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International Conference on Enterprise Systems, Accounting and Logistics Proceedings of the 7th ICESAL 2010 Rhodes Island, Greece, June 28-29 ISBN: 978-960-287-130-0 www.icesal.org Rhodes Island, 2010

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Page 1: International Conference on Enterprise Systems, …icesal.org/files/124188310.pdfInternational Conference on Enterprise Systems, Accounting and Logistics Proceedings of the 7th ICESAL

International Conference on Enterprise Systems, Accounting and Logistics

Proceedings of the

7th ICESAL 2010 Rhodes Island, Greece, June 28-29

ISBN: 978-960-287-130-0

www.icesal.org

Rhodes Island, 2010

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7th ICESAL 2010 - TABLE OF CONTENTS

No AUTHORS TITLE PAGES 1 Andreas I. Nicolaou KEYNOTE SPEECH: Gnosis: Doxa,

Episteme, and Relations to Accounting and IS Research

-

2 Somnath Bhattacharya KEYNOTE SPEECH: The Need for Options-Pricing Research in ERP Systems

-

3 Benita Gullkvist Diffusion of Digital Accounting Practice

1-3

4 Jian Cao, Andreas I. Nicolaou & Somnath Bhattacharya

A Longitudinal Study of Market and Firm Level Factors Influencing ERP Systems Adoption and Post-Implementation Decision Choices

4

5 Nazim Taskin & Jacques Verville An Exploratory Study on Strategic Alignment of Enterprise Systems and Business Strategies, Performance, and Flexibility

5-29

6 Oana Velcu & Ogan Yigitbasiouglu Dashboards adoption in the business controller’s decision making process

30-39

7 Rob Nehmer & Marilyn Prosch Privacy, Data Pollution, Organizational Responsibility, and the Sustainability of Information Ecologies

40-64

8 Akram Bodaghi & Ahmad Ahmadpour

Governance Decision Making Using XBRL 65-73

9 Trivellas Panagiotis, Kakkos Nikolaos & Reklitis Panagiotis

Investigating the impact of motivation, loyalty on performance intentions in the Greek banking sector

74-88

10 Akram Bodaghi & Ahmad Ahmadpour

The Effect of Corporate Governance and Ownership Structure on Capital Structure of Iranian Listed Companies

89-96

11 Thijs Emaus, Johan Versendaal, Vincent Kloos & Remko Helms

Purchasing 2.0: An Explorative Study in the Telecom sector on the Potential of Web

97-113

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2.0 in Purchasing

12 Jacques Verville & Nazim Taskin Critical Enterprise Software Contracting

Issues: Rights, Assurances and Responsibilities

114-124

13 Amy W. Ray, Wilson Wong, Susan Newell & Jesse Dillard

Beyond Privacy and Security: Ethical Dilemmas Resulting From Emergent Uses of Electronic Health Information

125-149

14 Vicky Arnold, Tanya Benford, Clark Hampton & Steve G. Sutton

Enterprise Risk Management: Re-Conceptualizing the Role of Risk and Trust on Information Sharing in Transnational Alliances

150-181

15 Daniel Selby Process-based view of agility: the value contribution of IT and the effects on process outcomes

182-195

16 Robyn L. Raschke Do Auditors Adjust Their Audit Plans Accordingly When They Encounter Material Automated Control Weaknesses?

196-198

17 Michael Wagner Analyzing Inventory Routing Costs

199-210

18 Kakkos Nikolaos, Trivellas Panagiotis & Fillipou Kaliopi

Exploring the link between job motivation, work stress and job satisfaction. A case study in the banking industry

211-230

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Gnosis: Doxa, Episteme, and , p ,Relations to Accounting and IS

ResearchResearch

Andreas I. NicolaouOwens Illinois ProfessorOwens‐Illinois Professor,

Bowling Green State University

EditorEditor, 

International Journal of Accounting Information Systems

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“All men by nature desire knowledge ”All men by nature desire knowledge.Aristotle

ίδ ό δέ ίδ«εν οίδα ότι ουδέν οίδα»“I know nothing except the fact of my ignorance.” g p f f y g

Socrates

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AgendaAgenda

• Philosophical Roots: what is knowledge howPhilosophical Roots: what is knowledge, how is it acquired?

• Dimensions of “doxa” and “episteme” capture differences in epistemological aspects of p g pknowledge.

• Critical to understanding modern paradigms• Critical to understanding modern paradigms of knowledge acquisition.

• Criticisms of unitary assumptions.

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Agenda cont’dAgenda – cont d

h d i b i l• Research domains: substantive, conceptual, methodological and their interplay in defining AIS research space.  

• Multiplicity in Dimensions of AIS Researchp y• Research Replication and Extension: Importance of Research ProgrammesImportance of Research Programmes

• AIS Research Opportunities: Multiple Th i M lti l M th dTheories, Multiple Methods

• Conclusions 

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GnosisGnosis• Gnosis in its philosophical origins: 

(a) Immutable forms that exist in and of themselves:(a) Immutable forms that exist in and of themselves:Plato: idea of forms ‐‐ ultimate knowledge (the “good”) relies on “forms of perception” that are independent of the perceiver (mind) and the object to be perceived and provide “forms” of intelligibility (myth of caves in Republic).p p g y ( y p )

(b) Unity of knowledge: Forms that make objects understandable cannot exist apart from particular objects; Individual objects, are a unity of a universal, repeatable form and a unique content or matter. 

Aristotle:moral evaluations of daily life do not presuppose a “good” that is independent of experience, personality, and circumstances.  Importance of knowledge as it develops in human endeavors – empirical focus.

(c) Orthological Rational: capturing the essence and causes of an event(c) Orthological – Rational: capturing the essence and causes of an eventPlato: reason is what maintains balance in human faculties (concept of justice)Aristotle: Reason is a distinctive human characteristic with properties of p p“potentiality” and “actuality”.By Aristotle, systematic perception and interpretation of reality is what helps develop intellectual virtues (Nicomachean Ethics).

5© A. I. Nicolaou 2010

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Gnosis = KnowledgeGnosis = Knowledge• Doxa • Episteme• Doxa

– Bios – biomatic: experiential / representational knowledge based on direct experience – emotive 

Episteme‐ Empirical – episteme: derived 

from systematic inquiry (Aristotle) – cognitive meaning.p

meaning.– Conceptual :  based on reason and 

multiple experiencesl h h b

‐ Familiarity with specialized area or field (Plato)

‐ Contrast to “experience”, “art” (Plato)– A personal opinion that might be 

true (person‐oriented philosophers viz. Homer, Herodotus)

– Contrary to “episteme” (Plato)

(Plato)‐ Closer to, but more 

encompassing than modernanglo‐saxon term of expertise –

iti iContrary to  episteme  (Plato)– Emotional/subjective basis of 

knowledge (thymos)

cognitive meaning.‐ Logical/objective  basis of reason 

(gnostic)

– What is believed to be true ‐‐What is known to be true

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Is episteme ever possible?Is episteme ever possible?

H d k hi i ?• How do we know something is true?• Answer to this question depends on how we acquire k l dknowledge.

• Is whole of our knowledge just a “doxa”?C di i l i f ?– Conditional – importance of context?

– Based on convention?Claims stand test of practical argument (cf Habermas)?– Claims stand test of practical argument (cf. Habermas)?

– Generally agreed understanding produced at a particular point in time? Is it changeable across time?point in time? Is it changeable across time? 

• Heraclitus: everything is in a state of flux • Parmenides: change is an illusion 

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Episteme=positivism?Episteme=positivism?

• Is positivism the answer? (modern North‐American research seems to have adopted pthis view)

• Empirical research: only source of knowledge?• Empirical research: only source of knowledge? Affirmative answer assumes empirical 

h fresearch is prerequisite for positivism.

• However, is gnosis only derived from empiricalHowever, is gnosis only derived from empirical research?

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Modern Philosophical DebatesModern Philosophical Debates

i i i l i i i i• Positivism versus alternatives: interpretivism; critical theory.

• Conflicting assumptions and criteria(Chua 1986; Orlikowski and Baroudi 1991) :; )– Beliefs about physical and social reality– Beliefs about the notion of knowledgeBeliefs about the notion of knowledge– Beliefs about the relationship between knowledge and the empirical world (theory andknowledge and the empirical world (theory and practice)

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Paradigm

Criteria/Assumptions

Positivism Interpretivism Critical Theory

Criteria/Assumptions

Beliefs about physical and social reality 

REALISM: There exists a single phenomenon of interest and there 

CONSTRUCTIVISM: Social world is not “given.” It is produced and reinforced via human actions and 

Humans alienated from theirpotential by prevailing economic, political, cultural 

(ontological assumptions )

exists a unique best description of any aspect of that phenomenon.

interactions. What is true changes with time, circumstances, objectives, constituencies.

, p ,authority. Social reality, while produced by humans, also dominates human action.p

Beliefs about the notion of knowledge (epistemological &

HYPOTHETICO‐DEDUCTIVE:Deductivism;generalizable

INTERPRETIVE: Understanding world of those generating the social process. Reciprocally interacting models of causality

Not enough to just interpret social world; must objectively analyze circumstances(epistemological & 

methodological assumptions)

generalizablerelationships; predict patterns of behavior across situations.

interacting models of causality. No researcher imposed structure and  attribution.

circumstances.

Beliefs about the relationship between knowledge and 

VALUE‐FREE: If lawsare given, and initial conditions can be manipulated, we can 

VALUE‐LADEN: Knowledge can never be value‐free. Weak constructionist view: mere description of phenomenon in 

VALUE‐LADEN: Can transform both actor and researcher. Role of theory is to initiate change in social 

empirical world (axiological assumptions)

produce a desired state of affairs, natural or social. Research is value‐

the words of actors; Strong‐form:  researcher’s interpretations intervene with actual meaning of the world – researcher enacts 

relations eliminating domination.

free. social reality of actors.

Adapted from Chua (1986) ;   Orlikowski and Baroudi (1991 )    10© A. I. Nicolaou 2010

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Not just a philosophical debateNot just a philosophical debate

• Differences in beliefs manifest themselves in the choice of research problems andmethods p(e.g., Chua 1986)

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Early criticismsEarly criticisms• Plausibility of Inductivism (Hume):

– what guarantees our knowledge about scientific knowledge?

– how do we know that we have identified the real– how do we know that we have identified the real causes?

– how do we delimit the conditions to be examined?• Supposition of symmetry between all future cases and the ones we have examined so far: the truth of any generalization is contingent, not necessary.P l i l b ifi i• Popper: logical asymmetry between verification and falsification. 

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More recent criticismsMore recent criticisms• Checkland (1981) and Galliers (1985): 

– Implausible characteristics of the scientific method: repeatability, reductionism (Descarté), refutability (hard to do in social systems – unlike physical ones, they can react to 

di ti d b t th )predictions made about them).• Hines (1988): Popper’s falsificationism should not be 

viewed as an attainable ideal by accounting researchers.  Its y guse limits the nature and domain of accounting research.  – A major misconception that exists in accounting research today 

lies in “the belief that the framing of an hypothesis in the ‘null’lies in  the belief that the framing of an hypothesis in the  null  form is a form of falsificationism”

• Kuhn (1970): a single methodological perspective designed for research in ‘normal science’ overlooks the anomalousfor research in  normal science  overlooks the anomalous quality of human experience.

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PitfallsPitfalls 

i l h• Wrong assumptions can severely threaten internal and external validity.

• Researcher should (Niessen 1985) :– Be aware of subjective nature of phenomena and achieve inter‐subjectively agreed knowledge.

– Support well grounded expectations rather than di t h b d l l / ditipredict phenomena based on general laws/conditions.

• Checkland: consider intended and unintended f f i f h i lconsequences of forecasting future human social 

activity events (self‐fulfilling prophecy).

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Some popular viewsSome popular views• Knowledge is a 3‐legged stool: 

– Injunction: dictate a method, follow sequence of acts– observe/examine/interpret– Verify: repeatability, symmetry

• Role of research is to turn “doxa” into “episteme” p(Berthon et al 2002; ISR)

• Aspects of Research / Range of Research Space:Aspects of Research / Range of Research Space:– Generation – ReplicationReplication – Interpretation

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Research NetworkResearch Network 

• Birnberg and McGrath: research domains include: 

• Substantive

• Conceptualp

• Methodological

Three Domains: interrelate – not independent andThree Domains: interrelate  not independent, and define research network.

Research Space a function of choices within theResearch Space a function of choices within the network of three domains, given the context.

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Interplay in Domains (substantive, conceptual, methodological)

I t f t l i l iti ( h t d k• Impact of ontological position (what do we know; substantive domain) on:

Epistemological basis of AIS research– Epistemological basis of AIS research• What counts as acceptable truth?• What phenomena do we study?• How do we distinguish between myths and reality? (e.g., myths about objective phenomena are created where empirical relations can be observed) p )

• Conceptual domain of AIS Research?– Methodological basis for AIS research

H h i d d i i i i i i i l i l• Hypothetico‐deductivism versus interpretivism, critical social theory, etc.

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Multiplicity in Dimensions of AIS Research

• Why multiplicity?– Broaden:

• Research domain

• Range of possible consequences of AIS researchRange of possible consequences of AIS research

– Eventually, help refine conceptual domain –develop quality of AIS research knowledge –develop quality of AIS research knowledge contribute to accounting and IS body of knowledgeknowledge.

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An example on why theoretical assumptions should be constantly questioned

“Th K h (1962) d ib d h t h d i th• “Thomas Kuhn (1962) described what happened in the field of physics when one theory could not predict properly. He said proponents of the theory would p p y p p f ycontinue to focus on those areas confirmed. This makes the theory more specific and focused. Several scientists would each refine different aspects of the theory Thewould each refine different aspects of the theory. The trouble is that the specific pieces of the theory became irreconcilable over time. As Van de Ven stated, “…such impeccable micro logic is creating macro nonsense.” (1989: 487).  One must either broaden or replace the theory that is used when the phenomena explainedtheory that is used when the phenomena explained becomes too hard for one simple theory to explain.”  (Nicolaou & McKnight 2010)

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AIS Research QuestionsAIS Research Questions• Wide net – Boundary Questions:Wide net  Boundary Questions:

– Would the questions and/or results be interesting?

W ld th h t d k l d ?– Would the research extend our knowledge?

– Is there a meaningful, unique contribution?

• N(ew)I(nteresting)T(rue) approach or N(ew)I(nteresting)B(alanced)D(omain)

approach?pp

• What overarching framework of thinking about research issues in an accounting or I S researchresearch issues in an accounting or I.S. research context? And not just a sub‐set of them?

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Related research areasRelated research areas

• IS and:• Accounting and:– Strategy– Modeling

D i

– Audit– Management control

V l ti – Design– Business Reporting– User behavior

– Valuation– Reporting– User behavior User behavior

– Organizational sociology– Individual beliefs

User behavior– Transparency– Relationship management

– Economics– etc

– Economics– etc

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How can we develop unique AIS Research opportunities?

• AIS and• AIS/IT and • AIS and:– Strategy– Modeling

• AIS/IT and:– Assurance– Control systems design g

– Design– Business Reporting

User behavior

y g– Business value– Reporting

User behavior – User behavior– Organizational sociology 

(trust, risk)

– User behavior– Transparency– Relationship (risk) 

– Individual beliefs– Economics– etc

management– Economics– etc etcetc

22© A. I. Nicolaou 2010

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Multiple theories multiple methodsMultiple theories, multiple methods

Th i R h Q i• Theories– Economic complementarity

• Research Questions:– Design scienceE icomplementarity

– Org. learning– Institutional

– Economic– Behavioral

M th dInstitutional– Org. sociology– Social network

• Methods– ExperimentalAnal ticalSocial network

– Agency– Analytical– Archival (qualitative, quantitative)quantitative)

– Field research– Case / critical/

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Theory Use in IS Research (Lee et al 2009)

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Diversity in Theory UseDiversity in Theory Use

• Many theories used with a small number of• Many theories used with a small number of articles using diverse theories

• Multiple theories/multiple methods recommendation does not imply thatrecommendation does not imply that distribution of theories used should have long and thin tailand thin tail. 

• Consider using multiple theories/methods in series of similar research studies/research programs.p g

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Usage of theories over time in IS research (Lee et al 2009)

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Influential Originating Disciplines over time (Lee et al. 2009)

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Top TheoriesTop Theories

d h• TAM, Resource‐Based, Game Theory, TRA.• Related risk is to apply theories that result in pp yindeterminate models.

• Indeterminacy is a common problem which isIndeterminacy is a common problem which is not so commonly acknowledged.

• Primary cause of indeterminacy: fluid state of• Primary cause of indeterminacy: fluid state of human behavior (random variables with a fat t il)tail).

• A major risk to research validity.

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(some) Recommended Solutions(some) Recommended Solutions

B ild fl ibili i d l h h• Build flexibility into models so they capture human actions/behavior across time.P i il t l bl d f d l• Primarily a temporal problem: need for model validation using inter‐temporal/longitudinal studies.

• Problems of deterministic models inability to make• Problems of deterministic models –inability to make time‐dependent predictions (consider few correct predictions of stock market crash).predictions of stock market crash).

• What is effect on social systems? – If we are forced to work with indeterminate models thenIf we are forced to work with indeterminate models, then there is more of a need to secure against the ‘fat tail’ phenomenon.

© A. I. Nicolaou 2010 29

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Visualization diagram of published articles in ISR & MIS Q (Lee et al)

Nicolaou & McKnight 2006 ‐‐the large blue node in the  Red: IT&Org

yellow community (IT&Individual):Th “ l “ i

Orange: ISD

Yellow: IT&IndThe “anomaly“ in IS research; Among 386 articles/nodes

Green: ITMarkets

Blue: IT& Groupsarticles/nodes, N&M distinctively could not pair with other 

Blue: IT& Groups

White: other

articles, because they used multiple theories and cut 

h

30

across research communities!) Main Finding: FRAGMENTED RESEARCH

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Virtues/Vices in ResearchVirtues/Vices in Research

• To attain “Intellectual Virtues”:– Work with an important problem, etcp p ,

– Acknowledge limitations and minimize risks to research validityresearch validity.

– Persistence

h i “ ld ?”– What is your “golden mean?”

– According to Aristotle, can only be found by habitual experience.

© A. I. Nicolaou 2010 31

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ConclusionsConclusions

• Multiple theories, multiple methods

• Assumptions are not monolithicp

• Accounting research has followed a monolithic path how can AIS contribute to the nature ofpath – how can AIS contribute to the nature of knowledge that is part of the accounting domain?

• Accounting ontological assumptions should be broadened – can accounting function and remain relevant without the information systems domain?

© A. I. Nicolaou 2010 32

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A Proposal for Real OptionsA Proposal for Real Options Research in ERPSResearch in ERPS

fÉÅÇtà{ U{tààtv{tÜçtYÄÉÜ|wt TàÄtÇà|v hÇ|äxÜá|àç

UÉvt etàÉÇ Y_ hfTUÉvt etàÉÇ? Y_? hfT

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PremiseDixit & Pindyck (1994) propose the theory of i ibl i d i ( iirreversible investment under uncertainty (accounting concept of sunk costs)According to Benaroch & Kauffman (1999), a project

l ti h t t kassumes a real option when management can take a future action based on events within the firm and the business environmentCl d G (2003) it th t ti i i lClemons and Gu (2003) posit that an option is simply the right to obtain an asset at a later time, at a prespecified price (call), or the right to sell an asset at a later time at a prespecified price (put)a later time, at a prespecified price (put).Real Options move away from the static DCF/NPV estimate of the outcome of technology deployment at a single point in timea single point in timeFichman (2004) posits that that when uncertainty and irreversibility are high, omitting the value of managerial flexibility can lead to substantialmanagerial flexibility can lead to substantial understatement of the value of investments in new IT

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PremiseThree conditions are prerequisite to using real options concepts to structure the evaluation and managementconcepts to structure the evaluation and management of technology investments: uncertainly regarding net payoffs, irreversibility in project costs, and managerial flexibility regarding how projects are structured (Dixit and Pindyck 1994)and Pindyck 1994).According to Clemons and Gu (2003)

Techniques exist that enable executives • (I) to identify and to delimit their range of investment

lt ti th t t b id d d t d idlalternatives that must be considered, and to do so rapidly and reliably

• (2) to divide investments into discrete stages that can be implemented sequentially(3) to determine which chunks can safely and profitably• (3) to determine which chunks can safely and profitably be developed as strategic options, with value that can be captured when subsequent stage investments are made later; and

• (4) to quantify and to estimate the value of these(4) to quantify and to estimate the value of these strategic options with a significant degree of accuracy, so that selections can be made from a portfolio of investment alternatives

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Models

Fi lit t id t j fi i lFinance literature provides two major financial option pricing models:

BinomialBlack-Scholes

However, real options differ from financial options in one critical aspect: there exist nooptions in one critical aspect: there exist no arbitrage-free markets where underlying assets are traded.All applications of option pricing models requireAll applications of option pricing models require identifying "twin assets" that are theoretically perfectly correlated with the underlying assets of the real options and use their prices andof the real options and use their prices and return variances in the pricing models

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Real Optionsp

I i l i i h i hIn its purest sense, a real option is the right to trade a physical asset, such as real estate, or manufactured durables, such as aircraft, at a future time and at a predetermined priceReal options are not common-value instruments in the sense that an option to upgrade an ERPS willthe sense that an option to upgrade an ERPS will have different values to different companies even within an industry depending upon their size and the age of their systems; likewise the value of anthe age of their systems; likewise, the value of an option to implement an ERPS will have different values to different companies depending upon th i t t d k t t d th t ftheir targeted market segments and the nature of their existing offerings. This greatly complicates any effort at valuation.g y p y

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Real OptionspAccording to Benaroch (2002), the valuation of complex options remains a difficult endeavor. As IT investments could pbe exposed to multiple risks, they may need to be configured using a series of cascading (compound) optionsStandard valuation models (e.g., Black-Scholes model) ignore the fact that the value of individual options in a series of cascading options may be lowered or enhanced by interactions with other options. Yet, custom-tailoring a separate valuation model for each viable investmentseparate valuation model for each viable investment configuration is not practical, especially when more than two risks are involvedRaiffa and Schlaifer in the 1960s put forward a simple but powerful idea that analyzing ex-ante the effects of taking a sequence of decisions, and allowing the combination of actions made based on previous decisions creates a decision treetreeThe end nodes of the tree can be evaluated, weighted by their ex-ante probabilities, and summed to determine the best initial course of actionOption valuation models are themselves special cases ofOption valuation models are themselves special cases of decision tree analysis

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Real Optionsp

A di t Cl d G (2003)According to Clemons and Gu (2003), “Some system investments can be divided into segments, tasks, or chunks that can g , ,be implemented sequentially.” To coin a term that is easy to remember, they call dividing a contingent IT investmentdividing a contingent IT investment opportunity into chunks strategic chunkification

the first chunk of investment is made because it may prove very useful, whereas thewhereas thesecond chunk is made only when it is known that it will be usefulknown that it will be useful

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Real OptionspConsider preparation for a future strategy, two tasks, which require investments E1 and E2, respectively.Investment E1, has duration of L1, and investment E2has duration of L2The options premium, or the cost of the option, can be estimated by E the cost associated with thebe estimated by E1, the cost associated with the investment needed to complete the first taskThe value of the option is determined by the value of the sequence of investments E1, at t1, E2 at t2, etc.,

d t th l f b th i t t dcompared to the value of both investments made beginning at t2Making the first-phase investment reduces the firm's response time in case changes in environmental espo se t e case c a ges e o e taconditions make the overall investment necessaryThe reduction in time will frequently be close to L1, the time required to complete implementation of the first task and thus the duration of the first phasefirst task and thus the duration of the first phaseBenaroch (2002) distinguishes between two classes of real IT options: operating options that allow to flexibly change investment configuration features (ti i l t ) d t t i th(timing, scale, scope, etc.), and strategic growth options that spawn new investment opportunities

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Real Options vs. Options Pricingp p g

O ti i i d l ll id i l i kOptions pricing models usually provide a single risk-adjusted expected value that is consistent with the objective of maximizing firms‘ market capitalizationProject managers' decision criteria however are oftenProject managers decision criteria, however, are often not aligned with maximizing firms' market capitalizationA strategic option represents a capability to deploy a selected strategy. Rather than being purchased, theseselected strategy. Rather than being purchased, these capabilities are synthesized by making the investments that will be needed for rapid deployment of the strategy later, if and when desiredDeciding not to implement the strategy has much in common with choosing not to exercise a real option, and much of the analysis used in real options theory is applicableapplicableStrategic options are context-specific rather than common-value

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Difference between Options Pricing & p gReal Options

Unlike financial options, where the value is determined by a large, anonymous, exogenous marketplace the variance of value in strategicmarketplace, the variance of value in strategic options, at least to some extent, is endogenous and is heavily contingent on the timing of actions and of the actions of competitorsactions and of the actions of competitorsPrior literature such as Nicolaou and Bhattacharya (2006; 2008), suggest that ERP i l t f i f h i timplementers face a series of choices post-implementation. These include upgrades, add-ons, deferments, and abandonmentsERP is inherently modular in design

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Additional Arguments for Real Optionsg p

IT i f t t i t t d ith tIT infrastructure investments are made without an expectation of immediate paybackEmerging technology investments make forecasting value payoffs difficultApplication design prototyping investments depend on how well the prototype mimicsdepend on how well the prototype mimics organizational needsTechnology-as-product investments: Google Android for GM fate of proprietary OnStar?Android for GM…fate of proprietary OnStar?Bardhan et. al. (2004) demonstrate the concept of Nested Options in IT projects using Real O tiOptions

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Kauffman & Li’s (2005) Brownian Motion( )Models

Unlike traditional real options modelsUnlike traditional real options models that stochastically model an investment project’s value, K&L (2005)’s model

B i ti t h tiuses a Brownian motion stochastic process to simulate changes over timeThe information that arrives typicallyThe information that arrives typically does so in random fashionMany observers believe that

i l d i i k ’managerial decision-makers’ expectations play a fundamental role in many strategic decision making y g gprocesses.. And these evolve constantly over a project’s duration

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Example of ROA from Schwartz & pZozaya-Gorostiza

Schwartz & Zozaya Gorostiza develop twoSchwartz & Zozaya-Gorostiza develop two models for the valuation of IT investment projects using the real-options approach

The first model is suited for the evaluation of ITThe first model is suited for the evaluation of IT projects in which a firm invests an uncertain amount of money during an uncertain period of time to develop an IT asset that can be sold totime to develop an IT asset that can be sold to third parties or used for its own purposesThe second model is suited for the valuation of investments in which a firm acquires an IT assetinvestments in which a firm acquires an IT asset for its own use. In this model, investment is assumed to be instantaneous, and the benefits associated with the investment are represented

f diff i l h fl d ias a stream of differential cash flows during a period of time in which the technology is considered to be useful

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ERP as a Software Platform

Taudes et al (2000) argue that softwareTaudes et. al. (2000) argue that software platforms are a bundle of functions serving as a basis for certain applicationsThese applications’ value change over timeThese applications value change over timeTherefore the value of the underlying platform depends on the applications created and the value of the applications themselvesppA platform can support many applicationsConversely, an application may be supported on multiple platformsp pTherefore, ERP can be construed as a software platform for a multitude of applicationsA resource will contribute to sustained advantage gonly if it is valuable, heterogeneously distributed among firms, and immobile (Fichman 2004)

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Option Valuing of Implementation Opportunities

Value of the software platform = NPV of fixed applications portfolio + option

l f i l t ti t itivalue of implementation opportunities (Taudes et. al. 2000)O ti V l P N B h POption Value… Pt = Nt x B, where Pt= benefit of the application at t; Nt is the no of times the activitiesthe no. of times the activities supported by the application are performed; B is the process costperformed; B is the process cost savings, say from ERP outsourcing

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Summary of Real Options for Project Escalations from Tiwana et. al. (2006)

• S it h Th ti t it h f t th ti t t t t• Switch: The option to switch use refers to the option to put an asset to a different purpose from that for which it was originally intended (Trigeorgis, 1993)• Change Scale: The option-to-change-scale allows the resources allocated to a project to be contracted or expanded in order to change the scope of the application (Pindyck, 1988)• Stage: The option to stage investments exists when a project is structured as a series of incremental outlays that allows the project to be

f fterminated if business conditions become unfavorable.•The pursuit of each stage is contingent on a reassessment of costs and benefits at the time the preceding stage is completed

• Abandon: An abandonment option is associated with a project if ith t j ti di ti thmanagers can, without major negative consequences, discontinue the

project prior to completion and redeploy remaining project resources(Hubbard, 1994)

• While any project can be terminated in principle, an abandon option f t it ti h t i ti d d l t brefers to a situation where termination and redeployment can be

carried out relatively easily• Strategic Growth: A growth option is embedded in a project when an initial baseline investment opens the door to pursue a variety of potential f ll i t t t ll f hi h il b ffollow-on investments, not all of which can necessarily be foreseen (Trigeorgis, 1993)

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OutsourcingFichman (2004) posits that prior literature on technology strategy yields a set of four broad factors that have especially large effects on uncertainty or flexibility, and hence, are especially salient from the y y p yoptions perspective:

(1) the radicalness of the technology(2) the strategic importance of affected products/processes(3) h i bili f d f d b i i h(3) the sustainability of advantages conferred by improvements in these products/processes, and (4) the extent to which the firm possesses innovative capabilities and endowments consistent with adoption of IT platformsendowments consistent with adoption of IT platforms

Each of the above increases the variance of potential returns in the futureFirms increasingly are utilizing outsourcing to enhance or maintain their g y g gcompetitiveness ((Premuroso, et. al., 2010)The U.S. Department of Commerce estimates 34% of the total services purchased by businesses were outsourcing-related (Premuroso, et. al., 2010)2010)

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Outsourcing as an IT (ERP) Innovationg ( )

According to Fichman (2004):Strong network externalitiesAdaptive FlexibilityInterpretive FlexibilityDivisibility

All add to the increase in variance of returns earned from IT innovations

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Outsourcing Options

Whether to outsource or not?Prior research finds capital markets valuePrior research finds capital markets value information about firms’ outsourcing activities (Hayes, Hunton, and Reck 2000; Florin, Bradford and Pagach 2005)Bradford, and Pagach 2005)

Whether to disclose outsourcing or not?There are currently no specific regulatory disclosure requirements in the US regarding firm outsourcing activitiesEconomic theory suggests firms willEconomic theory suggests firms will provide voluntary disclosures as long as the expected benefits to the firm exceed the related costs of voluntary disclosuresthe related costs of voluntary disclosures (Verrecchia 2001; Healy and Palepu 2001)

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Focus on the disclosure optionWhen regulatory standards do not require a particular disclosure, economic theory suggests management will trade-ff b t ki di l l t d t th fi ’ t doff between making disclosures related to the firms’ expected

future performance and managing expectations for contracting, political, and corporate governance reasons (Watts and Zimmerman 1986)Zimmerman 1986)Therefore, the choice to disclose outsourcing in the annual report is part of a firm’s accounting choicesGraham, Harvey, and Rajgopal (2005) find managers makeGraham, Harvey, and Rajgopal (2005) find managers make voluntary disclosures to reduce stock price riskGelb 2000 predicts firms will select an optimal mix of voluntary disclosures when conveying information to investors and the y gmarketA firm’s decision to make voluntary outsourcing annual report disclosures therefore is potentially part of this optimal mix of acco nting choicesaccounting choices.

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Results of the examination of the disclosure option

P l (2010) fi d h d f iPremuroso, et. al. (2010) find that underperforming firms (with respect to return on assets) and larger firms (in terms of total assets) are more likely to make disclosures related to outsourcing. They find that firms experiencing both short- and long-term negative capital market reactions, firms listed on the NASDAQ, p , Q,and firms that themselves (as opposed to the supplying firm) announce the outsourcing agreement in a press release are more likely to disclosein a press release are more likely to disclose outsourcing in their annual reports Finally, firms disclosing outsourcing in their annual

t b i li i t i th i h treports may be signaling improvements in their short-term market performance

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Focusing on the Outsourcing Option: g g pERP Outsourcing Reasons

Manpower deficienciesInadequate understanding of ERPq g

Poor trainingApprehension to changeApprehension to changeHalf-baked knowledge of ERPDeveloper-ERP or Consultant-ERP

Vagaries & politics of ERPVagaries & politics of ERP

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Merits & Demerits of ERP Outsourcingg

Merits DemeritsMeritsEnables concentration on core areas

DemeritsExposure of corporate strengths to outsiders

Allows routine BP to continue

Change management i

External vendor resistanceRestructuring causes problems

easierCost savingsTechnological advantages

pPolitical woes (Charles Schumer’s proposed bill re: outsourcing)g g

Increased opportunitiesMgmt. psychology & easeStrategic business

g)

Strategic business partnershipsInter-industry best practices

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Coordination Between ASP Use & Outsourcer

Should one attempt to coordinate between ASP provider and outsourced service provider?pPotential loss of ASP expertise as a result of ERP outsourcingas a result of ERP outsourcing

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ERP Outsourcing Issuesg

Ch i f dChoice of vendorsCost, function, and staffCustomization and degreeVendor credibilityy

Kind of outsourcing desiredStandard stand-alone one-timeStandard stand alone one timeApplications outsourcingAs part of BPOAs part of BPO

Performance Measurement

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Options for ERP Adoption & Subsequent p p qDecisions

ERPERPAdoption

Yes No

Outsource?

Yes

Modular additions?

Outsource/ASP?

Coordinate with ASP or not

Yes

No

Performance Evaluation in terms of ROA or

No

Choice of vendor(s)

of ROA or discounted NPV/DCF

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Discussion & QuestionsQ

ERP investments are inherently modular in natureERP investments are inherently modular in natureAs such, they lend themselves to ROA analysisManagers intuitively undertake ROA when considering ERP and other IT investmentsother IT investmentsThis opens up the possibility of new directions of ERP researchIntegration of IT, Finance, Accounting, Management research areasM id i i ht i t d ti it dMay provide insights into productivity paradoxManagers may implement projects otherwise rejected via traditional DCF analysisM l d t i ti l d i i i b d f fidMay lead to irrational decisions via overabundance of confidenceCloser tie-in to practiceApplicability to real-world decision making

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And so, at the end….,

Was Som good or was Som GOOD?

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7thInternational Conference on Enterprise Systems, Accounting and Logistics (7th ICESAL 2010) 28-29 June 2010, Rhodes Island, Greece

1

Diffusion of Digital Accounting Practice

Benita M Gullkvist

Hanken School of Economics, Finland [email protected]

Accounting has evolved with developments in business and information technology.

The mechanization and computerization of accounting has now moved into a new

stage, the digitization of accounting. Digital accounting does not as yet have a

standard definition but is usually referred to as changes in accounting due to

computing and networking technologies. Deshmukh (2005) defines digital

accounting, or e-accounting, as the representation of accounting information in the

digital format, which can then be electronically manipulated and transmitted.

Since the emergence of personal computers and the Internet, new ICT is constantly

being introduced. It is therefore of interest to analyze and try to understand how

innovative accounting practice (administrative innovations) spread within and across

organizations as the result of adoption and implementation of technological

innovations. An innovation, according to Rogers' (1962) theory of diffusion, is an

idea, thing, procedure, or system that is perceived to be new by whomever is

adopting it. Prior research has concluded that innovations often take years, if not

decades, to spread into wide use, and that accounting practice is rather slow to

change (Scapens,1994:317). Yet, the automation of financial accounting processes

was one of the first uses of computer systems in early years. Further, in order to

achieve and maintain a competitive edge in the business world of today, managers

are concerned with both the diffusion of efficient innovations and increasing the rate

of diffusion (Gurd et al., 2002).

The purpose of this study is to provide an analysis and review of the diffusion of

digital accounting practice emerging from the adoption of technological innovations

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7thInternational Conference on Enterprise Systems, Accounting and Logistics (7th ICESAL 2010) 28-29 June 2010, Rhodes Island, Greece

2

for corporate financial accounting among Finnish accounting services firms over the

last fifteen years. The working definition of diffusion of innovation here is the

adoption and implementation of new ideas, processes, routines, or services, with

particular emphasis on managing corporate financial accounting practice within

and/or across organizations. This study mainly focuses on the adoption and

implementation of more recently emerging innovations developed for automating

procedures, developing more appropriate information technology-based processes,

or replacing analog with digital information. Such innovations are for example

electronic bank statements, electronic reporting to tax authorities electronic invoicing

and web-based accounting software. Thus, technological innovations in this study

cover a number of various information technologies and systems, however all with

the purpose of digitalizing the financial accounting process and practice.

Drawing on Rogers’ (1962; 2003) research and theory on the diffusion of

innovations, the paper examines innovation adoption behavior over time. Further, by

using the Gartner Group’s hype cycle (see for example O’Leary, 2008) as a basis for

the analysis, the paper recognizes the diffusion of digital accounting practice as a

progression of emerging technology. Different stages of the curve is analysed with

regards to diffusion of digital accounting practice over time: technology trigger, peak

of inflated expectations, trough of disillusionment, slope of enlightenment and

plateau of productivity with regards to digital accounting practice and ICT adoption.

Data was collected over a fifteen-year period 1995 to 2009 from academic literature,

as well as academic and business journals. Primary data collected as part of a major

research project including tape-recorded semi-structured interviews and surveys

among Finnish accounting professionals and entities was further used in the

analysis.

Overall, the analysis show a hype in media and business journals with regards to

digital accounting practice in late 90s and early 2000. It was expected that the legal

consent to use machine-readable data records when setting up and archiving

bookkeeping entries, e.g. revised Accounting Act effective from 1998, would radically

change accounting practices. The available evidence is, however, that the change to

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7thInternational Conference on Enterprise Systems, Accounting and Logistics (7th ICESAL 2010) 28-29 June 2010, Rhodes Island, Greece

3

digital accounting practice has been slower than expected, and has not yet reached

the slope of enlightenment or plateau of productivity in many entities. Evidence of

that the hype ended in disillusionment could be found, and this stage has only more

recently turned into a slope of enlightenment in many companies. For innovators and

early adopters, however, there is evidence of changes in accounting practice,

however mainly further automation of previous practices and self services. True

innovative ways of managing digital accounting practices appear not to have

emerged so far. Further, and consistent with previous research, the diffusion

process shows a lag resulting from the difference in adjustment and evolution of

administrative innovations (new routines, procedures) to changes in technical

systems. Reasons for and obstacles to the development are discussed and

suggestions for further research are proposed.

References Deshmukh, A. (2006). Digital Accounting: The Effects of the Internet and ERP on

Accounting, The Ideal Group, Hershey PA. Gurd, B., Smith, M. & Swaffer, A. (2002). Factors impacting on accounting lag: An

exploratory study of responging to TQM, British Accounting Review, 34, p. 205-221. O’Leary, D.E. (2008) Gartner's hype cycle and information system research issues,

International Journal of Accounting Information Systems 9, p. 240–252 Rogers, E.M. (1962), Diffusion of Innovations, The Free Press, New York, NY. Rogers, E.M. (2003), Diffusion of Innovations, 5th ed., The Free Press, New York, NY Scapens, R.W. (1994) Never mind the gap: Towards an institutional perspective of

management accounting practices. Management Accounting Research 5, p. 301-321.

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7thInternational Conference on Enterprise Systems, Accounting and Logistics (7th ICESAL 2010) 28-29 June 2010, Rhodes Island, Greece

4

A Longitudinal Study of Market and Firm Level Factors Influencing ERP Systems Adoption and Post-

Implementation Decision Choices

Jian Cao1, Andreas I. Nicolaou2 and Somnath Bhattacharya3

1Department of Accounting, Florida Atlantic University, USA 2Department of Accounting & IS, Bowling Green State University, USA

3 Department of Accounting, Florida Atlantic University, USA

1 [email protected], 2 [email protected], [email protected]

Abstract Recent research in information systems has advocated use of real options theory

to analyze the option-like characteristics of information technology (IT) project investments. Various option pricing models have been adapted to determine optimal IT investments. However, little attention has been given to the economic determinants affecting the exercise strategy of investment and adaptation options in ERPS. We conduct a longitudinal study of factors influencing ERPS initial adoption and subsequent adaptation choices in the framework of strategic real options. We track the option exercise strategy profile of firms announcing initial ERPS implementation decisions during 1989–1998, and also examine their system enhancement and upgrade choices for a 10-year period post adoption (till 2008). We posit and find that product demand uncertainty and financial conditions of the firm and industry affect the exercise of investment options at the time ERPS are first adopted. However, while both product market concentration and firm market power significantly increase the probability of the initial ERPS adoption, they have no significant association with subsequent adaptation choices. These results are consistent with past research which suggests a negative effect of demand uncertainty on irreversible investments. As the process of ERPS implementation does not end with the adoption decision but continues to evolve over the long run, it seems to allow a system adaption to occur after the demand uncertainty is relatively more resolved. In contrast, firm-level characteristics, such as size, profitability, and investment opportunities, tend to have significant associations with both initial and post-implementation decisions.

Furthermore, the financial performance effect of the initial ERPS adoption decision, is found to be supplemented by the use of high-quality post-implementation activities (as posited by Nicolaou 2004 and examined in Nicolaou and Bhattacharya 2008) that relate to project planning, strategy, and process integration, in having a positive influence on the probability of a future enhancement or upgrade. On average, such option choices are more likely to occur during the medium-term of the ERPS implementation period (3 to 5 years post-adoption). Our findings suggest that some firms, due to their fundamental characteristics, are a priori more prone to invest in ERP systems. Their subsequent actions, modeled as exercises of additional investment options, further enhance their lead vis-à-vis their peers. Our findings also support prior studies such as Nicolaou and Bhattacharya (2006; 2008) that have posited that ERP implementation decisions are not binary in nature but tend to be modular and occur over a stream of investment decisions over an extended period of implementation.

Keywords: IT investments, ERP systems adoption, post-implementation, market effects, firm effects, longitudinal study, real options theory.

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An Exploratory Study on Strategic Alignment of Enterprise Systems and Business Strategies, Performance, and

Flexibility

Nazim Taskin1, and Jacques Verville2

1Faculty of Management, University of British Columbia, Okanagan, Canada 2 Faculty of Management, University of British Columbia, Okanagan, Canada

[email protected], 2Jacques [email protected]

Abstract

This paper examines the relationship between strategic alignment, performance and IS flexibility. We have used different strategy perspectives to form our strategy construct and have chosen the significant variables of these constructs based on the literature. While the alignment measurement method was moderation, several financial values have been used to form the performance construct. Our results indicate that alignment has a positive impact on performance. In addition, alignment has a mediating effect between flexibility and performance, while flexibility does not have a direct significant impact on performance.

Keywords: Strategic alignment, Enterprise Systems, Flexibility, Performance, Moderation

1. Introduction

The continuous change and intense competition has led organizations to either adapt new conditions or fail their businesses. Within the dynamic environment of business, companies usually tend to expand their market share, reduce resource consumption, and increase their products, efficiency, customer service, and their quality in order to meet the new requirements of businesses (Umble et al., 2003). Under these circumstances, in order to remain competitive, organizations need to improve their business practice and procedures (Umble et al. 2003) as well as the outputs. Information Systems (IS) has provided the opportunity for organization to improve the way they do business. In the past, technology has been used for three main objectives: initially, the goal of IS and technology was to automate information-based processes. The next objective was to enhance management efficiency, and finally the objective for adopting IS was to improve competitive advantage (Ward and Peppard 2002). In other words, organizations have a tendency to adopt systems that will allow them to gain competitive advantage over other companies.

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In addition, current developments and trends in business, market pressure, and technological developments have pushed organizations to collaborate (sometimes even mergers and acquisitions (Stefanou 2001)) and to improve business practices. This collaboration requires integration, sharing resources and information with customers, suppliers, and distributors (Umble et al. 2003). While Enterprise Systems (ES) have been developed to address these needs and accomplish these goals, Enterprise Resource Planning (ERP) systems aim to integrate the business functions, improve accuracy of information, and help to better decision making. ERP systems promise to reduce resource consumption, speeding up processes with improved accuracy, improved customer satisfaction, resource allocation, flexibility, information flow, business performance, etc. (Hsu and Chen 2004; Poston and Grabski 2000) through the integration of business processes and business functions across departments and functional/business units with the benefit of having access to real-time data. Several researchers state that Enterprise Systems are crucial for a competitive advantage (Porter 1987; Das et al., 1991). Reasons such as improved infrastructure (i.e., common platform), capability (process improvement, data visibility), and performance (i.e., advanced cost reduction, strategic decision making, and customer responsiveness) (Ross and Vitale 2000) are among the common motivations for adopting ERP systems. ERP systems are different from traditional software because of their structures. While choosing appropriate software and installing the software is usually easy and enough for traditional software, the situation is not the same for ERP systems. Time has shown that just adopting or installing a system, does not guarantee a competitive advantage or accomplishment of aforementioned motivations/benefits (Muscatello et al. 2003). The literature shows that successful ERP implementation have achieved the promised benefits. However, the literature also shows that the successful implementations of ERP are very limited. Most of the ERP projects either fail or have a conflict with strategy because of mismatch (Stefanou 2001). ERP systems may require several changes in business practices or even in strategy of the organization. ERP projects are more successful when management understands their strategic importance and give high priority to alignment. In other words, alignment (strategic) is a requirement for ERP systems (Gibson et al. 1999; Esteves and Pastor 1999). Importance of alignment has been recognized by both researchers and practitioners. The literature demonstrates that managers have agreed that alignment has been among the top priorities for management to improve the performance and add value to businesses during the last decade (Chan and Reich 2007). On the other hand, Davenport (1998; 2000), Bingi et al. (1999), Gable et al. (2001), Holland and Light (1999), Rao (2000), and Al-Mudimigh et al. (2001) state that organizations need to align their business strategies and even business processes in order to be able to fully benefit from ERP systems. Several researchers find that alignment when it is strategic has indirect positive impacts through effectiveness, and business profitability as well as direct impacts on performance (Porter 1987; Luftman 1996; Weill and Braodbent 1998; Venkatraman 2000; Avison et al., 2004; Sabherwal and Chan 2001). In addition,

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according to Kang et al. (2008) and Siswanto and Utomo (2008), aligning ERP with organizational goals would enhance the competitive benefits as well as the performance. In general, literature shows that alignment has a direct and positive impact on performance. However, considering the complexity and broadness of the concepts, there are different aspects that have significant impact on both. One of these significant aspects is the flexibility of IT. Duncan (1995) defines flexibility as “the ability of a resource to be used for more than one end product” (p: 42). Flexibility of IT enhances organizations ability to response to the needs and changes in practices and strategies (Duncan, 1995; Clemons and Row, 1991). In a dynamic environment such as today’s business world, flexibility is critical for the success and the performance of the organizations. The rest of the paper is organized as follows: the next section will briefly analyze the alignment, flexibility, and performance literature as well as the theoretical model; next, the methodology mentioning the design of the study will be followed by the results of our study. Finally, the paper will be finalized with discussion and conclusion section.

2. Prior Research

The literature has distinguished several types of alignment as well as several perspectives pertaining to alignment. Based on the literature, we have identified six types of alignment at business unit level that involves business and Information Systems components: (i) Strategic Alignment; (ii) Business Alignment; (iii) Structural Alignment; (iv) Information Systems Alignment; (v) Cross-Dimensional Alignment; and (vi) Alignment Mechanisms (Sabherwal et al., 2001). While the literature provides several examples of each type of alignment, majority of research have shown that alignment should be strategic in order to provide highest benefits such as improving competitive advantage and enhancing performance (Levy 2000; Chan and Reich 2007). The benefits of aligning business and IS strategies have been recognized by researchers and practitioners (Henderson and Venkatraman 1993; Chan et al. 1997; Croteau and Bergeron 2001; Sabherwal and Chan 2001). Some benefits of aligning IS to business strategies include (1) increasing organizations return on IS investment; (2) improving companies’ competitiveness; (3) enhancing flexibility (Avison et al. 2004); and (4) profitability of organizations (Galliers 1991; Papp 2001). With the alignment, organizations can have the traditional benefits of IT/IS in terms of having an organization’s operations accepted by executives (Huang and Hu 2007) as well as obtaining top management support (Lederer and Mendelow 1989). Studies also show that alignment is one of the critical issues for economic performance (Ciborra 1997). Studies of Chan (Chan et al. 2006; Chan and Reich 2007) also support the notion that alignment improves the performance by allowing organizations to use IS more strategically. In addition, Papp (1999) states that alignment is a key area that managers focus on in order to improve financial performance.

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Although there are several studies pertaining alignment, however, there is no agreed definition of alignment. Based on previous definitions, for this study, we define alignment as a continuous and dynamic process that requires appropriate and supportive use of IS with business strategies and objectives in order to contribute or enhance the business performance overtime. In addition, alignment can also relate to the synergy, fit, and integration between business and Information Systems strategies (Hirscheim and Sabherwal, 2000; Cuhng et al., 2003). The objective of this type of alignment is to support the business pertaining plans, missions, decisions, capabilities, and actions (Chan 2002). Current trend in alignment research encourages granularity as well as enablers and antecedents of alignment. While the majority of earlier research on alignment was on the whole Information Systems strategy or structure, several researchers have been encouraging increasing the granularity on many aspects. For example, Hong and Kim (2002) identify the contingency variables of IS that researchers have tended to examine. These variables include strategy, structure, size, environment, technology, task, and individual characteristics. Studies focusing on each of these variables separately and in more detail would bring useful information at least as much as examining all variables together. Considering the fact that alignment is between business and Information Systems, our focus will be on technology. The motivations of this stream of research come basically from the growth and broadness of technology field. Variety of technology products, and systems and lately attempt to integrate systems, components, data, and processes into a unified system has led developments of new systems that are strategic to organizations. These new systems have been formed into new structures (i.e., ERP, SCM, and CRM) that emphasize their own logic to organizations’ strategy that traditional Information Systems models may not capture every aspect. Therefore, while examining alignment, focusing on a specific technology rather than trying to cover whole IS would be more reliable. Researchers also agree that increasing the granularity of research would bring more benefits to alignment research (Farrell 2003; Chan and Reich 2007). Palmer and Markus (2000) and Chan and Reich (2007) also state that "one-size fits all" type research is not an appropriate method for alignment. Chan (Chan and Reich 2007; Chan et al. 2006) states that alignment should be examined in more detailed than it has been done today. Therefore, they signal towards a more specific research related to alignment. In addition Chan (2002) states that: “Due to the complex and daunting nature of overall business-IS alignment, perhaps successful alignment is more likely by emphasizing the management of specific components of alignment, rather than aiming for seemingly unreachable target of multifaceted, overall alignment. This is not to diminish the importance of maintaining a holistic view of alignment. It merely suggests that focusing on how individual components contribute to alignment may be more feasible, and yield better results, than tackling all the alignment challenges of the entire IS organization at once" (p.99).

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In the alignment literature granularity of research or aiming at specific component has been encouraged by several scholars. Granularity can be based on many perspectives such as industry effect on alignment, effects of company size, country effect as well as specific technology effects on alignment. For example, Chan and Reich (2007) support Farrell’s (2003) study and suggest that researchers should examine industry differences and effects of industry type on alignment rather than covering all industry types at once. In addition, in terms of granularity, Street (2006) and Chan and Reich (2007) recommend focusing on research based on specific firm sizes. In that sense, researchers also encourage examining specific technologies instead of whole IS while examining alignment. For example, Kang et al. (2008) use some components of Strategic Alignment Model (SAM) and measure alignment between organizational infrastructure and IS infrastructure by focusing a specific technology, ERP, where ERP alignment is defined as a state where the business activities of departments are changed to meet the requirement of ERP system in a way that there will be a harmony and internal coordination with organizational overall objectives. On the other hand, Wehmeyer (2005) apply strategic alignment model to distinguish database marketing and CRM. In this study the author examined alignment from not only a specific technology perspective like CRM but also from a specific business unit perspective like marketing. Alignment has been studied from several perspectives by many researchers. These studies reveal that there are different types of alignment. However, in spite of the variety of alignment work, there are few models proposed or studied by researchers. The main alignment models are shown in Table 1. Table 1: Main alignment models

Authors Discussion Henderson and Venkatraman (1989; 1991; 1992)

Presented SAM. The model has four domains as: (1) business strategy; (2) IT strategy; (3) organizational infrastructure and processes; and (4) IT infrastructure and processes. Examine alignment as (1) bivariate fit; (2) cross-domain alignment; and (3) strategic alignment. Derivatives of SAM include: Luftman et al. (1993): contributed to transformation of organization. Luftman (1996, 2000): added enablers and inhibitors, and presented Strategic Alignment Maturity Assessment instrument. Smaczny (2001): introduced fusion model Weill and Broadbent (1998): how to support business strategy through investing in IS. Maes (1999), Maes et al. (2000): developed unified framework.

Chan (1992), Sabherwal and Chan (2001)

Examine the relationship between IS strategy, business strategy, and performance. Main focuses of their model are IS strategic alignment, fit between business, and IS strategic orientation, IS effectiveness, and business performance.

Weill (1990; 1992) The present model examined the relationship between performance goals of an organization, business strategy, IS

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strategy, and firm performance. Benbya and McKelvey (2006)

Examine alignment based on coevolutionary and complexity theories. Alignment is a sequence of individual, operational, and strategic adjustments.

Alter (2002) The model does not require IS domain. The “work system” has both static and dynamic views that examine adaptation process.

Raffa and Capaldo (2007) Their process model examine alignment based on IS implementation and is limited to only issues regarding implementation.

Reich and Benbasat (1996; 2000)

Examine alignment based on social and intellectual dimensions as well as antecedents of alignment.

Sabherwal, Hirschheim, and Goles (2001)

Examine alignment, strategic, structural and cross-dimensional based on punctuated equilibrium model. According to the authors, alignment occurs in phases, which follows a series of evolutions and revolutions.

Baets (1992) Combines MacDonald's (1991) Strategic Alignment Process and Parker et al.'s (1988) Enterprise-wide Information Model. Their model allows improving an overall strategy and communication between functions and hierarchical layers; development of shared values; and knowledge management.

Ward and Peppard (2002) state that IS strategy that enhances the business value should be applications focused. Parallel to these, we will also focus on ERP, an Enterprise System instead of focusing alignment with whole IS. Following a generic perspective, which is covering whole IS on alignment may have several disadvantages. For example, Avison et al., (2004) argue that following a generic strategy may cause organizations to lose their flexibility. According to Klein and Calderwood (1991), the more generic the research, the wider range of applicability, the more multipurpose usage, the more common language and metric would apply. However, the assumptions (goals, choices, independence of utilities, and relationship between utilities) should be crucial while conducting a generic research. Generic IS models may have problematic assumptions that will not fit to specific technologies leading to less appropriateness, reduced sensitivity, abstractness or mismatch between the goals of specific technologies and the organization's elements. On the other hand, the more specific research would capture more details in terms of business processes (i.e., importance), flows, structure, etc. (Andersen and Fagerhaug 2001). Therefore, examining alignment from a specific technology perspective rather than an overall Information Systems approach can provide more reliable results. As discussed previously, majority of current research reveal that alignment has a positive impact on performance. Complex nature of these concepts requires more examination about the subject. Considering the fact that ES is a system that related to information technology, flexibility of this technology, its structure would have an impact on alignment.

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Duncan (1995) defines IT structure as the combination of technology components and management factors. Regarding technology component, the author defines IT structure as "a set of shared, tangible, IT resources that provide a foundation to enable present and future business applications" (p.39) while she includes IT plans, alignment, and skills as part of management factors. Byrd and Turner (2000) use three qualities, connectivity, compatibility, and modularity to describe IT structure. The authors define connectivity as "the ability of an technology component to attach to any of the other components inside and outside the organizational environment"; compatibility as "the ability to share any type of information across any technology component"; and finally modularity as "the ability to add, modify, and remove any software, hardware, or data components of the infrastructure with ease and with no major overall effect" (p.171). Considering the nature of business world in knowledge-era in which unplanned events may occur often, keeping the flexibility of IT is critical for organizations (Duncan, 1995). A flexible IT system can allow organizations to give better and quicker response to customers, suppliers, and changing demands and needs. Since the structure of IS plays a key role in performance (Broadbent et al., 1999; McKenney, 1995; Byrd and Turner, 1999), flexible IT can enhance the competitive performance of the firms. Byrd and Turner (1999) state that in order for IT to provide competitive advantage, it must be strategic, and have impact on the "goals, operations, products, or environmental relationships of organization" (p.43). In terms of technology, a flexible IS systems provides more opportunities for sharing, and reuse of resources, innovation (Duncan, 1995) as well as reengineering of business processes (Broadbent et al. 1999). These opportunities allow organizations to reduce their costs, increase the number of possible strategic options, maintain or enhance the competitive advantage, and therefore create a positive impact on their performance. In addition, Byrd and Turner (1999) found a positive relationship between IT structure and competitive advantage that generally leads to enhanced performance. On the other hand, Chung et al. (2005) did not find any direct impact of IS structure on the performance and suggested that IS structure may have impact on “intermediate performance variables and not overall business performance variables like ROI or market share (Barua et al., 1995))” (p.39). Therefore, in this study we will measure both direct and indirect effect of IS flexibility on performance. Henderson and Venkatraman (1993) argues that alignment and flexibility are related and mainly state that alignment is critical to the IS structure. On the other hand, Duncan (1995) states that although alignment will have positive impacts on the business and strategy, its impact on flexibility would be limited. In addition, Chung et al. (2003) state the positive relationship between flexibility and alignment, they hypothesize that flexibility has the impact on alignment, more specifically the strategic alignment of business and IT. In this study, we will be examining alignment at organizational level rather than process level as it is in many studies. We will measure alignment in terms of a synergy between

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business strategies and ES strategies based on Venkatraman’s (1989) fit measures. In addition, because of the fact that alignment can be affected by several factors (Luftman et al. 1999; Chan 2002; Pepperd and Breu 2003), we will examine the flexibility of IS and the relationship between alignment, flexibility, and performance.

2.1 Objectives of the Study

This study examines alignment between business strategies and Enterprise Systems rather than focusing on whole Information Systems. This study comprises three key objectives: (1) extend the strategic alignment concept by applying the combination of Miles and Snow and Porter’s typologies that will be mapped to strategy attributes as part of strategy; (2) identify one of the key antecedents of alignment, flexibility and see the impacts of this antecedent to business performance; and (3) finally examine the model through fit as moderation (Venkatraman 1989). Managements need to consider the ES strategy that will support and fit to their organizations' strategic orientation when they are conducting their IS planning. This study with its instrument will provide the quantification for evaluation of ES strategy, and ES strategic fit. In addition, organizations can enhance their competitiveness to assess their business and ES strategies (Chan 1992) through this study.

2.2 Conceptual Model

The main proposed model is composed of four constructs: business strategies (defined as Strategic Orientation of Business Enterprise (STROBE) by Chan (1992)), Enterprise Systems strategies (defined as Strategic Orientation of Enterprise Systems (STROES) by Chan (1992)), alignment or fit, and finally business performance. The conceptual model in Figure 1 depicts the relationship among these constructs. The conceptual model illustrates that business strategy, ES strategy and their alignment have an impact on business performance. This study is an exploratory one and aims to bridge the gap on several studies regarding alignment. With that purpose in mind, this study brings different business strategies that have proven to successfully have an impact on alignment or performance, together. Therefore, before strictly using one theory, that somehow has had conflicting result; we use an exploratory approach and test the relationship with the constructs. Therefore, after testing the main model, we introduce the flexibility concept to the model and examine the relationship between flexibility and alignment and performance. The constructs used for this study comes from the studies of Venkatraman (1989), Miles and Snow (1978), and Porter (1980), where the selection and combination of the factors have been done based on several studies such as Segev (1989), Chan (1992), Sabherwal and Chan (2001), Cragg et al. (2002), etc. The constructs have been grouped under three categories: Business Strategy Attributes, ES Strategy Attributes, and Performance Attributes.

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Business Strategy Attributes are based on Venkatraman (1989) study, where the author examined business strategy under seven categories: i) Company aggressiveness whose objectives include dominating the market and prices even if it requires reducing financial ratios, prices, profitability, etc.; ii) Company analysis, which focus on detailed analysis, effectiveness of IS, sophisticated outputs and planning for decision making; iii) Company defensiveness who focus on quality, effective relationships with supply chain network, performance monitoring, defending market share as well as a distinguished bargaining power over buyers/suppliers; iv) Company futurity focus on the ways to be more competitive in the future through forecasts, trend and “what-if” analysis; v) Company proactiveness focus on developing new products and services, acquiring businesses, and seeking new opportunities; vi) Company riskiness do not hesitate to take risk for businesses and projects (major decisions may require more conservativeness); vii) Company innovativeness focus on development of solutions through experimentation and creativity. ES Strategic Attributes have been developed through a mirroring approach (Chan 1992, 1997; Sabherwal and Chan, 2001; Cragg et al. 2002) and involves the same categories. The categories include ES Support for aggressiveness, analysis, defensiveness, futurity, proactiveness, riskiness and innovativeness. These attributes refer to the extent which current ES provide support for the company strategy attribute for each business strategy attribute. Both business strategy and ES strategy types are categorized based on Miles and Snow (1978) and Porter (1980) typologies. The typologies are selected based on the support found in literature and are merged based on Segev (1989) suggestions. The elements of typologies are as follows:

• Prospector – Seek market opportunities, first-in in new products, flexible, quick adaptation, broad and continuously developing product and service domains, less formalized and decentralized control, aggressive competition strategy, and profit making orientation.

• Differentiation (focus) - Unique product or service offering (technology, brand, customer service, etc.), satisfactory meeting with customer needs, has high loyalty from customers, low price sensitivity, creates high entry barriers for competitors.

• Analyzer - Focus on maintaining stability, cost-efficiency, limited product and service offer, second-in in new products, imitation of product after proven viability, seek for combination of both effectiveness and efficiency, and hybrid domain as well as same distance to centralization and decentralization (between defenders and prospectors).

• Cost focus/leadership - Cost leadership through a favourable access to raw material, high importance of efficient scale facilities, control over costs, tendency to minimize cost through reduction in R&D, advertising, etc. costs, target may be narrow.

• Defender - Niche, works in stable domain, limited range of products/services or market domain (segment), focus on quality, lower prices, etc., aims doing the best, centralized decision making and control, high bureaucracy, high cost-efficiency, market penetration, conservative in competition, follow "plan-act-evaluate" approach.

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The categorization does not include all the items from the typologies because of the problems stemming from the definitions of these items. For example, the forth type of strategy defined by Miles and Snow (1978) is Reactors. Characteristics and problems of organizations having this type of strategy include lack of a consistent product-market orientation, unclear strategy definition, unable to shape its structure to any strategy, lack of response to needs. In addition, the fifth element Porter's typology is "stuck in the middle" also removed from the analysis because of several reasons found in other studies. For example, these firms do not have specific market share or investments. They also lack the low-cost focus or differentiation focus unlike aforementioned firms. The third construct is the performance. There are several ways and types of metrics to measure performance. In this study, performance has been measured through several financial ratios such as market share, current sales growth rate as well as rate relative to major competitors, net profit, return on investment (ROI), relative revenue growth, and finally technological developments in business operations since we are measuring a technology’s impact on performance. These items can be grouped under categories such as market growth (market share, sales growth, revenue growth) and profitability (net profit, ROI). Another critical issue for the analysis is the calculation of alignment. Literature does not provide much variety of measurement for alignment. However, Venkatraman (1989) describes six types of calculation for alignment: Fit as Moderation (Interaction), Fit as Mediation, Fit as Matching, Fit as Gestalt, Fit as Profile Deviation, and finally Fit as Covariation. Each type of measurement requires specific type of data and objectives. For our analysis, three of analysis are suitable: fit as moderation, fit as matching, and fit as profile deviation. Studies of Chan (Chan, 1992, 1997; Sabherwan and Chan, 2001; Cragg et al. 2002) show that fit as mediation, which examines the interaction as well, is superior to matching in terms of explaining the outcome. Profile deviation requires additional variables and literature does not provide much comparison regarding the outcomes of two. Therefore, in this analysis we use fit as mediation approach to measure alignment. Figure 1 reveals the main theoretical model for this study.

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Figure 1. Theoretical model as part of a structural equation model

3. Research Methodology

Survey research is the appropriate method for collecting primary data pertaining to "describe, compare, or explain individual and societal knowledge, feelings, values, preferences, and behavior" (Fink 2008, p.1; Fink 2002). According to Salant and Dillman (1994), the objective of surveys is "to find out what percentage of some population has a particular attribute or opinion" (p.9). The most appropriate strategy based on the research questions, objectives, and foreseen analysis, is the quantitative data analysis in which data will be collected through questionnaire surveys. In addition, while measuring alignment, Reich and Benbasat (2000) suggest that researchers should examine the perceptions pertaining to the alignment rather than the structure of the artifact. The authors state that "... one should investigate the contents of the players' minds: their beliefs, attitudes, and understanding of these artifacts" (p.83). The instrument for this study has been developed based on Venkatraman’s (1985) study of STROBE and be mirrored for the IS strategy construct with a similar perspective of Chan (1992) and Cragg et al. (2002). Within this study, the appropriate aspects of works of Venkatraman (1985), Chan (1992), Sabherwal and Chan (2001), Segev (1989) (including Miles and Snow’s (1978) and Porter’s (1980) typologies), Luftmann et al. (1999) and Chan (2002) have been used as well as the dominant perspective, moderation of fit by Venkatraman (1989). As suggested by Dillman (2007), the questionnaire was refined through four stages: (i) reviewing the instrument by knowledgeable colleagues/analysts; (ii) conducting an interview with several participants in order to evaluate cognitive and motivational qualities; (iii) conducting a small pilot test; and (iv) final check. In our case, the initial instrument was sent to several PhD students, faculty, Post-Doc researchers as well as several IT professionals knowledgeable about the strategy of organization and their business unit. Based on the feedbacks, reviews, and survey data collected from the sample, the instrument was refined. A small number of individuals were interviewed regarding the questionnaire for additional refinement. Since the majority of instrument

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(with minor changes) has been tested and validated by several researchers (Venkatraman 1985; Chan 1992; Sabherwal and Chan 2001; Hale and Cragg 1996; Cragg et al. 2002; Chan 2002) as well as the high cost of collecting data (Bohmstedt 1970; Davis 1989), the pilot test has been skipped at this stage. As a final step, after the modifications instrument was sent to several faculty and PhD students for a final check. The questionnaires were sent to companies in North America (CANADA) from manufacturing and service industries. The survey questions are close-ended questions with five-point Likert scale. At the moment, we are still in the process of data collection. In this study, we will present the preliminary results of our study with the sample size of 60 that is collected from the IT/IS managers of companies of different sizes.

3.1 Analysis

In this study, we have utilized SPSS version 17 and WarpPLS, a Structured Equation Modeling (SEM) based statistical tool to conduct the analysis. More specifically PLS, Partial Least Squared (PLS) that is an SEM based tool was used in order to analyze collected data. PLS is a second-generation multivariate method that is capable of identifying both linear and nonlinear relationships among the variables/constructs. In addition, PLS and WarpPLS provide the estimated coefficients of the paths as well as the regression between latent variables/constructs. Another advantage of PLS and WarpPLS is that they simultaneously apprise the theoretical and measurement model (Chin et al., 2003). Factor analysis was conducted initially for 10 items, five for business strategy and five for ES strategy. The objective of this factor analysis was to examine whether the respondents were able to distinguish between two separate strategies. The results reveal that, indeed, the respondents were able to distinguish two strategies. As shown in Table 2, all items were loading to the right strategy. Although two items, leadership (BUST4) and defender (BUST5) were below 0.5 threshold value, these two variables were also successfully loading on the right factor more than the other factor. Table 2 also reveals information regarding the reliability of measurement. Cronbach’s alpha and composite reliability are two measurements to assess the reliability (Fornell and Larcker, 1981; Nunnaly, 1978). The acceptable values for Cronbach’s alpha is 0.7 while 0.6 is marginally acceptable. The threshold for composite reliability is 0.7. Our results show that while composite reliability is above 0.7, Cronbach’s alpha is 0.583. Since one of these measures should be above the threshold, our results indicate an acceptable reliability for the measurement model. Kaiser-Meyer-Olkin (KMO) statistics that indicate the sampling adequacy should be 0.6 or above so that data is suitable for factor analysis. Our results indicate the KMO value as 0.704 (with Chi Square as 205.823) and significant stating that factor analysis can be conducted. In addition, the Total Variance Explained was 53.03% for our analysis.

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Table 2: Factor loadings and reliabilities for business strategy and ES strategy items Construct Indicators Business Strategy ES Strategy Cronbach’s Alpha CR Business Strategy

BUST1 .839 .141 .583 .737 BUST2 .728 .019 BUST3 .672 .208 BUST4 .250 -.029 BUST5 .447 -.157

ES Strategy

ESST1 -.175 .767 .861 .900 ESST2 .153 .827 ESST3 .108 .775 ESST4 .030 . 787 ESST5 -.070 .838

Notes: Method: Principal Component Analysis with Varimax Rotation, Kaiser Normalization

In order to examine the convergent validity we checked the factor loadings and whether the items are loading to the same factor for the performance. As Table 3 shows, all the items are loading well on a single factor. Cronbach’s alpha and composite reliability tests show that the reliability is also acceptable, that is above 0.7 for Cronbach’s alpha and/or composite reliability (Fornell and Larcker, 1981; Nunnaly, 1978). Table 3: Factor loadings for performance items Construct Variables Loadings Cronbach’s

Alpha CR

Performance PERF1 .791 0.771 .841 PERF2 .807 PERF3 .803 PERF4 .866 PERF5 .418 PERF6 .616

Table 4 and 5 shows the descriptive statistics such as mean and standard deviation for the variables from the questionnaire. While first column shows the constructs such as strategy types, i.e. business and ES, and performance for Table 4, second column in Table 4 and the first column in Table 5 show the variables that comprise these constructs. Last two columns in both tables reveal the mean and standard deviations for these variables.

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Table 4. Descriptive Statistics for business strategy, ES strategy, and performance items Indicators Mean Standard

Deviation Business Strategy Prospectors 3.70 1.046

Differentiator 3.47 0.853 Analyzer 3.45 1.096 Cost/Leadership 4.07 1.177 Defender 4.33 0.774

ES Strategy Prospectors 2.62 0.904 Differentiator 2.63 0.956 Analyzer 3.12 1.091 Cost/Leadership 3.12 1.106 Defender 3.40 1.123

Performance Market Share 3.55 0.928 Sales Growth 3.17 1.044 Net Profits 3.38 0.976 Relative ROI 3.53 0.853 Revenue Growth 3.60 0.924 Relative Technological Developments/Innovations 3.13 0.929

The alignment between ES and business strategy has been calculated based on Venkatraman (1989) study. Venatraman (1989) identifies six types of alignment (the authors used the term fit) such as Fit as Moderation (Interaction), Fit as Mediation, Fit as Matching, Fit as Gestalt, Fit as Profile Deviation, and finally Fit as Covariation. Considering the objective of the study, type of data collected, and previous studies using these analysis, fit as moderation has been selected as the appropriate type of measurement for this study (please see Venkatraman (1989) study for detailed information regarding different types of measurements). Several researchers (Chan, 1992; Guest, 1997; Cragg at al., 2002) argue that moderation has advantage over match and profile deviation that are appropriate measurements of fit with our data. Table 5 shows the descriptive statistics for the single alignment measurements. Table 5. Descriptive Statistics for alignment between business strategy and ES strategy

Business/ES Alignment (Moderation)

Mean Standard Deviation

Prospectors 9.63 4.26 Differentiator 9.25 4.44 Analyzer 11.02 5.39 Cost/Leadership 12.83 6.40 Defender 14.75 5.85 Table 6 reveals the bivariate correlations among the performance related variables. The significance level of relationships are 0.05 for (*) and 0.01 for (**).

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Table 6: Correlations among the performance items, alignment, and IT flexibility Construct MarketS SalesG NetPr RelROI RevGrow Tech Align FlexMarketS 1 SalesG 0.603** 1 NetPr 0.512** 0.485** 1 RelROI 0.522** 0.488** 0.524** 1 RevGrow 0.261* 0.369** 0.210 0.340** 1 Tech 0.110 0.239 0.186 0.230 0.300** 1 Align 0.434** 0.275* 0.282* 0.385** 0.290 -0.030 1 Flex 0.083 0.152 0.051 0.234 0.096 -0.044 0.437** 1 Notes:

• MarketS: Market Share • SalesG: Sales Growth • NetPr: Net Profits • RelROI: Relative ROI • RevGrow: Revenue Growth • Tech: Relative Technological Developments/Innovations • Align: Alignment between Business and ES Strategies, Moderation • Flex: Flexibility

Table 6 shows that alignment is positively correlated with market share (β=0.434) and relative Return on Investment (ROI) (β=0.385) at 0.01 significance while positively correlated with sales growth (β=0.275) and net profit (β=0.282) at 0.05 significance level. On the other hand, alignment is not significantly correlated with either revenue growth or relative technological developments/innovations. Our results also indicate a positive and significant correlation between alignment and the IS flexibility (β=0.437, p<0.01). In order to measure discriminant validity, average variance extracted (AVE) values can be used. The square roots of AVE values for business strategies, ES strategies, alignment, and performance are as follows: 0.630 (for business strategies), 0.830 (for ES strategies), and 0.693 (for performance). In order to examine multicollinearity, we measured variance Inflation Factor (VIF) values. Our results indicate that all the VIF values are below the threshold value of five. While VIF values for business strategies and alignment, and ES strategies and alignment are 1.031, in this case average variance inflation factor (AVIF) is also 1.031; therefore indicating no risk for multicollinearity. The theoretical model with its path coefficients and significance levels are shown in Figure 2. In this figure, β represents the path coefficient, (*) represent the significance levels, and R2 represents the variance explained by the model.

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Figure 2. Path coefficients in structural equation model

Notes:

*: Correlation is significant at the 0.05 level (2 tailed) **: Correlation is significant at the 0.01 level (2 tailed)

The results from WarpPLS analysis reveal that the relationship between strategic alignment and both business strategies (β=0.40) and ES strategies (β=0.82) are positive and significant at 0.01 level. The variance explained (R2) is also 0.94. In addition, the relationship between strategic alignment and the performance that is calculated through six items such as market share, sales growth rate, net profits, revenue growth, relative ROI, and technological developments in business operations, is positive (β=0.39) and significant at 0.01 level. In addition to aforementioned analysis, we calculated the fit for the theoretical model shown in Figure 2. Kock (2010) suggests to use a set of measures such as average variance explained (ARS), average variance inflation factor (AVIF) and average path coefficient (APC) values to examine the quality of the model. Our results show that ARS value is 0.546, APC value is 0.534 and both of these measurements are significant at 0.01 level. In addition, AVIF value is 1.031, which is less than five; therefore indicating a good quality of the model. After analyzing the relationship between alignment and performance, ANOVA test was used to examine relationship between performance and flexibility. For this purpose, flexibility was converted into three levels: low, medium, and high for simplicity purposes. The ANOVA test has been used for testing the mean differences in performance for the three groups of flexibility.

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Table 7: ANOVA results Independent Variable

Dependent Variable SS F Val. Sig.

Flexibility Market Share 2.856 1.696 NS Sales Growth 5.710 2.776 Sig Net Profits 2.313 1.224 NS Relative ROI 6.480 5.066 Sig Revenue Growth 7.159 4.718 Sig Relative Technological Developments/Innovations 6.061 3.850 Sig Overall Performance 4.428 6.287 Sig

The F values shown in Table 7 are significant for relative ROI, revenue growth, and relative technology development while not significant for market share, sales growth, and net profits. In terms of the overall performance measurement, the mean differences are significant for flexibility. In addition, additional analysis reveals that flexibility is positively correlated with performance. In other words, while the flexibility increases, the performance outcome has increased as well. In addition to the aforementioned analysis, we examined the relationship between alignment, performance, and flexibility. With that purpose, we test the model: i) with flexibility as mediator between alignment and performance; and ii) with alignment as mediator between flexibility and performance. In the first case, shown in Figure 3, the relationship between alignment and flexibility (β=0.43) and performance (β=0.28) were significant at 0.01 level. However, the results reveal that the relationship between flexibility and performance was not significant. Therefore, we could not find any mediating relationship of flexibility and alignment, and performance. For this model, the recorded APC value was 0.451, ARS was 0.457 and significant at 0.01 level while the AVIF value was 1.068 stating a good fit of the model without any risk of multicollinearity.

Figure 3. Path coefficients in structural equation model for flexibility as mediator

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On the other hand, when we examine the relationship amongst alignment, performance, and flexibility where alignment is a mediator between flexibility and performance, our results indicated positive results. For this analysis, in order to examine the mediation, we used Sobel test (Baron et al. 1986), which would give the same results with WarpPLS. We followed the four steps, in other words four consequent regression analysis described by Baron et al. (1986): a) regression between independent variable and mediator; b) regression between independent variable and mediator; c) regression between mediator and dependent variable while controlling for the independent variable; and finally d) regression between independent variable and dependent variable while controlling mediating variable. The results of these analyses must show that the dependent variables should be significantly affected by their associated dependent variables in cases a,b, and c above. The next requirement for an appropriate mediation effect is that the results of the regression between independent variable and dependent variable while controlling the mediating variable should be non-significant. Table 8: Direct, indirect, and total effects of mediation through Sobel Test

Relationship Independent Dependent Control Coefficient S.E. t value SignificanceFlexibility Performance None .1146 .1151 .9951 .3238 Flexibility Alignment None 12.8404 3.1053 4.1349 .0001 Alignment Performance Flexibility .0116 .0047 2.4834 .0160 Flexibility Performance Alignment -.0342 .1255 -.2723 .7864 Value S.E. Z SignificanceMediation Effect .1488 .0714 2.0846 .0371 Our results, as shown in Table 8, indicate that alignment is a mediator between flexibility and performance. In other words, higher flexibility leads to higher performance because of alignment.

4. Discussion and Conclusion

There are several variables that affect performance directly and indirectly. Alignment and flexibility of information systems are among the most important factors that managers have cited for last several years that have impact on performance. Since Enterprise Systems are part of technology, the technology component should be studied as well as the strategy part. Therefore, in this study, we examined two constructs, one related technology, one related strategy, and their impact on performance. Our research examines the details to some degree to see the nature of relationship between these three factors.

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Considering the fact that alignment allows organizations to adapt the dynamic environment (Chung et al., 2003) faster and more efficiently, the more alignment between business and ES strategies will make the adaptation quicker. On the other hand, flexibility, as well, help organizations to update their technical structure quickly. Considering the fact that in an information era, having access to the u-to-date (and right) technology would bring competitive advantage over competitors and also an increase I performance. Our results show that alignment has significant and positive impact on performance. Although combination of different business strategies has been tested in our study, our results support the findings of previous studies. In addition, as we expected, we did not find a significant direct relationship between flexibility and performance. On the other hand, as we expected, our results show that flexibility has impact on performance through alignment. In other words, alignment in this study also mediates the relationship between flexibility and performance. Based on these results, we can argue that alignment is a critical factor that has impact o performance. On the other hand, although having access to the most-up-to-date technology, a flexible IS structure has impact on performance; this impact is more likely to be through alignment. In that sense, our results show similarities with Chung et al. (2005) whose findings indicate no direct impact of IS flexibility on performance. In addition to this, our results indicate that flexibility of IS systems has impact on performance when there is strategic alignment between business and Enterprise Systems. Our additional tests also reveal positive relationship between flexibility and performance. When the flexibility has an impact on performance (through the alignment), higher flexibility leads to better performance and vice versa. Main limitation of this study is the sample size. Since we are still in process of collecting data, the results are merely the preliminary results of our sample of 60 responses. However, these results can also provide us valuable information regarding the validity of the instrument, and shed light on possible findings with more data. In addition, the reader should keep in mind the difficulty reaching IT/IS managers for a survey. As mentioned earlier, alignment has several antecedents and testing the model with one or more of these antecedents (i.e. management support, communication between IS and business departments, ease of integration, connection between IS and business plans, mutual understanding between IS and business departments, etc.) would add to the alignment and strategy literature. Another future study may be examining other types of measurement for alignment (i.e., matching, profile deviation) with the current variables.

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Dashboards adoption in the business controller’s decision making process

Oana Velcu1 & Ogan Yigitbasiouglu2

1Department of Accounting, Hanken School of Economics, Finland 2Department of Economics, Hanken School of Economics, Finland

[email protected], [email protected]

Abstract

There has been discussion whether corporate decision-making can be helped by decision support systems regarding qualitative aspects of decision making (e.g. trouble shooting) (Löf and Möller, 2003). Intelligent decision support systems have been developed to help business controllers to perform their business analysis. However, few papers investigated the user’s point of view regarding such systems. How do decision-makers perceive the use of decision support systems, in general, and dashboards in particular? Are dashboards useful tools for business controllers? Based on the technology acceptance model and on the positive mood theory, we suggest a series of antecedent factors that influence the perceived usefulness and perceived ease of use of dashboards. A survey is used to collect data regarding the measurement constructs. The managerial implications of this paper consist in showing the degree of penetration of dashboards in the decision-making in organizations and some of the factors that explain this respective penetration rate.

Keywords: controllers, dashboards, decision-making, TAM.

1. Introduction

The purpose of this paper is to conduct an empirical study of business controllers’ adoption of dashboards as decision support tools. The aim is to understand how business controllers perceive and utilize dashboards as technology for business performance management. Decision-making needs to be supported with accurate performance measures and adequate information systems that enable decision-makers to have constant access to data. In the business intelligence literature, dashboards are the most useful analysis tools for business performance management and business activity monitoring (Negash and Gray, 2008). Dashboards fall under a broader concept known as decision support systems (more specifically a data driven DSS: http://dssresources.com/history/dsshistory.html) and are designed for decision-makers at operational, tactical and strategic levels in organizations. Around mid-1990’s, decision support systems started to be called dashboards or reporting systems (Adam and Humphreys, 2008). Dashboards represent graphical user interfaces that help decision-makers analyze and communicate throughout company the information regarding the performance

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metrics of the organization. They became popular after the Enron scandal in 2001, which drew the attention upon the need to have managers at all levels being able to monitor and control what happens in companies (Few, 2006). The idea of dashboards is to help visualize large amounts of data in a condensed representation to support the managers in their decision-making. For example, management may need to see condensed reports on profit and loss analysis, profits per product lines, fill rates for orders, gross margin analysis, balance sheets, etc. Research indicates that accountants differ in the extent to which they take initiatives to improve operational and decision-making process (Naranjo-Gil et al., 2009, Emsley, 2005, Johnston et al., 2002, Hopper, 1980). For example, Johnston et al. (2002) found that management accountants were reluctant to get involved in operational decision making, especially on this was related to change programs. Management accountants perceived their role as ‘preventing things from happening’ (Johnston et al., 2002, pp. 1331). Emsley (2005) examined management accountants’ willingness to adopt Management Accounting Systems (MAS) innovations, such as Balanced Scorecards, Activity Based Costing and Benchmarking. He found that the management accountants who were more involved in managerial decision-making were more likely to know whether a new MAS is appropriate for organization. In summary, the literature suggests that CFO’s tendency to support the adoption of MAS innovations vary widely based on the underlying cognitive and affective characteristics that determine managers’ decision making and that are predictive of organizational performance (Hambrick and Mason, 1984). Naranjo-Gil et al. (2009) identify demographic variables such as the age, the education and the tenure as good proxies for these cognitive and affective characteristics. They find that younger, less tenured and more business-oriented CFOs are more likely to adopt MAS innovations. These findings support the literature on management accounting innovation and change by showing that the adoption decisions are not only the outcome of rational decision-making process and institutional pressures (Malmi, 1999) but also of the characteristics of individuals responsible for such decisions. Software vendors market the new generation of dashboards that have three main characteristics: (1) align business process with live data to provide business intelligence at all levels of the company, (2) use intuitive and easy to digest visuals for delivering information to busy executives, and (3) visualize and navigate timely and accurate information. Despite the intensive effort to underline the benefits of dashboards for business, to our knowledge there is little evidence on the actual usage of dashboards by decision makers throughout organizations. We focus on business controllers in their role of supporting and advising the management of an organization in exerting control so that the financial goals can be reached (De Loo et al., 2006). In the light of literature on management accounting innovation and change, what are those factors related to the characteristics of business controllers that would incline them to adopt and use dashboards as decision support tools.

2. Prior Research

The Theory of Acceptance Model (TAM) is one of the most widely applied Information Systems (IS) theories that is grounded in the psychological theory, the Theory of Reasoned Action, which explains the users’ intention to perform a behaviour (Davis, 1989, Davis et al., 1989, Davis, 1996). The outcome construct in

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TAM is the users behavioural intention to use an IT. The behavioural intention is influenced by Perceived Usefulness (PU), the degree to which users using a system believe that it would improve their performance, and Perceived Ease of Use (PEOU), the degree to which users of a system perceive it to be effortless. PEOU influences PU. Some researchers argue that the influence of PEOU on behavioural intention is task related and have suggested careful task specifications as additions to TAM studies (Djamasbi et al., 2010). As a result, there are numerous expansions to TAM that can be investigated. For example, the role of individual characteristics that influence cognition, such as affect, computer playfulness and personal willingness to try out a new IS, is the focus of our study. There is evidence that affect is a necessary component of rational decision making (Damasio, 1994, Slovic et al., 2007). Affect helps individuals to eliminate the alternative solutions that don’t feel right, so that it remains a manageable subset of possible solutions. Therefore, a rational decision maker goes through a combined sequence of cognitive and affective processes. However, other studies showed the independence of affect from cognition, indicating that there may be situations of affective stimulus that do not require cognitive appraisal (Slovic et al., 2007). Affect is defined as an individual’s moods and emotions (George, 1989).Unlike volatile emotions, moods are more pervasive and enduring. These characteristics make moods more suitable affective framework for studying cognitive processes in organizational context, such is the use of IS innovations. There are different mood categories, such as positive, neutral and negative. This study is grounded in the positive mood theory and focuses on the effects of positive mood on use of dashboards. According to this theory, being in a positive mood influences how the thoughts are organized and accessed. Being in a positive mood, it enables decision makers to be more flexible in their cognitive process (PU and PEOU) and may be more inclined to use decision support systems to make informative decisions. H1: Positive mood is positively associated with the perceived ease of use of dashboards as a tool in the decision-making process. H2: Positive mood is positively associated with the perceived usefulness of dashboards as a tool in the decision-making process. Furthermore, TAM can be extended by incorporating antecedents to affect that have an influence on decision-makers acceptance of dashboards as decision support tools. Computer playfulness is one of the antecedents that we suggest in this paper. It describes the decision makers tendency to interact spontaneously, intensively and imaginatively with the IS implemented in their organization (Serenko et al., 2007). Computer playfulness has been used as an intrinsic-motivation antecedent for system adoption and use. In this paper, we suggest that computer playfulness affects indirectly the cognitive constructs of TAM (PU and PEOU) through the positive mood. H3: Computer Playfulness is positively associated with the positive mood of using dashboards for decision making.

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The personal innovativeness in information technology (PIIT) may also have an effect on the decision makers’ use of dashboards. Karahanna et al. (2002) find that PIIT has a positive support to an individual’s attitude and intentions to shop online. We suggest that PIIT affects the positive mood of individuals, which in turn affect the PU and PEOU of dashboards. H4: PIIT is positively associated with the positive mood of using dashboards as support for decision making. The age, tenure and education of decision makers may influence an individual’s decision to use a new IS (Naranjo-Gil et al., 2009). Hence: H5: Relatively young business controllers will be more willing to use dashboards as support for decision making. H6: Relatively short tenured controllers will be more willing to use dashboards as support for decision making. H7: CFO’s with a combined business and IT oriented educational background will be more willing to use dashboards as support for decision making. Dashboards should enable decision makers to access and quickly evaluate different aspects of a company’s performance (Hanoa, 2009). Hence: H8: The intention to use dashboards as support tools for decision making is positively associated with improved organizational performance. In summary, we suggest to empirically investigate the following model of antecedents of using dashboards for improving organizational performance (Figure 1).

Computer playfulness

PIIT

Business Controller’s Age

Positive mood

PU

PEOU

Intention to use dashboard

KPI Business Controller’s Tenure

Business Controller’s

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3. Research Methodology

The constructs in the proposed model will be measured based on a survey distributed among business controllers working in organizations. The measures for the construct „organizational performance” will be first collected as a result of interviews with preselected group of business controllers that will indicate what are the financial and non-financial performance indicators considered important in their organizations irrespective of the strategic position of their organization.

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Appendix A: Survey The following statement will be rated from 1 to 7, where 1 = not at all and 7 = to a very great extent.

1. I use dashboards as a support tool for the decision making process in which I am involved.

I use the dashboard for the following purposes: Business Controller’s Age

1. Kindly indicate the age group to which you belong:

20 – 30; 31 – 40; 41 – 50; 51 – 65. Business Controller’s Tenure

1. Kindly indicate for how many years you’ve worked as CFO within the company:

Less than 1 year; 1 – 5 years 6 – 10 years More than 10 years. Business Controller’s Education University degree and title Seminars on IT Special IT courses MBA PIIT

1. If I heard about a new information technology, I would look for ways to experiment with it (1 = Strongly disagree , 7 = Strongly agree):

2. Among my peers, I am usually the first to try out new information

technologies (1 = Strongly disagree , 7 = Strongly agree): 3. In general, I am hesitant to try out new information technologies (1 =

Strongly disagree , 7 = Strongly agree):

1 2 3 4 5 6 7

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4. I like to experiment with new information technologies (1 = Strongly disagree , 7 = Strongly agree):

Computer Playfulness (Serenko, 2007) For each adjective listed below, please indicate the number that best matches a description of yourself when you interact with computers (1 = Strongly disagree , 7 = Strongly agree):

1. Spontaneous 2. Unimaginative 3. Flexible 4. Creative 5. Playful 6. Unoriginal 7. Uninventive

Positive mood (Lin et al., 2008) For each item below, please indicate the number that describes best the way you are feeling when you use dashboards (1 = Strongly disagree , 7 = Strongly agree): Pleased / Annoyed Happy / Unhappy Satisfied / Unsatisfied Contended / Melancholic Hopeful / Despairing Relaxed / Bored PU (Djamasbi et al., 2010) On a scale of 1 to 7, please rate the following questions for the dashboard that you finished work with:

1. Using the dashboards enhances my effectiveness at work. 2. Using the dashboards enhances my productivity. 3. Using the software that I just worked with can be useful for communicating

with other colleagues. 4. Using the dashboard improves my performance.

PEOU (Djamasbi et al., 2010) On a scale of 1 to 7, please rate the following questions for the dashboard that you finished work with:

1. Learning to operate the dashboard was easy for me. 2. I find it easy to get the dashboard to do what I wanted it to do. 3. It was easy for me to become skilful at using the dashboards.

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4. I found the dashboards easy to use.

Behavioural intention to use (Djamasbi et al., 2010) Please rate on a scale from 1 (totally disagree) to 7 (totally agree) your willigness to use the dashboards by answering the following questions:

1. Assuming that I had access to dashboards, I intend to use it. 2. Given that I had access to the dashboards, I predict that I would use it. 3. I expect to use dashboards when they become available.

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References

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Serenko A., Bontis N., and Detlor B., (2007) “End-user adoption of animated interface agents in everyday work applications”, Behaviour & Information Technology, Vol. 26, No. 2, pp. 119-132.

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Privacy, Data Pollution, Organizational Responsibility, and the Sustainability of Information Ecologies

Rob Nehmer & Marilyn Prosch

Oakland University, Arizona State University [email protected], [email protected]

Abstract The literature on business and sustainability has been sparse to date. Theory building in the domain has been especially scarce. One-pillar models emphasize an ecological dimension to sustainable development. These models talk about sustainability in terms of the reduction of pollution and the sustainability of ecological environments without regard to human usage. That is, they emphasize sustainability from a “natural” point of view. Multi-pillar models, most of which are newer and appear post 2000, are usually the so-called three-pillar models. The usual pillars in these models are ecological, economic, and social. Each of the three pillars, the ecology, the economy and society, are separate, yet interconnected, systems. In this paper, we add to this formulation consideration of four distinct different theoretical perspectives that are employed to study privacy. The four perspectives are 1) design science and technical, 2) individual privacy and consumer behavior, 3) strategic, operational and financial, and 4) societal and public policy. To this, we add the concept of environmental pollution, where modeling is very advanced in the natural sciences and a large research stream exists. Modeling pollution in "information economies," however, is scarce. We assert that contaminants that exceed normal levels in data are "toxic data" in information system ecologies. We propose a digital ecology comprised of resources, economic activities, and society. Each component of this ecology is a system in its own right. The interactions among the three systems produce and re-produce an ecology of human and non-human, living and non-living interactions. The digital ecology models some of the underlying complexity of this natural ecology. Even at this relatively early stage of the development of this digital ecology, its own complexity and risks become an area of concern, especially in matters of privacy. In the paper, we attempt to model privacy responsibility within a corporate model, and then from there, we believe it makes sense to tie this concept of corporate privacy responsibility into the greater societal aspect of social responsibility. The model is based on the Dillard and Layzell’s (2008) model. Since the major stakeholders in current economic societies are organizations and individuals, we develop a model of organizational privacy that we then use as a backdrop to develop a model of privacy, data pollution, and sustainability.

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1. Introduction

The literature on business and sustainability has been sparse to date. Theory building in the domain has been especially scarce. One-pillar models emphasize an ecological dimension to sustainable development. These models talk about sustainability in terms of the reduction of pollution and the sustainability of ecological environments without regard to human usage. That is, they emphasize sustainability from a “natural” point of view. Multi-pillar models, most of which are newer and appear post 2000, are usually the so-called three-pillar models. The usual pillars in these models are ecological, economic, and social. Each of the three pillars, the ecology, the economy and society, are separate, yet interconnected, systems. We add to this model consideration of four distinct different theoretical perspectives that are employed in privacy studies. The four perspectives are 1) design science and technical, 2) individual privacy and consumer behavior, 3) strategic, operational and financial, and 4) societal and public policy. In the area of various types of environmental pollution, modeling is very advanced in the natural sciences and a large research stream exists. Modeling pollution in "information economies" is scarce. We assert that contaminants that exceed normal levels in data are "toxic data" in information system ecologies. We propose a digital ecology comprised of resources, economic activities, and society. Each component of this ecology is a system in its own right. The system of natural resources consists of the available goods on the planet and their interrelationships where the goods are both living and non-living. The economy is the system of interrelated human activities, which affords humans the means of sustaining their basic physical well-being across generations. Society is then the system of interrelated human activities, which allow humans to maintain their psychic well-being across generations. The interactions among the three systems produce and re-produce an ecology of human and non-human, living and non-living interactions. The digital ecology models some of the underlying complexity of this natural ecology. Even at this relatively early stage of the development of this digital ecology, its own complexity and risks become an area of concern, especially in matters of privacy. In the paper, we attempt to model privacy responsibility within a corporate model, and then from there, we believe it makes sense to tie this concept of corporate privacy responsibility into the greater societal aspect of social responsibility. Since the major stakeholders in current economic societies are organizations and individuals, we develop a model of organizational privacy that we then use as a backdrop to develop a model of privacy, data pollution, and sustainability. The paper develops in this way. First, we discuss the social sustainability and privacy literatures. Next, we consider the current state of the art concerning society, public policy, and privacy. Then the paper explores the relationships between privacy and the economy since the economy is one of the fundamental systems in social sustainability. Then we introduce and evaluate the concepts of pollution and data pollution, after which we define organizational privacy responsibility. We then use these descriptions to develop a model of privacy, data pollution, and social sustainability.

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2. Social Sustainability Research

The literature on business and sustainability has been sparse to date. Theory building in the domain has been especially scarce. Dillard and Layzell (2009) performed a deep textual analysis of one company’s Corporate Responsibility Report. From a theory building perspective, their paper is interesting because it conflates social sustainability with corporate responsibility. According to the authors “(T)he terms corporate responsibility and sustainability are used, but the term social sustainability is not officially part of the formal vernacular…The two phrases dance around each other, but generally coalesce around the term corporate responsibility.” Their paper then concentrates on exploring the self divulged information reported by the Intel Corporation in the areas of education, the community, and the corporation itself. While this is a useful approach in examining voluntary corporate disclosure, it is less helpful in providing a grounding of social sustainability in business contexts. As we continue to explore possible theoretical underpinnings for sustainability, we can consider whether and how sustainability is taught. In some cases, teaching actually leads practice and we may consider sustainability to be a good candidate for this since it has generally been a development pushed by less powerful interests. Following this leads us to volume 1 of Teaching Business Sustainability: from Theory to Practice (2004). This collection of papers contains additional guidance in the search for theories of sustainability. Foot and Ross’s (2004) paper “Social Sustainability” in that volume sees the beginnings of a three-pillar model in the Brundtland Commission’s definition of sustainable development, which is that it “meets the needs of the present without compromising the ability of future generations to meet their own needs” (p. 107). The Brundtland Commission was formed by Oxford University to investigate social sustainability in the 1980's. This definition adds an intertemporal characteristic to sustainability. Foot and Ross see social sustainability as being driven by two factors. The first factor is the need for the society to sustain its ecology in order to be able to preserve the ecological matrix in which it has developed and maintains itself. The Cary Institute of Ecosystem Studies defines ecology as "the scientific study of the processes influencing the distribution and abundance of organisms, the interactions among organisms, and the interactions between organisms and the transformation and flux of energy and matter." The second factor is the economic (business) need for going-concern business opportunities within cultural contexts. They promote the idea of a triple bottom line, first developed by Elkington, where the development of long term metrics in the areas of economics and finance, the ecology, and society becomes a driving force behind the movement towards sustainability. In their view, one of the most significant problems with this is the creation of these metrics, especially for social sustainability. From an economic and financial point of view, the metrics should correlate with risk reduction and the creation of business value. This triple bottom line idea is very similar to a balanced scorecard approach where the typical financial measures are supplemented with additional metrics for associated domains, typically operations, customers, and employees through organizational learning. A fairly extensive body of research on the balanced scorecard approach exists in the

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accounting literature. Using this literature in conjunction with what is available in the sustainability area will be explored in this paper in subsequent sections. Another paper in the Teaching Business Sustainability collection is by Elliott et al (2004) entitled “Approaching Sustainability through a Business-Science Synthesis.” This paper suggests a paradigm shift in business. This shift is from a view of the world in which business exploits the environment and shifts the externalities to future generations to one where “profitability and environmental responsibility are treated as complements rather than substitutes” (p. 151). In the authors view, this shift will be driven by consensus building activities involving business, citizens, and the political system. Each group will continue to act in its own self interest while also giving consideration to the larger system. Unfortunately, the authors do not provide a prescriptive approach to the operationalization of their shift in paradigms. A third paper of interest in this volume is “Teaching Sustainability: Whole-Systems Learning” by Brown and Macy (2004). The authors provide a prescriptive method for teaching sustainability principles through what they term “the work that reconnects.” The essentials of this work are to help people reconnect to what the authors call the self-correcting, self-organizing powers of living systems. Once this is accomplished, the student is in a position to “seek out, create, and apply sustainable business practices within the workplace and the larger world” (p. 219). This is done through a series of exercises. These exercises are designed to help people in the following areas. First, they are designed to help them to understand the costs of externalizing business and human costs of production. Second, they are designed to share personal experiences of these costs. Third, they provide a reframing of the distress in their lives caused by these externalities. Next, they provide experience in system science concepts and their application to daily life. Fifth is the exploration of personal responsibility to past and future generations of all life. Finally, the exercises help people clarify their intentions. A further method is used which unblocks feedback loops typically ignored in day to day human interactions. The method consists of recognizing mutuality, integrating painful information, expanding perceptual horizons, and finding creative responses. The final paper of interest in the volume is “Sustainability in a Business Context” by Wood et al (2004). The authors recommend that sustainability be integrated into the business’ strategic objectives. They present a process diagram with the following components. First, of course, the business must develop its strategic business objectives. Following that, the objectives are aligned with the focus on organizational learning objectives, much like a capability maturity model. Next there is the intellectual capital engine which consists of five pillars discussed below. Finally, the acceptance of real-time business sustainability projects will lead to measurable business results which can be fed back into the strategy process. The five pillars in this model represent the investment in intellectual capital and the existing process designs. The first pillar is business alignment which includes strategic leadership, effective communication and fact-based decision-making, among others. The argument here is that sustainability can be used as a rationale for business continuity and strategic leadership. The second pillar is sustainability knowledge. This includes a type of activity sometimes known as environmental scanning. It is the knowledge of the current state of the art in sustainability and the ability to forecast new developments as they evolve in the field.

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The third pillar is personal and organizational leadership. This includes purpose-driven leadership, visionary foresight and personal mastery. Not surprisingly, the details of how these items are to be operationalized are not clearly stated in the article. The fourth pillar is systems thinking. Systems thinking is defined here in terms which hearken back to the 1960’s. The main ideas are synergies and holistic, relational connections between the parts of the system. Also included here is the recognition that all systems are evolving and an appreciation of the role played by human mental models in helping to create the future. The fifth and final pillar is the enabling technology and processes. The emphasis here is on equity, transparency, and mutual benefit. Equity implies that all parties enjoy respect and equal status. Transparency refers to the requirement that the parties are all engaged in an open process devoid of gaming. Mutual benefit means that the connections between parties are known and understood. The material in this paper also lends itself to integration into a model of sustainability which we develop below. Another paper on social sustainability, Littig and Griessler (2005), looks at social sustainability from a more theoretical approach. Littig and Griessler begin their discussion with the Brundtland report (WCED, 1987) and the Rio documents (UN, 1992). They discuss one-pillar and multi-pillar models of sustainability development. One-pillar models develop directly from the WCED and UN reports and emphasize an ecological dimension to sustainable development. These models discuss sustainability in terms of the reduction of pollution and the sustainability of ecological environments without regard to human usage. That is, they emphasize sustainability from a “natural” point of view. Multi-pillar models, most of which are newer and appear post 2000, are usually the so-called three-pillar models. The usual pillars in these models are ecological, economic, and social. Continuing with the discussion of three-pillar sustainability models, Littig and Griessler point out that each of the three pillars, the ecology, the economy and society, are often considered as separate, yet interconnected, systems. Additionally, little consensus has been reached on what constitutes the society pillar. Often times other terms, especially culture and the political system, are made into separate categories distinct from the social pillar. From a theory building perspective, this leaves us with some problems. Indeed, Littig and Griessler point out that there are strong dichotomies between such constructs as social concerns versus social learning as well as science and politics in this area. They point out the need for an integrated sustainability theory which incorporates the ecology, the economy as well as society. Interestingly, their paper then emphasizes what is actually a two-pillar model of society and nature where society now includes the economic, political and social systems.

3. Privacy Literature Review

Much research on privacy has been conducted from many different perspectives. Before we review the literature, we will begin with a definition of privacy and its components. The AICPA has defined privacy as "the rights and obligations of individuals and organizations with respect to the collection, use, retention, disclosure, and destruction of personal information." (GAPP, 2009) GAPP is based on Fair

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Information Practices and legislation from around the world, and it has 10 Principles which are good guidance for protection of information: Managment, Notice, Choice and Consent, Collection, Use and Retention, Access, Disclosure to Third Parties, Security, Quality, and Monitoring and Enforcement. The dimensions of information privacy put forth by Smith et al. (1996), namely, the collection, unauthorized secondary use of information inside and outside of the organization, errors, reduced judgment, and data combination, closely align with GAPP. This alighnment indicates that the academic communities and industry agree somewhat on the basic parameters of privacy. However, drilling down into ownership issues and operationalizing these dimensions given business needs, social desires, technology and legal contexts makes it a very challenging area. Recently, two studies have developed research frameworks and synthesized prior research (Boritz et al. 2008 and Kauffman et al. 2009). The approaches are similar at first glance, but do have some dimensional differences. Boritz et al. (2008) examine Internet privacy, while Kauffman et al. (2009) examine enterprise-wide privacy. Boritz et al. (2008) develop a framework and review the literature organized around three main entities: customers, companies and government. Kauffman et al. (2009) develop a framework that represents five different kinds of stakeholders – individuals, organizations, privacy-enhancing solution providers, regulators and standard-setters,and independent assurance providers. Both studies place the customer as the center of focus and both have the government/regulators/standard setters as a main entity/stakeholder. Kauffman et al. (2009), however, separate privacy-enhancing solution providers from other organizations, while Boritz et al. (2008) has those two groups of stakeholders combined into one entity. One key difference between the studies, however, is that Kauffman et al. (2009) include a second key dimension in their framework. They consider the “variety of theoretical perspectives that are applicable to the analysis of information privacy.” They identify four distinct different theoretical perspectives that have been employed to study privacy. The four perspectives are 1) design science and technical, 2) individual privacy and consumer behavior, 3) strategic operational and financial, and 4) societal and public policy. Because we believe the theoretical perspectives add rich context, we will primarily focus on the Kauffman et al. (2009) framework, with a focus on the financial and societal and public policy perspective as these most closely align with the economic and societal pillars of the proposed ecological model of information We also consider technologies as being embedded in these perspectives, and the technological issues will be discussed within these sections. Further because both Boritz et al. (2008) and Kauffman et al. (2009) provide such comprehensive literature reviews, we limit our discussion to only those studies most relevant to this study.

4. Society, Public Policy and Privacy

Privacy has been considered one of the four ethical issues of the information age (Mason, 1986); the others are accuracy, property and access, which are also very closely related to issues of data pollution. Dillard and Yuthus (2002) assert that the

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"privacy of personal information is among the most volatile and difficult issues of the information age representing one of the most immediate dilemmas facing AIS." Warren and Brandeis (1890) wrote a seminal article with a legal perspective that contended that “[p]olitical, social, and economic changes entail the recognition of new rights, and the common law, in its eternal youth, grows to meet the new demands of society.” Warren and Brandeis (1890) wrote their article when they were outraged by the ability of photographers with new mobile cameras to take photographs on the street without the permission of individuals. Technologies continue to advance, more and more rapidly, and such concerns arise with the introduction of each new technology. At the time Warren and Brandeis wrote their article, mobile cameras were extremely large, heavy and obvious compared to today’s cameras, which even reside on mobile phones. Today, many types of new technologies impact an individual's privacy. Westin (1967) extended the perspective of Warren and Brandeis (1890) with a book that expressed a socio-political perspective on personal privacy. After providing a comprehensive analysis of the origins of privacy and its role in society, Westin (1967) called for four privacy-related areas of inquiries:

1. Privacy must be defined rather than simply invoked, and its psychological, sociological, and political dimensions must be described on the basis of leading theoretical and empirical studies.

2. New techniques of surveillance, their present uses, and their future prospects must be described, forsaking Orwellian imagery for hard facts.

3. The ways in which modern societies like the United States have reacted to the new surveillance techniques must be examined in depth to see what has been happening to the social norms for privacy, and whether there are trends among interest groups and in public opinion that may help to guide American policy-makers.

4. A discussion of how American law has dealt with the issue of privacy and surveillance is necessary, as the backdrop for an analysis of specific measures that public and private authorities might take to ensure the continuation of privacy as a cornerstone of a social liberty.

We assert that these four areas of inquiry reflect pressing issues for the 21st century, just as they did over half a century ago. Westin’s third point above regarding social norms and the fourth point about American laws being the solution to protecting privacy leads us to consider cultural lag theory, but in so doing, we do not just consider it in a legal context. We consider legal and regulatory actions as one of many possible privacy enhancing solutions. Cultural lag theory is important to the current study because it provides a context to assess whether society is indeed in a period of maladjustment and points out the need for action to be taken for sustainability of the original expectations of culture. The alternative is that adjustment never occurs and culture changes forever. Ogburn (1957) in his proposal of a cultural lag theory astutely observed that it takes time for a society’s culture to catch up with rapidly changing technology. Prosch (2008) adapted the theory to reflect the interplay of technology, users and culture. The theory has also been tied in to data privacy issues as illustrated in Figure 1. Although the technological advances and resulting maladjustment will affect all of the stakeholders discussed by Kauffman et al. (2009), individual expectations with respect to privacy protection will tend to lag

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behind the development and implementation of technologies that may exploit their private data. Eventually, as individuals learn about the risks, they will begin to demand protection collectively. So businesses, in order to maintain their customer base and be financially sustainable, must react. Theoretically, the privacy-enhancing technology solution providers, and the standards-setting and regulatory stakeholders will react, and begin to deliver solutions and require new privacy-enhancing technologies and privacy-related controls. These actions help to bring the socio-techno-cultural environment back into equilibrium. However, if such action is not taken the previous socio-cultural environment is not sustained and new cultural norms are developed.

Figure 1

Boritz et al. (2008) and Kauffman et al. (2009) both discuss an often hotly debated issue: whether regulation is necessary, or if self-regulation can be effective in the data privacy domain? Kauffman et al. (2009) point out that globally, different stances are being taken in different regions. National laws have been enacted in the European Union and Canada, for example, as well as state and provincial legislation. In stark contrast in the US, however, industry-specific laws have been passed in some cases, and a majority of the states, frustrated with laws at the federal level, have passed many of their own laws. Culnan (2000) has reported that only 67% of firms sampled posted privacy disclosures, and that only 14% of the disclosures were considered to be comprehensive. This indicated that a regime of self-regulation might not be effective in producing privacy policies. According to Ogburn’s theory we expect to see gradual increases in privacy-enhancing practices over time or as we argue in this paper, culture will be forever changed. The following quote sums up the potential loss of privacy in today’s technological culture:

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Protecting privacy in the face of ubiquitous data requires many tools: technology, education, market pressure but most of all it requires strong laws that impose serious obligations on industry to act as stewards, not merely processors, of our data, and firm limits on government access to those data. The United Kingdom, under a rigorously independent information commissioner, Richard Thomas, has made important strides in this area. Regrettably, the United States lags farther behind. But we all have a long way to go if we are going to accord individual privacy—the bedrock of human dignity—the respect it deserves. (Cate 2009) In related research, Solove (2004) provides a detailed analysis of the inter-relationship of individual privacy, society and privacy law. He provides a historical perspective on the conceptualization of privacy problems, their relationship to privacy law and ultimately society. He asserts that “we are still in the early days of the Information Age, and we still have the ability to shape the networks of information flows.” This line of thinking is promising from a cultural lag theory perspective. When viewed through a legal lens, he asserts that many of the privacy problems are not really technology driven, rather they are law-related – or the lack thereof – since he argues that privacy to a significant degree is legally constructed. He makes a strong case for this perspective by taking us back to the 19th century and quoting Ralph Waldo Emerson. Mr. Emerson while referring to the mail, declared that it was unlikely that “a bit of paper, containing our most secret thoughts, and protected only by a seal, should safely travel from one end of the world to the other, without anyone whose hands it had passed through having meddled with it” (quoted by Solove 2004, p. 225). In order to protect the post office’s mail and to integrate privacy into these practices laws were then created as a response to the relatively new mail system, rather than preserving the status quo. Even with technologies that are considered new to our generation, Solove contends that this can still be accomplished. Cavoukian (2009) agrees with this contention and is an advocate of designing privacy into systems of all types. Security and confidentiality of data has typically been considered as an afterthought in the design and implementation of new technologies and systems. However, over time, a fundamental paradigm shift is occurring, stimulating Privacy by Design (Cavoukian 2009), a concept embedding privacy enhancing technologies directly into new systems. Finally, Prosch (2008, 2009) argues that data needs to be protected from cradle to grave, meaning throughout its entire data lifecyle, and most importantly, unnecessary data should never be collected. These important concepts are key to preserving the privacy rights of individuals and as we will argue in the next section, sustaining a culture that values the fundamental right of privacy.

5. Privacy and Economics

Cavoukian (2002) touts the benefits to businesses of protecting personal information. However, many organizations do not see the value of deploying resources into privacy enhancing programs. Kauffman et al. (2009) point out that although individuals desire to have their information fully protected, organizations are constrained by budget and profit considerations. They acknowledge that full data protection may be

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socially desirable, but not necessarily feasible for organizations in a competitive environment. Kauffman et al. (2009a) find, in their review, that not many studies have examined the economic properties related to privacy issues. They take a risk management approach and assert that the goal of cost-benefit analysis for investments in data privacy solutions is to justify an investment in information security both within an organization and across its inter-organizational boundaries in order to mitigate unnecessary risks. They assert that “risk management theory provides a useful means to justify investments in data privacy protection and information security.” Value-at-risk theory is an approach for assessing privacy risk. This technique is especially useful when there is the possibility for the occurrence of extremely rare but severe cases of financial loss (Jorion, 1997). Both Wang et al. (2008) and Kauffman et al. (2009b) have used this approach. They utilized value-at-risk to measure the information security risk faced by companies, and applied extreme value analysis or worst-case scenario modeling to estimate the value-at-risk of daily losses. Kauffman et al. (2009b) argue from a purely financial perspective that the firm may be inefficiently investing in data privacy protection, both at the low and high end, not investing enough, or investing too much with little additional marginal value for the organization relative to the protection of its customers’ personal information. We contend that analysis needs to be conducted from more than just the firm’s perspective, including that of a societal perspective. Kannan and Telang (2005) found that for network and software vulnerabilities, social welfare is enhanced when the market is regulated (i.e. CERT) and incentives are given to market participants to report vulnerabilities.

6. Pollution and Data Pollution Review

In attempting to derive a meaningful definition of pollution, we begin with exploring various definitions of the term pollution. Princeton University's Wordnet defines it as an "undesirable state of the natural environment being contaminated with harmful substances as a consequence of human activities" and "the act of contaminating or polluting; including (either intentionally or accidentally) unwanted substances or factors". This definition refers to the "natural" environment, and we contend that "information" in the "information age" in which we live is a part of the natural environment. We also see that the unwanted substance or factors may be generated either intentionally or accidentally. The Merriam-Webster dictionary defines pollution as "the introduction of contaminants into an environment that causes instability, disorder, harm or discomfort to the ecosystem i.e. physical systems or living organisms." This definition focuses on contaminants, which can be naturally occurring or man-made. Contaminants are to be expected to some degree, but when they exceed a "normal leve”' they become pollutants. Helfland et al. (2003) assert that "pollution occurs because it is virtually impossible to have a productive process that involves no waste; economically pollution occurs because polluting is less expensive than operating cleanly." The industrial revolution which introduced new machines and factories resulted in a huge increase in water, air, soil and even noise pollution. Noise pollution is interesting because is leaves

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no contaminant residue, rather it disturbs the environment as sounds beyond the "normal level" are contaminating the environment. However, in certain cases, inhabitants of the eco-system can suffer long-term consequences, such as degraded ability to hear, even though the contaminant was only temporary. This is a spot-on example of Ogburn's cultural lag theory, the factories were built, the contaminants were let loose on the ecosystem until they were ultimately brought, at least somewhat under control in certain countries. In the US it resulted in many regulatory acts, including the The Clean Air Act, the Clean Water Act, the Noise Control Act, and the Environmental Pollution Act and the creation of the EPA. Unfortunately, not all countries have brought these forms of pollution under control through regulation. A list of the World's 10 Most Polluted Cities (Linfen, China; Tianying, China; Sukinda, India; Vapi, India; La Oroya, Peru; Dzershinsk, Russia; Norisk, Russia; Chernobl, Ukraine; Sumgayit, Azerbaijan; Kabwe, Zimbabwe) confirms that ecosystems can be greatly damaged when technologies get introduced that produce contaminants and are not controlled.1 Helfland et al. (2003) attack pollution policies and assert that "environmental goods provide the classic case where government intervention increases efficiency." Achieving efficient levels of pollution involves charging per unit of pollution based on damages caused by that unit. How such emission of pollution fines should optimally be levied when enforcement is costly has been researched for physical pollution (Stranlund et al. 2009) and those models have some corollaries with data pollution. In the information economy in which we live, the FTC has indeed issued a few large fines to companies with data pollution in the form of data breaches, and penalties in the HIPAA law also include per infraction fines for data pollution where personal health information is leaked out. In both of these cases, data pollution can be seen as a "leakage" of personal data into the public (non-personal) domain, and in both cases, the metric is the instance of the leakage, that is, the case or individual. Pollutants can also be categorized as primary or secondary pollutants defined by the EPA as follows. A primary pollutant "is one that is emitted into the atmosphere directly from the source of the pollutant and retains the same chemical form." An example of a primary pollutant is the ash produced by the burning of solid waste. A secondary pollutant is one that is formed by atmospheric reactions of precursor or primary emissions. Secondary pollutants undergo a chemical change once they reach the atmosphere. An example of a secondary pollutant is ozone created from organic vapors given off at a gasoline station. The organic vapors react with sunlight in the atmosphere to produce the ozone, the primary component of smog. The EPA contends that controlling secondary pollutants is generally more problematic because mitigation requires the identification of the precursor compounds and their sources as well as an understanding of the specific chemical reactions that result in the formation of the secondary pollutants. We contend that information ecologies also have primary and secondary pollutants and understanding their relationships and mitigating data pollution as it travels through multiple parties in "cloud" and other types of ubiquitous computing are akin to the increased difficulty of understanding the specific chemical reactions in the natural sciences in the formation of secondary pollutants. For example, when data is transferred among trading partners and combined and further processed we content the white "cloud computing" can

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quickly become a "brown cloud" of toxic waste. Consider Figure 2 (borrowed from one of NASA's websites). Figure 2

http://disc.sci.gsfc.nasa.gov/oceancolor/additional/science-focus/locus/images/pollution.jpg This diagram portrays water contamination, and we can see primary contaminants of 1) nitrates from pesticides, 2) PCBs/Benzene from chemical spills, 3) toxic waste from landfills, 4) gas from leaky tanks, and 5) refuse from septic tanks. We see by-products, however, of air pollutants to be a secondary pollutant that becomes a primary source of water contamination. In the next section, we will use these concepts to set forth a model of data contaminants that, when above normal levels, become toxic and are considered data pollution. Modelling the various types of environmental pollution is very advanced in the natural sciences and a large research stream exists. Modelling pollution in "information economies" is scarce, perhaps even non-existent. A stream of research does exist on "noise" in data and in data communication channels (Shannon, 1949). Reducing such noise, however, is really just considered to be a data quality issue and is somewhat akin to "normal" contaminants that exist in any ecosystem. Just like we speak of "air quality" the concept of "data quality" has been considered by researchers for some time (Juran, 1951; Lee et al, 2002; Wang and Strong, 1996; and Wang and Wang, 1996), but again typically at "normal" rather than pollutant or toxic levels. Thus, we assert that contaminants that exceed normal levels in data can be referred to as "toxic data" in information system ecologies, and this is the topic of this section. The dimensions of data quality put forth by these researchers tend to focus on accuracy, relevance, timeliness, completeness, representational faithfulness, and accessibility. Quantity of data is not discussed, meaning the propagation of the data and the number of footprints is not a factor, but it is one that we will greatly consider in the context of an information ecology and data contaminants that can become toxic. Relevance is considered an

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attribute of data quality, but "shelf life" of this relevance and what it means for retention/destruction of the data is not usually considered (Prosch, 2008). Again, we need better metrics which move from the existence of an “abnormal” condition to a quantification of the riskiness of that condition. Now that we have considered the basics of pollution and made a few suggestions towards how data pollution might be modelled, we will review the very few works that have made suggestions of data pollution. Gilb (1980) used the phrase "new industrial data pollution" in the context of a new data processing industry that utilized abstract numeric codes which impeded employee productivity because of their lack of understandability. This work is relevant to data pollution and privacy, as we will discuss in the next section, because in many countries, such as those in the EU, individuals have a right to access their personal information, but if it is given to them in format that is not understandable, then it is useless for that purpose. Knight (1992) provides evidence that data quality was suffering in large US companies, he called this data pollution. His study provides some interesting historical data of the quality issue, but does not really address the issue of toxic data pollutants. The causes given by him for poor data quality are 1) data entry errors, 2) data entered incorrectly because complete and accurate information was not available at the time of entry, 3) data "mismatched" during merging process, 4) poorly synchronized transaction processing. The first two causes we view as primary contaminants and the second two as secondary contaminants. He asserts the this poor data quality results in 1) violations of SEC reporting, 2) incorrect billing, 3) cost overruns and late deliveries, and 4) product line rework and recalls. Interestingly, the "damage" to individuals are not considered, just "operational" and reporting problems. We will extend the ramifications of poor data quality to privacy and data pollutants in the next section. Recently, Brandel (2008) questions whether it is time for a "digital diet" to help reduce "digital pollution" in response to information overload. However, her perspective was from one of data usefulness and information overload rather than "toxic" or harmful data. Schneier (2008) spoke about data pollution, people, and the death of privacy in an interview. The basic thrust of the interview closely aligns with Ogburn's (1957) cultural lag theory as adapted by Prosch (2008). Basically, he asserts that we have as much technology as we need, but security is what is lacking and that security needs to be designed so that it does not require educated users. We propose to take it much further than just security, as we propose the information ecology needs cleaning up and better processes than transcend just security concepts.

7. Organizational Privacy Responsibility

In this section, we develop a model of Organizational Privacy Responsibility2, and from that model we will next focus in on privacy and social sustainability. As mentioned earlier the differentiation between organizational responsibility and social sustainability has not been very clearly articulated, so we attempt to model privacy responsibility within a corporate model, and then from there, we believe it makes sense to tie this concept of organization privacy responsibility into the greater societal aspect of social

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responsibility. The resulting model can be seen in Figure 3, and is based on the Dillard and Layzell’s (2008) model. The model is divided into 3 areas: motivating forces, operational modalities and outcomes. Figure 3

The 4 motivating forces are 1) Corporate Culture, 2) Compliance, 3) Fiscal Viability, and 4) Expectations. Cavoukian and Hamilton (2002) assert that privacy can only be achieved if top management is on board . Specifically, Cavoukian (2007) asserts that:

1. A culture of privacy enables sustained collective action by providing people with a similarity of approach, outlook, and priorities;

2. The importance of privacy must be a message that comes from the top; 3. Privacy must be woven into the fabric of the day-to-day operations of an

organization, with adequate resources. Interestingly, this definition speaks to the issue of sustainability being enabled by a privacy culture, we will revisit this thought in our next section. What is key to corporate privacy responsibility is that the message come from the top and is designed into every facet of the day to day operations. This concept also ties into Wood et al.’s (2004) concept of purpose-driven leadership, with the purpose being to respect and protect personal information. Organizations are motivated to be in compliance with regulatory and legislative statues because if they do not they face fines, sanctions and even additional required

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audits. In recent years the US Federal Trade Commission has fairly uniformly required security audits every 2 years for the next 20 years for organizations that are investigated for security breaches and found to be lacking.3 In Canada, companies have been ordered to have audits and recently Facebook has been ordered to change its practices or shutdown operations in Canada.4 Similarly, Google is working in Switzerland to enhance its streetview image blurring or it faces being shutdown in that country.5 Thus, compliance is a compelling motivation force. Cavoukian and Hamilton (2002) and Cavoukian (2008) have advocated that privacy is an imperative for businesses and that in the long-run it adds value. The third motivating force, fiscal viability can be viewed from a risk approach (Jorion 1977, Wang et al. (2008) and Kauffman et al. (2009b)) where from a purely financial perspective, a firm may be investing too much or too little in data protection, implying that organizations need to find their “sweet spot” which is where the marginal value for the organization is optimized. Another indicator of fiscal viability is ability to keep market share, and Ponemon (2008) finds that data breaches result in a 3.6% customer churn rate. Finally, the fourth motivating force is meeting expectations of stakeholders. The primary stakeholder in privacy is the individual Individuals in society wear many hats (employee, consumer, and citizen) and their expectations of privacy may vary across cultures. Prosch (2008) dicusses the relationships between technological advances, societal expectations and resulting privacy enhancing technologies and processes. The expectations of these individuals will ultimately motivate organizations to align their privacy practice with societal expectations, unless society experiences a fundamental paradigm shift in their expectations. The operating modalities of coporate privacy responsibility are Goals, Programs and Resource Allocations. The program specifics and resource allocations should be driven by the specific privacy goals developed by the organization, which are developed based on the motivating forces or pressures. In privacy, goals may be to merely implement a privacy program or perhaps to move along the privacy maturity model (Prosch, 2008). The privacy maturity model is a tangible way that an organization can plot its journey in enhancing its privacy corporate responsibility. Once an organization assesses where it falls along the maturity model, it can then determine where it wants to be in certain intervals of time and set a path to get there. For example, an organization in the early stages of its privacy maturity lifecycle may have as a goal to develop repeatable, measurable privacy practices, such as responding to a request for information in a uniform way. Another organization, further along the maturity model, may have as its goal to “pass” a privacy audit, such as a GAPP audit. Once those goals are defined, the corresponding programs and resource allocations can be determined. What is key is that the motivating forces will drive the goals, and the goals drive the programs and practices and resource allocations. Finally, the operational modalities will have outcomes, specifically, community involvement, education, economic and environmental improvements. The first outcome will be the degree of community involvement that results. Community involvement involves having programs and policies in place to allow individuals to voice their opinion about their privacy desires. This will only happen if organizations value the opinions of their privacy stakeholders and provide venues (which takes) resources. As indicated in

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the diagram, the opinions need to provide feedback that is considered in the development/refinement of privacy goals, programs and resource allocation. Metrics here include the existence of programs and policies as well as the percentage of the relevant constituents providing feedback. Educational support is the second outcome, and organizations should take a proactive role in explaining and training employees on how they are using their data. Sears just recently got ordered by the US FTC to clearly explain to users exactly what data they were collecting and how they were going to use it when they offered to pay them $10 to download software to allow them to track their online browsing data. Most customers thought it was just their general browsing data, when it was actually the secure browsing as well, bank accounts, health information and others.6 Metrics in this dimension need to map from the organizations’ data and policies and procedures to the training and education that the relevant clientele receives. The third outcome, economic benefits is closely related to the motivating force of fiscal viability, it is just the actual achievement of the fiscal benefits. Metrics are designed for this dimension in a relatively straightforward ways. As one example of such an outcome, customer retention is shown to be higher for banks ranked higher on privacy. The Ponemon Institute finds the average length of a customer relationship for the five banks rated highest for privacy to be 7.68 years, and for the five lowest rated banks, the average relationship length was 4.6 years. Admittedly, the research design did not take into account possibly omitted variables that could be driving the results. However, Cavoukian and Hamilton (2002) assert that aligning an organization’s strategies with privacy protecting methods and technologies will pay off in the long run. At this point, this is really still an empirical question. The final outcome, and the primary focus of the next section, is on the environment. As previously discussed, we live in a digital society, but, not everything is digital. We recognize that good data privacy practices have the potential to reduce physical waste in the form of unnecessary paper usage and disk storage devices. We are more focused on the environmental improvements throughout the data lifecycle (Prosch, 2008) associated with 1) unnecessary data being collected, 2) data unnecessarily being shared, 3) data being erroneously processed, 4) erroneous data being shared, 5) obsolete data being retained longer than normal, and 6) data being inadequately destroyed. Good corporate privacy responsibility can help to reduce all of these sources of what we term “data pollution” as defined in the next section thereby creating an improvement in the digital economy.

8. Privacy, Data Pollution and Social Sustainability

In this section we drill deeper into the concept of environmental improvement from the organizational privacy responsibility model and draw upon the literature reviewed on social sustainability, privacy and pollution to develop a theory of data pollution in a digital ecology, with a special focus on privacy of personal information. In keeping with Foot and Ross's (2004) notion of a triple bottom line of sustainable ecologies, we propose a digital ecology comprised of resources, economic activities,

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and society. Each component of this ecology is a system in its own right. The system of natural resources consists of the available goods on the planet and their interrelationships where the goods are both living and non-living. The economy is the system of interrelated human activities which affords humans the means of sustaining their basic physical well-being across generations. Society is then the system of interrelated human activities which allow humans to maintain their psychic well-being across generations. The interactions among the three systems produce and re-produce an ecology of human and non-human, living and non-living interactions. For instance, the motivating forces of the organizational privacy responsibility model outlined in the last section are comprised of interaction between the economic and social systems. To the extent that these three systems are modeled as data and information, and their interrelationships measured and, to a certain extent, controlled via information technologies. We then conceive of a digital ecology which models some of the underlying complexity of this ecology. To this characterization, we add the concept from Nehmer (2009) that an information economy is composed of producers and consumers of information. We extend this idea to include parties with both information needs and information producing capacities. The interaction of the parties, corporations, individuals, and regulators within the three systems is what constitutes a digital ecology. Even at this relatively early stage of the development of this digital ecology, its own complexity and risks become an area of concern, especially in matters of privacy. The primary stakeholders in this proposed ecology are individuals and organizations (Elliot et al. 2004) as illustrated in Figure 4. We view the relationship between individuals and organizations as one that is optimized when a positive sum approach (Cavoukian 2009) to privacy is used and mutual benefit (Wood et al. 2004) is considered.

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When dramatically new technologies are introduced into society that use up resources and change the nature of economic activities, the challenge is for society to determine whether it desires a permanent change in culture. If not, then the ecology, so to say, needs to adjust, perhaps facilitated by changes made in the political (regulatory) system, economic activities (nature of transactions and processes) and resources (types of technologies employed) to return it to a steady state. As we begin to consider pollution in this digital ecology, consider the following statement regarding the advent of Web 2.0:

Essentially, the Web is shifting from an international library of interlinked pages to an information ecosystem, where data circulate like nutrients in a rain forest." (Johnson, 2005)

This is an interesting analogy for the flow of data, however, it is really referring to creating efficiencies and reducing information overload in the newer web computing environment. The issue of the exponentially increasing instances of data is illustrated in Figure 5, but data is not considered from a privacy perspective.

Figure 4

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Figure 5

http://web2.socialcomputingjournal.com/describing_the_web_20_information_ecosystem.htm Continuing with the rain forest analogy, we contend that rain forests can and are contaminated and can be forever changed by various environmental pollutants, as well as deforestation activities. Groups are formed, such as the Save the Rainforest, with the goals of education of the general population and actual protection of the rainforest. Ecosystems need to protect themselves from pollution, and so we consider digital pollution in a digital society. In doing so, we restrict ourselves here to considering only first and second order effects. We realize that there are in fact webs of interrelationships but defer discussion of such complex digital ecologies to further papers. Drawing from the definition of pollution given earlier, we define data pollution as the act of contaminating or polluting; including (either intentionally or accidentally) unwanted data. Where pollution in this case is the introduction of erroneous or unnecessary data into an environment that causes instability, disorder, harm or discomfort to the ecosystem. Potential sources of data pollution can be identified during all phases of the data lifecycle, which is illustrated in Figure 6. In this diagram, we call erroneous and essentially unnecessary data that leaves the organization and travels to third parties of any type “data pollution”, and similar data that stays within the organization “toxic data”. In essence, they are both potentially toxic and pollutants to the environment as a whole. However, because the data within an organization is “contained” within the organization and only hurts society if it is accidentally or maliciously released into the ecosystem, it is potentially toxic, and could be considered by risk management as a contingent liability. The riskiness of this data increases with its overall size and with its potential polluting impact if it enters the public sphere. Data sent to third parties, however, is data pollution, and indeed, it may be toxic. It is immediately considered data pollution because it has left the organization and been

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released into the ecosystem without feedback to the originating party. Again, metrics are needed to measure both the amount of data released and its negative potential consequences. Figure 6

Toxic Data

Organization Databases – Clean Data

Cloud Computing – Clean Data

Cloud Computing – Data Pollution

Data Creation Data Processing Data Sharing Data Destruction

Sources of data pollution and toxic data occur throughout the various phases of the data lifecycle. During the data creation phase, special care needs to be taken to only collect data that is necessary. Technologies and cheap data storage makes it tempting for organizations to collect more data than is really needed. Businesses need to identify the business purpose for which data is needed and collect only that data. Collecting other data contributes to data pollution and potentially toxic data, if it subsequently gets misused, lost or stolen. Restricting data collection in this way will significantly reduce the amount of potentially polluting data. Data pollution can also be generated if erroneous data is collected, so data quality is an issue as well. Collection of unnecessary and/or erroneous data can result in both primary and secondary pollution. Secondary pollution would occur if they are used in subsequent processes and cause further toxic data and/or data pollution to occur. During data processing, new fields of data may be created by inference programming and these fields of data, if not carefully designed and controlled, may result in the proliferation of additional toxins or pollutants. For example, profiling of purchasing habits to categorize individuals with a specific trait and then sharing or

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selling these inferred traits causes data pollution, which can also be potentially toxic depending on the sensitivity of the categorization, use and security of the data. Metrics need to include indices of the shifts in consumer preferences and the duration of those preferences with corresponding data retention policies. During data sharing techniques, the problem is controlling the intended use and allowed number and time frame of intended uses. Ensuring that the data is only used for intended purposes without directly observing the usage trail is almost impossible. Finally, data retention should not be “forever” and the data will ultimately need to be destroyed. In some cases, perhaps all instances except 1 instance for an audit trail may need to be destroyed. Determining when data should be destroyed, ensuring all appropriate instances are destroyed, and destroying the data in an appropriate manner must all be considered. Inadequately and inappropriately destroying data causes data pollution and toxic data to proliferate. Like other forms of physical pollution, regulatory bodies can step in and determine guidelines and control mechanisms. Similarly, globally, data protections laws have abounded, but not all jurisdictions have equivalent data standards, driven in part by culture, and not all laws and regulations are necessarily enforced. Another motivating force to clean up data pollution can be driven by society demanding such protections and organizations seeking methods to enhance data protection at all phases throughout the data lifecycle. Finally, good organizational governance practices lead to appropriate policies and metrics for controlling the organization’s exposure to data pollution. Organization’s can develop balanced scorecard measures of data privacy pollution using the four outcomes of the organization privacy responsibility. For example, the community involvement dimension measured by the percentage of people responding to requests to opt out. The educational support dimension needs to measure the mapping between an organization’s privacy policy declarations and the training outreach it provides on those provisions. The economic benefits dimension can measure customer retention and satisfaction. The environmental improvements dimension has six sub-dimensions, each with a privacy interpretation. First, we can measure the reduction in collected data. Next, we measure the reduction in data sharing without stated and measurable goals. Third, the overall assurance of proper processing controls is needed using a framework such as COBIT. The fourth dimension is measurable by the reduction of reported data errors in shared data. Fifth is measurable initially by better definitions of useful data life. Finally, the sixth sub-dimension is measurable again by better methods for defining old data and purging that data.

9. Conclusion

We have reviewed a diverse literature in sustainability, privacy, and pollution in order to begin to understand and measure a sustainable data ecology. Drawing on the works of Dillard and Layzell (2009), Cavoukian (2002, 2007, 2009), Foot and Ross (2004) and others, we develop a model of such a sustainable ecology and some of the metrics it will need in order to be operationalized. The processes that create, process, share and purge data all have the potential to create data pollution. Organizations need

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to develop policies, procedures and metrics to reduce both the amounts of data that they put through these processes but also the risk of data leakage in each of the processes. Limitations of the current research include primarily its exploratory nature. There is little research at present in the areas of organizational responsibility, sustainability, and the digital ecology. The present work is a first look only. Additionally, the analysis presented here considered only primary and secondary effects of privacy pollution concerns in the digital ecology. More realistically, models of digital ecologies need to recognize the complex web of interrelationships among the three systems in order to fully represent the complex dynamics of these ecologies.

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M. and Erekson, O. Homer. (2004). Approaching Sustainability through a Business-Science Synthesis. In Teaching Business Sustainability. Chris Galea (ed.). pp.151 - 155.

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Helfland, G. Berck, and T. Maull. (2003). "The Theory of Pollution Policy," in Handbook of Environmental Economics, Vol. 1: 249-303. Elsevier B.V.

Johnson, S. (2005). “Software upgrades promise to turn the Internet into a lush rain forest of information teeming with new life,” Discover. October, 2005.

Jorion, P. (2000). Value at Risk: The Benchmark for Controlling Market Risk. McGraw-Hill Professional Book Group, Blacklick, OH.

Juran, J. (1951). Quality Control Handbook. McGraw-Hill. New York, NY. Kauffman, R.J. Y.J. Lee, M. Prosch, B. Shao, and P.J. Steinbart. (2009). “Theory and

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Oxford University Press, Oxford. Endnotes 1 http://www.time.com/time/specials/2007/0,28757,1661031,00.html 2 We prefer the term organization rather than corporate because it is more inclusive. 3See FTC Orders against BJ’s, ChoicePoint, CVS, DSW, among others. http://ftc.gov/privacy/privacyinitiatives/promises.enf.html 4 PIPEDA Case Summary #2009-008: CIPPIC v. Faceboook Inc. 5 www.the.register.co.uk/2009/09/15/streetview_switzerland/ 6 FTC. File No. 082 3099 Sept. 9, 2009.

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The Improvement of Governance Decision Making Using XBRL

Akram Bodaghi, Ahmad Ahmadpour

Department of Management, Mazandaran University of Science and Technology, IranDepartment of Accounting, Mazandaran University, Iran

[email protected] , [email protected]

ABSTRACTExtensible Business Reporting Language (XBRL) has the potential to influence users’ processing of financial information and thus, their judgments and decisions based on this information. XBRL is a XML1-based language, developed specifically for financial reporting. Using XBRL, as a search-facilitating technology, contributes to direct searches and simultaneous presentation of related financial statement facilitate, and also footnote information which could potentially help financial statements’ users. XBRL is more than a distribution mechanism for data or facilitating technology. XBRL, in effect, has the potential to significantly improve corporate governance. Putting that potential into practice requires an XBRL taxonomy model that is data based than is the document based focus of the current specifications, as well as a flexible rendering system.

KEYWORDS: XBRL (EXTENSIBLE BUSINESS REPORTING LANGUAGE), CORPORATE GOVERNANCE, DECISION MAKING, TRANSPARENCY

1. Introduction

XBRL is an issue that is directly related to one of IFAC’s key goals: enhancing the credibility of financial reporting and auditing across all sectors of the economy, public and private, for profit and not-for-profit, listed and unlisted.

The importance of this issue is underscored by the accelerating demand for enhanced financial reporting through the use of technology. All the information users in the financial reporting supply chain are demanding more extensive financial and nonfinancial information. They need this information on a timely basis. It needs to be easily accessible and, where possible, tailored to specific needs. Listed companiesare responding by exploring ways to provide financial information faster, more accurately, and with greater confidence. Success, however, ultimately depends on two key factors: the quality and timeliness of the information produced; and the evolution of web-based technology tools that increase the flexibility and comparability of reported information, thus enhancing its value.

1 Extensible Markup Language

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Additionally, the economics of XBRL are clear: XBRL provides a powerful return on investment, significantly lowering the cost of information production AND consumption. It is a flexible, scalable solution that facilitates exchange of data, streamlines communications, expedites information delivery and enhances transparency of reported information. It enables all those directly or indirectly involved in developing or using financial information to do so more cost-effectively.

XBRL is not a software application; also it is not a new accounting standard. XBRL is a so-called semantic data format additional to an open and free electronic language providing each data element with a tag that identifies it unambiguously.

The data tags, prepared by XBRL format, provide information about the structure of financial data that allows software applications, such as search engines, parsers and so forth, to more effectively process the data. For example, software developed to search for these predefined data tags allows users to extract and simultaneously view all similarly coded information, notwithstanding where the information is presented in a firm’s financial statements. This search capability has the potential to contribute to increase the transparency of different accounting treatments, decrease users’ costs of processing information, and perform as a decision aid for users by facilitating the providing related information.

While search-facilitating technology has implications for numerous financial statement issues, recognition versus disclosure of financial information likely is one of the subjects which are most affected. In not too distant past, managers have strongly opposed standard setters’ proposals to recognize in the financial statements items such as stock-based compensation and unrealized gains/losses on financial assets, preferring instead that these items be disclosed in the footnotes. One possible explanation for this vigorous opposition to recognition is that there existeconomic costs accompanied by recognition if debt covenants or other contracts are restricted by recognized, but not disclosed, amounts. A second explanation is that managers believe that the items in question do not meet the FASB2’s relevance and reliability criteria for recognition, and thereby deem disclosure the appropriate reporting alternative. A third explanation is that managers believe that users fixate on recognized items and discount disclosed items in view of processing costs or cognitive limitations. Such a belief would lead managers to disclose information they believe would harm firm value if recognized in the body of the financial statements.

2. Recognition Versus Disclosure

The FASB’s conceptual framework states that an item should be recognized in the body of the financial statements only if it (1) meets the definition of a financial element (e.g., asset, revenue); (2) is measurable with sufficient reliability; (3) is relevant to users’ decisions; and (4) is reliable in the sense of being representationalfaithful, verifiable, and neutral (SFAC No. 5, para. 63). Items that fail to meet one or more of these criteria are candidates for disclosure.

Items primarily fail recognition tests due to uncertainty about the item’s existence or its monetary value (Johnson and Storey, 1982). Financial accounting standards rarely allow recognition and disclosure as acceptable alternatives for presenting financial statement information; one exception is stock option compensation. In the early 1990s, corporate managers and standard setters engaged in vigorous debate

2 Financial Accounting Standard Board

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over the placement of stock-based compensation within a firm’s financial statements. After intense lobbying, the resulting standard, Statement of Financial Accounting Standard (SFAS) No. 123, Accounting for Stock-Based Compensation (FASB 1995), encourages firms to report the fair value of stock option compensation as an expense in the income statement but does not require this treatment. Under SFAS No. 123, firms can choose to follow the prior standard, Accounting Principles Board (APB) Opinion No. 25, Accounting for Stock Issued to Employees (APB 1972), and recognize only the difference between the market value of the stock and the option exercise price on the date of grant (usually zero) as an expense in the incomestatement. In this case, firms also must disclose the fair value of the stock options compensation and pro forma income in the notes.

Until 2002, only two firms in the Fortune 500, the Boeing Company and Winn Dixie, elected to recognize stock option compensation in the income statement. Recently, however, the number of companies that disclosed stock option compensation information in their footnotes is increasing. Thus, in the future, there is likely to be greater diversity in reporting for stock option compensation among companies, enhancing the importance of understanding users’ reactions to recognition versus disclosure.

3. The Role of XBRL in Governance Decision Making

One of the prominent models in decision making is that of Elliott (Elliott, 1998) which illustrates the steps in that process both sequentially, and in order of increasingvalue added:

The decision process is initiated with the completion of the business events that the firm engages in, be it selling products or purchasing raw material and labor, which are recorded in the firm’s data systems, such as its general ledger. But that raw data itself is too voluminous and disaggregated for meaningful interpretation. Hence, users have to apply their training and experience, aided by rules and tools for data manipulation, to extract the information contained within that data. For example, from the data contained in that general ledger, an accountant prepares statements of the firm’s annual earnings, cash flows and balance sheet, relying upon accounting standards to guide the aggregation of transactional data into those summary statements.

It is that information which users analyze to convert into decision relevant knowledge: for example, all business students are taught financial statement analysis to unravel the summarization inherent in financial reports and to conclude about trends and firm performance relative to competitors. Finally, that knowledge allows feasible decision alternatives to be derived and assessed, and a final decision made in the light of the goals and incentives of the decision makers. Elliott uses his model to argue that accountants need to move up the value chain, with the value added from decision making being orders of magnitude larger than at the data entrystage. The problem, as he sees is that much of accounting practice is focused on activities on the lower end of the value chain, such as data summarization and

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aggregation into financial statements, as opposed to knowledge generation and decision making.

XBRL cannot change those practices, but we can use this decision making framework as applied to governance to ask how, and to what extent, tagging impacts governance decision making. And, in particular, whether XBRL can be pushed up the chain to higher value added activities, for as this value chain analysis indicates, XBRL cannot fundamentally change governance if its main application is to data processing and handling. Of course, there is no question that XBRL will have a major impact on the data stage of the value chain, as XBRL International makes clear (emphasis added):

“XBRL is a language for the electronic communication of business and financial data which is revolutionizing business reporting around the world. It provides major benefits in the preparation, analysis and communication of business information. It offers cost savings, greater efficiency and improved accuracy and reliability to all those involved in supplying or using financial data.” (XBRL 2008)

While these benefits are not be underestimated, they do not speak to the higher end of the value chain, particularly knowledge generation leading to different decisions about governance—as opposed to the same decisions arrived at faster or more cheaply. We now turn to discuss stakeholders in the decision making process and specifically their governance motivations.

4. Stackholders in Governance Decision Making

Boards of directors, internal and external auditors, analysts, ratings agencies and investors all have a role to play in governance, but their informational needs and analytical capabilities differ. Those stakeholders inside the firm already have access to a larger set of information than the financial statement summaries that will be tagged under XBRL. Therefore, for these players the process by which data is transformed into governance-related decisions is what tagging has to impact if it is to add value. By contrast, for stakeholders outside the firm, the primary use of XBRL will be to facilitate the communication of firm information.

XBRL supports most of the goals expressed by participants of different reporting scenarios. It is an empirical issue whether XBRL will increase transparency mainly through the use of official taxonomies so the reported facts are clear and well documented for the users, or whether firm-specific taxonomies will signal important differences more than causing confusion and ambiguity. The fair view on the company and on the group is achieved also through the validation procedures which can be applied to the reported instance documents. Further automatic consumption of instance documents enhances the protection of market participants, reveals malpractices and mistakes of tax payers as well as secures the borrowing.Supervisors, tax offices and borrowing banks have the ability to import XBRL reports into their analysis systems without necessity of manual data input. Also automated warning systems can be introduced where the focus is not on data integration but on data analysis.

Finally, the use of XBRL combined with the other user readable formats allows providing the general public with the user-oriented publication of financial information clearly referenced to XBRL taxonomy. It is important to differentiate between open and close XBRL reporting in this case. Manual input can be fully eliminated in case of closed reporting where reporting entities are not allowed to extend the

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taxonomies. In such a case it is enough to provide a mapping between taxonomy elements and analysis system (usually represented by a database schema). In open reporting scenarios it is necessary to manually (or semi-automatically) map additional concepts in company-specific extension. More difficult to assess is the impact of XBRL on the assurance of financial reports. Trites (Trites, 2006) indicates that issues concerning XBRL in the context of assurance are numerous, pervasive and evolving. It always has to be kept in mind, though, that the ultimate source of governance is the discipline of market forces, through the stock price and the entity’s cost of capital. Thus to facilitate that mechanism, the efficient and error free transmission of entity information to users outside the entity always has to be the main priority of XBRL. Decision making, however, is more than having access to information, as is obvious from the fact that boards of directors are continuously striving (and lately, often failing) to figure out how to analyze all the information they can freely request. The communication of information is only a means towards the end of better decision making using that information, and hence, it is on that process where the emphasis must lie. Moreover, with a general purpose tool like XBRL the identity of users is less important than the understanding of the method by which any such user makes their decisions about governance. Developing high value adding applications of XBRL to governance decision making requires a more detailed understanding of how such decisions are made, but before turning to that topic, we need to explain where the decision model really begins, with data.

5. XBRL and Search-Facilitating Technology

XBRL uses predefined data tags that provide information about the content and structure of a dataset, allowing search technology to more efficiently and effectively categorize and present the information. With knowledge of the labels associated with the data tags, users of electronic financial reports can easily extract and custom-format information to suit their analyses. For example, in our context of stock option compensation, a user can search for “salary expense” and retrieve simultaneously all items in the financial statements with that data tag, whether in the body of the statements or in the footnotes. Technology that facilitates directed searches potentially alleviates cognitive processing costs and limitations that lead to differences in users’ judgments and decisions between firms that choose recognition versus disclosure. XBRL data tags contribute to the accomplishment of this by providing detailed information about the content and structure of the data, providingsearch engines to effectively perform a directed search and simultaneously present related financial statement and footnote information. Furthermore, search-facilitating technology can overcome users’ knowledge limitations by acting as a decision aid that identifies related information and presents it simultaneously, providing users with an opportunity to integrate data better and make appropriate comparisons between firms that choose recognition versus disclosure. In other words, presenting information in a way that enhances the structure of the data and facilitates users linking relevant information allows users to more efficiently (and often effectively) acquire and use the information (Larkin and Simon, 1987).

For a company with outstanding stock options, reported net income is higher when the firm chooses to disclose stock option compensation in the footnotes than when the firm recognizes stock option compensation on the face of its income statement. We expect the difference in reported net income to influence users’ financial

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performance judgments unless they adjust net income for stock option compensation disclosed in the footnotes. It is flagrant that search-facilitating technology will decrease the influence of recognition versus disclosure by making the firm’s stock option compensation reporting choice more transparent and directing attention to the pro forma income statement effects of stock option compensation that is disclosed in footnotes. In concise, in the presence of search-facilitating technology, users’ financial performance judgments will be less influenced by the choice of recognition versus disclosure of stock option compensation than in the absence of search-facilitating technology.

If users’ investment decisions incorporate their financial performance judgments, investment decisions should reflect the predictions made in the prior hypothesis. Specifically, search-facilitating technology will lead users to be less influenced by differences in financial performance (net income) between recognition and disclosure. We state the corresponding hypothesis with respect to investment decisions below.

In the presence of search-facilitating technology, users’ investment decisions will be less influenced by the choice of recognition versus disclosure of stock option compensation than in the absence of search-facilitating technology.

Search-facilitating technology may also affect other reasons for a differential user reaction to recognition versus disclosure: specifically, reasons related to perceived financial statement reliability such as inherent differences in relevance/reliability and managers’ use of disclosure to decline negative information. Search-facilitating technology probably will make differences in recognition/disclosure policies between companies more transparent so that this technology retains placement signals (i.e., shows where different information items originate (Hodge, 2001), which XBRL does. Thus, search-facilitating technology can draw attention to a firm trying to play down stock option compensation by choosing disclosure in the footnotes rather than recognition on the income statement. This heightened sensitivity to a firm’s disclosure choice will result in users having negative perceptions about the reliability of financial statements of firms that choose disclosure. These arguments lead to the following hypothesis related to reliability.

In the presence of search-facilitating technology, users’ judgments of financial statement reliability will be more influenced by the choice of recognition versus disclosure of stock option compensation than in the absence of search-facilitating technology.

6. Proposed Method

Our experiment is consisted of sixty nonprofessional financial statement users. We choose these users as participants in that research suggests that they are more likely to be affected by cognitive processing limitations and costs than professional users (Hunton and McEwen, 1997). Furthermore, nonprofessionals play an important role in the capital markets. We randomly assigned participants to one of four conditions in a 2x2 between subjects design. In the one hand, the two independent variables are presentation format including non-searchable and searchable; on the other hand, placement of data including recognition and disclosure. The searchable condition contained a search-facilitating engine by XBRL at the bottom of the computer screen that provided participants to retrieve all information pertained to a specific account. The non-searchable condition contained

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the same information (financial statements and notes) in a PDF-formatted document, which did not have the search engine at the bottom of the screen. We manipulated recognition versus disclosure by having one of the two companies (Company Y)recognize stock option compensation expense on the face of the income statement (recognition condition) or disclose it in the notes (disclosure condition). Company Xalways disclosed stock option compensation expense in the notes. In the disclosure condition, where both companies disclosed stock compensation, company Youtperformed company X on four key income statement ratios. Given identical financial reporting, the difference in key ratios reflected economic differences between the two companies. In the recognition condition, where company Xdisclosed and company Y recognized stock option compensation, company Xoutperformed company Y on the four key income statement ratios unless participants adjusted company X’s income statement to reflect stock option compensation, i.e.,put the two companies on equal footing.

We examine two primary dependent measures: acquisition and investment decisions. We capture acquisition by asking participants in the questionnaire to identify whether company X and company Y disclosed or recognized stock option compensation information. Our acquisition dependent measure is the percentage of participants who correctly identify how each company reported stock option compensation information. We capture participants’ investment decisions by asking them to allocate an investment of $10,000 between company X and company Y. Our investment decision dependent measure is the percentage of $10,000 participants invested in company Y.

7. Experimental Result

Out of 40 participants who accessed to materials in the XBRL-enabled search engine, 21 used the search engine to view footnote information. These 21 users set search group in our experiment. Other 19 users who were exposed to the search-facilitating technology but selected not to use it, along with 20 users who were not exposed to search engine constitute non-search group. Table 1 shows The Effect of Search-Facilitating Technology on Users’ Acquisition, and also Table 2 illustrates The Effect of Search-Facilitating Technology on Users’ Investment Decisions.

Table 1. Percentage of participants who correctly identified whether company X and company Yrecognized or disclosed stock option compensation information

Participants Information’s Placement Difference(Disclosure-Recognition)Disclosure Recognition

Search group 87% 69% -18%

Non-search group 94% 43% -51%

Table 2. Mean percentage invested in company Y

Participants Information’s Placement Difference(Disclosure-Recognition)Disclosure Recognition

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Search group 57% 55% -2%

Non-search group 67% 34% -33%

8. Conclusion

Current paper investigates the potential for search-facilitating technology to improve nonprofessional investors’ use of financial information in investment decisions, using the context of recognition versus disclosure of stock option compensation. We find that when stock option accounting varies between two firms, search technology contributes users to both acquire and integrate relevant information. Participants whoused XBRL-facilitating technology were more likely to acquire footnote information, and also they were more likely to integrate the footnote information with related information on the face of the income statement when making judgments and decisions. When compared to participants who did not use search-facilitating technology, differences in investment decisions were detected. It can be seen that the implementation of XBRL improves the transparency of a firm’s financial statement information and managers’ choices for reporting that information.

Additionally there is a link between search-facilitating technology and managerial decisions apropos of financial reporting. Consequently, they predicted that the effect of XBRL on users’ decisions may alleviate the benefits of managers lobbying for and choosing financial reporting approaches that artificially enhance the financialperformance or condition of the firm. As XBRL increases the transparency of financial reports in general and managers’ financial reporting system choices anddisclosure management efforts in particular, the disclosure reputation of firms along with user’s interpretation of the quality of financial information presented will be enhanced. Accordingly, as XBRL increases the transparency of management’s financial reporting choices and disclosure management to the uses of financialinformation, the reliability and the reputation of their financial information will be more easily analyzed and evaluated. As a result, as the adoption of XBRL to support financial reporting process becomes more common, managers may become aware that their capital market positions are being affected; hence, their attitudes and decisions concerning financial reporting system choices and financial disclosure management may change. Provided managers conceive that disclosure management may damage firms’ reputation and affect users’ investment decision after XBRL adoption, they will become more likely to choose more precise or neutralaccounting policies and procedures on their financial reporting. Alternatively, if managers perceive that XBRL adoption makes their financial disclosuremanagement transparent, they will become less likely to engage in financial disclosure management that is harmful to the users of financial reports.

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ReferencesElliott, R., K., (1998). Accounting in the 21st century. http://newman.baruch.cuny.edu/digital/saxe/saxe_1998/elliott_98.htm (last accessed on 30.11.2008)

Hodge, F., (2001). Hyper linking unaudited information to audited financial statements: Effects on investor judgments. The Accounting Review 76 (October): 675-691.

Hunton, J. and McEwen, R., (1997). An assessment of the relation between analysts’ earnings forecast accuracy, motivational incentives and cognitive information search strategy. The Accounting Review 72(October): 497-515.

Johnson, L., and Storey, R., (1982). Recognition in Financial Statements: Underlying Concepts and Practical Conventions. Stanford, CT: FASB.

Larkin, J. and Simon, H., (1987). Why a diagram is (sometimes) worth ten thousand words, Cognitive Science (11): 65-99.

Trites, G., (2006): Interactive Data: The Impact on Assurance – New Challenges for the Auditing Profession. XBRL International.

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Investigating the impact of motivation on loyalty and performance intentions in the Greek banking sector

Panagiotis Trivellas1, Nikos Kakkos2 & Panagiotis Reklitis3

1,3Department of Logistics Management, TEI of Chalkida, Greece 2Department of Management, TEI of Larissa, Greece

[email protected], [email protected], [email protected]

Abstract Enhancing the job performance of employees has been put in the centre of interest for many motivation theories, especially the need theories. However, limited research investigating the relationship between need satisfaction and job performance poses questions on motivation theories’ soundness. Furthermore, few researchers have been focused on the link between work motivation and employee loyalty. This paper addresses these research gaps by examining the impact of need satisfaction (as suggested by the Alderfer theory) on employee loyalty and on performance intentions (the surrogate measure for job performance). Drawing upon a sample of 220 employees of bank branches located in central Greece, a structured questionnaire was developed to measure need satisfaction (existence needs, relatedness needs and growth), employee loyalty, and performance intentions. The empirical results indicate that four out of five need satisfaction elements are significantly and positively related to loyalty, and three out of five motivation elements are positively associated with job performance. In particular, relatedness needs referring to superiors proved to be the most crucial predictor for both individual outcomes. Understanding the nature of the association between needs satisfaction and both employee loyalty and job performance, would enable academics and administrators to target on specific incentives so as to foster productivity, improving banks’ competitiveness. As specific motivators are linked to individual outcomes, practitioners gain insight into which practices, policies and behaviors may lead to loyal employees and high performers, enabling them to develop strategies for enhancing organizational outcomes.

Keywords: Motivation, employee loyalty, job performance, banking, Greece.

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1. Introduction

Many of the problems encountered in the banking sector are attributed to their ineffectiveness to energize and align human capital with their strategy in order to respond to a vulnerable and uncertain economic environment. On the other hand, in a highly dynamic and complex business environment, competitive advantages stemming from human resources become more than vital for organizations’ success. In particular, frontline bank employees play a decisive role in delivering high quality services, promoting the corporate image and improving customer satisfaction (LeBlanc and Nguyen, 1988; Lewis and Gabrielsen, 1998; Yavas et al., 2003; Karatepe and Tekinkus, 2006). A large-scale survey by the Gallup Management Consulting Group has revealed that intrinsic motivation is one of the key talents that the best salesmen (front-line employees) possess (Brewer, 1994). Though the motivation literature has been mainly dominated by theoretical vagueness (Arnolds & Boshoff, 2002; Miao et al., 2007), need theory has attracted research interest because it is considered as one of the ‘the most enduring ways to understand motivation’ (Aram and Piraino, 1978). These theories assume that the satisfaction of certain needs is the main driving force for employees’ motivation in order to enhance their job performance. The identification of these specific needs, the understanding of their nature and the way that they will be satisfied, will provide insight into work-related behaviours promoting job performance and loyalty (Stein and Hollowitz, 1992; Arnolds & Boshoff, 2002; Miao et al., 2007). Building on this logic, the need theory of Alderfer (1967, 1969) has been adopted to investigate the impact of need satisfaction on individual outcomes, exploiting its advantages of robustness and job specification orientation. In addition, previous research relied on global internal and external motivation aspects without incorporating a multi-dimensional perspective as Alderfer’s theory, has failed to reveal important relationships among motivation and performance according to social psychology (Amabile et al., 1994; Miao et al., 2007). On top of that, few scholars have explored causal linkages between dimensions of motivation theory and work behaviours, grounded on sound theoretical foundations. Consequently, most of the previous studies attempting to link work motivation and employees’ outcomes, are theoretical and adopt global constructs leading to inconclusive findings, while only few provide robust empirical evidence in a more holistic and thorough perspective. This research bridges this gap by exploring the impact of needs satisfaction on frontline employees’ job performance and loyalty, and spots light on relevant areas of betterment. The identification of these specific needs which cause certain behaviours referring to individual outcomes, will enable HR managers to develop motivation strategies fitting to specific individuals or groups of employees.

2. Theoretical Background

2.1 Motivation Theories – ERG The extant literature focused on the link between motivational elements and job performance is mainly dominated by theoretical vagueness and inconclusive empirical results (Arnolds & Boshoff, 2002; Miao et al., 2007). One of the most

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popular and appealing motivation theories is Maslow’s (1943) needs hierarchy, comprised by five categories of needs, namely the physiological needs, safety and security needs, affiliation needs, esteem needs and self-actualization needs (Bryan, 1983; Hoffman, 1988; Stahl, 1986). On the other hand, various criticisms have been raised for its applicability (De Cenzo and Robbins, 1988; Steers and Porter, 1991). For example, it has been accused of failing to describe work motivation (Landy, 1985). In the light of Maslow’s theory, Alderfer (1967, 1969) attempted to address this deficiency by developing a synthesis based on empirical research (Robbins, 1998). He advocated that three categories of core needs motivate individuals, namely Existence, Relatedness and Growth needs, for this reason the name ERG theory. The existence needs incorporate the human basic needs necessary for existence, corresponding to the physiological and safety needs of Maslow’s theory. The relatedness needs describe individual’s desire to uphold important interpersonal relationships referring to social, acceptance, belongingness and status desires. The last needs category is the growth needs, which reflect the desire for personal development, self-fulfilment and self-actualization. Though, ERG is considered as a more robust perspective of needs theory (Robbins, 1998) and has been recently gained the support of contemporary scholars investigating work motivation (Luthans, 1998; Arnolds & Boshoff, 2002), it has not instigated a stream of research (Ivancevich and Matteson, 1999). Alderfer’s theory advantages stem from its job specification orientation reflected especially by pay fringe benefits, relatedness needs from co-workers and superiors and growth need satisfaction at work. In addition, this theory has been examined by empirical researchers who are mainly focused on the correlational relationship between its content and work behaviours with few exceptions (Fox et al., 1993; Salancik and Pfeffer, 1977; Wanous and Zwany, 1977). Alternatively, the investigation of causal linkages between the elements of a motivation theory and work behaviours may provide a more holistic and thorough assessment of the phenomenon (Wahba and Bridwell, 1976; Arnolds & Boshoff, 2002). Although, some of the strict assumptions for causation may not be met in social sciences, ‘strong causal assertions can possibly be made if the relationships are based on a theoretical rationale’ (Hair et al., 2010). In our study, based on Alderfer’s theory, we attempt to explore what needs cause certain behaviours reflected on employee loyalty and performance intentions, rather than just showing the correlation between global constructs of motivation and behaviour. Understanding the nature of the association between needs and behaviour, would enable HR managers to reflect critically on specific needs adopting the related practices, methods and techniques in order to develop motivation packages fitting to specific individuals or groups of employees. In this frame, Arnolds and Boshoff (2000) examined the causal relationship between the groups of need satisfactions as recommended by Alderfer (1969) and employee job performance measured by performance intentions as proposed by several researchers (Carkhuff, 1986; Shore et al., 1990; Sumerlin and Norman, 1992). Given that our sample is comprised frontline employees such as bank tellers, secretaries, office staff, we will focus only on relative findings. Based on a sample of frontline employees in the manufacturing, trade and service sectors, Arnolds and Boshoff (2000) reported that the satisfaction with pay and relatedness need satisfaction from peers exert a significant positive influence on job performance, while the satisfaction with fringe benefits, relatedness needs from superiors and growth needs failed to confirm significant associations with performance intentions.

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Their findings impinge upon certain general claims on motivational theories. Maslow’s theory assumes that lower-order need satisfaction (fringe benefits) should motivate lower-level employees, while Arnolds and Boshoff (2000) failed to support this hypothesis. Similarly, Hong et al. (1995) concluded that lower level employees are more concerned about security (which includes fringe benefits) need satisfaction. Furthermore, Alfred’s (1991) conclusion that frontline employees are motivated to raise their job performance by the satisfaction of higher-order needs (respect, recognition and personal development) is dissonant with Arnolds and Boshoff (2000) evidence. The impact of satisfaction with pay on job performance is a subject of considerable debate with mixed results (i.e. Igalens and Roussel, 1999; Pfeffer, 1998; Arnolds and Boshoff, 2000). For example, Pfeffer (1998) reflects on the influence of individual incentive pay on employee performance. Bloom (1999) and Bloom and Milkovich (1998) advocated that highly dispersed pay structures mould less co-operative employees and less inclined to work in teams, thus they are apt to adversely influence organizational performance. Other researchers put forth that ‘pay for performance’ schemes fail to improve creativity because they undermine intrinsic motivation (Amabile, 1988). Along this reasoning, employees are relatively less likely to be involved in a process of producing new and creative solutions to problems against well established and trusted methods of working, when they feel controlled by supervisors who are responsible for determining the payment scheme (Pfeffer, 1998; Thorpe, 2000). On the contrary, Lawler (1995) stated that pay for performance schemes provide a ‘line of sight’ linking performance and reward, thereby guiding individuals to make and implement appropriate decisions. It is obvious, that research has overemphasized on the content of the payment schema which should best motivate employees, neglecting the latent social psychology of it (Heath, 1999; Arnolds and Boshoff, 2002). The social psychological foundation of the payment schema sheds light on the degree of principals’ understanding (the supervisor who motivates) of the agent (the employee been motivated), so as to develop the appropriate compensation package in alignment with their motivational needs. This strategy will prevent an overemphasis of extrinsic incentives in motivation, as it is frequently reported in literature (Heath, 1999). Furthermore, according to social psychology, pay as an external reward should influence work behaviour only if the employee is apt to these rewards due to an internal desire. Similarly, intrinsic rewards would affect work behaviour only when something in the external environment constitutes this behaviour as valuable (Heath, 1999; Ryan and Deci, 2000). Motivation is one of the top priorities which organisations should successfully deal with in order to tap on human capital and retain skilled and talented employees (Bennett, 2000). The often recommended perception of adopting different motivational strategies especially designed to fit to different individuals or groups via customized compensation packages may provide a remedy for this problem (Scharge, 2000). Most researchers have treated intrinsic (comprised challenge seeking and task enjoyment) and extrinsic motivation (consisted of compensation seeking and recognition seeking) as a global construct leading to mixed findings about their impact (Ingram et al., 1989; Leach et al., 2005; Tyagi, 1985), though these first-order motivation dimensions has been proved to be conceptually and empirically distinct (Amabile et al., 1994 ; Miao et al., 2007). Furthermore, Amabile et al., (1994) revealed that individuals vary in the cognitive and affective components of their

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motivational needs (Amabile et al., 1994). Supporting evidence was provided by Miao et al. (2007) who found that the positive impact of intrinsic motivation on job performance principally stems from its cognitive dimension challenge seeking, while extrinsic motivation strength comes mainly from compensation seeking. A large-scale survey by the Gallup Management Consulting Group has found that intrinsic motivation is one of the key talents of the best salespeople (Brewer, 1994). Building on this discussion, the present study attempts to elaborate a more complete assessment of the impact of need satisfaction on individual outcomes by revisiting the Alderfer ERG theory. In order to achieve this aim, the associations between Alderfer needs, and job performance (measured by performance intentions) as well as loyalty (reflecting commitment or reversely retention) are explored. 2.2 Individual outcomes Employee loyalty and performance intentions as a surrogate measure of job performance are the individual outcomes under investigation in this study. Employee loyalty or organizational commitment may be defined as the relative strength of individuals' identification with the involvement in a particular organization (Steers, 1977). Commitment has served as a major construct of research for investigating the ties that bind employees in a particular organization. It represents the binding force that inspires individuals to take part in a course of actions that are relevant to both the organization and the individual. This definition is in alignment with Allen and Meyer’s (1990) perception of affective organizational commitment, which refers to the strength of an employee’s emotional attachment to an organization. Loyalty is viewed as an employee’s positive connection to the organization and a willingness to contribute toward the accomplishment of organizational goals (Bou and Beltran, 2005). A behavioral perspective put forth by Salancik (1977), is derived primarily from the commitment model of Kiesler (1971), where the focus is on behavior. This view is grounded on essential conditions, such as explicitness, revocability, volition and publicity. Several researchers have proposed a positive relationship between motivation and loyalty or organizational commitment (i.e. Orphen, 1997; Mak and Sockel, 2001; Karatepe and Uludag, 2007). Mak and Sockel (2001) empirically found that motivation has an impact on retention of IS workers and loyalty as one of its indicators. Furthermore, conceptual reasoning as well as empirical evidence has indicated that intrinsically motivated employees are inclined to be more connected and loyal to the organisation (Miller 2002; Mathieu and Zajac, 1990; Eby et al., 1999; Low et al., 2001; Karatepe and Uludag, 2007; Miao et al., 2007). Thus, high committed individuals are more likely to employ actions and behaviours supporting the achievement of firm's strategic goals and shared purposes due to intrinsic rather than extrinsic motivations (Porter et al., 1974). Frontline bank employees due to the extensive work and time efforts invested into their jobs are likely to become attached to their organization (Karatepe and Tekinkus, 2006). In some empirical studies, organizational commitment or loyalty is treated as one of the predictors of job performance (e.g. Boshoff and Mels, 1995), while others consider job performance as one of the antecedents of affective organizational commitment in banks (Karatepe and Tekinkus, 2006). This study examines the association between motivation and performance intentions as a proxy for individual performance, treating loyalty and performance like two distinctive variables. Theoretical and empirical evidence indicates that motivated employees, especially intrinsically, are innovative and high performers in the workplace (Miller, 2002;

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Teigland, and Wasko, 2009). In marketing literature, surveys point out that salespeople’s intrinsic motivation has a significant positive impact on their work performance (Tyagi, 1985) as well as on their perceptions of performance (Babakus et al., 1996). In a similar vein, DeVoe and Iyengar (2004) concluded that there is a strong association between both intrinsic and extrinsic employee motivation, and performance appraisal in three distinct cultural regions (North America, Asia, and Latin America) within a single global organization. In order to bridge the gap between motivation and individual outcomes, as well as provide banking organizations with practical assistance in dealing with this issue, this research uses a sample of frontline employees to examine, whether need satisfaction results in an improvement of employees’ loyalty and job performance.

2.3 Research Hypotheses

Based on the above discussion, we set up the following hypotheses: H1: Need satisfaction (as measured by the Alderfer needs) is positively associated with employee loyalty. H2: Need satisfaction (as measured by the Alderfer needs) is positively associated with performance intentions. In figure 1, the proposed conceptual framework of the current study is illustrated. Figure 1. Conceptual framework of the research

3. Research Methodology

3.1 Questionnaire Design

The field research was based on a structured questionnaire. It was built by adapting existing scales in the literature measuring Alderfer needs satisfaction, loyalty and employee performance intentions. Based on the ERG theory, Alderfer (1967) developed a construct for the operationalization of the extent to which the respondents’ job permits the satisfaction of needs. The five ERG sub-scales were proved to meet convergent and discriminant validation criteria. Performance intentions of respondents were assessed by adopting a tool proposed by Shore et al. (1990). Several researchers consider performance intentions as a potent or surrogate measure of employee job performance (Carkhuff, 1986; Shore et al., 1990; Sumerlin and Norman, 1992). Aiming to make this scale a 4-item one, for reasons of consistency, as all other constructs comprised four or more items, an item from the Cranny et al. (1992) scale was added.

H1

H2

Alderfer needs • Existence • Relatedness • Growth

Employee Loyalty

Performance Intentions

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Employee loyalty items were developed and validated by Eskildsen et al. (2003). As it is illustrated in table 1, the instrument rests on a solid foundation of academic research in the field. Furthermore, similar items can be found at the research of several authors (Arnolds and Boshoff, 2002). Table 1. Basic references of all scales Alderfer needs satisfaction

Items

Basic References

Alderfer needs existence needs-pay 4 Alderfer (1967)existence needs-fringe benefits 4 Alderfer (1967)relatedness needs-respect from superiors 4 Alderfer (1967)relatedness needs-respect from peers 4 Alderfer (1967)growth needs 4 Alderfer (1967)Dependent variables Performance intentions 4 Shore et al. (1990), Cranny et al. (1992) Employee Loyalty 6 Eskildsen et al. (2003)

For each item of the aforementioned scales, respondents were asked to indicate their agreement or disagreement on a five-point Likert scale. Finally, reliability tests and principal components analyses were performed to confirm scale construct validity, while multiple regression analyses were conducted to test research hypotheses.

3.2 Sample

The field research was conducted in 2007, using a structured questionnaire in order to test the research hypotheses. The measurement instruments were checked twice before it was released. Firstly, it was examined by ten branches’ managers from different banks. Secondly, it was submitted to academics for in depth discussions. This process was fruitful, since after minor adaptations, they confirmed the cognitive relevance of the questionnaire to the banking sector. This research is focused on frontline employees of banks, at the region of Thessaly (Central Greece). A total of 500 questionnaires were delivered. The final sample comprises 220 valid questionnaires, resulting to an adequate response rate of 44%. The 58.2% of the respondents were male. The majority of the respondents (53.2%) were between 31 and 45 years old. The employees in the sample were allocated to a variety of departments (deposits, cash (teller), loans, customer services).

3.3 Principal Component Analysis

Principal Component Analysis (PCA) was conducted to identify latent factors within Alderfer needs scale. Five factors were extracted from the data, as it is shown in table 2. These principal components accounted for over 71% of the total variation. A cut-off of 0.50 was used for item scale selection and it was adopted a normalized varimax rotation to bring about simple and interpretable structure. According to

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Lewis-Beck (1994), this method is the most commonly used in order to reduce the number of items in a survey questionnaire. Following an inspection of the items’ loadings on each factor, five distinct principal components were identified, corresponding to: existence needs (pay), existence needs (fringe benefits), relatedness needs (respect from superiors), relatedness needs (respect from peers) and growth needs. Also, one principal component was extracted (Kaizer criterion) explaining approximately 78% of the overall variance for the employee loyalty scale, as well as one component for the performance intentions construct (49% of the total variation, Kaizer criterion).Preceding PCA, the Bartlett sphericity testing on the degree of correlation between the variables (p<0.001) and the appropriateness of the sample according to Kaiser–Meyer–Olkin (KMO over 0.70) verified the appropriateness of the sample (Norusis 1990). Inter-item analysis was used to verify Alderfer needs, employee loyalty and performance intentions scales for internal consistency or reliability. Specifically, Cronbach’s coefficient alpha (Cronbach, 1960) was calculated for each scale, as recommended by Flynn et al. (1990), ranging approximately from 0.77 through 0.91. Thus, all sub-scales exhibited well over the minimum acceptable reliability level of 0.7 (Nunnally and Bernstein, 1994).

Table 2. Descriptive statistics and internal reliability analysis of all scales Variables

Mean

S.D.

items

Cronbach’s

alpha

KMO*

Alderfer needs 0.802 existence needs (pay) 3.14 0.675 4 0.836 existence needs (fringe benefits) 3.37 0.687 4 0.801 relatedness needs (superiors) 4.17 0.823 4 0.887 relatedness needs (peers) 3.73 0.586 4 0.790 growth needs 3.48 0.685 4 0.778 Dependent variables Performance intentions 4.25 0.731 4 0.907 0.721 Employee Loyalty 3.92 0.671 6 0.805 0.796

*The Kaiser–Meyer–Olkin (KMO) indicator was calculated to assess sample size adequacy. The minimum acceptable level is 0.5. Bartlett's test of sphericity is significant at p<0.001 for all scales. Valid N=220.

4. Statistical Analysis

Two multiple regression analyses were conducted for employee loyalty and performance intentions as dependent variables, testing their relationships with need satisfaction elements (existence needs (pay), existence needs (fringe benefits), relatedness needs (respect from superiors), relatedness needs (respect from peers) and growth needs).

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Results show that the predictor variables have captured a significant proportion of change in the dependent variables, explaining 57.6% of variance in loyalty and 48.6% of variance in performance. No serious problems of multi-collinearity exist between the independent variables as Variance Inflation Factors (VIF) is far below the 3 points limit suggested in Social Sciences literature. The results of regression analyses (standardized betas, adjusted R square, significance levels) are exhibited in table 3. The data were examined for outliers, skewness, kurtosis, and multivariate normality using statistical procedures and plots available by SPSS (14.0) Table 3. Results of regression models testing the relationships of needs satisfaction, employee loyalty and performance intentions Dependent variables Independent variables Employee loyalty Performance intentions Control Variables Gender -0.003 -0.041 age 0.075 0.074 Working experience -0.075 -0.084 Alderfer needs existence needs (pay) 0.168** 0.187** existence needs (fringe benefits) 0.066 0.003 relatedness needs (peers) 0.113* 0.039 relatedness needs (superiors) 0.513*** 0.462*** growth needs 0.143** 0.208**

Adjusted -R2 0.576*** 0.486*** * Significant at the 0.05 level, ** significant at the 0.01 level, *** significant at the 0.001 level. Valid N=90. Examining H1, the values of standardized betas reveal that four out of five need satisfaction elements are significantly and positively related to employee loyalty. In particular, those crucial elements are relatedness needs referring to superiors (stand. b= 0.513, p<0.001), as well as peers (stand. b= 0.113, p<0.05), existence needs stemming from payment (stand. b= 0.168, p<0.01), and growth needs (stand. b= 0.143, p<0.01). Examining H2, relatedness needs referring to superiors (stand. b= 0.462, p<0.001), existence needs stemming from payment (stand. b= 0.187, p<0.01), and growth needs (stand. b= 0. 208, p<0.01) are significantly and positively associated with employee performance intentions. Existence needs stemming from fringe benefits are not found to have any significant relationship with the dependent variables.

5. Conclusions

The overall objective of this study was to investigate the impact of needs satisfaction on loyalty and job performance of bank frontline employees, based on Alderfer’s

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theory. Our results revealed that relatedness needs referring to respect from superiors is the most important predictor of both employee loyalty and performance, followed by growth needs and the existence needs of pay. In addition, the relatedness needs stemming from peers’ respect has the weakest impact on employee loyalty among the statistical significant components. On the contrary, the existence needs based on fringe benefits failed to be associated with any individual outcomes. Regarding the relatedness needs, the frontline employees hunt for the acceptance of their superiors rather than the respect and acceptance of their peers. This finding clearly contradicts Arnolds and Boshoff (2000) study about relatedness needs. It may also be interpreted as a quest for the favour of management, against the “fellowship” of the co-workers. However, employee loyalty is not only affected strongly by relatedness needs linked to superiors, but also influenced by relatedness needs stemming from peers at a lesser degree. The acceptance of supervisors found to be more crucial than the respect of coworkers referring to performance. Thus, endeavors to improve commitment to superiors would increase performance intentions among frontline employees, while strategies to improve work group cohesion and the image of the employee among his/her co-workers could be a path to improved employee loyalty. As regards growth needs, this study is in disaccord with Arnolds and Boshoff’s (2000) point that growth need satisfaction is not associated directly with performance intentions of frontline employees, but only through the intervening factor of self-esteem (Arnolds and Boshoff, 2002). Our findings suggest that teaching frontline employees new things on the job, nurturing their competences, using a wide range of their abilities, providing for opportunities to do challenging things and cultivating autonomy to take initiatives and to make more decisions at work will improve job performance and employee loyalty. In a challenging working environment that provides opportunities for autonomy and self-fulfilment, the need for creativity and advancement can be satisfied when the employee is in charge of his/her work from the initiation till its completion. In this frame, an organizational culture that endorses a fair and constructive judgement of ideas, supports confidence, rewards creativity in workplace and provides opportunities for self-development rather than adheres to evaluation, surveillance, authoritative dictates from superiors and the promise of fringe, should prevail (Amabile, 1997). Unhealthy internal politics in the firm, destructive criticism of new ideas, fierce internal competition, risk aversion and adherence to status quo, must be abolished. On the contrary, HR managers should pay particular attention to promotion policies, career development plans and educational programmes for the cultivation of skills and competences. Furthermore, when growth needs are satisfied, individuals will be more loyal to the organization, adopting behaviors that promote organizational goals and consequently bank competitiveness (Porter et al., 1974). Considering existence needs, our results confirm Arnolds and Boshoff (2000) conclusions that only satisfaction with pay has a direct impact on job performance of frontline employees, while satisfaction of fringe benefits has no association with performance intentions. The same pattern is proved to be present for employee loyalty. Hence, the Alderfer model appears to be merely in agreement with Herzberg et al.’s (1959) theory that pay and fringe benefits are not employees’ motivators enhancing job performance but hygiene factors, which avert from job dissatisfaction. By contrast to Maslow’s theory, assuming that higher-order needs, such as growth needs, are not usually motivators of lower-level employees, the empirical findings

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reported in this study put forth the view that higher-order needs do motivate frontline employees. Thus, motivation strategies including training, job enrichment, recognition and rewards systems, and promotion policies should be focused on these higher-order needs in order to boost individual performance and employee loyalty. Our conclusions promote the notion that management should develop a monetary compensation schema considered as fair and equitable reward for the tasks delivered by frontline employees and at the same time it could create synergies in satisfying higher-order needs, namely growth needs and respect from superiors. Other researchers also converge to this rationale that pay may yield a much stronger motivation power under the condition that it is related to higher-order needs (Glassock and Gram, 1995; Arnolds and Boshoff, 2000). In an era of depression in economic and business activity, remedies such as downsizing or rightsizing are being adopted devastating employee goodwill, trust, morale, loyalty and group coherence (Tzafrir and Merav, 2005). Given that downsizing as a result of financial crisis induces a deep sense of loss, long-term job insecurity, and distress and inflicts a wound upon credibility and trust in management on the part of survivors (Clark and Koonce, 1995; Tsai et al. 2007), a suitable personal development and growth plan should be proactively deployed (Gandolfia, 2006). Our findings challenge the conventional view and highlight the necessity to reconsider job security as well as the bond between management and employees. Several researchers have raised severe objections promoting an alternative perspective that ‘reduced profit and redistribution of generated wealth over a wider spectrum of humanity are morally and economically sound’ (Slabbert, 1996; 1997 cited in Arnolds and Boshoff, 2000), while jobless growth may contribute to the vicious weakness and downward cycle of the economy.

Limitations

Drawing from a sample in the banking sector, this study contributes on the literature investigating the link between motivation and outcomes of frontline employees, however, certain other antecedents of work motivation should be considered for inclusion in the theoretical models apart from age, gender and working experience, such as organisational culture, management style, promotion opportunities, personality, and alienation (Sdrolias et al. 2005).

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The Effect of Corporate Governance and Ownership Structure on Capital Structure of Iranian Listed Companies

Akram Bodaghi, Ahmad Ahmadpour

Department of Management, Mazandaran University of Science and Technology, IranDepartment of Accounting, Mazandaran University, Iran

[email protected] , [email protected]

ABSTRACT

This paper presents the relationship between corporate governance and capital structure oflisted companies from Tehran Stock Exchange. Measures of corporate governance employed are board size, board composition, and CEO/Chair duality. Impact of shareholding on financing decisions has also been examined by using institutional shareholding. Similarly influence of controlled variables like firm size and profitability on firms’ financing mechanism is also investigated. Results reveal that board size is significantly negatively correlated with debt to equity ratio. However corporate financing behavior is not found significantly influenced by CEO/Chair duality and the presence of non-executive directors on the board.However, control variables firm size and return on assets are found to have a significant effect on capital structure. Therefore results suggest that corporate governance variables like size and ownership structure play important role in determination of financial mix of the firms.

KEYWORDS: CORPORATE GOVERNANCE, CAPITAL STRUCTURE, OWNERSHIP STRUCTURE

1. Introduction

Corporate governance is mechanism that entails processes and structure which facilitate the creation of shareholder value through management of the corporate affairs in such a way that ensures the protection of the individual and collective interest of all the stakeholders. Sound corporate governance principles are the foundation upon which the trust of investors and lenders is built. Good corporate governance practices may have significant influence on the strategic decisions of a company, e.g. external financing, that are taken at board level. Therefore corporategovernance variables like size of board, composition of board, skill set at board and CEO/Chair duality may have direct impact on capital structure decisions.

Corporate governance is generally associated with the existence of agency problem and its roots can be traced back to separation of ownership and control of

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the firm. Agency problems arise as a result of the relationships betweenshareholders and managers and are based on conflicts of interest within the firm. Similarly conflict of interests between controlling shareholders and minority shareholders is also at the heart of the corporate governance literature.

According to modern corporate finance theories, agency cost is one of the determinants of capital structure. However empirical literature on corporate governance does not provide any conclusive evidence on the existence of relationship between corporate governance, ownership structure and capital structure of firm.

The corporate governance has been a growing area of management research. A comprehensive review of literature reveals that empirical work is mostly focused on the impact of corporate governance on firm’s performance or examines the influence of ownership structure on firm value. However relationship between corporategovernance and capital structure has not been fully explored. Only few studies discuss the said relationship. Some studies discuss the influence of corporate governance on the capital structure decisions of firms for developed and emerging markets (Berger, 1997; Friend and Lang, 1988; Wen, 2002; Abor, 2007). But no such study has been conducted to investigate the relationship between corporate governance and capital structure for Iranian listed companies.

According to modern corporate finance theories, agency cost is one of the determinants of capital structure whereas corporate governance is structured to alleviate agency issues; hence corporate governance and capital structure are linked through their association with agency costs. This paper integrates various strands of the literature and examines the effects of corporate governance and ownership structure on capital structure decisions of Iranian listed companies. Study examines the impact of three sets of variables on capital structure. The first set includes corporate governance variables represented by Board Size, Composition of Board and CEO/Chair Duality. The second set comprises ownership variables which include Institutional Shareholding. The third set consists of control variables which include Size of Firm and Profitability. The capital structure is represented by debt to equity ratio. In the following sections, firstly, we provide an overview of existing literature on the subject, then explain the data, variables and methodology employed during empirical work in section 3, then experimental results are summarized in section 4 and eventually the conclusion is drawn in section 5.

2. Literature Review

Corporate governance has been identified in previous studies (Berger, et al. 1997;Friend and Lang, 1988; Wen. et al. 2002; Abor, 2007) to influence the capital structure decisions of firms (especially large and listed companies). The extant literature identified the main feature of corporate governance to include board size, board composition and CEO duality. However, empirical results on the relationship between corporate governance and capital structure appear to be varied and inconclusive.

The board of directors is charged with the responsibility of managing the firm and its operation. There is a remarkable relationship between capital structure and board size (Pfeffer & Salancick, 1978; Lipton & Lorsch, 1992). Firms with larger board membership have low leverage or debt ratio (Berger, et al. 1997). They assume that larger board size translates into strong pressure from the corporate board to make

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managers pursue lower leverage to increase firm performance. However, other researcher argues that firms with high leverage or debt ratio rather have larger boards (Jensen 1986). The researches also show a positive relationship betweenboard size and financial leverage (capital structure) (Wen, et al. 2002; Abor, 2007). Their findings suggest that large boards, which are more entrenched due to superior monitoring by regulatory bodies, pursue higher leverage to raise company value. Another reason is that larger board membership could result in difficulty in arriving at a consensus in decision-making. These conflicts arising from bigger board size have the tendency of weakening corporate governance resulting in high leverage. Other research also show that the cost of debt is lower for larger boards, presumably because creditors view these firms as having more effective monitors of their financial accounting processes (Anderson, et al. 2004).

The resource dependence approach emphasizes that external directors enhance the ability of a firm to protect itself against the external environment, reduce uncertainty, or co-opt resources that increase the firm’s ability to raise funds or increase its status and recognition (Pfeffer, 1973; Pfeffer and Salancick, 1978). Highproportion of outside directors is believed to be associated with high leverage position. Other researchers find a significantly negative relationship between number of outside directors on the board and leverage (Wen, et al. 2002). They argue that outside directors tend to monitor managers more actively, causing these managers to adopt lower leverage for getting improved performance results. Also, firms with higher proportion of outside directors tend to pursue low financial leverage with a high market value of equity. On the contrary, firms with higher leverage rather have relatively more outside directors, whiles firms with low percentage of outsidedirectors experience lower leverage (Jensen, 1986; Berger, et al. 1997; Abor, 2007).

CEO duality, i.e. where the CEO is also the chairman of the board, also influences the financing decision of the firm. A two-tier leadership structure is one in which the chair of the board of directors and the CEO position are not held by the same person. The rationale for this was decision management as the right to initiate and implement new proposals for the expenditure of the firm's resources and decision control as the right to ratify and monitor those proposals (Fama and Jensen, 1983). By not allowing an insider to have both decision management and decision control authority over the same proposals, a series of checks and balances areimposed that make it more difficult for managerial insiders to engage in any type ofopportunistic behavior. At the highest levels, this implies that the person with the senior decision management authority, the CEO, should not be allowed to exercise the senior decision control authority as well. Since the board of directors is thehighest level decision control structure in the firm, this requires that the board must not be under the control of the CEO. If the board is controlled by the CEO, “this signals the absence of separation of decision management and decision control ...” (Fama and Jensen, 1983). Since the chairman has the greatest influence over the actions of the board, the separation of decision management and decision control is compromised when the chairman of the board is also the CEO of the firm. Thus, requiring the chair and CEO positions to be held by different people (a two-tier leadership structure) should more effectively control the agency problems associated with the separation of ownership and control typical in the modern corporations. Firms with a two-tier leadership structure should be more likely to employ the optimal amount of debt in their capital structures than firms in which the CEO is also the board chair (a unitary leadership structure or CEO duality) (Fosberg, 2004). He finds

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that, firms with a two-tier leadership structure have higher debt/equity ratios. However, the relationship is not statistically significant.

3. Data Description and Methodology

This study analyses relationship between capital structure and corporate governance for 50 companies listed at Tehran Stock Exchange. The sample period is 1380 to 1385. Total data consists of 110 observations for 42 companies. Board Size, Board Composition, Proportion of Non-Executive Directors, CEO/Chair Duality, Institutional Shareholding are used as measures of Corporate Governance. Similarly, impact of control variables like Return on Assets and Firm Size on capital structure has also been studied. Variables included in study have been measured as follows:

3.1 Dependent Variable: Capital Structure - Leverage

Capital Structure is the dependent variable and it is quantified by using debt to equity ratio. Debt to equity ratio can be calculated either by using market value or by using book value. The use of book value measure of leverage has been preferred in this study. The reason is that optimal level of leverage is determined by the trade-off between the benefits and costs of debt financing. It is an established fact that prime benefit of leverage is debt-tax shield and it is available on book value of the debt. Secondly, leverage can be calculated either by using total debt or by using long term debt as a percentage of total equity. Long term debt is better option but in this study total debt to equity ratio has been used because in Iran a tendency to use short-term financing even for longer term funding needs is fairly prevalent. There are number of companies that do not have long term debt. There are a number of causes for this state of affair. The first is unwillingness of commercial banks to extend longer term facilities. The second is relative absence of financial institutions specializing in long term financing. The third reason is the nascent state of capital market for long term debt in the country. Under these circumstances, we considered it wise to take the total debt figure for measuring the companies’ gearing level.

3.2 Independent Variables

3.2.1 Board Size

The board of directors is apex body in the corporate set up, playing central role in a firm’s strategic decisions like financial mix. It is therefore considered an important variable to study the impact of corporate governance on capital structure. The variable Board size is measured as logarithm of number of board members. It is hypothesized that larger boards have negative relationship with leverage.

3.2.2 Board Composition

Presence of non-executive directors on a company’s board gives signal to the market that company is being monitored efficiently so lenders consider company more credit worthy. In turn, this makes it easier for the company to raise long term funds through debt financing. It is hypothesized that higher representation of non-executive directors on board leads to higher gearing levels. Variable Board

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composition represents the proportion of non-executive directors on board and is calculated as the number of non-executive directors divided by total number of directors.

3.2.3 CEO/Chair Duality

If a person holds both slots of chief executive officer and chairman than it may create agency problems. Higher level of control by CEO may lead to managerial opportunistic behavior and can lead to lower gearing levels under entrenchmenthypothesis. It is hypothesized that CEO/Chair duality is negatively related to leverage levels. The variable CEO/Chair duality is included as a dummy variable. It is taken as 1 if CEO is chairman; otherwise it is taken as 0.

3.2.4 Institutional Share Holding

Presence of institutional shareholding in a company helps it to raise long term finance at an advantageous cost. In the first place, these institutional investors themselves act as a source of long term debt as they are willing to provide debt to a company over whose board they enjoy an influence. Secondly, these institutional investors serve as an effective monitoring device over the company’s strategic decisions. They bring down the company’s agency costs and also reduce managerial opportunism. This gives confidence to general public and other lenders – resulting in favorable terms of borrowing by the company. It is therefore hypothesized that firms with higher Institutional Shareholding are likely to have a higher debt to equity ratio. Institutional Shareholding is measured as percentage of shares held by institutions as disclosed in annual financial reports.

3.2.5 Size of Firm

Large firms generally have close links with their lenders and find it easy to arrange debt on favorable terms. So it is hypothesized that there exists a positive relationship between the Size of Firm and leverage level of the firm. The variable Size of Firm is measured as logarithm of total assets.

3.2.6 Profitability- Return on Assets

Pecking Order Theory of capital structure states that companies use internally generated funds as first priority to finance project. Then as second priority debt is used and finally option of equity is exercised to finance company projects. Therefore it is hypothesized that profitability of firms has negative relationship with leverage levels. In this study Return on Assets (ROA) is used as measure of profitability and it is calculated by dividing a company's net earnings by its total assets.

3.3 Specifications of the Econometric Model

This study employs multivariate regression analysis in a panel data framework to measure the dependence of capital structure on corporate governance variables.

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The panel data analysis helps to explore cross-sectional and time series data simultaneously. Pooled regression has been used with assumption of constant coefficients. Constant coefficient model assumes intercept and slope terms are constant. The general form of model is:

)())(()()(%)(%))((

65

43210

DUALITYTotalAssetLogROAINSTSHARENEDBZLogLEVERAGE

4. Experimental Result

Table 1 shows the descriptive statistics. Results reveal that average size of board in is 8.79. Non-executive directors (NEDs) constitute 42% of boards which is a fairly good representation. Institutional shareholding is 15% which is reasonable; however it is not as well spread out across companies as it should be. Average rate of return on assets is 7%. Average (total) debt to equity ratio is 1.43 representing a fairly reasonable overall capital mix.

Table 1. Descriptive Statistics

Variables Mean Median Std. Dev Minimum Maximum

Leverage 1.43 1.08 1.05 0.05 6.52Board Size 8.79 8.00 2.06 5.00 19.00%NED 0.42 0.56 0.23 0.00 1.00Inst. Hold 0.15 0.11 0.11 0.00 0.52ROA 0.07 0.05 0.06 -0.15 0.32Firm Size 3.37 3.42 0.65 1.93 4.76

Table 2 shows the results of correlation analysis. Profitability is negatively correlated with debt to equity ratio which is consistent with pecking order theory that firms use internally generated funds as first option to finance projects before resorting to debt. There is a positive relationship between leverage and the size of firm. This appears rational as larger firms have more assets for collateral and it is easier for them to negotiate better terms with lenders. Institutional share holding is positively correlated with capital structure. This positive relationship is result of efficient monitoring and reduction of the agency cost and managerial opportunism. The size of board is found negatively correlated with debt to equity ratio indicating larger boards may exertpressure on managers to follow lower gearing levels and enhance firm performance. An aspect of this observation is that larger companies have larger boards – and larger companies with larger assets base are more inclined to incur debt at favorable terms.

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Table 2. Correlation Matrix

Variables Leverage Board Size

% NED Inst. Hold

ROA Firm Size

Duality

Leverage 1.00Board Size -0.06 1.00%NED 0.03 0.07 1.00Inst. Hold 0.15 0.11 -0.25 1.00ROA -0.23 0.21 0.00 -0.07 1.00Firm Size 0.11 0.42 0.03 0.15 0.29 1.00Duality -0.05 0.12 0.20 0.04 -0.04 -0.07 1.00

Table 3. Multivariate Regression Analysis

Variables Coefficient t Statistics P-value

Intercept 2.41 2.74 0.02Board Size -1.79 -1.87 0.06%NED 0.19 0.52 0.65Inst. Hold 0.84 1.04 0.33ROA -4.73 -4.11 0.00Firm Size 0.29 2.14 0.03Duality -0.14 -0.61 0.50

Table 3 presents results of multivariate regression analysis. Multivariate regression analysis provides that an increase of 1% in Profitability leads to 4.73% decrease in leverage. Debt to equity ratio is significantly affected by Size of the firm and an increase in size increase the tendency of the firm to exercise the mode of debt financing. Regression analysis also provides evidence about the existence of significant negative relationship between size of board and debt to equity ratio. Presence of NED on the board has no significant impact on leverage. Institutional share holding is positively correlated by debt to equity ratio.

5. Conclusion

This paper empirically examines the relationship between corporate governance, ownership structure and capital structure by using multivariate regression analysis. Results reveal that board size is significantly related to capital structure. However representation of NEDs on board and CEO/Chair Duality has no significant relationship with capital structure. Correlation analysis suggests that CEO/Chair Duality is negatively correlated with profitability. Institutional ownership has positive relationship with capital structure which is consistent with corporate governance philosophy but this relation is statistically insignificant. This may be due to the fact that corporate governance practices are still in an infancy phase in Iran. Traditional determinants of capital structure like size and profitability have significantly effect on corporate financing decisions. Profitability is negatively related with debt to equity ratio and it is consistent with pecking order hypothesis. Similarly, size has positive relationship which shows that large firms can arrange debt financing due to long termrelationship and better collateral offering. Therefore we can conclude that corporate

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governance and ownership structure has important implications on the financing decisions.

References

Abor, J., (2007), Corporate Governance and Financing Decisions of Ghanaian Listed Firms, Corporate Governance: International Journal of Business in Society, 7.

Anderson, R., Mansi, S. and Reeb, D., (2004), Board Characteristics, Accounting Report Integrity and the Cost of Debt, Journal of Accounting and Economics, 37, 315-342.

Berger, P. G., Ofek, E. and Yermack, D. L., (1997), Managerial Entrenchment and Capital Structure Decisions, Journal of Finance, 52(4), 1411-1438.

Fama, E., and Jensen, M., (1983), Separation of Ownership and Control, Journal of Law and Economics, 26(2), 301-325.

Fosberg, R.H., (2004), Agency Problems and Debt Financing: Leadership Structure Effects, Corporate Governance, International Journal of Business in Society, 4(1), 31-38.

Friend, I. and Lang, L.H.P., (1988), An Empirical Test of the Impact of Managerial Self-interest on Corporate Capital Structure. Journal of Finance, 47, 271-281.

Jensen, M. C., (1986), Agency Costs of Free Cash Flow, Corporate Finance and Takeovers, American Economic Review, 76, 323-329.

Lipton, M. and Lorsch, J.W., (1992), A Modest Proposal for Improved Corporate Governance, Business Lawyer, 48, 59-77.

Pfeffer, J., (1973), Size, Composition and Function of Corporate Boards of Directors: the Organisation-environment Linkage, Administrative Science Quarterly, 18, pp. 349-364.

Pfeffer, J. and Salancick, G.R., (1978), The External Control of Organisations: a Resource-dependence Perspective, Harper & Row, New York.

Wen, Y., Rwegasira, K. and Bilderbeek, J., (2002), Corporate Governance and Capital Structure Decisions of Chinese Listed Firms. Corporate Governance: An International Review, 10, 2, pp. 75-83.

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Purchasing 2.0: An Explorative Study in the Telecom sector on the Potential of Web 2.0 in Purchasing

1SDC Logistics, Doetinchem, The Netherlands 2Department of Information and Computing Sciences, Utrecht University, The Netherlands

3Research Centre Technology & Innovation, HU University of Applied Sciences, Utrecht, The Netherlands

1{thijs,vincent}@sdclogistics.nl, 2{j.versendaal,r.w.helms}@cs.uu.nl, [email protected]

Abstract

Internet technology has had and continues to have major impact on businesses: organizations have changed the way they run their business, and there is an ongoing potential in new ways of leveraging the Internet. Web 2.0 services like Amazon.com and Wikipedia are undeniable, yet particularly present for the consumer market. The applicability of Web 2.0 services within the business-to-business area remains rather undetermined. This paper describes the Web 2.0 potential in the specific business-to-business purchasing process of telecommunication solution providers: a study is performed aiming at the determination of the potential of Web 2.0 to enhance the purchase function. Based on literature and qualitative, explorative interviews a positive view on the potential of Web 2.0 for purchasing is identified. The literature and interview results indicate that Web 2.0 has the most potential within two phases of the purchasing process in the telecom service sector: the phase of ‘determination of specifications’, and the phase of actual ‘order management’. Validation of the potential has been executed through a pluralistic walkthrough. Based on the two purchasing phases of most interest, two usage scenario’s were developed and a software prototype was built. This validation method proved its value by providing, in our case, confirmation of the potential of Web 2.0 services and principles, while placing the participants in the role of a user.

Keywords: purchasing, Web 2.0, business/it-alignment, pluralistic walkthrough.

1. Introduction

General opinion and experience is that the Internet has changed the way organizations operate. Many companies indeed have implemented e.g. e-business solutions. Procurement and sales strategies founded on Internet-based infrastructures are executed upon: new sales channels, purchasing capabilities and interorganizational integration have become manifest. Yet, the penetration of e-business still shows opportunities. The European e-Business Report (e-Business W@tch), a European Commission initiated research on European e-business impact and adoption, indicates that ‘basic ICT infrastructure’ has become a commodity, however ‘advanced e-business activity’ still holds competitive

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advantage. Especially for Small and Medium Enterprises (SMEs) e-business can still be leveraged to improve the organization’s performance (p.9). Within the business-to-consumer market the rise of Web 2.0 as defined by O’Reilly (2005) can be noticed. The influence and rise of Web 2.0 is clearly visible in, mostly, the Business to Consumer (B2C) and Consumer to Consumer (C2C) market where companies and services as Google, YouTube, eBay, Amazon.com, Flickr and Wikipedia raised. There are indicators though that Web 2.0 could enhance business by sustaining their competitive advantage (Gilchrist, 2007). Specifically for the telecommunication service sector the growth of products related to the Internet, like Voice over IP (VoIP) and hosted voice, indicate a growing influence of the Internet on this particular sector. Companies in this sector seem, almost by definition, technology visionary. For the purpose of showing the potential of Web 2.0 on the B2B area, we therefor choose the telecom service sector as our focus industry. In this sector, typically Original Equipment Manufacturers (OEMs) of telecom products have their products installed through resellers (telecommunication solution providers). In the telecom service supply chain, between the OEMs and resellers, distributors can add value by normalizing a suitable telecom solution for companies. In terms of Timmers’ (2001) definition of e-business models, distributors act as ‘value chain service providers’, which Timmers classifies as having a high degree of innovation. SDC Logistics is such an innovative distributor in the supply chain of telecom services in the Netherlands, wanting to improve the support to their customers (resellers) as much as possible. SDC provides a comprehensive portfolio of products and services towards their resellers. Taking the perspective of the resellers, and specifically the collaboration in purchasing between distributor and resellers, we define the context of our explorative research as follows (figure 1): Figure 1. Context of our explorative study

Specifically we address the following research question:

What is the potential of Web 2.0 to enhance the maturity of purchasing from resellers to distributor in the telecom service supply chain?

Note that we specifically look how a distributor (as the value chain service provider) can support the purchasing process of resellers. From a scientific viewpoint we can position this research in the domain of ‘strategic alignment’ as defined by Henderson and Venkatraman (1993). See figure 2.

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Figure 2. Positioning of our research in the context of strategic alignment of Henderson and Venkatraman (1993), taking a telecom distributor perspective

Henderson and Venkatraman identify business/IT-alignment as the degree to which there is functional integration between the business and IT dimension, and the degree to which there is a strategic fit between the strategy and operations dimension. In our research we operationalize this by determining an operationalization of Web 2.0 principles (strategic fit perspective), and by verifying to which degree the collaborative purchasing processes are supported by suggested Web 2.0 applications (functional integration perspective). The rest of the paper is organized as follows. We identify characteristics of both Web 2.0 and the purchasing process seperately. Subsequently, we define a framework for a conceptual model in which Web 2.0 principles and services can be mapped onto the purchasing process steps. Explorative interviews with several reseller organizations provide a further operationalization of the conceptual model. The operationalized model provides potential applications of Web 2.0 for the purchasing process between resellers and distributor. The suggested potential of Web 2.0 is subsequently validated using the pluralistic walkthrough method, with participants from SDC, who act as experts on how resellers may collaborate with the distributor. We finally end this paper with conclusions and further research.

2. Web 2.0 principles

Although the concepts of effective and smart collaboration, self servicing, leveraging user knowledge, etc. were earlier mentioned in relation to leveraging the Internet, it was O’Reilly (2005) who first mentioned Web 2.0 explicitly by defining its scope in terms of among others these concepts. Others have taken O’Reilly’s definition further, by creating their own interpretations and operationalizations of it for their own situational context (e.g. Anderson, 2007; Knol et al., 2008; Wijaya et al., 2009).

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For our purpose we stay close to the principles of O’Reilly (2005), and associate these with examples of available software (see table 1). Table 1. Web 2.0 principles (with example applications) as used in this research

Principle Example

• Software above level of a single device • iTunes, Yammer, YouTube

• Harnessing collective intelligence • Wikipedia

• Rich user experiences • Gmail

• The Web as a platform • Google Maps and Gmail

• Data is next intel inside • Amazon.com, YouTube

• Lightweight programming models • Google Code

• End of software release-cycle • Gmail

3. Purchasing process and purchasing maturity

Van Weele (2005) defines the purchase function in an organization as: “The management of the company’s external resources in such a way that the supply of all goods, services, capabilities and knowledge which are necessary for running, maintaining and managing the company’s primary and support activities is secured at the most favorable conditions.” He defines the following steps (see figure 3). Figure 3. The Purchase Process Model (van Weele, 2005)

Determining specifications. The specifications that result from this phase can be of any kind e.g. technical, functional, costs, etc. These specifications are used to select a product that meets these specifications. In the case of new (possible) suppliers these specifications can be presented to the suppliers and suppliers are asked to offer the product against conditions. When the purchaser already selected a certain product the supplier will be asked to make an offer based on the specific product. The specifications can be based on one single product, batch sizes, project-based products where several products are required to be compatible and

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interoperable, etc. Based on the specifications a single supplier or several suppliers will be contacted. Selecting supplier. A single or set of suppliers will be contacted and asked to create an offer based on the previous determined specifications. This will result in a list of possible suppliers, and there are several options to determine the best supplier. These options are outside the scope of this research and therefor will not be elaborated as we assume that the distributor is already selected. Contracting. The selected supplier is contacted and an agreement is settled where price, delivery conditions and other related conditions are negotiated. Ordering. The actual order can be incidental or according to agreements that are made for a longer period. In the first case the selected supplier is contacted and an order is placed and agreed. If the buyer selected a supplier for a long term agreement an agreement is negotiated and the buyer orders the goods against this long term agreement. Expediting and evaluation. Monitoring the delivery and inspection of the delivered goods to determine damage, incompleteness of the delivery, etc. are performed in this phase. Follow-up and evaluation. Administration of the delivered goods and internal handling of the received goods are dealt with in this phase. Collaboration in purchasing and the usage of Web 2.0 should ideally be executed on a rather mature purchasing process. We explore the concept of maturity in purchasing by referring to van Weele et al. (1998). Their purchasing maturity model has 6 maturity stages; 1. Transactional orientation 2. Commercial orientation 3. Purchasing co-ordination 4. Internal integration 5. External integration 6. Value chain integration The maturity stages are listed in ascending order, from lowest maturity (1) to the highest maturity level (6). Based on their descriptions a short explanation will be given for each maturity level; Transactional orientation. The first mature stage is characterized by the lack of a purchasing strategy. The only and main goal of the function is to ensure a certain level of incoming goods to prevent provisioning problems. Commercial orientation. Purchasing is characterized by a focus on low prices. All purchasing related activities are focused on minimizing costs.

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Co-ordinated Purchasing. In this stage there is, for the first time, a kind of strategy formulated. Besides prices and costs there is acknowledgement for the importance of quality. Internal integration; cross-functional purchasing. The purchase function is becoming more process focused. Information systems are internally integrated and the strategic importance of the purchase function is recognized. The internal integration is the key characteristics in this phase. The purchase function is involved within strategic issues and key suppliers are becoming partners and joint problem solvers. The information systems are integrated within the organization, covering other departments, functions and divisions. External integration; supply-chain management. There is an outspoken sourcing strategy and the purchase function is concentrated on the effects from the supply chain on company resources. Information systems are present where users can place their orders, while these systems are linked with those of major suppliers. The purchase function has become more supply chain management. Information systems are integrated with, besides key supplier, partner suppliers. The purchase function is substantiated by information systems and services like digital catalogues, EDI (electronic data interchange), e-business, etc. Value chain orientation. The purchasing strategy is end-user focused. The whole supply/value chain is optimized to deliver value for the end-customer. Information systems are linked through the whole chain substantiating an efficient and effective chain. E.g. Batenburg and Versendaal (2008) have succesfully indicated that the steps as identified by van Weele (1998) are useful, and can be used as a strategy to increase purchasing performance. Therefore, we also use the identified maturity levels, and as Web 2.0 implies external orientation, in our research we specifically look at the transition from maturity level 4 to level 5. Based on the characteristics as determined by van Weele et al. (1998) a comparison between level four and level five is made, the results are presented in table 2. Table 2. Level 4 and 5 maturity characteristics derived from van Weele et al. (1998)

Level 4: Internal integration Level 5: External integration

• Cross-functional problem solving, reducing totalsystem costs

• outspoken sourcing strategy, attention for co-operation with supply partners

• Information systems are integrated with that of other departments/functions. (but not yet with those of the most important suppliers)

• Information systems are not only internally integrated, but also with those of the partner suppliers.

• Users order through advanced computers systems to which some major suppliers have been hooked up.

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Level 4: Internal integration Level 5: External integration

• purchasing is involved in strategic issues like core / non-core questions and make-or-buy decisions

• Purchasing works hard to make things simple for their internal customers, by using system contracting, purchasing-cards, Electronic business and catalogues and/or EDI

• Improvement actions are aimed at integrating the purchasing process over the different division

• integration with other disciplines, divisions and especially suppliers is in full speed.

4. Framing and operationalization of the conceptual model

In this section the operationalization is described. Explorative interviews have served to make the operationalization. We describe the interview set-up and results subsequently.

4.1. Interview set-up Based on the identified Web 2.0 principles and purchasing process steps we are able to identify a frame for our conceptual model (see figure 4). Figure 4. Frame of the conceptual model

The data for operationalization is gathered by conducting explorative open interviews amongst resellers; a qualitative research method is chosen as the most appropriate method. Kaplan and Maxwell (2005) defined the qualitative research method as “The goal of qualitative research is understanding issues or particular situations by investigating the perspectives and behavior of the people in these situations and the context within which they act.” This definition is in line with the ideas and reasons behind our research question.

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The respondents were selected in consultation with SDC. SDC preferred that they were at least a member of the Partner & Friends program, have a significant turnover and a good personal relation with SDC’s employees. To ensure reliable information and a certain level of knowledge regarding the research area the respondents should be a member of the organization’s management team, preferably CIO or purchasing manager. In relation with the research question, the organizations were also selected in line with the definition of purchase development level four. This level is characterized by a process oriented purchase function where information systems are (not yet fully) integrated between buyer (reseller) and supplier (distributor). In total seven resellers from SDC were interviewed according to the following three-phased set-up. Introduction and evaluation Purchase Process Model and Maturity Stage 4 and 5 The Purchase Process Model (van Weele, 2005) was introduced and the respondent was asked if he could identify his own process with this model. Moreover, the maturity levels, internal integration and external integration, as determined by van Weele et al. (1998) were introduced. This was done based on the characteristics as displayed in table 2, where these characteristics were used to explain the differences between maturity level four and five. As the interviews were conducted in an open form, these characteristics were elaborated and the interpretation was left to the respondents. Introduction to current organizational knowledge regarding Web 2.0 This interview phase was meant to establish a certain level of knowledge regarding Web 2.0. Its purpose was to enable the respondent to discuss the potential of Web 2.0 in the following interview phase. As Web 2.0 has become a buzzword within the business world, practical examples based on O’Reilly (2005) like Amazon.com, Google Maps and Mail, Wikipedia, etc. were presented to explain the principles behind web 2.0. Perceived potential of Web 2.0 to enhance the purchasing process This interview phase was structured around the Purchase Process Model phases. For each phase the potential of Web 2.0 was discussed aiming at the determination of added value to enhance the purchasing process phase. Web 2.0 was explained with the use of examples as presented in the previous interview step. The respondent was asked to elaborate on the added value compared to the current situation.

4.2. Interview results The conceptual model served as a tool to direct and describe the interview results. In the operationalization the corresponding respondent is indicated by his number shown in parentheses (see table 3). The operationalization is described per purchasing process step. Determining specifications. Within this phase the potential of Web 2.0 was recognized by all respondents. In particular the sharing of knowledge by using Web 2.0 applications like Wikipedia was acknowledged. Besides knowledge

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sharing Web 2.0 could improve the service by making it available at all time and from anywhere, again comparable with Wikipedia and the services provided by Google like Google Mail and Google Maps. In relation with the maturity growth the involvement of the distributor to improve this phase was obvious, almost all respondents stressed out that the distributor has the responsibility to ensure reliable and qualitative information. The services should also be provided by the distributor and they should ensure a 24/7 availability. Table 3 lists all relevant quotes and remarks from the interviewees on this purchasing process phase. Table 3. Web 2.0 potential in the Determining specifications phase

WEB 2.0 Determining specifications Maturity characteristic Software above level of a single

device • knowledge base that is

accessible from everywhere (4)• accessible by PDA, laptop,

iPhone, etc to enable anytime-anywhere availability (2)

Harnessing collective intelligence

• wiki’s could provide them with the needed information (2)

• continually enhanced with knowledge and information from other TSPs (2)

• provides a service with information about product combinations from several manufacturers (3)

• Yammer functionalities are already used (3)

• information gathered from technicians to improve their purchasing process (3)

• Provide knowledge transfer (7)

• Information systems are not only internally integrated, but also with those of the partner suppliers

• integration with other disciplines, divisions and especially suppliers is in full speed.

Rich user experiences • all options at all time(1)

The web as a platform • access every time, everywhere (1)

• accessible by PDA, laptop, iPhone, etc to enable anytime-anywhere availability (2)

• Wiki-like service could provide a knowledge base that is accessible from everywhere (4)

• Information systems are not only internally integrated, but also with those of the partner suppliers

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WEB 2.0 Determining specifications Maturity characteristic Data is next intel inside • purchasing statistics could be

used as a trigger (5) • A Wiki could ... to global(6) • purchasing statistics to

determine product combinations could be used as a trigger (7)

• Users order through advanced computers systems to which some major suppliers have been hooked up.

Lightweight programming models

• accessible by PDA, laptop, iPhone, etc to enable anytime-anywhere availability (2)

• information is fast and easy accessible (3)

• coupling of the systems provides an advantage (4)

• linked to the distributor’s ERP to provide realtime product information (7)

• Purchasing works hard to make things simple for their internal customers, by using system contracting, purchasing-cards, Electronic business and catalogues and/or EDI

End of software release-cycle • access every time, everywhere (1)

Selecting supplier. Within the selecting supplier phase the potential of Web 2.0 was considered minimal. In general if distributors are chosen based on certain requirements, those requirements are delivery time and price. Here the selection procedure could be enhanced by providing the reseller with this information through web interfaces/services and/or coupling distributor and reseller ERPs. These two requirements were mentioned by the majority of the resellers, but they are not exclusive. All respondents emphasized that selection is mostly based on trust, gut feeling, personal experience and relationship between reseller and distributor. These factors are in general decisive, and delivery time and availability are secondary factors. Contracting. The contracting phase was not recognized as an actual phase within each purchasing process. Within the researched area the resellers often have contracts with suppliers, based on turnover and growth in turnover. Despite the lack of recognition there is a moment, or action, where contracting is present. This moment is where the purchaser orders the selected goods and thus agrees with the terms and prices as provided by the distributor. Here Web 2.0 could improve the service by offering a realtime, up-to-date and continuous availability of information regarding costs and pricing. Ordering. Within the ordering phase the potential of Web 2.0 was clearly recognized. Especially the coupling of information systems between reseller and distributor was mentioned as useful. This is in line with the coupling of information systems as in the “determining specifications” phase. Although in that phase (determining specifications) the coupling of information systems is aimed at sharing product information and knowledge, in the ordering phase it should enable the reseller to place an order and retrieve the order status at any moment. Here, as mentioned before, a part of the contracting phase is present. When placing an

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order and submitting it a reseller agrees with the delivery terms and price specifications. Here techniques like Amazon.com uses to provide product combinations and suggestions based on users purchasing statistics where found to be useful as a trigger. The main difference here is that, in common, resellers order at project level. As project configurations differ from each other and each project has its own characteristics and dependencies, purchase statistics cannot easily be used to provide a direct solution, however, they could be useful to act as a trigger to review the order and check why “the others did order that suggestion/combination and I didn’t”. This service should be monitored carefully by the distributor so that suggestions and combinations are reliable. This was stressed out by several respondents, they wanted to have the certainty that the provided suggestions and combinations were valid. This should prevent that purchasing statistics produce a product combination that is incompatible and causes malfunction.Table 4 lists the remarks and comments. Table 4. Web 2.0 potential in the Ordering phase

WEB 2.0 Ordering Maturity characteristic Software above level of a single

device • accessible by PDA, laptop,

iPhone, etc to enable anytime-anywhere availability(2)

• when ad-hoc purchasing occurs (6)

• Information systems are not only internally integrated, but also with those of the partner suppliers

Harnessing collective intelligence

• consensus of the users based on purchasing behavior. (2)

• Statistical analysis ... could be used as a trigger to reexamine the offer (6)

• Users order through advanced computers systems to which some major suppliers have been hooked up.

Rich user experiences

The web as a platform • accessible by PDA, laptop, iPhone, etc to enable anytime-anywhere availability (2)

• when ad-hoc purchasing occurs (6)

• Information systems are not only internally integrated, but also with those of the partner suppliers

Data is next intel inside • analyze purchase orders of buyers on consistency and compatibility(1)

• the purchasing statistics could be used as a source (7)

• Users order through advanced computers systems to which some major suppliers have been hooked up.

Lightweight programming models

• EDI’s (1) • coupling of their own system

with their suppliers (3) • coupling of the systems

provides an advantage(4) • Here the coupling ... and

availability of data (7)

• Purchasing works hard to make things simple for their internal customers, by using system contracting, purchasing-cards, Electronic business and catalogues and/or EDI

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WEB 2.0 Ordering Maturity characteristic End of software release-cycle • ad-hoc en 24/7 ordering

functionalities (2)

Expediting and Evaluation. The last two purchasing process phases are combined for analysis. The expediting is a particular operational phase where goods are received, checked and further handled. These actions are all performed within the organization and only affect internal administration. Only when situations, like receiving damaged goods, occur, contact with the distributor is needed by phone or email to ensure a quick reaction and solution. Web 2.0 was not considered useful for this. Although it seemed logical that services like instant messengers could be helpful, they were not mentioned. The last phase where evaluation of the distributor takes place is done based on experience and relationship between reseller and distributor. None of the respondents base their evaluation on statistics; problems are solved in an ad-hoc manner and if necessary the distributor is contacted directly to consult the problem and accommodate a solution. Conclusion. We analyzed and categorized the interview data in terms of Web 2.0 as defined in chapter two. From this analysis it became clear that Web 2.0 has the most potential within the ‘determining specifications’ and ‘ordering’ phase. Web 2.0 could contribute by enabling knowledge sharing and transfer, coupling of information systems and thus integrating the distributor in business processes. This contribution is in line with the characteristics of purchase maturity levels as defined by van Weele et al. (1998). These findings could be established by using Web 2.0 examples like Wikipedia, Google, Amazon.com, etc. Within the tables presented in this section quotes from the interviews where literally categorized against the Web 2.0 principles. In some cases quotes can be categorized against multiple Web 2.0 principles, this is a consequence as the principles have some overlap and are closely related to each other. Based on the two phases (determining specifications and ordering) a prototype and two scenarios will be developed to validate the results.

5. Validation by pluralistic walkthrough

Pluralistic walkthrough (Bias, 1991; Nielsen, 1993) was chosen as the technique to validate the correctness and usefulness of the found Web 2.0 services in the purchasing proces. Bias (1991) identified pluralistic walkthrough to determine the usability of software before it is developed. As we wanted to make the identified Web 2.0 services as tangible as possible for our validation participants, we judged the walkthrough, that uses mock-up screens and scenario’s to lead users through a workflow, an appropriate validation method. We defined two walkthrough scenario’s, based on the two purchasing process phases that were considered as most beneficial for Web 2.0 services, as explained in the previous section. We included tangible Web 2.0 applications (services) that could be identified from the explorative interviews, in combination with the example Web 2.0 applications from table 1.

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Seven experts confirmed to take part in the walkthrough: all employees from SDC (so the distributor organization). Three persons from the board (walkthrough session group 1), and four other (account) managers (walkthrough session group 2) that had insight in and good contact with the customers (the reseller organizations). For the walkthrough mock-up screens were developed. During each of the walkthrough sessions the full scenarios were completed, and provided feedback through mutual discussing and thinking aloud, and answering repeatedly the questions: 1) What would you do in this screen, 2) why would you do that, and 3) what do you expect to happen next? (For more details on how to perform usability and pluralistic walkthroughs we refer to Bias (1991) and Nielsen (1993)). For space limitation we only discuss the scenario on ‘determining specifications’.

5.1. Scenario 1, Determining specifications In this scenario the participants were asked to play the role of a user and look for some general information regarding Swyx products. The trigger for this scenario is the situation where a customer (SME/end-user) contacts a reseller with a request for a software based telecommunication solution. The reseller is familiar with Swyx, a software based telephony system, but needs some more information regarding this product. The current situation is that the reseller contacts the distributor by telephone or mail or the reseller searches for a user manual. In our scenario the users used Web 2.0 applications to perform their activities. The Web 2.0 services that were used in this scenario, to determine the potential of Web 2.0, were; • Wiki; the Wiki represents the gathering of user knowledge and information with a

service that is web-based and accessibly from multiple devices. • Youtube; providing a service where the users can substantiate their findings with

the option of video. This service was extended with videos from suppliers and OEMs.

• Yammer; Yammer provides an instant messenger service, document sharing

functionality and discussion groups. This service is organizational oriented: the distributor takes the role of administrator and grands certain rights to users.

• Blog; this service provides the opportunity to share knowledge towards a large

number of users. The Blog can be publicly available or only for registered users. Other users, who have access to the Blog, can write comments providing feedback and information. Once a Blog is written other users can not change the Blog, unlike a Wiki.

For a comprehension of the flow of this scenario, figure 5 shows the flow of screens.

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Figure 5. Flow of screens of the first walkthrough scenario

5.2. Analysis Scenario 1 The comments made by the participants while performing the walkthrough scenario were in line with the research results, though there were nuances. The major comments are listed below, and normalized towards the different Web 2.0 services/applications (see table 5). Table 5. Web 2.0 potential in the Ordering phase Web 2.0 service Comments • Blog • Personal view, could be (too) subjective

• Usability and reliability could be improved when distributor grants users to write

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• Chance to find needed information is small, need to have the luck that a person wrote a blog regarding the subject

• Wiki • Information based on personal experience, could be “colored”

• Identity source should be known to improve reliability • Ensure independent information (manufacturer should not be involved) • Distributor should remain not responsible as information is user contributed • Risk of user “shouting” and create negative image • Provided information could substantiate service • 24/7 availability and accessibility to the service

• Youtube • More reliable than Wiki • Easy to embed and add content • Easy to reuse • Can be used for technical, commercial, general information • Practical • Obvious/clear

• Yammer • Creates expectations regarding reaction time • No added value in comparison to telephone/email • Possibility to manage user’s permissions and restrictions contributes to

reliability and quality of the service. This should be monitored by the distributor

• More reliable than wiki, identity of source is more obvious • Added value when usable via PDA/iPhone etc. • Perceived value based on experience

To determine the potential of Web 2.0 the participants were asked if the Web 2.0 features had an added value compared to the traditional way. This added value was acknowledged, information was found to be accessed easier, and usability and reliability could further be improved by involvement of the distributor. We conclude that with this scenario the potential of Web 2.0 was identified within the ‘determining specifications phase’. Web 2.0 services (Blog, Wiki, Youtube and Yammer) and consequently principles (‘Software above level of a single device’, ‘Harnessing collective intelligence’ and ‘Data is next Intel inside’) can enhance the purchase maturity (see also table 1). The second walkthrough confirmed the above found suitable Web 2.0 services and principles; no additional principles and services were validated.

6. Conclusions

This research identified the potential of Web 2.0 to enhance the purchasing process of telecom solution providers. Our research confirms the indication in the European e-Business Report (e-Business W@tch, 2008) that SMEs can still leverage e-business through, in our approach, identifying Web 2.0 services.

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In this paper we have also elaborated and instantiated Henderson en Vankatraman’s (1993) Strategic Alignment model by creating an operationalization of Web 2.0 principles in the purchasing process of telecom solution providers. We have identified Web 2.0 services from Web 2.0 collaboration strategy (implementing ‘strategic fit’), and we have an implementation of ‘functional integration’ by validating the usefulness of our identified Web 2.0 services with the purchasing process of telecom solution providers. The research also confirms the innovativeness of the telecom distributor being a ‘value chain service provider’, as identified by Timmers (2001): the distributor validated the value of Web 2.0 services. Specifically the purchasing process phases ‘determine specifications’, and ‘ordering’ were found (and validated) to be suitable for Web 2.0 services. Blogs, Wikis, Yammer and Youtube were validated useful, implying that at least ‘Software above level of a single device’, ‘Harnessing collective intelligence’ and ‘Data is next Intel inside’ are explicit suitable Web 2.0 principles. With the interrelation between individual Web 2.0 principles and with the results of the explorative interviews the other four principles as identified by O’Reilly may in addition further support the purchasing process. These research results should, nevertheless, be retained within the researched area. This area, the inter-organizational boundaries within the telecommunication solution providers sector, is driven by strong customer intimate focused business strategies. This could be of influence regarding the potential of Web 2.0 as the maturity growth is aimed at intensifying the relationship between and involvement of the distributor in their reseller’s business processes. In general, limitations of our research lie in the fact that more cases can be applied in the telecom solution providing sector. Also other industries could be explored: especially those indistries that seem to be quite mature in their purchasing processes (e.g. Electronics and Automotive). We conclude by stating that pluralistic walkthroughs are a convenient means for validating innovative ideas and concepts.

References

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Batenburg, R. and Versendaal, J. (2008), “Maturity Matters: Performance Determinants of the Procurement Business Function”, Proceedings of ECIS 2008, European Conference on Information Systems (ECIS 2008), Galway, Ireland.

Bias, R. (1991). “Walkthroughs: Efficient collaborative testing”, IEEE Software, Vol. 8, No. 5, pp.94-95.

e-Business W@tch (2008), The European e-Business Report 2008: The impact of ICT and e-business on firms, sectors and the economy, 6th Synthesis Report of the Sectoral e-Business Watch, European Commission, Retrieved on April 26th, 2010 from http://www.ebusiness-watch.org/key_reports/documents/EBR08.pdf.

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Gilchrist, A. (2007). “Can Web 2.0 be Used Effectively Inside Organizations?” Bilgi Dünyası, Vol. 8, No. 1, pp.123-139.

Henderson, J.C., Venkatraman, N. (1993), “Strategic alignment: Leveraging information technology for transforming organizations”. IBM System Journal, Vol. 32, No. 1, pp.4-16.

Kaplan, B., Maxwell, J.A. (2005), “Qualitative Research Methods for Evaluating Computer Information Systems”. In Anderson, J.G., Aydin, C.E., Evaluating the Organizational Impact of Healthcare Information Systems (2nd edition), Springer, New York, NY, pp.30-55.

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O’Reilly, T. (2005), What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software, Retrieved on April 26th, 2010 from http://oreilly.com/web2/archive/what-is-web-20.html.

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Critical Enterprise Software Contracting Issues: Rights, Assurances and Responsibilities

Jacques Verville1, and Nazim Taskin2

1Faculty of Management, University of British Columbia, Okanagan, Canada 2 Faculty of Management, University of British Columbia, Okanagan, Canada

[email protected], [email protected]

Abstract

Unfavourable contractual agreement can be detrimental to the well-being of an organization. Often software contracts are written to favour the vendor and their terminology is vague and in a high-level language that can make organizations vulnerable. Among all IT applications, Enterprise Software is particularly critical due to integrating various critical business processes. In addition, ES implementations are among the most expensive types of IT implementations. Thus, ES contracting mistakes can be particularly costly for organizations.

Keywords: Enterprise Software, Contract issues, Rights, assurances, responsibilities, warranties

1. Introduction

Just as the wrong Enterprise Software (ES) acquisition can be detrimental to an organization’s survival, so too can an unfavourable contractual agreement be detrimental to its well-being. Often software contracts are written to favour the vendor and their terminology is vague and in a high-level language that can make organizations vulnerable to additional fees or restrictions that the buyer does not anticipate (Disbrow, 2005). Further complicating the acquisition of ES is a global competitive environment that adds additional layers of complexity to the overall process, with mergers and acquisitions within the ES industry itself. In the past few years, several companies have either merged or have been acquired by others. Prime examples of these are the merger of PeopleSoft and J.D. Edwards and later on the acquisition of PeopleSoft by Oracle. These added dimensions of the acquisition process for ES technologies have created a level of uncertainty and/or complexity beyond the technology itself. It is, therefore, critical that organizations approach the contractual negotiations with utmost care. To ensure that ES contracts meet the needs and requirements of the organization,

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contractual negotiations cannot exist in a vacuum. These must be part of the overall acquisition process for an ES. Consideration must be taken to ensure that ES contracts are multi-dimensional in nature and involve legal, economic, managerial, change and technological issues. In the past decade, acquiring ES has become an alternative to in-house development and information technology (IT) managers have been faced with the need to understand the various issues of ES contracting for them to better manage the acquisition of these technologies. The technological revolution of the past decades has created a volatile and complex systems environment. This paper focuses on the acquisition of ES and the perceived importance of various key/critical contractual issues by IT managers within both the manufacturing and service industries.

2. Enterprise systems contracts

From the perspective of contract law and contract theory, contractual issues set the foundation for the type of relationship between the buyer and the vendor and provide the directives and the completeness (or lack thereof) of the contract. The contract can be “an integral part of an organization’s strategy, including its risk management strategy, by circumscribing relationships among interdependent parties seeking to create projects jointly around a multiplicity of diverse purposes” (Gilbert, 1993). It can be viewed as a dynamic obligation which results from agreements that are structured to allow consensual changes in the obligations imposed in order to fulfill the ES contract in uncertain conditions (Salbu, 1997). As such, a contract, as a dynamic obligation, allows the organization and the vendor to mutually adjust commitments while maintaining a shared perception of reciprocal responsibility (Smith, 1991). Contractual issues are important to the establishment of the relationship between an organization and the vendor of an ES. Within the context of ES acquisition, each of the parties involved in the contractual agreement determines, during the decision-making phase, how their baseline activities prior to entering the relationship should be altered to achieve relational gains, the results of which are the rights and conditions of both parties. All parties then institute any chosen adjustments that in turn, may affect the consensual alterations ‘by forging a binding agreement’ (Salbu, 1997). In ES acquisition, this can be construed as a ‘give and take’ between all parties. Within the realm of contract theory, there are two distinct types of agreements or contracts: complete and incomplete. Complete contracts are defined by the time allocated to write the contract wherein all contingencies are addressed (Gifford, 1999; Williamson, 1975). Complete contracts are rare in IT (Richmond, Seidmann & Whinston, 1992) and incomplete or relational contracts are more the norm. For example, in complex acquisitions such as for ES, it is difficult to anticipate every outcome, problem or action and thereby include every possible contingency in a contract. This, in turn, leads to the incompleteness of the contract. This incompleteness, however, provides the opportunity for repeated interaction between the

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vendor and the buying organization and hence sets the framework for the evolving relationship. According to Gifford (1999), when transactions warrant repeated interaction over time, the acquisition is governed by a “relational contract”. This type of contract is “intentionally incomplete because of a desire to be flexible in response to future contingencies rather than to stipulate responses to all future contingencies in the initial contract (Gifford, 1999). Relational contracts “are those which the trading parties feel deserve periodic attention for the purpose of supervision, monitoring, consultation, and renegotiation” (Gifford, 1999, p. 470). In some cases, the complexity of the acquisition’s contractual environment opens the way for establishing the foundation for a long-term relationship between the buying organization and the vendor. Relational contracting frames the relationship where all parties concerned agree “on goals and objectives, on general provisions that are broadly applicable, on the criteria used in deciding what to do when unforeseen contingencies arise, on who has what power to act and the bounds limiting the range of actions that can be taken, and on dispute resolution mechanisms to be used if disagreement occurs” (Milgrom & Roberts, 1992, p. 131). Another perspective on contracting comes from contract law whose primary concern is coordination and control, i.e., “minimizing disputes among market transactions and resolving disputes when they arise” (Salbu, 1997, p. 329). When organizations are threatened with legal recourse, the parties in question are encouraged to fulfill their contractual obligations with the minimum of fuss. Thus, contracts operate as a means of control. However, according to Salbu (1997, p. 330), modern day relationships are better served by “management forms that sacrifice control in favor of flexible coordination”. In this light, contracts can be viewed as mechanisms for flexible coordination and control. As for ES, contractual relationships (relational contracts) are the basis for the long-term cooperation and coordination of all parties, helping to create a less adversarial environment which benefits all parties. In the establishment of a relationship between the organization and the vendor, each party would like to assume some type of control within the relationship. This produces two possible scenarios: conflict or compromise/concession; the latter being essentially a give and take. In the second scenario the organization acquiring the ES finds it is relinquishing control of the technological environment within the organization. Since ES systems are considered critical systems, the organization would like to assume some type of control over the systems. To do so, certain issues within the contract would allow them to perceive an aspect of control. For example, IT managers would request the right to establish the acceptance procedure for the software. To maintain harmony within the relationship, the vendor could accept this condition as part of the contractual agreement. In return, the vendor might request to retain ownership of the source code or to place it in escrow. This creates a “give and take” where each issue is negotiated. To do so or to facilitate this allows for the negotiability of rights which in turn reduces the risks (Salbu 1997).

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The question that requires answering, however, is about which contract issues are critical to the establishment of the relationship. The organization has the choice to accept, modify or reject fundamental issues within the contract. These issues may become the focal point or baseline for the relationship between the vendor and buyer. For instance, the organization has the choice to accept the standard ES solution or decide to customize/tailor the ES package to better fit the organization’s overall objectives and needs. Thus, the right to customize the ES to meet the organization’s overall objectives is most likely a critical issue for the establishment of the relationship. Since most ES solutions are ‘one size fits all’, they do not meet the precise needs of an organization and often lack key components needed by the organization to fully utilize the software’s capabilities (Harris, 2000), some organizations may want to customize the technology to fit their needs or to enhance the system in its capabilities to meet overall organizational objectives. As such, it may be more important or even critical for certain organizations/industries to have the ability to customize the technology to meet their needs, but less important for others. Another issue that might be perceived as more important or even critical is the ability for the organization to port the technology to any platform supported by the vendor. In this age of global competition, certain industry sectors such as manufacturing might perceive the portability of the technology as an important issue. The ability to port the technology to any platform supported by the vendor would allow manufacturing firms that desire to re-locate their manufacturing facilities either domestically or overseas, the flexibility, upon setup of the new site, to continue with the existing/known technological environment.

ES software is licensed with warranties and guarantees. Warranties and guarantees, expressed or implied, are important issues in the majority of ES acquisitions and are often negotiated. The contract issues pertain to warranties and the expectation of what the organization is acquiring, in other words, what they are expecting to get. More often than not, vendors want to make sure the contract does not find the vendor giving a warranty to a product that is not within its power to control. In the instance of software acquisition, the Uniform Commercial Code (UCC) provides warranty guidelines for the majority of software transactions to which United States law applies. The UCC is the express warranty of being able to make the deal, i.e., to grant the license, an affirmation of fact or promise made by the vendor to the acquiring organization and or, any description of the product. Under the Uniform Computer Information Transactions Act (UCITA) the express warranty pertains to specific computer information transactions.

3. Research Methodology

We collected a set of data on ES contracts via a mail survey sent to IT executives in charge of ES contracting. The survey questionnaire was developed based on a previous research project on ES acquisition practices (Verville, 2000), a study of software contracting by the Society of Information Management (SIM, 1995), The Software Legal Book, and a literature review in the area of contract law and theory. The survey

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instrument comprised two major sections. The first section focused on measuring the perceived importance of each featured ES contracting issue and was comprised one question and 36 items/issues. Each respondent was asked the following question: In your opinion, how important are the following contracting issues in the purchase of Enterprise Software (i.e., ERP, CRM, SCM, KM, etc.)? Items perceived as important were measured using a seven-point Likert scale range from 1 (Not Very Important) to 7 (Very Important). The second section focused on demographic information such as the type of industry, size of the organization in terms of number of employees, job title and area of responsibility, type of ES acquired and from which vendor. A small pilot study (N = 30) was used to pre-test the instrument and to identify any ambiguities and other problems with the survey questionnaire. A total of 279 completed questionnaires were received. Approximately 50.2% of the respondents were from the manufacturing sector, and 49.8% from the service sector. In terms of size, based on number of employees, 38.4% of the organizations had less than 1000 employees; 46.2% had between 1,000 and 9,999 employees; 10.4% had between 10,000 and 49,999 employees; and 5% had more than 50,000 employees. In terms of types of ES acquired, 48.7% of the organizations had acquired enterprise resource planning systems; 39.4% had acquired a customer relationship management, supply chain management, or other type of ES; and 23.9% had acquired more than one type of ES. In terms of the ES vendors, in 18.6% of the cases it was SAP; 25% was Oracle; 5.4% was BAAN; 41.6% was other vendors; and 9.3% was more than one vendor. In addition, Table 1 reveals the sample distribution for job title for our sample. Table 1: Sample distribution for the job title Category Frequency Percentage Job Title CIO 70 25.1

IT Management 45 16.1 Purchasing 36 12.9 Legal 5 1.8 User 59 21.1 Other 64 22.9

3.1 Critical contract issues: Rights, assurances and responsibilities

The Survey results (see Table 2) were analyzed using non-confirmatory (exploratory) factor analysis. Loadings (within the shaded cells) obtained by the non-confirmatory factor analysis are shown in Table 2 in the columns labelled “Right To” (RT), “Contractual Assurance” (CA) and “Responsibilities, Indemnities and Warranty” (RIW). In this non-confirmatory factor analysis the extraction method used was principal component analysis, and the rotation method was Varimax with Kaiser Normalization. To interpret and ensure significance of the loadings, a cut-off value of 0.50 was set. In respect to the reliability coefficient, the most widely used measure is Cronbach’s alpha being. It is generally agreed upon the lower limit for Cronbach’s alpha is .70 while 0.6 is also marginally acceptable. For our study, Cronbach’s alpha were 0.691 for rights,

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0.829 for contractual assurance, and responsibilities, indemnities, and warranties, which are acceptable (Hair, 1998).

Table 2: Results of the factor analysis RT CA RIW Cronbachs’

Alpha RT1 .810 .114 .093 0.691 RT2 .844 .046 .218 RT3 .892 .131 .215 RT4 .823 .291 .246 RT5 .605 .105 .245 CA1 .012 .667 .282 0.829 CA2 .331 .769 .156 CA3 .281 .774 .181 CA4 .123 .745 .069 RIW1 .042 .395 .579 0.700 RIW2 .255 .043 .844 RIW3 .244 .250 .797 Extraction Method: Principal Component Analysis Rotation Method: Varimax with Kaiser Normalization

From our initial thirty-six (36) issues, twelve (12) issues (see Table 4) were retained and are classified in three categories (factors), “Right To” (RT), “Contractual Assurance” (CA) and “Responsibilities, Indemnities and Warranty” (RIW). The first category, “RT”, is related to the ‘right’ of the organization to assign, re-assign, define, establish and port the ES. The second category, “CA” is related to ‘contractual definition and assurance’ and pertains to the forward compatibility of the software. Finally, the third category, “RIW” is related to ‘responsibilities, indemnities and warranties’ pertaining to the software. Table 3 reveals the bivariate correlations between the constructs. According to our findings, Rights are positively and significantly (p<0.01) correlated with contractual assurance (0.527), and responsibilities, indemnities, and warranties (0.422), which is also positively and significantly correlated (p<0.01) with contractual assurance (0.549). Table 3: Bivariate correlation RT CA RIW Right To (RT) Contractual Assurance (CA) 0.527** Responsibilities, Indemnities & Warranties (RIW) 0.422** 0.549** **. Correlation is significant at the 0.01 level (2-tailed).

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Table 4: Critical contracting issues The Right To…(RT) RT1:The right to assign the software license to a new corporate entity resulting from a merger, consolidation, acquisition or divestiture RT2:The right to re-assign software licenses within the corporate entity RT3:The right to define software acceptance as occurring only upon your written notice RT4:The right to establish acceptance procedures RT5:The right to port the software to any platform supported by the vendor at no or minimum charge Contractual Assurance (CA) CA1:Contractually defined difference(s) between (1) enhancements, releases, versions, etc., that you receive by subscribing to software support, and (2) those the vendor insists are a new product requiring new license CA2:Contractual Assurance regarding forward compatibility of the software with changes in operating systems CA3:Contractual assurance regarding forward compatibility of the software with changes in hardware CA4:Contractual assurance regarding forward compatibility of the software with changes in other software from the same vendor Responsibilities, Indemnities & Warranties (RIW) RIW1:Vendor’s responsibility to meet the cost of procuring alternative third-party support if the vendor fails to provide adequate and timely service RIW2:The vendor accepts to indemnify the organization for all losses, damages, or liabilities arising from the infringement or alleged infringement of such patents, trademarks, trade secrets, copyrights, or any other pertaining to intellectual property RIW3:The vendor warrants that the services provided to the organization shall not infringe upon any patent, trademark, trade secret, copyright, or any other right relating to intellectual property: rights are in force, recorded, or recognized

These factors revolve around the need for the organization to feel secure, in control and at ease with their acquisition. If the organization deems that ES is a critical and strategic system for its overall organizational competitiveness in the global market place, then its value is relative the importance of the technology to the extent to which the organization requires it for continued operations and survival. It is important for the

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organization to have as much control over the technology as possible. This issue of control is linked to another important factor, that of dependence and reveals the level of dependency of the organization has on the technology. More often than not, in ES acquisition, organizations feel that they are relinquishing control of their systems when they adopt enterprise wide software systems from the vendors. Thus, it is important that the organization maintain a semblance of control over the technology by factoring in or embedding in the contract terms that give them the sense of control. In other words, the organization derives control from its ability to set terms or regulate the acquisition and from its ability to enforce the terms or regulations in it’s of contractual agreements. In the case of ES acquisition, although the organization is relinquishing the development of the technology to a vendor, it does so without giving up control of its own objectives. The factor (Control) as a psychological effect on those involved in the acquisition is that it reduces the level of uncertainty and/or risk in the acquisition of an ES. In the case of the ‘Right to...’ assign and/or define contractual issues and/or assurances and/or warranties protect the organization forms a partial base for an ES contract against possible litigation in the future litigation. This being said, ES contract management issues/activities are proactive in that they attempt to anticipate and deal with ES acquisition circumstances before they arise. ‘An ounce of prevention is worth a pound of cure’. ANOVA results reveal that there is a significant difference between the job titles regarding rights and responsibilities, indemnities, and warranties; however, there is no significant difference between the job titles for contractual assurance. The results indicate that CIO’s find the rights most important while employees of attorneys find the software rights as least important compared to other job types. In terms of responsibilities, indemnities, and warranties, while employees of attorneys find these concepts as least important, IT managers and “Others” find these concepts the most important. Table 4 shows the ANOVA results based on the job title. Table 4: ANOVA results with job title Independent Variable

Dependent Variable

Mean Rank F Value

Sign. T1 T2 T3 T4 T5 T6

Job Title of the Respondent

RT 6.13 5.92 6.02 5.36 5.69 5.85 2.551 0.028 CA 6.05 5.75 5.89 5.70 6.08 6.03 1.011 0.411 RIW 5.69 6.03 5.98 5.13 6.01 6.12 2.703 0.021

4. Discussion and conclusion

Rights. This first factor pertains to the rights, in contractual terms, of the organization to either assign or re-assign the software licenses to either a new corporate entity or to other areas/departments within the existing corporate entity; the right to define the software acceptance procedure and to establish to said procedure; the right to port the software to any platform supported by the technology at minimal or no cost to the organization. In this age of uncertainty, as it pertains to the dynamic global environment, the need for organizations to be able to assign or re-assign software

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licences to other entities within organization and/or to port the ES to any platform is critical to the organization’s overall competitive well-being. With the proliferation of corporate mergers, acquisitions and divestitures, the need for the organization to be able to do so with as few difficulties as possible is important. Another important issue is the ability for the organization to define and establish acceptance procedures. In other words, this is to protect the organization in terms when the organizations feels that the software does not meet the organizations criteria for the overall deployment of the ES within the organization. Defining and establishing acceptance procedures set the terms and conditions under which the software is accepted. As shown by the data, this is an important issue and should be negotiated ‘up front’ at the time of the contract. As shown in our results, upper level management such as CIO, IT managers, and purchasing managers are among the ones who realize the importance of rights regarding Enterprise Systems Assurances. The second factor pertains to contractual assurances. The issue involves contractually defining the difference(s) between enhancements, releases, versions and those changes the vendor insists are a new product. In addition, contractual assurance pertains to forward compatibility of the software as it pertains to changes in hardware and software. To contractually define the differences between improvement, enhancements and releases of new versions of the software is an important issue. According to Hoffman (1992), improvements and enhancements are usually considered aspects of the software which either improve or extend the software capabilities. The issue is that if the software provides improvements on existing features without enhancing functions to the point where only a small part of the overall ES is affected. It is not unusual that vendors are unwilling to provide a blanket commitment to furnish future enhancements, improvements and versions of their technology, but rather insist on retaining control over which improvements, enhancements, and/or versions are ‘given free’ to the customer as part of a software warranty or in connection with a maintenance contract, and which are provided as options for an additional charge. Thus, it is important to the organization that the distinctions be made clear. Another factor that is deemed important under contract assurance is the forward compatibility of the software in terms of changes in hardware and software. Within the dynamic environment of technology, it is deemed important for the organization to be protected in the event of change to the technological environment which may render the recently acquired ES obsolete. The importance of this issue is to alleviate the uncertainty surrounding these types of acquisitions, uncertainty, arising from the concern over whether the organization is receiving the most recent product and whether this product can be easily or with minimal effort transferred to another technological environment. Although it is difficult to assess future trends in today’s technological environment, it is not unreasonable for the organization to be contractually assured that the ES they are acquiring can be transposed to another environment as defined within the contract.

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Responsibilities. The third factor pertains to responsibilities, indemnities and warranties. This issue involves contractually defining the vendor’s responsibilities, liabilities and warranties for the software. In terms of a vendor’s responsibility it is important that the organization define it to include assurance of adequate and timely service. Other responsibilities that could be defined by the organization would be for the vendor to provide an alternative third-party support if the vendor fails to live up to the customer’s expectations or if there are unacceptable delays during the implementation of the ES. These responsibilities need to be clearly thought out by the organization and set forth in their contract. As for indemnities, the organization can define that the vendor accepts to indemnify the buyer for all losses, damages or liabilities arising from the infringement or alleged infringements of patents, trademarks, copyrights, trade secrets or any other pertaining to intellectual property. As for the warranties, the organization can contractually define that the vendor warrants that the services provided do not infringe on said patents, trademarks etc. In this day and age where the patent infringement law suits are a common thread within the industry, whether the law suits are frivolous or real, an organization has the obligation to protect itself from such litigation. Case in point, Microsoft has had numerous patent infringement lawsuits in the past several years. Patent, copyright, intellectual property infringement lawsuits and other similar suits have not only been the purview of distributors and/or vendors but they have also transcended to end-user companies. For example, SCO Group filed a copyright infringement lawsuit against AutoZone, a Tennessee-based auto parts chain with more than 3000 stores nationwide. In the lawsuit, SCO alleges that AutoZone ran versions of Linux that “contain code, structure, sequence and/or organization from SCO’s proprietary Unix System V code in violation of SCO’s copyright”. SCO seeks an injunction to stop AutoZone’s use of Linux as well as an unspecified amount of damages. Red Hat named AutoZone as a customer. This is a case where an End-User company is being sued. According to Mark Radcliffe, an intellectual-property attorney not involved in the case, SCO needed to start legal actions against users, because the end-user companies will be the least inclined to spend money to defend against these types of litigation. The end-user companies are going to ‘scream for their distributors to come and save them’ (Shankland, 2004). IT managers entering into ES contracts on behalf of their organizations are advised to closely consider the issues discussed above in connection with rights, assurances, and responsibilities. Among all IT applications, ES are particularly critical due to integrating various mission-critical business processes of the organizations in which they are implemented. Compounding that, ES implementations are also among the most expensive types of IT implementations. Thus ES contracting mistakes can be particularly costly for organizations.

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References

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Harris, R. (2000), “Customization versus Standardization: Striking a Balance in ERP Software”, Machine Design, V.72, No.14, pp. S64-S69.

Hoffman, P.S. (1992), “The Software Legal Book”, Croton-on-Hudson, NY, Shafer Books.

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Richmond, W.B., Seidmann, A., and Whinston, A.B. (1992), “Incomplete Contracting Issues in Information Systems Development Outsourcing”, Decision Support Systems, V.8, No. 5, pp. 459-477.

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Shankland, S. (2004), “SCO Suits Target Two Big Linux Users”, CNET News.Com, URL: http://www.zdnetasia.com/news/business/0,39044229,39170786,00.htm.

SIM (1995), “Current Practices In Software Contracting”, The Society of Information Management: Working Group on IT Procurement, The School of Information Studies, Syracuse University, Syracuse, NY.

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Verville, J. (2000), “An Empirical Study of Organizational Buying Behavior: A Critical Investigation of the Acquisition of “ERP Software””, Ph.D. Dissertation, Faculty of Administrative Science, University Laval, Quebec, Canada.

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Beyond Privacy and Security: Ethical Dilemmas Resulting From Emergent Uses of Electronic Health Information

Amy W. Ray1, Wilson Wong2, Susan Newell3 & Jesse Dillard4

1,2,3Bentley University, 4Portland State University

[email protected], [email protected], [email protected], [email protected]

Abstract

Numerous countries are engaging in the development of national electronic health record systems in efforts to reduce administrative costs and healthcare treatment errors while improving healthcare quality. To date, most of the research related to these systems has focused on measuring or maximizing expected benefits or on related privacy and/or security concerns. Yet electronic health systems have social and organizational consequences that go beyond improved decision-making and task-performance and reports of rising privacy and security breaches of electronic health data are indicators that we also are not doing enough to protect this information. Beyond concerns for privacy and security, a number of additional ethical dilemmas are emerging that are not being addressed by existing management and legislative controls. These under-recognized ethical dilemmas are the primary focus of this research. More specifically, in this paper, we address the ethical tensions among different stakeholder groups in relation to the use of patient data from electronic health records (EHR) and demonstrate that evolution in use of EHR data increases privacy and security risks and that the complexity of these systems and variety of users and uses makes it more difficult to identify unethical behavior. We further consider factors that may be contributing to the creation of these ethical tensions and discuss how these tensions might be managed in ways to benefit the individual as well as the broader society.

Key words: ethics, inter-organizational information systems, electronic health records, emergent use of technology, security

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1. Introduction Around the globe, numerous countries are engaging in the process of developing a

national electronic health record system in efforts to reduce administrative costs and healthcare treatment errors while improving healthcare quality. Efforts to increase use of electronic health record systems are taking place at local, regional, national and international levels. At the local or community level, organizations with more sophisticated technology and business processes are engaged in efforts to drive community-wide adoption of these systems while national and international efforts are directed toward developing new standards to increase the interoperability of adopted systems.

Related research on electronic health systems has focused primarily on how technology can accomplish business objectives such as reducing costs or improving the decision-making capabilities of one or more stakeholders, or alternatively on methods for improving the privacy and security of sensitive health information. However, electronic health systems are influenced by the specific conditions under which they exist, and they have social and organizational consequences (intended and unintended, negative and positive) that go far beyond improved decision-making and task-performance, and reports of rising privacy and security breaches of electronic health data are symptoms that there is a gap in our understanding of how these systems are used and how to secure them. In addition, a growing number of ethical dilemmas beyond privacy and security of information are coming to light. More specifically, tensions between different stakeholder groups in relation to the use of patient data from electronic health records are emerging. These under-recognized ethical dilemmas and the factors that are contributing to the creation of these ethical tensions are the primary focus of this research. We argue that new ethical dilemmas are emerging as a combined result of the growing number and diversity of systems stakeholders and the increasing size and complexity of standards-based electronic health record (EHR) systems. We further argue that the emergent systems, applications and uses of these systems will continue to grow in complexity and thus become harder to predict, much less control. After a review of the relevant literature and a brief description of some of the expected benefits from widespread adoption of standards-based electronic health record systems, we provide evidence of new and unexpected uses of health information that may increase opportunities for unethical behavior, including but also beyond increased opportunity for privacy and security breaches. We propose that to fulfill its societal responsibilities, stakeholders in electronic health information systems must consider broader organizational and social antecedents and consequences surrounding the use of advanced information technology before these investments begin to mature.

We begin by exploring prior research on standards and complex systems and on ethical use of information. We subsequently develop our theoretical arguments and use grounded theory to support these arguments with data from practice in healthcare in the United States. We conclude with suggestions regarding changes in decision models of organizations with access to electronic health information. We also discuss the potential role of information security and privacy researchers and professionals in the mitigation

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of information abuse opportunities in standards-based national EHR systems. As most national and international efforts to implement integrated electronic health record systems are in early stages, there is still time to explicitly consider potential misuses of such systems and consider appropriate policies and controls to reduce these risks.

2. Interoperability Standards and System Complexity

Information standards are foundational to information sharing across variant computer systems. Many technical standards are widely used across industries and organizations, including TCP/IP, HTML, XML, SMTP, SOAP, and EDI to enable inter-organizational information sharing. In addition to official ratified IT standards developed by standards-setting boards, technologies may become de facto standards when there is widespread adoption. For example, many consider Microsoft Office for business applications on personal computers and SAP for enterprise systems to be de facto standards.

In the US healthcare industry, many of the commonly known ratified standards are used for sharing information, but a number of additional industry-specific standards have also been developed to improve record sharing. One of the first comprehensive efforts in the US to develop healthcare specific standards came with the passage of the Health Information Portability and Accountability Act (HIPAA) in 1996. Prior to HIPAA, there were more than 400 different formats for filing electronic insurance claims. This heterogeneity slowed down inter-organizational information sharing, created confusion for claims entry personnel, thereby increasing the likelihood of mistakes, and made validation of claims information more challenging. Part of the justification for HIPAA was the expectation that the legislation would reduce costs, speed access to information and improve information integrity (Hellerstein, 2001). For example, with standards the record of one person in an electronic health information system may be accessed to file an insurance claim, book an appointment, treat a patient for an illness, identify possible wellness programs for a patient or for a population of patients, fill a prescription, calculate insurance enrollment costs, assess the quality of patient care, research the efficacy of prescriptions and conduct research on a particular disease. Standardized EHR systems make all of these tasks potentially more efficient to complete and can also potentially improve the overall healthcare system which is why there are various ongoing local, regional, national and international efforts to identify mutually agreeable standards for the contents of the records (e.g., patient identification, diagnosis coding and physician identifiers) as well as for the methods of transmitting and sharing information among users.

Technology standards simplify development of individual, local systems and increase the extensibility of systems by enabling quicker and easier systems enhancements. However, ironically, standards-enabled extensibility also leads to organizational and inter-organizational systems that are increasingly complex and difficult to manage at a comprehensive level. When complex systems are used by a diverse set of inter-organizational users, opportunities for new, emergent functions and processes are created (Truex, Baskerville and Klein, 1999). For example, codes for patient and medical data and processes in most EHR systems are standardized and digitized to facilitate interoperability of the systems of care providers and insurers of

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individual patients and to encourage information sharing among these participants. However, in any system, standardization of data and processes offers opportunities for ‘reuse’ of those data and processes in new and emergent ways, with potentially positive or negative consequences. Examples of productive, emergent use of standardized patient data in an EHR in the US is the development of additional systems functionality once the basic system is in place (e.g., the ability to submit prescriptions electronically), extension of users and uses of data through investment in new mobile hardware (e.g., addition of handhelds for use in exam or hospital rooms), or development of new business practices (e.g., physician access to records from a home computer as well as office computers). Emergent uses where the consequences are potentially more questionable and may lead to violation of security, privacy or ethics, such as the development of patient systems by third party vendors not traditionally in healthcare or emerging practices among employers to monitor employee health records are discussed in greater detail in this paper. We know from prior research that the complexity of large technical systems undermines the ability to plan and regulate them (LaPorte, 1994), so complex systems will inevitably give rise to errors (social and technical), which can lead to failure (Perrow, 1984), including breaches of privacy and security.

Non-linearity refers to the phenomenon that occurs when there is an interaction between two or more elements in a system that cannot be predicted at the time of design (Rocha, 1999). The combined complexity created by the growing size and sophistication of EHR systems along with the growing diversity and volume of stakeholders is increasing non-linearity in this environment. We know that in such situations, management complications can arise where inputs lead to unexpected outputs (Daft and Lewin, 1990). Prior research shows that small changes in the behaviors of any one of the actors in a network can have multiple, small, secondary effects on the rest of the system and that these changes can ultimately lead to dramatic consequences. The result is that complex systems can be upset by small unanticipated events (Arthur, 1989) and may be said to have emergent properties: “They baffle us because we acted in terms of our own designs of a world that we expected to exist – but the world was different” (Perrow, 1984).

Information systems are implemented with the intention that they will be used in particular ways and for particular reasons, just as EHR systems were originally implemented to support patient care and related administrative issues such as filing insurance claims. Use of systems often change and evolve, however, after their initial implementation, and users of those systems collectively establish “normal” and “unacceptable” behavior (Gersick and Hackman, 1990). These behaviors evolve into social structures encompassing the rules and norms that guide future behavior (Giddens, 1979), however, as the behaviors change so too do the structures. There is a duality of interaction, action creates structures and structures constrain action (DeSanctis and Poole, 1994). Action is, thus, ‘both conditioned by existing cultural structures and also creates and recreates those structures through the enactment process’ (Walsham, 1993; 34). This constant interplay of action and structure eventually settles into a somewhat stable state for most sets of users (DeSanctis and Poole, 1994). However, as we will demonstrate, EHR systems have grown to involve multiple groups or communities of users across multiple organizational settings, often with

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contradictory and conflicting needs and demands. So while a temporary equilibrium may be achieved within a set of users, the interdependencies between user groups means that any small alteration in use by one group can impact the structures governing the behavior of other groups and can amplify across the whole system of use.

EHR systems will provide a variety of capabilities, each designed to enable particular groups to interact in a specific manner (DeSanctis and Poole, 1994) and to accomplish a specified task. Each of these capabilities inherently contains both the actual structural features of the EHR and the spirit of the EHR. The structural features are the specific components of the EHR, their capabilities, and the “specific types of rules and resources, or capabilities, offered by the system” (DeSanctis and Poole, 1994, p.126). For example, the record format will modify the work process of an insurance company by providing a specific set of data using a specific set of standards. The formation of the documents, the entering of the data and the degree of accessibility the insurance company has to the EHR is a specific structure captured by the technology that provides its own rules (who can see what when).

The spirit of the technology is the general intent of its structural features, and is broadly defined to include the system design, its features, user interface, and training materials (DeSanctis and Poole, 1994). This holistic view is more open to interpretation than are the structural features. Both the designer's intentions and the users’ perceptions influence the spirit. The faithful adoption of the structural features of a system should lead to the intended use patterns of the technology, whereas an unfaithful adoption is more likely to lead to unintended use patterns. For example, one feature of most EHR systems is the use of a set of standards for the entry of specific medical data. The spirit of this feature is to improve the quality and reliability of the health related data. A faithful appropriation of it would be the entry of the data in a timely and accurate manner, while an unfaithful use would be to manipulate the data (by entering codes that may not be entirely accurate) in order to alter insurance reimbursement. The combination of spirit and features affects how organizations and users adopt the EHR into their work processes.

Thus, while information content and transmission standards will most certainly improve systems interoperability and information interpretation for the original intended uses of information systems, they also facilitate development of unexpected applications and enable new and unexpected uses of existing applications. The Internet and email systems are two readily recognizable, non-healthcare examples of this phenomenon. Employing a few of the first simple standards developed in the early days of the Internet, such as TCP and IP, uncountable numbers of diverse computers and computing devices around the world can connect to each other and operate in centrally unpredictable and non-linear ways. It is literally impossible to manage the network of computers, devices and systems created using these simple standards for interoperability. Using multiple standards together further enhances the ability to develop applications locally but increases potential complexity of overall systems architectures. For example, using TCP/IP for transferring data and XML for defining data enhances systems inter-operability and extensibility beyond the interoperability provided by just one standard. Thus, the overall effect of interoperability standards is

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decreased complexity of local interoperability (i.e., it is easier to connect system A to system B) but increased overall complexity of the systems architecture.

In addition, email has certainly fulfilled its promise as a rapid facilitator of communications, but the inherent flexibility of email’s structure led to the development of many unforeseen uses. Immeasurable sums of time and money have been spent on efforts to control spam, malware and non-work related use of email systems. Another cost of email that was unforeseen in the early days is the cost of upgrading the networks to provide enough bandwidth for transmissions that continuously increase in volume and size. Had there been a way to accurately forecast these additional costs and then include them in the decision model, would management have decided to invest in email? Still broader questions may be asked about whether society as a whole is better off as a result of the Internet and email. Certainly it is easy to identify examples of how society has benefited from both, but some may argue that there may not be a net gain to society as a whole after weighing all of the societal costs (e.g. provision of a vehicle for a variety of criminal and predatory behaviors) and organizational costs (e.g. time and money spent on information repair and protection and related legal costs) against the benefits described above. In any event, we are currently too invested in these systems to go backward. However, while foretelling the impact of the Internet and email may have been impossible, it is not too late to examine the positive and negative consequences of the standards-enabled, uncontrolled proliferation of technology associated with standards-based inter-organizational investments.

We can examine this kind of standards-enabled complex system growth in relation to EHRs. The HIPPA legislation addresses a number of different issues, including one section which established a set of uniform standards for electronic transmission of healthcare data, as well as standards for privacy of data and security of data. The idea behind this act was to simplify administrative exchanges between care providers such as doctors and payors such as insurance companies. However, as we show next, the HIPAA standards also provided a good foundation for developing other applications including EHRs and ePrescribing systems, which in turn are serving as the foundation for applications like the personal health record systems created by Google.

The HIPAA standards for electronic transmission of data include unique standards for medical codes, identifier codes for individuals, payers, providers and employers and electronic transaction standards to facilitate activities such as insurance claims processing and health plan enrollment. These privacy and security standards were put in place to protect against privacy and security violations from covered entities, including insurers, clearing houses, healthcare providers and business associates engaged in providing services specifically to one of the covered entities. It is these standards that have served as a good starting point for other standards-based information sharing applications in healthcare, including electronic health record systems (EHR) that are typically owned and managed by care providers such as hospitals and physicians offices, and personal health record systems (PHR), which may be managed by health care providers but are also increasingly offered by third parties. Thus, as more healthcare systems of various care providers, insurers, researchers, pharmacies and other entities become increasingly interoperable, other third party commercial entities including but not limited to Google, Microsoft and Wal-Mart have

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identified market opportunities to develop healthcare application systems where the target user is the patient.

By using the standards developed for HIPAA and subsequently for EHR systems, these organizations have begun to architect applications that will pull information from systems of the same entities that use the EHR plus offer additional resources to customers/patients for such activities as researching a prescription or a diagnosis or offering the ability to chat online with other care providers. One major difference with PHRs vs. EHRs is that if a third party vendor such as Google is used, the consumer must initiate the request for the PHR from the vendor, rendering the primary stakeholders in the relationship the consumer and the vendor. Since HIPAA privacy and security legislation was created to protect information shared between care providers and payors, such as insurance companies and HMOs, neither the consumer nor the vendor are bound by the privacy and security laws. Additionally, since PHRs may be populated in part by content from EHRs, that information may become more vulnerable in the hands of organizations not bound by HIPAA rules, but authorized to access the records by the consumer. Also, Google and Microsoft have made it clear that they intend to offer key word-based advertising in their PHRs, increasing the number of stakeholders with some access to information related to the PHR and accordingly, increasing the likelihood of possibly inappropriate but authorized access to information by advertisers. While all of these issues decrease the customers/patients control over their data, PHRs have been marketed by the vendors as providing consumers more control, simply because the consumer may have easier access to all of their health data. Thus the consumer may have an increased ability to correct any detected errors in their medical records, but what they may not understand (because they are not being told) is that there is increased access to their sensitive and personal information by a variety of new stakeholders.

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3. Ethical Issues And Emergent Complex Health-Record Systems

As we have seen, as EHR systems and associated technologies become increasingly complex they involve a growing number of stakeholders. Thus, EHR systems are today used by a wide range of end users, including patients, doctors, nurses, administrators and insurance companies, as well as a growing list of external health organizations (e.g. CDC, WHO), not to mention third party vendors such as Google, Microsoft and Wal-Mart. These different stakeholders may have different perspectives on what constitutes the ethical use of the data and this may create ethical tensions. An ethical tension arises when ‘one party in pursuit of its goals engages in behavior that affects the ability of another party to pursue its goals’ (Thong and Yap, 1998; 214).

In examining these issues we can consider the contrast between teleological and deontological ethical reasoning, the two dominant normative ethical approaches that form the basis for thinking about the morality of behavior (Thong and Yap, 1998). While these two views about ethical reasoning have a long history, they are still relevant today since the underlying moral issues associated with ethical tensions remain the same, even though there may be some unique characteristics of current ethical issues associated with IT systems (Johnson, 1994). Teleological or utilitarian reasoning considers the consequences of an action. An action can be considered right or wrong depending on whether it promotes the most good. Teleological theories vary in terms of whose good is the focus, but the dominant theory in this tradition follows John Stuart Mills who argued that the ethical choice is the one that promotes the greatest good for the greatest number of people who will be affected; that is, the common good rather than self-interest of the particular actor. Deontological or non-consequentialist reasoning follows Emanuel Kant who argued that the ethical choice should be determined by basic principles that can be universally applied – the categorical imperative that for example, indicates that an action should ‘do no harm’ to any other human being, regardless of the consequences to others or self. Thus, teleological principles are based on outcomes (getting the best outcome possible for the most number of people) whereas deontological principles are based on rules of action (ensuring that one acts according to moral rules of justice and fairness) regardless of outcomes.

Arguably, the nature of the payor businesses such as insurance companies and HMOs dictates a utilitarian perspective while the nature of care provision by physicians and other care providers dictates a deontological perspective. Given that these two stakeholders must work closely together, these different ethical perspectives automatically create tension. Patients are more likely to have a deontological perspective, as they are primarily interested in making sure that they are treated fairly. Most of the remaining traditional healthcare-related stakeholders (health research, laboratories, CDC) understand the importance of protecting sensitive information but still may view ethical use of health data through a utilitarian lens (e.g., data mining should be done if it helps improve treatment of a particular disease). Currently, the greatest ethical tensions are created by the entry of new stakeholders such as Google and Microsoft who have never been responsible for protecting sensitive health data before and thus there is concern that they may not explicitly incorporate ethical issues

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such as the societal consequences of their data mining practices at all. In other words, we can expect ethical tensions in relation to EHRs to arise because morality will vary across the population of systems stakeholders (Moores and Cha-Jan Chang, 2006); these tensions are likely to increase as the number of stakeholders grows and includes more companies that have not historically worked with healthcare data.

These tensions between stakeholders relate to the issue of how to determine the ethical use of sensitive patient health information. In exploring this issue we can draw upon Mason (1986) who identifies four ethical issues associated with the application of information systems: privacy, accuracy, property and access. Privacy can be conceptualized as freedom or immunity from the judgment of others (Introna and Pouloudi, 1999). A host of ethical and legal concerns relate to the privacy of health information and whether it is appropriate for various stakeholder groups to capture, own, and disclose it. Important dimensions of information privacy include: collection, unauthorized secondary use of information inside and outside of the organization, errors, reduced judgment, and combining data (Smith, et al, 1996). Privacy is a particularly important issue with regard to health information systems because the data is sensitive. The more standardized the content and transmission methods, the easier it is to share this information. The HIPAA privacy rules initially stated that patients must authorize use of their records beyond the personnel responsible for providing immediate care, but the fine print of the HIPAA privacy statements that new patients sign often state that by signing the statement, patients are authorizing the care provider to share, as necessary, the record with other physicians, nurses, healthcare researchers, laboratory technicians, insurance company employees, third party data entry personnel, pharmacy employees and possibly human resources personnel for patient employers. In other words, the HIPAA privacy statements are not intended to ensure patient privacy but rather to explain what are the care providers’ privacy policies.

Accuracy of records was a concern of McMahon (1995) who argued that the public and private sectors should raise the status of corporate records, ensuring that they are accurate and complete, in addition to encouraging moral behavior and emphasizing democratic values. Detecting errors in stored data is also a concern in relation to accuracy and interestingly Moyes (1996-1997) finds that different groups have differing opinions regarding the degree of effectiveness of current audit techniques for fraud detection, though some techniques are perceived to be more effective than others. Regarding property, the rights to, and ownership of, personal information is central to the ethical tensions around health care information. Spinello (1998) argues that privacy rights hinge on clarifying ownership rights associated with personal data. This perhaps plays an important role in the current problems in healthcare with privacy breaches, as it is unclear which stakeholders may own personally identifiable health information. Finally, historically speaking, the purpose of information systems and IS professionals has been to provide rapid and rich access to information than was previously unavailable. However, where information systems deal with highly sensitive information, the ethical issues that surround electronic health records illustrates the conflicting forces associated with the design and use of these systems. These four basic issues will continue to increase in importance with greater degrees of interconnectivity.

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In terms of finding ways to resolve these ethical tensions, some argue for the importance of codes of ethics (Harrington, 1996) and others address the importance of dialogue, with all stakeholders fairly and equally represented (Introna and Pouloudi, 1999). In the discussion, following the analysis of tensions which emerge in the use of EHRs, the paper will explore ways in which this stakeholder dialogue can be effectively encouraged.

In summary, we have, in the first part of the paper, argued that if uncontrolled proliferation of personal health information use and users continues to take place in healthcare, ethical abuses of this information will proliferate as well. In the following sections, we present our methodology and then in our findings section, present data which illustrates that these abuses are already happening.

4. Methodology We use grounded theory (Glaser and Strauss, 1967) in this study to demonstrate

the ethical conflicts arising among healthcare industry stakeholders as a result of changes in development and use of healthcare records. In the information systems field, grounded theory has been employed in researching requirements engineering in health care (Garde and Knaup, 2006), the use of CASE tools (Orlikowski, 1993), the perceptions of Internet technology use (Al-Kahtani et al., 2006), , the personalization of computers and mobile phones (Blom and Monk, 2003) and the use of group support systems (Trauth and Jessup, 2000).

Grounded theory typically starts with the process of collecting, coding and organizing data into similar conceptual groups as the basis for supporting theoretical arguments. It may be used to inductively build theoretical arguments from empirical data (Martin and Turner, 1986) and is appropriate for understanding complex organizational and inter-organizational interactions (Orlikowski, 1993). Thus, grounded theory is an appropriate methodology for research that is “inductive, contextual and processual” (Orlikowski, 1993) Given the large number of stakeholders in the healthcare industry and their complex interactions, grounded theory is especially useful in this study. Furthermore, grounded theory can be used to build theories of process and change pertaining to organizations (Glaser and Strauss, 1967) and accordingly is helpful in our efforts to demonstrate how the changes in healthcare systems and processes are leading to increases in ethical dilemmas..

Using Glaser and Strauss’ (1967) technique of theoretical sampling, the examination units were selected for their ability to further develop the theory rather than for statistical representation. Two criteria were applied in collecting data: 1) the event involves adoption of standards-based healthcare information systems and 2) multiple stakeholders make use of the system. This second criterion is necessary because it permits us to analyze possible conflicts between stakeholders arising from healthcare system functionality.

Data were collected through both secondary data sources in the form of news articles and primary data sources such as United States federal regulations and federal code of law. News articles were chosen to provide evidence of existing risks and emergent risks arising from the use of information systems based on electronic health

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care standards. Criteria for the selection of these news articles were that they included 1) factual information on the use or capability of electronic health information systems, and 2) information regarding stakeholders involved or affected by the change in healthcare information systems development and/or use. In the following section, our findings are presented.

5. Findings

Table 1 presents a summary of findings from 20 articles that highlight recent investments in new uses of health information that are expected to benefit at least one stakeholder group but also have consequences for other stakeholders. In general, our data indicates that as new applications for electronic health information emerge, there is a high likelihood that the interests of some members within a stakeholder group may be sacrificed for the benefit of another stakeholder group and in some cases the interests of an entire stakeholder group may be ignored. Further, where different stakeholder interests are taken into consideration this will be done primarily in relation to broad efficiency or effectiveness criteria, ignoring the security and privacy issues as well as broader issues related to trust and ethics.

Shading changes in the table indicate emergent uses initiated by different stakeholder groups. The first 6 table entries describe new uses of health information that were initiated by payors/insurers. Entries 7-11 were initiated by employers, 12-14 were initiated by providers/clinicians, entry 15 was initiated by an electronic device manufacturer, 16 by a third party health portal, 17 by a search engine company, 18-19 by government, and 20 by a clearinghouse. The first column includes a brief description of the new systems function or new use of existing systems. The second column includes a list of different stakeholder groups that will be influenced, positively or negatively, by the introduction of the new system or use. The third column includes a description of the overt goal, or in other words the benefits expected to accrue from the investment, while column four includes a brief description of possible ethical issues or tensions that may arise as a result of the investment.

As noted earlier, payors may be more likely than providers/clinicians to view the use of electronic health information through a utilitarian ethical lens. All of the payor-initiated events will reduce their costs and in some cases may provide value to other stakeholders but in others, may not. For example, patients/customers would benefit from quality of care information on area physicians. However, there is concern that payor rankings of physicians will be biased toward cost-effective providers rather than higher quality of care providers. Both care providers and patients will lose if all stakeholders must accept quality rankings that really reflect low cost rankings. An independent third party designated to rank physician performance may be less biased, but introducing such an organization may involve increasing the number of stakeholders with authorized access to sensitive information. Entry 5 in the table refers to introduction of new wellness programs by the 4th largest payor/insurer in the US. Customers are given the opportunity to sign up for these programs and if they do, they can earn points toward gift cards by making progress toward wellness goals such as weight loss. Benchmarks of wellness will be measured and recorded in the patient’s EHR. Clearly, encouraging healthier life habits is a good idea, but payors may not be the most

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appropriate stakeholder for instigating such programs. It is currently against US law for payors to refuse an individual of insurance, but it is easy to see how individuals who either refuse participation in wellness programs or do not meet specified goals, may ultimately face discrimination through unaffordable insurance premiums. Similarly, in the US, a large percentage of the insured population is covered through employer benefits programs. Thus, employer initiatives related to electronic health information are largely designed to reduce costs to themselves. Additional benefits may fall to some parties in other stakeholder groups, but other individuals may face new forms of discrimination as a result of the employer initiatives. Employers who offer PHR systems to their employees will have access to personal health information that they would not have otherwise, and many privacy advocates and lawyers would argue the employers should not be allowed to have access to such information. Employer-managed PHR systems may serve as a convenience to healthy employees but could be used to discriminate against employees with chronic diseases, mental health issues, or other sensitive health problems. Just as it is technically illegal for payor organizations to discriminate against consumers, it is illegal for employers to publicly discriminate against employees for health reasons, but given the knowledge, employers may identify other excuses for letting an employee go.

Entry of existing, successful third party software and service companies such as Google and electronic device vendors such as Verichip potentially create a whole host of new ethical dilemmas as these organizations are new to healthcare and either may be unaware of the full complement of ethical consequences from their offerings or may choose to ignore them given the profit potential of the new investments. As noted earlier, the contract between a consumer and Google is not covered by HIPAA privacy rules. Since there is less oversight, new alternative uses of electronic health information are expected to emerge around third party PHR systems. For example, pharmaceutical companies are expected to buy access to keyword searches for the purpose of direct marketing of drugs to potential patients. Healthy consumers and privacy savvy consumers who manage their PHRs judiciously may reap benefits of PHRs without suffering negative consequences, but other members of the patient/consumer stakeholder group may suffer from increased spam, new fraud scams and at worst, discrimination or blackmail as sensitive information becomes easier to access. Similarly, patients with their health information recorded in a chip that is implanted in their bodies are vulnerable to rogue scanning of that information and subsequent violation of privacy and even basic human rights. In fact, it is possible that only a minority of individuals such as Alzheimer’s patients would reap more benefits than losses from having microchip implants.

Still other stakeholder groups are initiating new applications of health information. Given the increasing use of EHR systems with standardized data, the CDC is now offering grants to research organizations that can develop surveillance applications to collect health data for use in public health programs. While this is a legitimate and worthwhile cause, concerns arise over possible alternative uses of this information by the government once it is collected. Who will have access to the information and how it will be protected is unclear. Similarly, initiatives by clearinghouses and portals are designed to simplify their processes and increase their revenues but may also lead to exploitation of sensitive health information.

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6. Discussion

Perhaps one of the greatest advocates for standards-based electronic health records is Dr. John Halamka, Chair of the National Health Information Technology Standards Panel (HITSP), CIO of Harvard Medical School, CIO of Beth Israel Deaconess Medical Center, and Chairman of the New England Health Electronic Data Interchange Network (NEHEN). He has been implanted with a microchip to facilitate access to his EHR, and much of the content from his own personal medical record is available through his blog to the public, as an example of what might be included in an extensive electronic medical record. It is worthwhile noting that his grandparents lived into their 90s,and that Dr. Halamka does not suffer from any serious medical conditions whatsoever. He does not have any weight issues, does not smoke and is a vegan. It appears from his blog that his only risky behavior is rock climbing. Thus, it is clear to see how such members of the consumer/patient population will have very little to lose from having their electronic records shared. In fact, individuals with this sort of health record are likely to receive benefits from payors and employers as a result of making this information public – they are indeed good investments to employers and insurers. However, other individuals with health problems or even family members with health problems will not receive the same treatment as will Dr. Halamka. As we go forward with rapid development of new applications using electronic health information and as the information becomes richer and more personal through the addition of factors such as genetic profiles, it is important to explicitly consider the possible ethical and social consequences of allowing an increasing number of stakeholders access to this information in a growing number of ways.

Traditional information systems research focuses on effective and efficient resource utilization by controlling means-ends relationships. The focus of system development is on meeting a set of predetermined objectives. Success is defined as the extent to which the functional specifications are met and the extent to which the economic well being of the organization is enhanced. Individual needs and values are ignored. All are assumed to share common economic oriented goals. Social processes associated with system design, implementation, and application are not considered. Ethical considerations are ignored, couched in neoclassical economic utilitarian terms, or specified more in terms of socially acceptable behavior than moral or right actions. A more enlightened perspective recognizes the need for information technology applications to consider unique elements of an individual decision setting. We propose a perspective that explicitly incorporates moral responsibility focusing on commitment and accountability among affected individuals and groups. The primary ethical consideration is whether the technology is used to enhance the overall human condition.

In order to effectively pursue ethical action, the fundamental form of human association must be seen to go beyond the social contract in which atomic individuals make partial commitments to each other for the purpose of gaining limited common ends or satisfying certain laws. Commitments made within the context of an ongoing community represent the fundamental form of human association. Niebuhr’s (1963) responsibility ethic is an abstract framework whereby ethical dilemmas can be identified and evaluated by recognizing that health information systems are implemented within

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the context of an ongoing community (Dillard, 1995; Yuthas and Dillard, 1996). The responsibility ethic provides a framework for identifying responsible action associated with the design and application of medical information systems. Responsible action represents a nexus of rights and responsibilities of members of an ongoing community. A responsibility based approach for systems development in the health care domain might include stakeholder committees, in conjunction with the information systems steering committee, to carry out a development cycle based on the four components of the responsibility ethic.

Acting responsibly requires that the decision to act recognizes and incorporates four primary components: solidarity, interpreted actions, the anticipated action, and accountability. Solidarity refers to the system implementer’s recognition of their situated and interrelated status as a responsible member of an ongoing health care community. Interpreted actions are the observed outcomes associated with past actions that over time reveal the physical and historical interrelatedness of any actions undertaken within the context of the community. Contemplated action considers possible outcomes on anticipated actions in light of observed and interpreted past actions. In deciding to act, decision makers are obliged to consider the anticipated act and its propriety in light of its projected effect and to formulate realistic projections with respect to the anticipated implications for community members based on an intentional awareness of the effect of past actions and a sensitivity to circumstances that supplement these observations. Accountability refers to the operationalization of an ethic of accountability that is being held to account for one’s actions in light of the rights and responsibilities enjoyed.

Conceptually, an ethic of accountability requires a genuine and ongoing conversation among all affected parties. An ethic of accountability does not seek “the good” in a utilitarian sense or “the right” in a deontological sense, though both are consistent with the ideal. The good and the right are delineated as part of the process of determining the appropriate action within the context of the ongoing community. Fitting action as well as the act of holding, and being held, accountable depends upon open and trustworthy communication between the actor and the community members as well as among the community members themselves.

A preliminary condition in implementing an inclusive and ethically sensitive health care information system requires the stipulation of what constitutes legitimate communal dialogue whereby the rights and responsibilities of all affected stakeholders are recognized. Trustworthiness among the actors grows out of the ongoing interactions and is central to establishing a sense of loyalty and responsibility. If the communal discourse is controlled by powerful, self interested members who exploit the social and physical resources to achieve self serving objectives, legitimate dialogue becomes impossible, and its pretense becomes a means for manipulation and exploitation with any possibility of solidarity and social sustainability destroyed.

The trend toward standards-based interoperability is rationalized with the promise of reduced costs and enhanced revenues. The belief is that these economic enhancements will be more instrumental to organizational success than anything else. Not enough thought is given to the potential moral consequences of these systems. There isn’t even enough thought put into the potential negative economic consequences of socially-agreed upon moral failures of the companies. For example, it is illegal for an

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insurance company to discriminate against an individual because the individual has expensive health problems, but the combination of complex systems and ubiquitous access to information increasingly makes it possible for insurance companies to do this without being obvious about it.

Organizations in healthcare and beyond invest in information and transmission standards as a way of achieving interoperability for particular intended business processes, without explicitly considering the fact that once implemented, interoperability standards enable emergent applications and uses. Some emergent systems and uses are desirable but others are not. The dilemma then becomes how to enable positive emergent applications while protecting against harmful emergence.

The use of well-understood standards to develop uniquely complex systems such as EHR systems creates a double threat for security managers. First, standards are well-documented and well-understood, which means that any known flaws are typically documented. Documented flaws, for example the ability to exploit knowledge about the IP standard to spoof an IP address, means that hackers as well as security professionals have access to this knowledge. If security professionals understand the vulnerabilities in standards applied in an existing system, they can implement effective security controls. However, as the number and variety of standards applied in systems like EHR systems increases, the likelihood that known weaknesses in those standards will be well understood by the security and privacy personnel decreases. Accordingly, the burden on security professionals to know about all of the weaknesses, know where the standards have been applied in each system and know how to effectively manage associated risk is potentially overwhelming. More research is needed to understand what security and privacy professionals are as well as should be doing to deal with these growing risks is needed.

In addition, the growing complexity of overall systems architectures that result from systems extensions and integration translates into decreasing abilities to understand and manage systems while simultaneously increasing opportunities for exploitation among hackers. Carnegie Mellon’s Computer Emergency Response Team (CERT) website includes the following statement, which is evidence of this problem:

“Given the widespread use of automated attack tools, attacks against Internet-connected systems have become so commonplace that counts of the number of incidents reported provide little information with regard to assessing the scope and impact of attacks. Therefore, as of 2004, we will no longer publish the number of incidents reported.”

Current privacy security guidance is inadequate for protecting sensitive health data.

Information security guidance has typically either been strategic-level guidance based on organizational goals and primary uses of data or alternatively, focused on highly detailed technical issues such as protecting access points with effective passwords, and protecting networks with properly configured firewalls and appropriate use of encryption. The latter category of security investment dominates organizational spending and using terminology from complexity theory, may be referred to as a reductionist approach where security management strategies involve application of solutions to one dimension

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of a system at a time (e.g., passwords to secure access to an application, firewalls to protect network traffic, etc.). Reductionism is described as the driving force behind most scientific inquiry (John Holland, 1999). Even strategic security guidance such as HIPAA and ISO17799 make recommendations based on consideration of methods for securing systems components rather than considering the vulnerabilities from a holistic perspective. Reductionism is clearly necessary, but it is also increasingly insufficient for effective security management in an environment of increasingly standards-enabled systems proliferation.

7. Possible future research

Health information systems are beginning to form some of the characteristics of systems that have been defined as ‘complex adaptive systems’ with features such as self-similarity, complexity, emergence and self-organization (Lewin, 1992). As data and processes become increasingly standardized, the systems will possess greater self-similarity. As the network of users grows in size and diversity, complexity increases, and this diversity combined with the standards-created self-similarities enables emergent applications and uses. In-depth comparisons between standards-based EHR systems and other known complex adaptive systems such as the nervous system, ant colonies and the stock market could yield some new insights into what should be expected from these systems in the future.

Additionally, more research is needed to identify the appropriate balance of detail in standardized records. At what point do we have enough detail to improve decision making without invading the privacy of the individuals? For example, how standardized should the coding be for psychiatric treatments? Is it enough to include standards to represent general diagnoses like ‘anxiety disorder’ or is it necessary, as some insurance companies will say, to include more specific coding such as ‘post-traumatic stress disorder’?

Similarly, much has been written about the benefits to consumers from use of EHRs and PHRs. Arguments indicate that consumers will be empowered by these investments and will be able to control information in their records. It seems true enough that patients will be able to view and verify information, but they will not be able to control who else sees their records. More research needs to address the privacy risks associated with use of these systems.

8. Conclusions

Following HIPAA, EHR data is standardized and can be electronically transmitted to others, or stored in a central repository where others can access it. These features mean that personal health information can be accessed by a wide variety of stakeholders using various methods and access points. Our findings have demonstrated that EHR systems are evolving as new uses of the available health data are developed and as new stakeholders are getting involved. More importantly, our findings demonstrate how this evolution is giving rise to new ethical tensions in relation

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to the use of medical information. Specifically, our examples illustrate how uncontrolled emergent use of EHR systems poses increased privacy and security risks as well as increased opportunity for other unethical behavior as a result of the increased complexity of systems and uses. In a sense, the real nature of a technology and its consequences emerge from the actions of individuals as they engage with and use the technology-in-practice (Orlikowski, 2000).

Formal mechanisms for justifying investments are important for a number of reasons including the fact that humans can only intake and process a small amount of information at once while the amount of information that should be considered in an IT investment decision is increasing exponentially. It is imperative, therefore, that decision models designed to aid investment decisions in this environment of complex consequences be implemented and that they formally include a broader set of socially responsible factors, especially where vulnerable populations are involved. In order to develop more comprehensive and socially responsible decision models, stakeholders must find new ways to more effectively communicate. A rallying cry for better methods for quantifying qualitative costs and benefits has long been made. However, considering the unexpected costs associated with emergent uses of prior simpler standards-enabled applications such as email, juxtaposed with the potential emergent uses of highly sensitive and personal health information, it is possible that society will not be able to bear the burden of the cost of uncontrolled proliferation.

We propose that to respond to the critical ethical issues related to EHR, we must seriously and consistently exercise our moral imagination. Following Johnson (1993), ‘moral’ pertains to the “ongoing imaginative exploration of possibilities for dealing with our problems, enhancing the quality of our communal relationships, and forming significant personal attachments that grow.” (1993, 209) By ‘imagination’ we mean “that capacity which allows us both to experience present situations as significant and to transform them in light of our quest for well-being…the means for going beyond ourselves as presently formed, moving transformatively toward imagined ideals of what we might become, how we might relate to others, and how we might address problematic situations,” (1993, 209). Moral imagination is the “capacity to see and to realize in some actual and contemplated experience possibilities for enhancing the quality of experience, both for ourselves and for the communities of which we are a part, both for the present and for future generations, both for our existing practices and institutions as well as for those we can imagine as potentially realizable,” (209). The purpose of this paper is to challenge the IS community to take up this call for exercising moral imagination in relation to the use of EHRs.

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Table 1: Emergent Uses of Electronic Health Information and Stakeholder Consequences System Implementation/Use (description) Stakeholders Overt Goals Potential Ethical Issues Sources 1. BCBS-CA requests categorical

searches from providers on patient pre-conditions

Patients Payors Providers

Payor Cost reduction

Provider concerns over patient-trust violation; possible payor discrimination against patients

LA Times (Girion & Rau, 2008)

2. BCBS-TN rates physicians based on provider quality of care scores based on categorical treatment data

Patients Payors Providers

Improve care Reduce errors

Concern that cost to payor prioritized over care quality in physician ratings

The Tennessean (Ward, 2008)

3. BCBS-TX publishes affordability and quality information for doctors in Texas

Patients Payors Providers

Improve transparency to patients/customers

How metrics are selected is not transparent – concern that cost more important than quality

Dallas Business Journal (Gordon, 2007)

4. CareFirst BCBS investing 1.5M in statewide EHR system

Patients Payors Providers

Improve efficiency and effectiveness

Concern that payor financing of EHR projects may optimize benefits to them at expense of others

The Daily Record(Buckelew, 2008)

5. HC Services, 4th largest insurer in US, adds wellness to their mission, recruits enrollees for programs with offer of points for gift cards

Patients Payors

Improve patient health, reduce costs to payors

Concern that customers who opt out may face discrimination

Chicago Tribute (Japsen, 2008)

6. Standardization of insurer reimbursement rates across multiple insurers/payors in NY

Patients Payors Providers

Administration simplification for payors

Patients and providers are concerned that unreasonable rate limits will be set

Washington Post(Freifeld, 2008)

7. EMC employees use proprietary PHR system that combines EHR data, WebMD and other sources

Patients/EmployeesPayors Employers 3rd Party Portal

Convenience for employees

Concern that employer owned PHR can facilitate discrimination against employees

Employee Benefit News (Carlson, 2008)

8. Employers work together to lobby for more cost transparency in healthcare

Patients Payors Providers Federal Agencies

Reduce costs to employers, provide more information

Concern for rewarding low cost providers over quality care providers.

Mercer Health and Benefits LLC (2007)

9. Ford, GE, Wal-Mart fund e-prescribing efforts.

Patients Providers Employers

Improve quality of care Reduce errors Payor cost reduction

Concerns over employer access to electronic health systems that they sponsor

ABC News (Perrone, 2008)

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Table 1 (page 2): Emergent Uses of Electronic Health Informtion and Stakeholder Consequences

System Implementation/Use (description) Stakeholders Overt Goals Potential Ethical Issues Source

10. Safeway is using FoodFlex, an online service that tracks nutrional quality of food purchased, to reward employees with a discount on health care premiums

Payors Employers Patients/Employees3rd Party Vendors Supermarkets

Improve health of employees Cost reduction

Inappropriate surveillance of employee habits, employee discrimination based on health factors

The Financial Times LTD (Birchall & Bowe, 2008)

11. Texas legislature first in nation to force insurers to share employee EHRs with employers

Patients/employeesEmployers Payors

Reduce costs to employers

Violates employee-employer trust, confidentiality of patient information. Would not be possible with paper health records

Houston Chronicle (SIxel, 2008)

12. Doctors using web sites to answer patient questions

Patients Payors Providers

Quicker response to patients

Reduced quality of care by substituting e-mails for doctor visits; concerns for privacy of communications

Associated Press(Change, 2008)

13. Maryland eCare LLC will use VISICU’s eICU technology to remotely monitor patients in an ICU

Patients Payors Providers 3rd Party Vendors

Improve quality of care Reduce costs

Reduced physical contact and thus quality of care; concern for network eavesdropping

VISICU (Bazzoli, 2003

14. Survey of hospital executives used to rate health care insurers in response to insurers rating care providers

Patients Payors Providers

Balance of transparency among providers and payors to patients

Concern over ratings motivated by vendettas. Patient confusion may result from biased ratings from both providers and payors

LA Times (Girion, 2008)

15. Verimed system used in 400 facilities to implant patients with microchips to enable fast connection to EHRs

Patients Providers 3rd Party Vendors

Improve Access to EHR for unconscious or mentally incapacitated patients

Concerns over abuse of patient tracking, human rights violations, unnecessary implants, some evidence that chips may cause cancer

New York Times (Feder, 2007)

16. Southwest Medical Associates provide patient access to their EHR through a personal webpage on MySMA Health Online

Patients Providers 3rd Party Health Portal

Reduce costs Improve transparency Improve Access

Privacy of patient-entered data (not covered by HIPAA). Also concern over 3rd party access to information.

Las Vegas Review-Journal (Wells, 2008)

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Table 1 (page 3): Emergent Uses of Electronic Health Information and Stakeholder Consequences

System Implementation/Use (descriptions)

Stakeholders Overt Goal Potential Ethical Issues Source

17. Google partners with Cleveland Clinic to design an independent PHR

Patients/employeesProviders Payors Vendors Advertisers Researchers

Improve patient management of personal health records

Google not covered by HIPAA privacy, concerns over access to traditional Google customers: advertisers, market researchers, other vendors

USA Today (Kornblum, 2008)

18. AT&T and state of TN partnering to create statewide information exchange

Patients Payors Providers 3rd Party Portal

Improve data sharing capabilities

Increased access points for data, more third party access

Associated Press(Schelzig, 2008)

19. CDC Center of Excellence in Public Health Informatics awards grants to scientists who can build effective EHR surveillance applications.

Patients Providers Federal Agencies Researchers

Improve response to epidemics

Concerns over governmental abuse of new source of access to patient records

Bio-Medicine (Gourley & Lacey, 2005)

20. Availity, largest claims processing organization, and Health Market Science, data mining experts, partner to mine claims provide access to data on more than 4.5m providers

Payors Providers Clearinghouses

Improve transparency to customers

Partnership changes nature of relationship between claims processor and providers; trust concerns

Availity, LLC (Shepse & Padron, 2008)

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Enterprise Risk Management: Re-Conceptualizing the Role of Risk and Trust on Information Sharing in Transnational

Alliances

Vicky Arnold1,2, Tanya Benford1, Clark Hampton3 & Steve G. Sutton1

1Kenneth G. Dixon School of Accounting, University of Central Florida 2Department of Accounting and Business Information Systems, University of Melbourne

Australia 3University of Waterloo, Waterloo, Canada

[email protected], [email protected], [email protected], [email protected]

Abstract Globalization places greater emphasis on the development of transnational alliances. The greatest benefits from alliances are derived from high-level information sharing, but risk escalates with information sharing. This study examines risk in transnational alliances based on a theoretical model drawing from enterprise risk management (ERM) as a strategic management effort. This theoretical model posits that ERM strategies focus on business risk as the primary determinant of alliance partner selection and continuity, whereas prior management control research focused on trust. The purpose of this study is to examine the influence of ERM on risk and trust associated with transnational alliances and the resulting impact on interorganizational information sharing. Survey data is gathered from 200 senior managers monitoring transnational alliances. Structural equation modeling is used to test the hypothesized relationships. Results provide strong support for the research model, showing that high ERM leads to decreased risk, increased trust, and improved information sharing. Given the on-going debate over the relationship directionality between trust and risk, we conducted additional sensitivity testing. Using competing models reversing the relationship between risk and trust, and including (excluding) ERM, we find that neither competing model has as much explanatory capability as our research model. Overall, our results show ERM substantially alters strategic management of transnational alliances, and has become a major influence on inter-organizational risk, trust, and information sharing. Keywords: enterprise risk management, trust, business risk, transnational alliances, information sharing, supply chain management

The authors would like to thank Carlin Dowling, Andreas Nicolaou, Kim Zahller, and participants in workshops at the University of Melbourne and the University of Auckland for their feedback on earlier versions of this manuscript.

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Introduction

Competition between organizations is increasingly driven by global alliances of firms as opposed to individual firms (Buhman et al., 2005; Chapman & Corso, 2005; Langfield-Smith, 2008). Strategic alliances are formed between alliance partners and become nested within larger, more complex networks of interorganizational relationships (Chua & Mahama, 2007). Partners in these relationships understand that coordination with suppliers can provide skills and knowledge not available within the firm, but that these linkages also generate risks that must be controlled (Bensaou & Anderson, 1999). Many—perhaps even a majority—of these strategic alliances fail (Das & Teng, 2001; Langfield-Smith & Smith, 2003; Dekker, 2004). Yet, the competitive landscape pushes organizations to forge these alliances with full awareness that the most successful alliances come from high levels of information sharing and associated knowledge sharing (McEvily & Marcus, 2005; Cousins et al., 2006). The result of these tight collaborations is generally a mutual interdependence, meaning that each party is vulnerable to its partners, substantially escalating the potential risk (Nicolaou, 2008). Accordingly, firms with successful alliances generally understand that risks should be initially identified when the alliances are forged and that such risks should be mitigated to the degree possible during the start-up phase (Das & Teng, 2001; Aron et al., 2005; Miller et al., 2008).

Miller et al. (2008) make the case that managerial control systems (MCS) researchers should focus more on the risk aspect of these alliances, rather than the current focus on trust, as a control mechanism. This focus on risk is consistent with Power’s (2007) observation that risk management has become the overriding dominant corporate strategy for evaluating internal and external relationships. Triggered in large part by the CICA’s Criteria of Control Board (CoCo) move in the mid-90s to extend the strategic significance of risk-based control activities, corporate management has rapidly moved toward re-classifying control activities under the broader umbrella of enterprise risk management (ERM) (Kinney, 2000; Maijoor, 2000; Power, 2007). Power (2007, 66) makes the case that the rapid evolution and adoption of ERM has created a “global governance structure” designed to meet the needs of transnational organizations in the absence of any overarching regulatory mandates. ERM has become recognized as a standard form of organizational governance from the perspective of important stakeholders, and the central focus for most public and many private companies in managing internal and external operations and relationships.

The purpose of this study is to examine the influence of ERM practices on both the risk and trust associated with transnational alliances and the resulting impact on interorganizational information sharing. The specific risk area of concern in this study is e-commerce business risk (i.e. business risk) which encompasses both the IT-enabled nature of these alliances (Langfield-Smith, 2008; Nicolaou, 2008) and the strategic nature of the associated risks (Khazanchi & Sutton, 2001; Sutton et al., 2008). The impact of these risks on information sharing is perceived to be most critical to successful transnational alliances (Buhman et al., 2005; McEvily & Marcus, 2005; Cousins et al., 2006).

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Traditionally, the theoretical basis for MCS studies on alliance relationships is formed by transaction cost economics and resource based views. Approaching the theoretical relationships from an ERM view provides a different lens through which to view alliance relationships. In this study, we adopt Power’s (2007) views that ERM has become the central strategic focus for organizations, and the primary criterion used to assess potential alliance partners is risk. ERM dictates that assessment of potential partners should begin with risk analysis, and related decisions will align with risk management outcomes. In the past, research has focused on trust as the primary organizing principle necessary for organizations that are relative strangers to form alliances; in an ERM focused world, management and stakeholders will first demand evidence that supports the desired level of trust before trust will be offered (Power, 2007, 39). This study examines Power’s (2007) strategic view of ERM by investigating the relationships between business risk and trust in transnational alliances.

To test the theoretical model, we collect data from 200 North American managers monitoring relationships with transnational alliance partners. A survey was used to collect data related to strategic ERM practices, partner’s business risk, trust in the supply chain partner, and the level/quality of information sharing with the partner. Consistent with ERM philosophies, we view the strength of ERM as a precursor to decreased business risk and increased trust. ERM is viewed as directly influencing information sharing, but also indirectly affecting information sharing through identified risks and established trust levels. Testing the overall model using structural equation modeling (SEM) provides strong support for the theorized relationships. Competing models that focus on trust as the key control mechanism are also tested to assess the strength of our research model. Our risk-oriented research model demonstrates stronger explanatory power than the competing models.

The research provides several important advancements to the MCS literature. First, the study addresses recent calls by researchers to recognize the shifting organizational focus where risk management, governance, MCS, and trust work together to reduce risk and optimize interorganizational relationships (Langfield-Smith, 2008; Bhimani, 2009). Second, the research addresses the shifting relationship between risk and trust that is evolving arguably due to the ERM movement among organizations. While trust has generally been seen as a means for mitigating risk, in an ERM-driven strategy, risk analysis occurs first and the evidence gathered determines the level of trust that should be afforded the alliance partner. Third, results show that the level of information sharing is simultaneously a function of the strategic nature of ERM processes, the perceived riskiness of the partner, and the trust placed in that alliance partner.

The remainder of this paper is divided into four sections. The next section provides the theoretical foundations for the research model and the hypotheses formulation. Section three presents the research method, while the fourth section provides the results of the analyses. The fifth and final section provides a discussion of the findings, a review of possible limitations to the study, and some concluding perspectives on the implications for future research.

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Theory and hypotheses

Power (2007), in his widely acclaimed book on the rise of ERM practices, notes that a dozen years ago there were no risk committees, but now they are considered “mandatory features of organizational life” (p. viii). CoCo initiated the rapid escalation of ERM practices through its extension of the strategic significance of risk-based control activities (Kinney, 2000). COSO’s subsequent defining of ERM as a process requiring senior management direction, an enterprise-wide focus on risk analysis and control, and an emphasis on establishing risk appetite as a necessary component of organizational consciousness, served to raise ERM to the strategic level of the firm (Power, 2007, 78). In essence, internal control processes and managerial control processes were placed under the umbrella of risk analysis and risk management, and engrained into the strategic focus of organizations. This focus on ERM becomes a key component to strategic management of the firm under the auspices of governance structures that focus on risks from internal and external operations and relationships (Power, 2007, 42).

Several recent reports in practice support Power’s view of ERM as a key cultural component in strategic management decision making. Recent joint meetings of the North American and European Audit Committee Leadership Networks yielded discussions of how talk about strategy and risk occurred “at every board meeting” (EACLN & NAACLN, 2008). Ernst & Young surveys on internal control and ERM find that executive management has heightened expectations for ERM and its underlying risk mitigation efforts that support overall business performance (E&Y, 2008b). Survey results also reveal that 61percent of institutional investors avoid investing in companies with inadequate ERM, 48percent de-invested in companies with poor ERM, and 82percent put premiums on share prices for companies with good risk management (E&Y, 2008a). Reflective of this movement, Standard and Poor’s has said it will downgrade companies with inadequate ERM (EACLN & NAACLN, 2008). Ernst & Young also notes that ERM’s focus on risks and controls extends across functional activities and business processes that cross traditional boundaries (E&Y, 2008b), and ERM benefits are maximized in areas such as alliance relationships (E&Y, 2008a).

This perspective of ERM as a part of strategic management and culture re-shapes the way organizations view interorganizational relationships such as strategic transnational alliances. ERM supplants trust as an organizing principle necessary for unfamiliar alliance partners by focusing on risk and risk analysis as a precursor to trust. Demands for trust create corresponding demands for evidence justifying trust—i.e. risk analysis (Power, 2007, 39). This orientation shifts the dominant focus from trust to risk and places this focus at a higher and more strategic level of the firm. This strategic orientation toward risk suggests the focus in such relationships begins with risk analysis—but primarily at the strategic levels of decision making as opposed to the transactional level.

As Power (2007, 66) notes, corporate stakeholders’ demands for enhanced organizational governance have forced a standardized adoption of ERM across organizations and created a de facto “global governance” standard that transcends country borders. These transnational alliances are nested networks consisting of a

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myriad of strategic relationships among different firms (Chua & Mahama, 2007). In these alliances, supplier partners are frequently managed as if they were a part of the buyer firm (Caker, 2008). However, the complexity and embeddedness of alliance relationships increase the volatility and risk in the supply chain (Seal et al., 2004). Controls enforced within these relationships can be a key strategy for improving supply chain performance (Dekker, 2004). Still, the biggest concern in these relationships persists—the concern that suppliers will be unable to perform (Anderson & Dekker, 2005). A strategic ERM approach necessitates that risk be assessed for every alliance partner and the associated risk reduced to an acceptable level.

The establishment of these transnational alliances and the management of associated risks are further complicated by the inevitable dependence on technology and information sharing (Buhman et al., 2005). Technology has been the chief catalyst in the explosion of alliances (Langfield-Smith, 2008). The leveraging of technology requires another layer of alliance coordination, and the complexity and cost involved in linking alliance partners’ internal systems increases “lock-in” costs as well as switching costs (Nicolaou, 2008). These linkages greatly escalate the risks assumed through the alliance as an organization’s internal systems become more vulnerable (Vasarhelyi & Greenstein, 2003; Vasarhelyi & Alles, 2008). Technology is critical to the existence and continuity of strategic alliances, yet the technology component has received scant attention from researchers (Cuganesan & Lee, 2006).

ERM advocates stress that before entering into alliances, firms should carefully assess the risks associated with sharing information (Lam, 2003, 143). Lam notes the importance of evaluating risks across a variety of capabilities and business congruencies in order to achieve a desired level of support for answering the question, “Can we trust each other?” This view is consistent with Power (2007, 39) and his acknowledgement that ERM demands evidence in order to meet demands for trust. Lam (2003, 144) also notes the importance of on-going monitoring and reassessment of relationships with reassessment of risks and risk management being a priority. Again, the ERM focus on risk emerges as the overarching concern and the dominant governance theme (Power, 2007, 66). This places risk at the center of efforts to coordinate and manage these networks of alliances across the supply chain. ERM influences on trust, risk and information sharing

The central role of ERM in governing all aspects of interorganizational relationships is symptomatic of Power’s (2007, 186) concept of organized uncertainty. Our risk management oriented society has led to an emergence of contemporary views on ERM that focus on the construction of risk objects and management of those risks. This view of ERM as an overarching strategic governance mechanism suggests that risk management will drive all aspects of an interorganizational relationship. In our research, the primary focus is on the direct effects of ERM on trust, risk, and information sharing within transnational alliances.

Little is known about the nature of the relationship between ERM and trust. Power (2007, 39) recognizes trust as a key organizing principle for building relationships between remote organizations. ERM does not displace trust; rather, the demand for trust still exists. However, the demand for trust instead creates demands for evidence to support trust. While we would expect that this evidence will largely be derived from

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identifying, assessing, and monitoring risks, other sources of evidence supporting trust will likely still exist. Trust is not sufficient for effective risk management, but it can contribute (Emsley & Kidon, 2007). While there has been conflicting perspectives on the relationship between trust and control, trust is generally viewed as complementary to control (Caglio & Ditillo, 2008). Thus, trust will not be displaced as a control mechanism by the implementation of ERM strategies, but will become one component of risk management as it is applied to each alliance partner. Prior studies highlight the fact that control can strengthen trust (Coletti et al., 2005).

Trust has traditionally been a focal point in establishing new alliances as a means for improving interorganizational relationships (Langfield-Smith, 2008). Repeated cycles of transactions and performance can strengthen the trust within such alliances (Free, 2008). While ERM may shift the focus toward gathering evidence indicating violations of trust, ERM strategies also emphasize mechanisms that make key trust variables more visible (Power, 2007, 40). Thus, risk management efforts should identify evidence to support trust in an alliance partner by monitoring on-going relationship experiences as well as other mechanisms for monitoring and assessing that are not necessarily focused just on risks. Where risk management efforts provide confirming evidence, trust should be greater. Alternatively, when risk management efforts provide disconfirming evidence, discontinuance of the alliance would likely occur. This leads to the first hypothesis:

H1: As the strength of ERM increases, the level of trust in alliance

partners increases. The primary focus of ERM, however, is inevitably on risks that go beyond just the

establishment of trust (Miller et al., 2008). In transnational alliances, these risks are heavily tied to information technologies from an operational and reliability standpoint as well as from a strategic vision that supports opportunity and growth in the relationship (Khazanchi & Sutton, 2001; Sutton et al., 2008). Technology is key to making global supply chains work (Prater & Ghosh, 2006), and the risks can be substantial if alliance partners have only made minimal investments in the technologies to meet current operational needs and have not developed a strategic understanding on how to leverage such technologies for future supply chain improvements (Khazanchi & Sutton, 2001). In many business-to-business (B2B) e-commerce enabled relationships, there is a lack of evidence that alliance partners have made investments in such key areas as linking external systems with internal, back-office systems (Khazanchi & Sutton, 2001; Anderson & Lanen, 2002). To thrive in B2B e-commerce enabled relationships, the appropriate technologies must be in place to participate and survive (Straub & Watson, 2001). The biggest risk in these alliances is a supplier’s inability to perform as required (Anderson & Dekker, 2005).

From an ERM perspective, the primary concerns in assessing alliance partner risk are at the more global, strategic level. Research has shown that participants in B2B e-commerce relationships are much more concerned with the overall reliability and capability of an alliance partner than with simply the reliability of transaction information (Mauldin et al., 2006). Sutton et al. (2008) discuss three levels of B2B e-commerce

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risks—technical, application, and business. Evaluation of these three levels of concern shows that technical and application level risks feed into business level concerns. These business level concerns focus more comprehensively on an organization’s technical and application strength (or weakness), as well as on assimilated knowledge for leveraging the technology and comprehending strategic opportunities. This includes how technologies and relationships can be leveraged for competitive gain. B2B e-commerce risk at the business level (i.e. business risk) is a strategic concern in alliance relationships. Given the strategic orientation of contemporary ERM practices, business level risks are of greatest strategic interest. Given an alliance partner with high business risk, the likely continuity of the relationship with that alliance partner is low. Further, Khazanchi & Sutton (2001) posit that helping an alliance partner improve their strategic capabilities is in the best interest of the organization and the entire supply chain. A strong ERM-oriented firm may be willing to assist an alliance partner in reducing their level of business risks. In combination, this leads us to the second hypothesis:

H2: As the strength of ERM increases, the business risk of alliance

partners decreases. While many alliances fail, a characteristic of successful alliances is a high level of

information sharing protected through risk mitigation (Buhman et al., 2005). Information sharing relates to the willingness of alliance partners to exchange strategic information (Mahama, 2006). This may include information on a range of joint interests including product design, open book arrangements, cost data sharing, and strategic initiatives. Increased familiarity, generated through on-going alliance relationships, facilitates the development of more extensive governance structures to coordinate advanced relational activities such as information sharing (Dekker, 2008). These complex relationships are characterized by interdependencies that leverage and grow from high levels of information sharing, but also create increased risk potential (Nicolaou, 2000; Seal et al., 2004; van der Meer-Koistra & Scapens, 2008). From an ERM perspective, we expect that high levels of ERM will provide the type of extensive governance structures required to help coordinate information sharing. This leads to the third hypothesis:

H3: As the strength of ERM increases, information sharing with alliance

partners increases.

Risk, trust, and information sharing The relationship between risk and trust is increasingly of interest to MCS

researchers, a predictable phenomenon amidst our rapidly emerging risk management oriented culture (Power, 2007). The relationship between risk and trust is tenuous with earlier MCS research focusing on trust as a means for mitigating risk (Langfield-Smith & Smith, 2003). Trust evolves from experience with an alliance partner and the resulting perceptions of competence, integrity, and benevolence (Free, 2008). However, Tomkins (2001) notes that no matter the level of trust in a relationship, information on the alliance partner will be needed in order to plan and execute a collaborative relationship. Further,

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while trust is particularly relevant to alliance relationships, trust is only important where there is risk and, as such, risk management becomes a critical aspect of the relationship (Das & Teng, 2001).

As noted earlier, a strategic ERM approach necessarily demands an initial focus on risk. While there are culturally constructed trade-offs between demands for evidence and trust (Power, 2007, 164), the impact of the ERM movement and its “global governance structure” dictates that risk analysis and risk management take prevalence in transnational alliances (p. 66). Thus, while trust was traditionally the organizing principle necessary to enable the formation of transnational alliances, in an ERM focused global environment, demands for trust create corresponding demands for evidence to support that trust (p. 39). Thus, the perceived level of business risk associated with an alliance partner will dictate whether sufficient evidence exists to support demanded levels of trust. This leads to the fourth hypothesis:

H4: As business risk decreases, the level of trust in an alliance partner

increases. Greater information sharing in alliance relationships provides better coordination

and a greater opportunity for high levels of success, but sharing such information subjects a firm to substantial risks, which should be assessed before information is shared (Kulp, 2002). Before a firm makes the commitment to collaborate with an alliance partner, a firm needs to understand the competency of the alliance partner and the reliability of the supporting systems that allow information sharing (Malhotra et al., 2005; Nicolaou & McKnight, 2006). The dual need to coordinate and share information must be countered by the risks that are inherent in tight collaborative relationships with the alliance partners (Klein et al., 2007). Controlled levels of risk should lead to increased information sharing (Klein et al., 2007). The expected relationship is captured in the fifth hypothesis:

H5: As business risk decreases, information sharing increases. Research suggests that risk and trust have a dual and complimentary impact on

information sharing (Nicolaou & McKnight, 2006). Chae et al. (2005) argue that the relationship is more important than the technology infrastructure, and that trust should be the primary driver behind increased information sharing. Research shows that as experience is gathered and familiarity with an alliance partner increases, less governance is often required and the focus evolves to implementing the processes necessary to increase information sharing (Mellewight et al., 2007; Dekker, 2008). High trust provides a foundation where positive experiences encourage alliance partners to cooperate further and increase information sharing (Velez et al., 2008). Others argue, however, that reliance on trust when interdependencies are created produces tensions (Mouritsen & Thane, 2006). However, this tension would seem to be mitigated when trust is coupled with risk management efforts. This leads to our sixth hypothesis:

H6: As trust increases, information sharing increases.

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The six hypotheses create a unified view of the interrelationships between ERM,

trust, and risk on information sharing in transnational alliances. The six hypotheses in combination provide an overall framework for understanding how information sharing can be increased in alliance relationships. Given these interrelationships, the six hypotheses are considered in tandem and can be conceptualized as presented in Figure 1.

Research method

This study examines the antecedents of information sharing for organizations engaged in transnational alliances using B2B e-commerce as a primary communication and information sharing medium. Participant demographics, instrument development, and model validation are presented in the following sub-sections. Participants

To examine how ERM, trust, and business risk impact information sharing between alliance partners, we needed respondents with considerable business-level and technical e-commerce level knowledge of their organization as well as their alliance partner’s organization. Identifying a pool of potential participants possessing this complex knowledge set was problematic; thus, a survey company was employed to reach the targeted population. The survey company sent email solicitations to 18,500 potential participants; job title was the criteria used for the initial email solicitation. From that solicitation, 6,668 (36.04 percent) potential participants responded and were further screened to insure that they met the study requirements. 1 Potential participants were asked the following screening questions:

1. In which country is your company based? 2. What are your job responsibilities? 3. Does your company use any non-North American supplier or outsourcing

companies? 4. How familiar are you with these non-North American supplier or

outsourcing relationships. A five point Likert scale (1= not at all familiar and 5 = very familiar) was used to

solicit responses to question four. Of the respondents that satisfactorily answered the first three screening questions, 268 responded either four or five to screening question four. These respondents were deemed appropriate for this study, and the survey company provided them the link to this study’s survey materials. Responses from 268 participants were logged. These responses were examined, and participants who responded “no basis for answering” or failed to answer a majority of the questions were excluded from further analysis. This process yielded a sample of 200 participants who worked for organizations with supply chain partners located in an array of different countries and geographical locations.                                                             1 The survey company is paid for a pre-determined number of respondents. Once those responses are obtained, the survey is closed. Hence, there could have been many more respondents who were subsequently turned away after the survey closed. 

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A summary of the participant demographic data is presented in Table 1. These data reveal that 160 (80 percent) of the 200 participants were in high level managerial positions and 176 (88 percent) of the participants had three or more years experience with their current employer. The majority (61.50 percent) of the participants were from 25 to 40 years old, 119 (59.5 percent) were male, 76 (38 percent) were female and five participants choose not to disclose this information. Industry representation included manufacturing (32 percent), wholesale/retail (15.5 percent), construction (6 percent), consulting (6 percent), technology (5.5 percent), and health (5 percent). (Table 1)

Survey development

As shown in Figure 1, the theoretical constructs for the current study are enterprise risk management, business risk, trust, and information sharing. An on-line survey was used to collect item measures for each of the constructs. The items used to measure each of those constructs are shown in Table 2. Each item was measured using a five point Likert scale where 1 represented the strongest positive response, 5 represented the strongest negative response, and 6 represented “no basis for answering”. In order to capture diversity in the alliance partner’s cultural and geographic dispersion, respondents were asked to provide data on an offshore alliance partner. Thus, in an effort to capture feedback on a range of transnational alliances, the data collection process was designed to promote a diverse set of partner organizations in the sample. Although culture is not a construct of particular interest in this study, a supply chain partner’s geographical location and cultural orientation could systematically affect analysis and results. To rule out the possible effects of culture, supply chain partners were classified into three primary groups, Anglo, European, and Asia-Pacific, based on cultural orientation (Ronen & Shenkar, 1985). All scale item responses are examined for culture effects using one-way ANOVA with Bonferroni correction. The results indicate culture does not significantly influence any of the individual scale item measure. (Table 2)

Power (2007, 67) notes that ERM should not be assumed to be representative of some explicitly coherent set of practices, but rather ERM “represents a mixed bag of reformist, organizing sensibilities in the name of risk.” ERM is fundamentally about the governance of risk metrics as these mechanisms have evolved (p. 76). The global identification and recognition of the COSO Framework (COSO, 2004) provides a framework for delineating the confines of ERM, defining it as a process that requires senior management direction, extends across the whole organization , and signifies a new organizational consciousness of ‘risk appetite’ and assurance (Power, 2007, 78). In an effort to measure ERM across its more globally accepted strategic objectives, we adapted the ERM global strategic benefits measures developed in Arnold et al. (2008). Arnold et al. describe the measures as being reflective of ERM activities typically found in organizations that are actively engaged in strategically addressing opportunities and the associated risks at the enterprise level. The instrument is designed to measure a firm’s own ERM proficiency.

Existing instruments for measuring business risk as conceptualized for our research purposes could not be found. As a result, a multi-item measure consisting of

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questions reflective of the overall business risk associated with an alliance partner’s strategic B2B e-commerce capabilities were designed specifically for this research study. Based on prior research (Khanzanchi & Sutton, 2001; Nicolaou & McKnight, 2006; Sutton et al., 2008), five aspects of B2B e-commerce risk—strategic nature, understanding benefits, reengineering business processes, management of data processes, and obligation fulfillment—are identified. These five categories of business risk are designed to capture the key facets of B2B e-commerce alliances necessary for current and future success that are applicable across a wide range of B2B e-commerce instantiations.

The items used to measure trust in this study were previously developed and validated by McKnight et al. (2002) and further tested by Nicolaou & McKnight (2006). These measures incorporate general trust in a supply chain partner in addition to trust issues distinct to B2B e-commerce based alliances. Thus, the trust scale captures generalized trust beliefs that may impact specific trust issues associated with supply chain alliances.

Mahama (2006) defines information sharing as the willingness of alliance partners to exchange important, possibly proprietary, information about their relationship among supply chain members. Malhotra et al. (2005) identified four critical dimensions of information sharing; these dimensions include the breadth, quality, privileged nature, and coordination of the information exchanged. Breadth in the exchange between supply chain partners represents the diverse nature of the information exchanged, while quality is a measure of the value of the information exchanged. Another dimension of information sharing, the privileged nature of the exchange, is the extent to which supply chain partners share confidential information. The fourth dimension, coordination, represents the extent to which supply chain partners synchronize their processes. Each dimension is captured in the current study using questions previously validated by Malhotra el al. (2005). The four dimensions are combined to form a second order construct of information sharing.

Measurement model validation

Scale items with their corresponding means, medians, and standard deviations are presented in Table 2. Scale items for the latent constructs, ERM (Arnold et al., 2008), trust (Nicolaou & McKnight, 2006), and information sharing (Malhotra et al., 2005) are adapted from measures used in prior research studies. Business risk scale items are developed for this study based on Sutton et al. (2008). For analysis purposes, responses coded “no basis for answering” were treated as missing values. Initial analysis of scale item data values indicates excessive missing values for ERM scale item ERM1a21.1.6. This item is not included in the remaining analyses. The remaining missing data appear to be completely at random (chi-square = 35.34, df = 39, p-value = 0.638), and EM (SPSS 15.0, 2006) was used for imputation of these data (Hair et al., 2010). The validity of the measurement model constructs as well as the overall measurement model fit is assessed prior to testing the structural model (Hair et al., 2010). The measurement model is estimated using a two-step approach. Step one

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uses confirmatory factor analysis to assess the convergent and discriminant validity of the four information sharing first order constructs. As previously noted, information sharing is a second order construct comprised of four first-order constructs: breadth of exchange, quality of exchange, privileged information exchange, and coordination of information exchange. As shown in Table 2, each of the item measures of the four first order constructs has a standardized factor loading greater than 0.70. The composite construct reliability of each of the first order constructs is greater than the recommended 0.70, and the related average variance extracted is greater than 0.50 supporting the convergent validity of the constructs. The first order constructs were then used as measures of the second order information sharing construct (Hair et al., 2010). As shown in Table 3 Panel A, the average variance extracted for each of the four first-order constructs is greater than the related squared inter-construct correlations providing evidence that these constructs are unique and distinct (Hair et al., 2010). The maximum inter-construct correlation of 0.73, between quality of exchange and coordination information exchange, is below the standard threshold of 0.85, which also supports construct discriminant validity (Kline, 2005). Indices used to assess the overall fit of this portion of the measurement model include the chi-square statistic (X2 = 198.55, df = 106, p = 0.00), the root mean squared error of approximation (RMSEA = 0.07), the Tucker Lewis Index (TLI = 0.94), and the comparative fit index (CFI = 0.96). These results suggest an overall acceptable fit for this portion of the measurement model (Hair et al., 2010). (Table 3)

In step two, confirmatory factor analysis is used to assess convergent and discriminate validity of the four measurement model constructs (ERM, business risk, trust, and information sharing). As shown in Table 2, each of the item measures of the measurement model constructs has a standardized factor loading greater than 0.70 with the exception of ERM, which has one factor (ERM1a21.1.1) with a factor loading of 0.67. However, the ERM composite construct reliability (0.89) exceeds the recommended .70, and average variance extracted (0.63) exceeds the recommended 0.50. Accordingly, ERM1a21.1.1 is retained as a measure of ERM. In addition, the composite construct reliability and average variance extracted of business risk, information sharing, and trust all exceed the recommended 0.70 and 0.50 respectively. These results support the convergent validity of the constructs used in the measurement model (Fornell & Larcker, 1981). As shown in Table 3 Panel B, the average variance extracted for each of the measurement model constructs is greater than the related squared inter-construct correlations providing evidence that these constructs are unique and distinct (Hair et al., 2010). The maximum inter-construct correlation of 0.71, between trust and information sharing, is below the standard threshold of 0.85, which also supports construct discriminant validity (Kline, 2005). Indices used to assess the overall fit of the measurement model include the chi-square statistic (X2 = 892.90, df = 501, p = 0.00), the root mean squared error of approximation (RMSEA = 0.06), the Tucker Lewis Index (TLI = 0.91), and the comparative fit index (CFI=0.92). These results suggest overall acceptable measurement model fit (Hair et al., 2010).

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Common method bias Misleading results stemming from common method bias are always a concern when a single source of survey data is used to measure attitudes and beliefs (Bagozzi & Yi, 1990, Bamber & Iyer, 2002, Podsakoff et al., 2003). One strategy is to use multiple respondents; however, to reduce potential social desirability bias and obtain candid responses from senior management regarding their transnational alliances, we opted to assure anonymity for all respondents (Podsakoff et al., 2003). Thus, we use a common method factor to test for the existence of a common method bias. A first-order common factor latent variable was added to the research model, and all of the research model indicators were used as indicators of this common factor latent variable (Podsakoff et al., 2003). As shown in Table 4, when the common method factor is included in the model, all of the research model factor loadings remain significant, while the common method factor loadings are not significant (p < 0.05). The average variance explained by the research model constructs is 63 percent while the common method factor explains an average of 2 percent of the variance. These results suggest that common method bias is not a concern for the current study results (Podsakoff et al., 2003, Liang et al., 2007). (Table 4) Structural model estimation and results

The research model, presented in Figure 1, is tested using covariance based

structural equation modeling (SEM) (Amos 16.0, 2007). As shown in Figure 2, indices used to assess the goodness of fit of the structural (research) model include the chi-square statistic (X2 = 841.54, df = 504, p = 0.00), the root mean squared error of approximation (RMSEA = 0.06), the Tucker Lewis Index (TLI = 0.92), and the comparative fit index (CFI=0.93). These results suggest an overall good fit of the research model (Hair et al, 2010). All hypothesized relationships are significant in the predicted direction at a minimum of p<.05. As indicated by an r2 of 0.74, enterprise risk management, trust, and business risk explain 74 percent of the variance in information sharing. (Figure 2)

H1 predicts that an organization’s ERM proficiency is associated with the level of trust in the alliance partner. The model results indicate a significant (p < 0.05) positive association (+0.20); that is, higher levels of ERM are associated with higher levels of trust in the alliance partner. The positive relationship between ERM and trust suggests that the enhanced levels of identification, evaluation, and monitoring of alliance partners provide a better understanding of alliance partner capabilities and motives, and result in a higher level of trustworthiness among the alliance partners in which the organization maintains relationships.

The effects of an organization’s ERM on the alliance partner’s business risk are addressed by H2. As predicted, higher levels of ERM are associated with lower levels of alliance partner business risk (-0.63, p < 0.001). In addition, ERM accounts for 40 percent of the variation in business risk. The negative relationship between ERM and business risk suggests that holistically focused ERM processes can impact organizational perceptions of unique risk associated with alliance partners.

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The relationship between ERM and information sharing with a supply chain partner is addressed in H3. Specifically, H3 predicts that stronger ERM will be associated with greater information sharing with alliance partners. Consistent with predictions, a positive (+0.28) and significant (p < 0.001) relationship exists between ERM and information sharing with an alliance partner.

H4 posits a negative relationship between alliance partners’ business risk and levels of trust in those partners. The results indicate a significant (p < 0.001), negative association between business risk and trust. In combination, the strength of an organization’s ERM and the level of business risk associated with an alliance partner explain 54 percent of the variation in trust. These results suggest that a large part of trust in an alliance partner is a function of both the holistic risk processes incorporated within ERM and the unique business risks of that partner.

H5 predicts that as business risk associated with an alliance partner decreases, information sharing with the partner will increase. The hypothesized relationship is supported. A negative (-0.40) and significant (p < 0.001) association exists between alliance partner business risk and information sharing.

The final hypothesis posits that higher levels of trust in a supply chain partner will be positively associated with higher levels of information sharing with that alliance partner. As predicted by H6, a positive (+0.30) and significant (p < 0.001) association exists between trust in an alliance partner and information sharing with that partner. As previously noted, ERM, trust, and business risk explain 74 percent of the variation in information sharing. These finding suggest organizations consider both the global risks identified by ERM processes and unique alliance partner factors incorporated within trust and business risk, when determining the breadth, depth, coordination, and quality of information to be shared.

Additional model tests and results

The results of model estimation and testing indicate strong support for the research model developed in this paper. However, given the conflicting views on the relationship between business risk associated with an alliance relationship and trust of that alliance partner, tests of competing models were deemed appropriate. The ERM view of transnational alliances, as modeled in Figure 1, predicts that business risk is the primary concern, and evidence on risk must be gathered before trust develops (Power, 2007, 39). The traditional MCS view has been shaped by transaction cost economics and/or resource based views of the firm. This view generally suggests that trust is used to mitigate business risk (i.e. trust influences risk) (see Langfield-Smith, 2008). Because theoretical and empirical support exists for the relationship between risk and trust to be reversed from our conceptualization and recognizing the importance and active nature of this debate, two alternative models are compared to the research model. X2 difference tests are used to evaluate the competing models (Hair et al., 2010).

The first alternative model, presented in Figure 3, modifies the relationship between business risk and trust to emanate from trust to business risk. The relationship between ERM and trust is also removed as ERM would no longer be expected to impact trust. The results of the re-estimation are presented in Figure 3. X2 for the first

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alternative model is 904.65 (df = 505) and the increase in X2 is 63.11 (df = 1), which is significant (p < 0.001). Thus, the research model is a better fit than the first alternative model. Similar to the previous alternative model, the second alternative model, shown in Figure 4, depicts the relationship between business risk and trust as emanating from trust to business risk; in addition, this alternative removes the relationships between ERM and trust, business risk and information sharing. X2 for the second alternative model is 948.31 (df = 507) When the second alternative is compared with the research model, the increase in X2 of 106.77 (df = 3) is significant (p < 0.001). Again, the research model is a better fit than the alternative. In addition, the research model explains more of the variance in information sharing (r2 = 0.74) than either alternative model (Figure 3 r2 = 0.67and Figure 4 r2 = 0.69). (Figures 3 and 4)

Conclusions, limitations, and future research In this study, we examine key factors affecting information sharing in transnational alliances. The focus is specifically on the effects of risk and trust and the overarching influence of the organizations’ strategic ERM and its effect on partner risk and trust, and in turn on information sharing with the alliance partner. The conceptual model underlying the research is based on Power’s (2007, 66) conceptualization of ERM as a representation of organizational governance that transcends country specific governance practices and creates a “global governance structure” to meet the needs and demands of transnational organizations. Power (2007, 186) posits that ERM has developed as a response to the desire of firms and their stakeholders for organized uncertainty that has rapidly emerged in today’s risk management oriented society. The result is that risk analysis and risk management, as key tools supporting ERM, have become the central concerns in the strategic management of internal and external firm operations and relationships. One critical by-product of the rapid emergence of ERM as an overriding dominant corporate strategy is that trust, which has served as the organizing principle for facilitating relationships between diverse firms, is being supplanted as the enabling force in such relationships by a focus on risk analysis and risk mitigation. ERM driven organizations now react to demands for trust with demands for evidence on risk minimization to support the establishment of trust (Power, 2007, 39). Actual trust develops only when business risk can be reduced to levels acceptable for establishing trust levels. This study presents a conceptual model for understanding the interrelationships of ERM, risk, and trust in influencing information sharing with alliance partners. The research model specifically addresses recent calls by researchers to recognize the shifting organizational focus where risk management, governance, MCS, and trust work together to reduce risk and optimize interorganizational relationships (Langfield-Smith, 2008; Bhimani, 2009). The results of the study are strong and support the model. Consistent with recent strategic views espoused on ERM, the results show that ERM has an overarching effect on the interorganizational relationship as stronger ERM is associated with decreased risk, increased trust, and increased information sharing in transnational alliances. This impact on increased information sharing is particularly critical given that research suggests that successful alliances generally include high

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levels of information sharing that facilitate strategic reaction and innovation by supply chains as a whole (Buhman et al., 2005; McEvily & Marcus, 2005; Cousins et al., 2006; Dekker, 2008). The research also highlights a shift in the relationship between trust and risk. Traditionally, this relationship has been viewed as trust is formed to reduce risk (see Langfield-Smith & Smith, 2003; Power, 2007, 39). Other researchers have often questioned this relationship with the belief that risk should always be a critical concern within itself (e.g. Das & Teng, 2001; Tomkins, 2001). Power (2007, 39) posits that in an ERM focused environment, risk analysis is a necessary precursor to the development of trust. Our research model and results provide strong support for this conceptualization. Several limitations to the study should be considered when assessing the research findings. First, our sample was drawn from individuals in North American firms managing transnational alliances. Thus, while our study examines the effects of ERM within transnational alliances, the emphasis on ERM could potentially be biased if there are either stronger or weaker sentiments about ERM among North American firms versus other regions of the globe. Power (2007, 66) notes that the ERM movement is a global phenomenon, but the impact of mandatory reporting on internal controls for most North American firms could influence their views. Further examination of the phenomena reported in this study among firms in other global regions could extend the external validity. Second, we use a survey method to collect the data; survey data captures perception data as opposed to observable or archival data. However, for the phenomena of interest in this study, perceived risk, trust, and information sharing are the variables of interest; and, as such, the method applied is the most appropriate for this research. Future research using alternative data sources could further extend our understanding of the dynamics surrounding alliance relationships. Overall, the research provides several contributions to the MCS literature on transnational alliances and the underlying drivers of information sharing in these alliances. By integrating an ERM effect into the MCS research on the relationship between risk, trust, and information sharing, we are able to demonstrate the effects of the rapidly emerging ERM movement in strategic management on the nature of MCS in transnational alliances. Additional sensitivity tests support the ERM-based theoretical model as opposed to traditional views that trust is the first line of control. Identification of this ERM and risk mitigation focus suggests future research should further examine the ERM phenomena. This study provides an initial lens into the critical role of ERM and risk mitigation on the rapidly developing global networks of firms. Much about the ERM movement and the effect on interorganisational relationships is still unknown. There is great opportunity to enhance our understanding of the emerging world of global supply chains and influence their development and viability.

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Table 1 Demographic Information

Item Frequency (n = 200) Percentage

Panel A: Gender Male 119 59.50 Female 76 38.00 Not answered 5 2.50 Panel B: Age Under 25 3 1.50 25 to 40 years 123 61.50 40+ years 71 35.50 Did not answer 3 1.50 Panel C: Experience with current employer

1 to 2 years 15 7.50 3 to 10 years 119 59.50 10+ years 57 28.50 Did not answer 9 4.50

Panel D: Current Position C-level executive / Owner 59 29.50 Vice President / Director 33 16.50 Managers 68 34.00 Supervisors/Consultants / Analysts 21 10.50 All Other 19 9.50

Panel E: Organizational Structure Publicly traded 113 56.50 Not publicly traded 86 43.00 Did not answer 1 0.50

Panel F: Organizational Size Less than 50 employees 21 10.50 51 – 100 employees 11 5.50 101 – 500 employees 29 14.50 501 - 1,000 employees 42 21.00 More than 1000 employees 96 48.00 Did not answer 1 0.50

Panel G: Industry

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Manufacturing 64 32.00 Wholesale/retail 31 15.50 Construction 12 6.00 Consulting 12 6.00 Technology 11 5.50 Health 10 5.00 Financial 8 4.00 Telecommunications 7 3.50 Energy 6 3.00 Insurance 5 2.50 Aerospace 5 2.50 Education 5 2.50 Transportation 5 2.50 All other 15 7.50 Did not answer 4 2.00

Table 2 Descriptive Statistics for Item Measures and Tests of Convergent Validity

Scale Item Item Measure Name Mean Median Standard

Deviation Factor

Loadings Composite Reliability

Average Variance Extracted

Information Sharing First Order Constructs (Malhotra et al., 2005) Breadth of Exchange Regarding the sharing of information between Your Company and Company ABC 

0.90 0.60

To what extent do you exchange details of upcoming product or service related changes with Company ABC? ISboe1m9.1.1 1.99 2.00 0.92 0.76

To what extent do you exchange future plans such as promotion and marketing plans, long-term production plans, capital investments and capacity utilization with Company ABC?

ISboe1m9.1.2 2.15 2.00 1.01 0.82

To what extent do you exchange information related to market demand trends and forecasts with Company ABC? ISboe1m9.1.3 2.17 2.00 0.96 0.75

To what extent do you exchange information on demand shifts and changes in customer preferences with Company ABC?

ISboe1m9.1.4 2.10 2.00 0.93 0.77

To what extent do you exchange information related to ISboe1m9.1.5 2.17 2.00 1.05 0.75

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Scale Item Item Measure Name Mean Median Standard

Deviation Factor

Loadings Composite Reliability

Average Variance Extracted

changes in supply chain structure—such as addition or dropping of partner companies, merger and alliances, with Company ABC? To what extent do you exchange process information needed to support changes in product features or volumes, with Company ABC?

ISboe1m9.1.6 2.00 2.00 0.96 0.79

Quality of Exchange How would you rate the information exchanged with Company ABC in terms of its: 

0.89 0.66

Relevancy to your business needs, compared to information exchanged with other similar partners? ISqoe2m10.1.1 1.89 2.00 0.82 0.84

Value-added to your business needs, compared to information exchanged with other similar partners? ISqoe2m10.1.2 1.89 2.00 0.79 0.76

Timeliness, compared to information exchanged with other similar partners? ISqoe2m10.1.3 1.96 2.00 0.91 0.86

Its completeness, compared to information exchanged with other similar partners? ISqoe2m10.1.4 2.01 2.00 0.86 0.79

Privileged Information Exchange In our relationship with Company ABC

0.86 0.62

We provide each other proprietary information if we feel it can help our business partner. ISpie3m10.2.1 1.91 2.00 0.99 0.75

We share confidential information if we feel it can help our business partner. ISpie3m10.2.2 2.03 2.00 1.03 0.78

We share information with each other that is not available from other sources. ISpie3m10.2.3 1.99 2.00 0.95 0.76

The information exchange helps us provide each other a unique perspective that neither of us could have developed on our own.

ISpie3m10.2.4 1.94 2.00 0.95 0.84

Coordination Information Exchange When performing processes that are inter-linked

0.82 0.61

To what extent does your company and Company ABC exchange coordination information to synchronize your activities?

IScie4m11.1.1 1.97 2.00 0.86 0.81

To what extent does your company and Company ABC exchange information to track each other's internal processes?

IScie4m11.1.2 2.18 2.00 0.97 0.75

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Scale Item Item Measure Name Mean Median Standard

Deviation Factor

Loadings Composite Reliability

Average Variance Extracted

To what extent does your company and Company ABC exchange operational information (such as inventory levels, product availability, production volumes etc.)?

IScie4m11.1.3 2.06 2.00 0.94 0.78

Information Sharing Second Order Construct (Malhotra et al., 2005) 0.87 0.63 Breadth of Exchange 0.73 Quality of Exchange 0.92 Privileged Information Exchange 0.70 Coordination Information Exchange 0.81 Enterprise Risk Management (Arnold et al., 2008) Regarding risk management activities in Your Company:

0.89 0.63

Our company performs a thorough enterprise-wide risk assessment at least once a year. ERM1a21.1.1 1.80 2.00 0.89 0.67

Our company is able to identity events that may affect the achievement of our objectives. ERM1a21.1.2 1.82 2.00 0.88 0.77

Our company regularly evaluates the effectiveness of internal controls for mitigating identified risks--management has effective processes to respond to identified risks.

ERM1a21.1.3 1.82 2.00 0.84 0.80

Our risk management procedures provide the necessary information top management needs to monitor changes that could impact our company’s well-being.

ERM1a21.1.4 1.84 2.00 0.88 0.86

One focus of our ERM is the strength of our internal control system for risk identification. ERM1a21.1.5 1.93 2.00 0.98 0.85

Our company has a Chief Risk Officer. ERM1a21.1.6b NA NA NA Global Business Risk Considering B2B e-commerce capabilities, how would you rate Company ABC's 0.91 0.66

Understanding of the strategic nature of the B2B e-commerce relationship? GBR1ts19.1.1a 4.14 4.00 0.82 0.73

Understanding of the benefits of the B2B e-commerce relationship? GBR1ts.19.1.2a 4.24 4.00 0.78 0.86

Reengineering of business processes to facilitate B2B e-commerce transaction requirements? GBR1ts19.1.3a 4.05 4.00 0.91 0.76

Management of data, transmission security, and auditability? GBR1ts19.1.4a 4.17 4.00 0.81 0.86 Ability to fulfill legal obligations initiated via B2B e-commerce transactions? GBR1ts19.1.5a 4.19 4.00 0.82 0.85

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Scale Item Item Measure Name Mean Median Standard

Deviation Factor

Loadings Composite Reliability

Average Variance Extracted

Trust (McKnight et al., 2002 and Nicolaou & McKnight, 2006) Regarding Company ABC 0.95 0.71

I believe that Company ABC would act in my best interest. T1n12.1.6 2.05 2.00 1.06 0.89 If I required help, Company ABC would do its best to help me. T1n12.1.7 1.95 2.00 0.96 0.80

Company ABC is interested in my well-being, not just its own. T1n12.1.8 2.08 2.00 1.06 0.82

Company ABC is truthful in its dealings with me. T1n12.1.9 1.91 2.00 0.99 0.91 I would characterize Company ABC as honest. T1n12.1.10 1.89 2.00 0.94 0.85 Company ABC would keep its commitments. T1n12.1.11 1.90 2.00 0.93 0.79 Company ABC is sincere and genuine. T1n12.1.12 1.94 2.00 0.98 0.85 aReverse Coded bDropped

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Table 3 Tests of Discriminant Validity

Panel A: Information Sharing First Order Constructs

Quality of Exchange

Breath of Exchange

Privileged Information Exchange

Coordination Information Exchange

Average Variance Extracted 0.66 0.60 0.62 0.61 SQUARED INTER-CONSTRUCT CORRELATIONS Quality of Exchange 1.00 Breath of Exchange 0.39 1.00 Privileged Information Exchange 0.31 0.50 1.00 Coordination Information Exchange 0.54 0.44 0.44 1.00

Panel B: Research Model

Business Risk

Information Sharing

Enterprise Risk

Management Trust

Average Variance Extracted 0.66 0.63 0.63 0.71 SQUARED INTER-CONSTRUCT CORRELATIONS Business Risk 1.00 Information Sharing 0.49 1.00 Enterprise Risk Management 0.31 0.47 1.00 Trust 0.43 0.50 0.31 1.00 Table 4 Common Method Bias

Construct Indicator

Research Model Factor Loadings

(R)

Research Model

R2

Common Method Factor Loadings

(R)

Common Method

R2

Information Sharing

ISboe1m9.1.1 0.74*** 0.54 0.00 0.04 ISboe1m9.1.2 0.84*** 0.71 -0.08 0.02 ISboe1m9.1.3 0.74*** 0.55 -0.08 0.00 ISboe1m9.1.4 0.78*** 0.60 0.07 0.00 ISboe1m9.1.5 0.73*** 0.54 0.22 0.00 ISboe1m9.1.6 0.80*** 0.64 0.02 0.00 ISqoe2m10.1.1 0.79*** 0.62 -0.20 0.04 ISqoe2m10.1.2 0.77*** 0.60 -0.13 0.02 ISqoe2m10.1.3 0.81*** 0.65 -0.05 0.00 ISqoe2m10.1.4 0.82*** 0.67 0.01 0.00 ISpie3m10.2.1 0.72*** 0.52 -0.03 0.00 ISpie3m10.2.2 0.76*** 0.58 -0.06 0.00 ISpie3m10.2.3 0.77*** 0.59 -0.01 0.00

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ISpie3m10.2.4 0.86*** 0.74 0.04 0.00 IScie4m11.1.1 0.77*** 0.59 -0.05 0.00 IScie4m11.1.2 0.75*** 0.57 -0.11 0.01 IScie4m11.1.3 0.75*** 0.57 -0.09 0.01

Enterprise Risk Management

erm1a21.1.1 0.67*** 0.45 -0.10 0.01 erm1a21.1.2 0.77*** 0.59 -0.09 0.01 erm1a21.1.3 0.80*** 0.63 -0.04 0.00 erm1a21.1.4 0.87*** 0.75 0.06 0.00 erm1a21.1.5 0.85*** 0.73 0.01 0.00

Business Risk

GBR1ts19.1.1 0.77*** 0.59 0.23 0.05 GBR1ts19.1.2 0.80*** 0.64 0.36 0.13 GBR1ts19.1.3 0.78*** 0.61 0.01 0.00 GBR1ts19.1.4 0.83*** 0.69 -0.34 0.12 GBR1ts19.1.5 0.80*** 0.63 -0.32 0.10

Trust

T1N12.1.6 0.87*** 0.76 -0.04 0.00 T1N12.1.7 0.82*** 0.68 -0.16 0.02 T1N12.1.8 0.85*** 0.73 -0.19 0.04 t1n12.1.9 0.83*** 0.69 -0.07 0.01 t1n12.1.10 0.80*** 0.64 -0.02 0.00 t1n12.1.11 0.82*** 0.67 -0.05 0.00 t1n12.1.12 0.84*** 0.71 -0.01 0.00

Average 0.79 0.63 -0.05 0.02 ***p<0.001

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Figure 1 Theoretical Model

H5 (-) H3 (+)

H6 (+)

H4 (-)

H2 (-) Enterprise Risk Management

Trust

Business Risk

Information Sharing

H1 (+)

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Figure 2 Research Model

*p < .05 **p < .01 ***p < .001 X2 = 841.54, df = 504, p = 0.000 TLI = 0.92 CFI = 0.93 RMSEA = 0.06

Enterprise Risk

Management

Trust

H4 -.0.59***

H3 +0.28***

H2 -.0.63***

H5 -0.40***

H6 +0.30***

Business Risk

Information Sharing

r2 = 0.54

r2 = 0.74

r2 = 0.40

H1 +0.20*

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Figure 3 Competing Model - Alternative 1

Information Sharing

Business Risk

Trust

r2 = .50

*p < .05 **p < .01 ***p < .001 X2 = 904.65, df = 505, p = 0.000 TLI = 0.91 CFI = 0.92 RMSEA = 0.06

r2 = .67

+0.32***

-.0.37***

-.0.40***

-.0.60***

Enterprise Risk Management

+0.36***

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Figure 4 Competing Model - Alternative 2

 

r2 = .088 

r2 = .425 *p < .05 **p < .01 ***p < .001 X2 = 948.31, df = 507, p = 0.000 TLI = 0.90 CFI = 0.91 RMSEA = 0.07

-0.52***

Enterprise Risk

Management

Business Risk

r2 = 0.51

Information Sharing

r2 = 0.69

Trust

-0.72***

0.37***

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Do Auditors Adjust Their Audit Plans Accordingly When They Encounter Material Automated Control Weaknesses?

Daniel D. Selby

E. Claiborne Robins School of Business,Department of Accounting, University of Richmond [email protected]

Abstract Automated control weaknesses are associated with defects in computer hardware or computer applications. AU 319 does not require auditors to add professionals with specialized skills in automated controls (or IT audit specialists) to the audit engagement. Auditors may encounter a material automated control weakness and adjust their audit plan without the assistance of an IT audit specialists. Thus, auditors may not adjust their audit plan enough to gain an understanding of the material automated control weakness. This is an important problem for auditors because this problem may result in misstated financial statements and incorrect internal control opinions if auditors fail to effectively examine automated control weaknesses. I conduct an experiment where Big Four auditors and IT audit specialists make audit planning adjustments. Results suggest that auditors’ audit plan adjustments are influenced by non-diagnostic evidence. Results also suggest auditors do not adjust their audit plans for material automated control weaknesses as they much as they do for material manual process evidence. Finally, auditors do not adjust their audit plans for automated control weaknesses as much as IT audit specialists’ adjust their audit plans for automated control weaknesses. Keywords: material automated control weaknesses, non-diagnostic evidence, audit planning

adjustments, IT audit specialists, evidence domain

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1. Introduction

A material control weakness is a deficiency in internal control that has a reasonable possibility of producing a material misstatement in the financial statements (AICPA 2008). Once a material control weakness is identified, auditors are encouraged to improve their understanding of the material control weakness (PCAOB 2007, AICPA 2008, IAASB 2010a, IAASB 2010b). In order to understand the material control weakness, auditors may have to perform additional audit procedures (Kaplan 1985).

In this paper, I investigate whether auditors’ audit plan adjustments for material control weaknesses are reduced by non-diagnostic evidence. I also investigate whether auditors’ audit plan adjustments depend on the domain of the internal control evidence. For the purpose of gathering internal control evidence, auditors encounter control weaknesses that stem from automated controls or manual processes (Fogelman et al. 2007). Automated control weaknesses are weaknesses that are created by defects in computer hardware or computer applications (Hall and Singleton 2005; KPMG 2007). Manual process weaknesses, on the other hand, are created by human personnel within the control system. Although auditors tend to possess internal control skills that are best suited for manual processes (e.g., Duffy 2004), AU 319 does not require auditors to call on professionals with specialized skills in automated controls (AICPA 2002). Hunton et al. (2004) suggests that auditors tend to be overconfident in their ability to evaluate automated controls. So auditors, who lack the necessary automated control skills, may bypass the help of skilled professionals and insufficiently adjust their audit plans for material automated control weaknesses. For example, Brazel and Agolia (2007) conclude that auditors with low automated control skills may assign more resources than necessary to the substantive test phase of the audit. However, non-diagnostic evidence may influence auditors to insufficiently adjust their audit plans for material automated control weaknesses. My study provides empirical evidence on this issue.

Auditors’ ability to effectively adjust their audit plans for material automated control weaknesses is important for three reasons. First, automated controls impact financial statement line items. Auditors may fail to modify the degree and extent of their planned audit procedures and limit their ability to understand the material automated control weaknesses. If the examination of the effect of the material automated control weakness on the financial statements is not effective, the financial statements may be issued with undetected material misstatements. Second, failure to adjust the audit plan after identifying a material automated control weakness may also limit the auditor’s ability to issue the correct internal control opinion. If auditors’ fail to gain the appropriate understanding of internal control weaknesses, based on the audit procedures that they perform, auditors’ may give unqualified opinions of internal control structures when adverse opinions would be more appropriate. Third, if the procedures performed by auditors are insufficient to determine the effectiveness of internal controls, audit failure may occur. Specifically, stakeholders may rely on financial statement opinions and internal control opinions made by auditors that are materially incorrect.

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I conduct an experiment in which I assess auditors’ adjustment to the audit hours necessary to test controls of a material control weakness. I vary material control weakness as automated control or manual process. I also measure auditors’ adjustment when non-diagnostic evidence is present. To determine whether fifty-two auditors adjust their audit plans sufficiently for a material automated control weakness, I compare their adjustments to the audit plan adjustments of thirty-seven IT audit specialists.

I find that auditors provide lower audit plan adjustments of material control weaknesses when they are exposed to non-diagnostic evidence. This finding is consistent with the prior literature on the influence of non-diagnostic evidence. I also provide evidence that auditors’ audit plan adjustments for material internal control weaknesses are dependent on the domain of the internal control evidence. Specifically, auditors are less susceptible to non-diagnostic evidence when they evaluate material manual process weaknesses versus material automated control weaknesses. Most importantly, I find that auditors’ audit plan adjustments for material automated control weaknesses are significantly lower than the adjustments made by IT audit specialists. My results suggest that auditors might not adjust their audit plans as much as they should when they examine material automated control weaknesses without the assistance of professionals with skills in automated controls.

My inferences are based on an experiment that captures an important environmental aspect of the audit that prior studies on the influence of non-diagnostic evidence have not captured. AU 319 permit auditors to evaluate material automated control weaknesses even though they may lack the information technology (IT) skills that would allow them to fully understand automated control weaknesses (AICPA 2002). This would mean that the magnitude of the audit plan adjustments by auditors exposed to non-diagnostic evidence could be sub-optimal for material automated control weaknesses. My experimental design captures this element by exposing auditors and IT audit specialists to (1) a material automated control weakness and a material manual process weakness, and (2) the respective material control weakness with non-diagnostic evidence from automated control risks and manual process risks. My evidence suggests that the influence of non-diagnostic evidence documented in prior studies may depend on whether the auditor has the skills to complete the task. I believe that this study provides the first empirical demonstration that internal control skill is another dimension of the influence of non-diagnostic evidence.

The remainder of the paper is organized as follows. Section II discusses the previous literature and develops my hypotheses. Section III describes the experiment. Section IV presents the results. Section V summarizes the findings and comments on the study’s implications.

2. Hypothesis Development

Individuals reduce their assessments of diagnostic cues in prediction tasks when

they are exposed to non-diagnostic cues (Nisbett et al. 1981; Tetlock et al. 1989; Tetlock et al. 1996). Prior research posits that individuals predict future events of interest based on the perceived similarity of features. Judgment based on similarities between mental models and diagnostic features of available information is normative behavior (Tversky 1977). Conversely, individuals have also been found to base their

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perceptions on features that are non-diagnostic to the event of interest (Nisbett et al. 1981; Tetlock et al. 1989; Tetlock et al. 1996). Nisbett et al. (1981) referred to this non-normative behavior as the dilution effect.

The dilution effect has been widely used in the accounting literature to show that auditors reduce their fraud risk judgments (Hackenbrack 1992; Glover 1997; and Hoffman and Patton 1997) and going concern judgments (Shelton 1999) when they encounter non-diagnostic evidence. For example, the auditors in Hackenbrack’s (1992) study evaluated diagnostic evidence initially in conjunction with non-diagnostic evidence and subsequently when the non-diagnostic evidence was removed. He found that auditors provided less diluted fraud risk assessments when they evaluated diagnostic evidence simultaneously with non-diagnostic evidence versus evaluating the diagnostic evidence alone.

The auditors in Glover’s (1997) study were allowed to update their fraud risk judgment after reviewing each of his eight diagnostic evidence cues. Then he assigned the auditors to one long case that embedded one of the diagnostic evidence cues with non-diagnostic client information, non-diagnostic workpapers, and the non-diagnostic results of other audit procedures. Glover found that the auditors’ fraud risk assessment was more diluted in the long case than their fraud risk assessments for the eight short cases.

Like Hackenbrack (1992) and Glover (1997), Hoffman and Patton (1997) also used a within-participant experimental design to examine dilution. The auditor judgments in Hoffman and Patton’s (1997) study were made after participants read two diagnostic cues alone and then again after reading the same two diagnostic cues mixed with four additional non-diagnostic cues. Hoffman and Patton found that auditors’ fraud risk assessments were less diluted when they were exposed to the diagnostic evidence cues than when they were exposed to the mixed evidence cues.

Shelton (1999) used a between-subject design. The auditors in her study were either provided with diagnostic evidence only or diagnostic evidence plus non-diagnostic evidence. She observed that the going concern assessments of less experienced auditors were affected by the presence of non-diagnostic evidence. She also found that the going concern assessments of the experienced participants in her study did not vary significantly based on the presence of non-diagnostic evidence. Shelton concludes that experience mitigates the effects of dilution.

The existing accounting literature (Hackenbrack 1992; Glover 1997; Hoffman and Patton 1997; and Shelton 1999) did find that auditors provided lower judgments when they were exposed to non-diagnostic evidence. Like Hackenbrack, I use a within-subject design and asked auditors to make an initial assessment based on a combination of diagnostic and non-diagnostic evidence. Then I asked auditors to make their subsequent assessment with diagnostic evidence alone. However, I investigate dilution in an internal control evidence setting where the non-diagnostic evidence is expected to reduce audit planning judgments of material control weaknesses. I predict that auditors will reduce their audit planning judgments of material control weaknesses when non-diagnostic evidence is present. The hypotheses, stated in the alternative form, are:

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H1a: Auditors’ audit plan adjustments for material manual process weaknesses when non-diagnostic evidence is present will be lower than when non-diagnostic is not present.

H1b: Auditors’ audit plan adjustments for material automated control weaknesses

when non-diagnostic evidence is present will be lower than when non-diagnostic is not present.

Domain knowledge has been used to explain differences in performance. Audit

firms facilitate the acquisition of domain knowledge by assigning auditors to areas of domain specialization. For example, Brazel and Agoglia (2007) describe how Big Four audit firms minimize business risks by encouraging auditor specialization in computer assurance. As auditors acquire specialization computer assurance, they improve their ability to transfer their knowledge from previously solved problems with automated controls to new problems that are related to their specialization (Frederick and Libby 1986; Vera-Munoz et al. 2001, Owhoso et al. 2002).

Vera-Munoz et al. (2001) found that management accountants outperformed financial auditors when both groups were asked to identify opportunity costs. Management accountants and financial auditors both have declarative knowledge in identifying opportunity costs. However, Vera-Munoz et al. (2001) attribute their results to the fact that management accountants have superior domain knowledge in measuring opportunity costs because they routinely consider opportunity costs. Financial auditors do not consider opportunity costs on a routine basis. But, financial auditors do work with manual process evidence more often than automated evidence (Tarantino 2006). I predict that auditors will recognize a greater need to adjust the audit plan for material manual process control weaknesses than for material automated control weaknesses. The hypothesis, stated in the alternative form, is:

H2: Auditors’ audit plan adjustments for a diagnostic automated control

weakness will be lower than their audit plan adjustments for a diagnostic manual control weakness.

IT auditors and financial auditors assess the strengths of the control points within

an internal control system. The control points involve two internal control evidence domains: manual processes and automated controls (AICPA 2008). Manual process evidence is created by humans within the internal control system. Automated control evidence is created by the IT infrastructure. Auditors tend to have more internal control skills in manual process evidence than automated control evidence (Tarantino 2006, Singleton 2007). Auditors will not adjust their audit plans for material automated control weaknesses as much as professionals with skills in automated controls. The hypotheses, stated in the alternative form, are:

H3: Auditors’ audit plan adjustments will be lower than IT audit specialists’ audit

plan adjustments for diagnostic automated control weaknesses.

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H4: Auditors’ audit plan adjustments will be lower than IT audit specialists’ audit plan adjustments for a diagnostic automated control weakness with non-diagnostic evidence

3. Research Method

3.1 Participants

Fifty-two auditors from each of the Big 4 accounting firms volunteered. The auditors had an average of 49 months of audit engagement experience. The auditors had worked on an average of 9.05 client engagements that involved manual processes risks. The auditors also worked on an average of 2.57 client engagements that involve automated control risks. The IT audit specialists also had an average of 49 months of experience. The IT audit specialists worked on an average of 20.76 client engagements that involved automated controls.

3.2 Pre-testing

Two rounds of pre-testing were used. The cues were pre-tested in the first round by two Big Four senior managers who were both licensed as Certified Public Accountants and Certified Information System Auditors. Both senior managers were employed with two different Big Four accounting firms. Round one pre-testing helped to identify the diagnostic and non-diagnostic evidence cues. During the second round of pre-testing, the evidence cues were rated between 1 (least diagnostic) and 100 (most diagnostic) by four Big Four IT audit specialists and four Big Four auditors. Both rounds of pre-testing revealed which evidence cues were diagnostic cues and which cues were non-diagnostic cues. The average rating for each evidence cue is provided in Figure 1. The average rating for the diagnostic material manual process weakness is 90. The average rating for the diagnostic automated control weakness is 80. The average ratings for the four non-diagnostic manual process cues ranged between 2.6 and 15.9. The average ratings for the four non-diagnostic automated control cues ranged between 3.8 and 22.6.

Figure 1: Evidence Cues and Round Two Pre-test Relevance Rating

(Average ratings in parentheses, 1=least diagnostic and 100=most diagnostic)

______________________________________________________________________

Diagnostic Control Weaknesses

Automated: “ABC Banking Corp. implemented an ERP module for electronic funds transfer that receives data from a legacy system that does not transfer hash totals, control totals, and record counts.” (80)

Manual: “ABC Banking Corp. EFT personnel can send wire transfers before obtaining authorization.” (90)

Non-diagnostic Automated Control Risk Cues

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“During the current year under audit, ABC Bank Corp. modified their PIN system to restrict personnel access to the Human Resource system via the company’s Intranet after three failed login attempts.” (3.8)

“ABC Bank Corp. uses IT to initiate orders for the purchase and delivery of supplies based on predetermined decision rules of what to order and in what quantities based on system-generated decisions. No other documentation of orders placed or supplies received is produced or maintained, other than through the IT system. Changes to this process are documented.” (16.3)

“New packaged software applications were installed this year to manage the travel expense files for ABC Banking Corp.’s Retail Banking Operation managers. Their IT staff has formal training and experience using this new software.” (16.5)

“ABC Bank Corp. uses automated fraud prevention technology to monitor and data warehouse accountholder card usage and activation in the current year under audit. They also used the technology to monitor closed accounts, dormant accounts, and deceased accounts in the current year under audit.” (22.6)

Non-diagnostic Manual Process Control Risk Cues

“Fraud prevention department personnel attend mandatory fraud training on a routine basis. They notify accountholders of dubious account activity.” (15.9)

“Human resource and employee benefits hotline personnel verify the identity of all callers before ensuing phone conversations.” (12.4)

“Travel expense reimbursement forms require inspection and authorization by the employee's immediate supervisor and the supervisor's manager before the authorized form is entered into the travel reimbursement system.” (2.6)

“ABC Bank Corp. maintains physical security over purchase orders for the purchase and delivery of supplies by limiting access to blank order forms and supplies received to appropriate personnel.” (7.4)

Figure 1: Evidence Cues and their Round Two Pre-test Task-Relevance Ratings

3.3 Case Material Big Four auditors and IT audit specialists read an overview that summarized the

purpose of the study. The auditors and IT audit specialists then acknowledged that they were interested in the results of the study and volunteered to participate (the participant response rate was 91%). Then I provided each participant with a password and a personal identification number (PIN). Participants used their password to enter the program. After reading the general instructions, participants entered their PIN and provided their formal consent to participate in the study.

Participants initially rated the effectiveness of the prior year’s controls after reading a brief narrative about a hypothetical financial institution and an excerpt from

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the hypothetical company’s unqualified independent internal control opinion of the previous year. The 7-point scale was labeled from left to right as “extremely effective” (coded as 1), “effective” (coded as 2), “somewhat effective” (coded as 3), “neutral” (coded as 4), “somewhat ineffective” (coded as 5), ”ineffective” (coded as 6), and “extremely ineffective” (coded as 7). The purpose of this step was to allow the participants to establish a baseline perception of the effectiveness of internal controls in the prior year. The average baseline rating was 2.15. So the participants felt that the internal controls were effective in the prior year.

Half of the participants were randomly assigned to the manual process evidence domain first then to the automated control evidence domain. The remaining participants were assigned to the automated control evidence domain first then to the manual process evidence domain second. The order that the participants encountered the control risk settings were not found to be significant (t = 0.64, p-value = 0.19).

Participants’ audit planning judgments were collected via a computer program that was designed according to the Tailored Design Method (Dilman 2007). The program controlled for order effects by randomizing the presentation order of the control risk setting evidence cues and the program also controlled the order in which the participants completed the tasks in the experiment (Favere-Marchesi 2006). The program mandated responses and prevented the changing of responses once participants had already answered a question and proceeded to the next webpage. Participants were not subject to any time pressure and spent an average of 38.15 minutes completing the experiment. Similar to Nisbett et al. (1981) and Hoffman and Patton (1997), I gave participants four non-diagnostic cues and one diagnostic cue (for each internal control evidence domain). Participants were given the opportunity to adjust the audit plan after reading four non-diagnostic manual process cues (or automated control cues depending on initial order assignment) with the diagnostic material manual process weakness cue (or diagnostic automated control weakness cue depending on the order of the initial assignment). Participants were then given the opportunity to adjust the audit plan based only on the diagnostic manual process weakness cue (or diagnostic automated control weakness depending on the order of the initial assignment). Participants repeated these steps for the remaining internal control evidence domain.

Participants were asked to provide their audit planning judgments. They rated the number of audit hours necessary to effectively complete the audit relative to the prior year on an 11-point scale. The scale contained three labels, “Significantly Decrease” (coded as 1), “Do Not Adjust” (Coded as 6), or “Significantly Decrease” (coded as 11). The remaining points on the scale were not labeled. The participants then responded to six multiple choice questions related to internal control risks from Gleim and Hillison’s (2006) professional examination preparation guide. The multiple choice questions were intended to distract participants from the next control risk setting case. Participants were then prompted to repeat these steps for the next internal control evidence domain case. After completing the second internal control evidence domain case, participants completed a background questionnaire, six new multiple choice questions that dealt with Electronic Fund Transfers, and a manipulation check.

The manipulation check asked participants to rate the relevance of each evidence cue on a 7-point scale. The left extreme of the scale was labeled as

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“extremely non-diagnostic” and coded as 1. The right extreme of the scale was labeled as “extremely diagnostic” and coded as 7. The mean rating and standard deviation of the diagnostic material control weakness cues was 5.99 and 1.41, respectively. The mean rating and standard deviation of the non-diagnostic evidence cues was 5.22 and 1.52, respectively. A two-sample t-test of the diagnostic control weaknesses cues and non-diagnostic control risk evidence cues revealed that the participants were able to differentiate diagnostic material control weaknesses from non-diagnostic evidence (t = 4.91, p-value < .001).

4. Analysis and Results

Table 1 provides mean and standard deviations of the auditors’ planning

adjustments. The mean response and standard deviation of the auditors’ judgments for the diagnostic material manual process weakness was 8.90 and 1.84, respectively. The mean response and standard deviation of the auditors’ planning adjustments for the same diagnostic material manual process weakness combined with non-diagnostic manual control evidence was 7.67 and 1.58, respectively. The mean response and standard deviation of the auditors’ judgments for the diagnostic material automated control weakness was 7.96 and 1.64, respectively. The mean response and standard deviation of the auditors’ planning adjustments for the same diagnostic material automated control weakness combined with non-diagnostic automated control evidence was 7.17 and 1.91, respectively. The mean response and standard deviation of the IT audit specialists’ judgments for the diagnostic material automated control weakness was 8.35 and 1.44, respectively. The mean response and standard deviation of the IT auditor specialists’ planning adjustments for the same diagnostic material automated control weakness combined with non-diagnostic automated control evidence was 7.73 and 1.36, respectively. Table 1: Within-Participant Audit Planning Adjustments, Descriptive Statistics ______________________________________________________________________

Auditors IT Audit Specialists Evidence Mean S.D. Mean S.D. Manual Process Control Weakness 8.90 1.84 -- -- Mixed Manual Control Process Evidence 7.67 1.58 -- -- Automated Control Weakness 7.96 1.64 8.35 1.44 Mixed Automated Control Evidence 7.17 1.91 7.73 1.36 ______________________________________________________________________ Notes: Manual Process Control Weakness: One Diagnostic Manual Process Evidence Cue

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Mixed Manual Control Process Evidence: One Diagnostic Manual Process Evidence Cue & Four Non-diagnostic Manual Control Risk Setting Cues Automated Control Weakness: One Diagnostic Automated Control Evidence Cue Mixed Automated Control Evidence: One Diagnostic Automated Control Evidence Cue & Four Non-diagnostic Automated Control Risk Setting Cues _____________________________________________________________________

The results to the test of my hypotheses are provided in Table 2. H1a predicts that auditors’ manual control risk planning adjustments would be lower when additional non-diagnostic evidence is present than when it is not present. This would mean that the non-diagnostic evidence cues would influence auditors to reduce their manual control risk planning adjustments. As predicted, H1a is significant (t = -5.12, p = <.000).

H1b also predicts that auditors’ automated control risk planning adjustments will be lower when non-diagnostic evidence is present than when it is not present. This would mean that the non-diagnostic evidence cues would influence auditors to reduce their manual control risk planning adjustments. As predicted, H1b is significant (t = -2.89, p = .002).

H2 predicts that auditors’ planning adjustments of the diagnostic material automated control weakness will be lower than their planning adjustments of the diagnostic material manual control weakness. This would mean that financial statement auditors do not anticipate that the material automated control weakness used in this study warrants the same magnitude of audit plan adjustment as the material manual process weakness used in this study. As predicted, H2 is significant (t = 3.49, p = <.000).

H3 predicts that auditors’ planning adjustments of the diagnostic material automated control weakness will be lower for auditors than IT audit specialists. This would mean that financial statement auditors may insufficiently adjust their audit plan for material automated control weaknesses. The statistical results are displayed in Panel B of Table 2. As predicted, H3 is significant, but only marginally significant (t = 1.49, p = <.064).

H4 predicts that auditors’ planning adjustments of the diagnostic material automated control weakness with non-diagnostic automated evidence is lower for auditors than IT audit specialists. This would mean that financial statement auditors may insufficiently adjust their audit plan for material automated control weaknesses with non-diagnostic automated evidence. As predicted, H4 is significant, (t = 1.91, p = <.023).

Table 2: Audit Planning Adjustments Statistical Tests of Hypotheses ______________________________________________________________________ Panel A: Dependent Tests

t p-value Mixed Manual Process Evidence < Manual Process Weakness -5.12 <.000 Mixed Automated Control Evidence < Automated Control Weakness -2.90 .002

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Automated Control Weakness < Manual Process Weakness 2.75 <.000 ______________________________________________________________________ Panel B: Dependent Tests

t p-value Auditor Automated Control Weakness < IT Automated Control Weakness 1.49 .064 Auditor Mixed Auto Control Evidence < IT Mixed Auto Control Evidence 1.91 .023 ______________________________________________________________________ Notes: Manual Process Control Weakness: One Diagnostic Material Manual Process

Weakness Cue Mixed Manual Control Process Evidence: One Diagnostic Material Manual Process Weakness Cue & Four Non-diagnostic Manual Process Evidence Cues Automated Control Weakness: One Diagnostic Material Automated Control Weakness Cue Mixed Automated Control Evidence: One Diagnostic Material Automated Control Weakness Cue & Four Non-diagnostic Automated Control Evidence Cues ______________________________________________________________________ 5. Conclusions

This study presents evidence that auditors reduce their planning adjustments when they encounter non-diagnostic evidence. This finding is consistent with the existing literature on non-diagnostic evidence. The results in this study also suggest that auditors may not adjust their audit plans as much for material automated control weaknesses as they would for material manual process weaknesses. Auditors also did not adjust as much for material automated control weaknesses as material manual process weaknesses when non-diagnostic evidence is present.

The implication of my findings is that auditors may not revise their audit plans enough to gain an understanding of material automated weaknesses. Auditors have the option to add professionals who have automated control skills to the audit engagement team. However, AU 319 does not require them to add professionals with automated control skills to the engagement. Auditors, without automated control skills or the assistance of professionals with automated control skills, could issue unqualified internal control and financial statement opinions when adverse opinions would be more appropriate. If auditors were to add professionals with automated control skills to the engagement and follow the professional’s recommendations to adjust the audit plan when material automated control weaknesses are identified, the influence of the non-diagnostic evidence may be mitigated. I leave these issues for future research.

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One limitation of my study is that I used one material weakness for each of the two internal control evidence domains. This limitation results from the pre-testing phase as a condition of my research design. My goal was to identify an internal control weakness for both internal control evidence domains that would resonate as diagnostic between auditors and IT audit specialists. The diagnostic cues met the expectations of the auditors involved in the pre-test phase and appear to be diagnostic to the actual experimental participants for the manipulation check. However, other cues may have elicited different results.

Another limitation of my study is that my participants were only exposed to one internal control domain at a time. This too was a condition of my research design. In practice, auditors and IT audit specialists may encounter automated control evidence simultaneously with manual process evidence. I restricted the participants to one internal control domain at a time with the intent of simplifying the task. Adding that level of complexity to the task could potentially make the instrument more time consuming and harder to complete. If this were to be the case, the results from my experiment could be unreliable (Dilman 2007). This issue awaits empirical investigation.

References

American Institute of Certified Public Accountants (AICPA). 2002. AU SEC. 319 – Consideration of internal control in a financial statement audit. http://www.aicpa.org/download/auditstd/AU319DispositionSept02ASB.pdf

American Institute of Certified Public Accountants (AICPA). 2008. Codification of Statements on Auditing Standards. New York, NY: AICPA.

Brazel, J.F. and C.P. Agoglia. 2007. An Examination of Auditor Planning Judgments in a Complex Accounting Information System Environment. Contemporary Accounting Research 24 (Winter): 1059-1083.

Dilman, D.A. 2007. Mail and Internet Surveys: The Tailored Design Method 2 nd.ed. Hoboken, NJ: John Wiley & Sons.

Duffy, M.N. 2004. Section 404 Opens a Door. Journal of Accountancy. May: 55-64.

Favere-Marchesi, M. 2006. “Order Effects” Revisited: The Importance of Chronology. Auditing: A Journal of Practice and Theory 25 (May): 69-83.

Frederick, D.M. and R. Libby. 1986. Expertise and Auditors’ Judgments of Conjunctive Events. Journal of Accounting Research 24 (Autumn): 270-290.

Fogelman, S.L., B.H. Peterson, W.G. Heninger, and M.B. Romney. 2007. Opportunity Detected. Journal of Accountancy 204 (December): 62-65.

Gleim, I.N., and W. A. Hillison. 2006. Auditing and systems: Exam questions and Explanations, a supplemental text. Gainesville, FL: Gleim Publications, Inc.

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Glover, S.M. 1997. The influence of time pressure and accountability on auditors’ processing of nondiagnostic information. Journal of Accounting Research 35 (Autumn): 213-226.

Hackenbrack, K. 1992. Implications of seemingly non-diagnostic evidence in audit

judgment.” Journal of Accounting Research 30 (Spring): 126-136.

Hall, J.A. and T. Singleton. 2005. Information Technology and Assurance 2nd ed. Mason, OH: Thomson South-Western.

Hoffman, V.B., and J.M. Patton. 1997. Accountability, the dilution effect, and

conservatism in auditors’ fraud judgments. Journal of Accounting Research 35 (Autumn): 227-237.

Hunton, J.E., A.M. Wright, and S. Wright. 2004. Are financial auditors overconfident in their ability to assess risks associated with enterprise resource planning systems? Journal of Information Systems 18 (Fall): 7-28.

International Auditing and Assurance Standards Board (IAASB). 2010a. International Standard on Auditing 200 – Overall objectives of the independent auditor and the conduct of an audit in accordance with international standards on auditing. Available at: http://web.ifac.org/download/a016-2010-iaasb-handbook-isa-300.pdf.

International Auditing and Assurance Standards Board (IAASB). 2010b. International Standard on Auditing 300 – Planning an audit of financial statements. Available at: http://web.ifac.org/download/a008-2010-iaasb-handbook-isa-200.pdf.

Kaplan, S.E. 1985. An Examination of the Effects of Environment and Explicit Internal Control Evaluation on Planned Audit Hours. Auditing: A Journal of Practice and Theory 5 (Fall): 12-25.

KPMG. 2007. PCAOB current activities and selected other items.

Nisbett, R.E., H. Zukier, and R.E. Lemley. 1981. The Dilution Effect: Nondiagnostic Information Weakens the Implications of Diagnostic Information. Cognitive Psychology 13: 248-277.

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Selby, D.D. 2009. The effects of Auditor Type and Evidence Domain Type on the Influence of Non-diagnostic Internal Control Evidence and the Potential for Audit Failure. Ph.D. Dissertation, Florida State University.

Shelton, S.W. 1999. The Effect of Experience on the Use of Non-diagnostic Evidence in Auditor Judgment. The Accounting Review 74 (April): 217-224.

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Singleton, T. “Emerging Technical Standards on Financial Audits: How IT Auditors Gather Evidence to Evaluate Internal Controls.” Information System Control Journal 4 (2007): 9-1.

Tarantino, A. 2006. Manager’s Guide to Compliance. Hoboken, NJ: John Wiley & Sons.

Tetlock, P.E., L. Skitka, and R. Boettger. 1989. Social and Cognitive Strategies of Coping with Accountability: Conformity, Complexity, and Bolstering. Journal of Personality and Social Psychology: Interpersonal Relations and Group Dynamics 57: 632-641.

Tetlock, P.E., J.S. Lerner, and R. Boettger. 1996. The Dilution Effect: Judgmental Bias, Conversational Convention, or a Bit of Both? European Journal of Social Psychology 26: 915-934.

Vera-Munoz, S.C., W.R. Kinney, and S.E. Bonner. 2001. The Effects of Domain Experience and Task Presentation Format on Accountants’ Information Relevance Assurance. The Accounting Review 76 (July): 405-429.

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Process-based view of agility: the value contribution of IT and the effects on process outcomes

Robyn L. Raschke

Department of Accounting, University of Nevada Las Vegas, USA

[email protected]

Extended Abstract

This paper focuses on the development and validation of a measure of agility at the process level. This measure is then tested using Sambamurthy et al.’s (2003) theoretical model of agility in which IT is a platform for agility in which subsequent value is derived. The business value of IT intersects accounting and information systems. Although the majority of prior research that examines the relationship between IT and performance is at the firm level, value is created or lost at the process level. Business process agility is defined as “the ability to add and/or reconfigure a business process by quickly adding new capabilities to the set of business process capabilities to accommodate the potential needs of the firm” (Raschke and David, 2005). An example of business process agility is adding RFID capability into the order fulfillment process to improve order tracking and reduce shipping errors

A review of the literature identifies two main competencies of agility: the ability to adapt to change and to do so in a responsive manner (Burgess, 1994; Sharifi and Zhang, 1999). Development of agility at the business process level begins with the general understanding of these two primary agility competencies but also considers other conceivable components that may theoretically represent business process agility. Therefore, Business Process Agility is conceptualized as a construct comprised of the following four components: reconfigurability, responsiveness, employee adaptability, and a process-centric view. Reconfigurability and responsiveness incorporate the fundamental characteristics of agility whereas employee adaptability and a process centric view incorporate the ability to leverage the human aspect as it relates to the process. As it is theorized that these four components form Business Process Agility, this research proposes that Business Process Agility is a second-order formative latent construct.

The theoretical model used refines the conceptual elements of (Sambamurthy et al., 2003) within the context of business process agility and uses the following measurable constructs to test the theoretical model: IT Infrastructure Flexibility, Business Process Agility, and Process Outcomes. A flexible IT infrastructure can facilitate the ease of “adding to” or “modifying” the set of business process capabilities; therefore, allowing the firm to respond quickly to opportunities or threats (Ray et al., 2005). This occurs because a flexible IT infrastructure enables the delivery of technical solutions effectively and quickly (Ravichandran and Lertwongsatien, 2005). Correspondingly, an inflexible IT infrastructure creates difficulty to add or modify a set of process capabilities. Therefore, due to the variation of IT infrastructure flexibility between firms and the potential effect on the firm’s ability to add or modify to a set of process capabilities, the following is hypothesized: H1: IT Infrastructure Flexibility is positively related to Business Process Agility

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Immediate effects of IT are manifested in process improvements (Ravichandran and Lertwongsatien, 2005). By focusing on the business process, value is derived through process outcomes adding granularity between the conceptual relationship of IT and performance. Because IT impacts firm performance via intermediate business processes, it is appropriate to use an intermediate-level dependent variable such as process outcomes (Dehning and Richardson, 2002; Melville et al., 2004). Process Outcomes in prior literature are described as having the characteristics of quality and efficiency (Matolcsy et al., 2005; Wieder et al., 2006). IT is a platform to the capability of agility that then leads to performance; thus, variations in Business Process Agility accounts for variations in Process Outcomes. Therefore, the effect of Business Process Agility for a specific process is hypothesized as:

H2a: Business Process Agility is positively related to Efficient Process Outcomes. H2b: Business Process Agility is positively related to Quality Process Outcomes.

A survey was sent to operations and purchasing managers from a purchased list of 880 manufacturing firms. Dillman’s (2000) Tailored Survey design is used. To validate the agility measure, order fulfillment and purchasing processes are used with a response rate of 18% and 22%, respectively. Covariance based structural equation modeling is used to validate the Business Process Agility measure and to test the hypotheses. The fit indices indicate a good fit of the agility measurement model (Chi-square of 159.36, df=80, p=<.01; CFI=.97; TLI=.96, and RMSEA=.057) with a conclusion that the four components identified from the literature is a relatively good capture of the conceptualization of Business Process Agility. In addition, the test for invariance indicates that the agility measurement is equivalent across the two processes.

Hypothesis 1 examines the assertion that IT enables agility through the strength of the relationship between IT Infrastructure Flexibility and Business Process Agility. The path between IT Infrastructure Flexibility and Business Process Agility is positive and statistically significant (p < .001), the standardized path is .33 thus supporting Hypothesis 1. Hypothesis 2a and 2b examines the relationship between Business Process Agility and Process Outcomes. Both structural paths between Business Process Agility and Process Outcomes (Efficiency and Quality) show a positive and meaningful relationship (.28 and .31, respectively) and are statistically significant (p=<.001 and p=<.001, respectively), thus supporting Hypothesis 2a and 2b. All standardized paths meet Meehl’s (1990) recommended threshold of .20.

Drawing from dynamic capabilities within a process oriented perspective this study adds granularity to the understanding of how IT enables agility and derives subsequent value through its process outcomes. Both IT and process managers benefit from the results of this study in understanding not only the components of business process agility, but also the impact of the relationships between IT, business process agility, and process outcomes. Developing and operationalizing Business Process Agility is a necessary and needed first step prior to examining the effects of IT on agility. Thus, our contribution provides empirical support as to the components of Business Process Agility. Envisioning Business Process Agility as a second-order formative construct allows for an understanding into the components that form Business Process Agility. The components of Business Process Agility are core drivers of agility. Understanding these core drivers allows management to focus investments towards specific components to reach desired agility results. Therefore, the possibility of understanding Return on Agility Investments and risk assessment are possible.

With an understanding of the contributions to the literature, it is important to also acknowledge the limitations of this research. One limitation is that this research focuses on manufacturing firms and the specific processes of purchasing and order fulfillment. Future research should examine if different industries place a different emphasis on the components of Business Process Agility (e.g. service versus manufacturing industries).

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Additionally, Business Process Agility should be validated for other specific processes other than purchasing and order fulfillment. An additional limitation is that the data collected to validate Business Process Agility and evaluate the theoretical model is cross-sectional and collected at one point in time. Therefore, the conclusions reached during this study must be considered within the contexts in which they apply.

Keywords: Business process agility, agility, dynamic capabilities, order-fulfillment, purchasing, process outcomes

References

Burgess, T.F. (1994), “Making the leap to agility: Defining and achieving agile manufacturing through business process redesign and business network redesign”, International Journal of operations & Production, Vol. 14, No.11, p, 23.

Dehning, B., and Richardson, V.J.(2002), "Returns on investments in information technology: a research synthesis", Journal of Information Systems, Vol. 16, No. 1, pp. 7-30.

Dillman, D.A., (2000), Mail and internet surveys: the tailored design method. New York: John Wiley & Sons.

Matolcsy, Z.P., Booth, P., and Wieder, B. (2005), "Economic benefits of enterprise resource planning systems: some empirical evidence", Accounting and Finance, Vol. 45, pp. 439-456.

Meehle, P.E. (1990), "Why summaries of research on psychological theories are often uninterpretable", Psychological Reports, Vol. 66, pp.195-244.

Melville, N., Kraemer, K., and Gurbaxani, V.(2004), "Review: information technology and organizational performance: an integrative model of IT business value", MIS Quarterly, Vol. 28, No. 2, pp. 283-322.

Raschke, R.L., and David, J.S., (2005), "Business process agility", Proceedings of the Eleventh Americas Conference on Information Systems, Omaha, Ne.

Ravichandran, T., and Lertwongsatien, C., (2005), "Effect of information systems resources and capabilities on firm performance: a resource-based perspective", Journal of Management Information Systems, Vol. 21, No. 4, pp. 237-276.

Ray, G., Muhanna, W.A., and Barney, J.B., (2005), "Information technology and the performance of the customer service process: a resource-based analysis", MIS Quarterly, Vol. 29, No. 4, p. 625.

Sambamurthy, V., Bharadwaj, A., and Grover, V., (2003), "Shaping agility through digital options: reconceptualizing the role of information technology in contemporary firms", MIS Quarterly, Vol. 27, No. 2, p. 237.

Sharifi, H., and Zhang, Z.(1999), "A methodology for achieving agility in manufacturing organizations: an introduction", International Journal of Production Economics, Vol. 62, pp. 7-22.

Wieder, B., Booth, P., and Matolcsy, Z.P.(2006), "The iImpact of ERP systems on firm and business process performance", Journal of Enterprise Information Management, Vol. 19, No. 1, pp. 13-29.

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Analyzing Inventory Routing Costs

Michael Wagner

Department of Accounting, Hanken School Economics, Finland [email protected]

Abstract

Inventory routing problems involve determining the optimal replenishment schedule that integrates inventory management and vehicle routing. This paper investigates efficiency gains from combining the two distinct problems. Mixed-integer models are applied to determine the optimal replenishment schedule. Total costs of a hierarchical approach are contrasted with an integrated approach in order to capture the impact of set-dependent cost structures. The proposed methodology applies simulation and a factorial design to analyze the role of cost factors, factor levels and interaction effects. Cost benefits are evaluated in a deterministic environment using nominal range sensitivity analysis and repeated measure ANOVA. The approach is illustrated for a network of Automated Teller Machines (ATMs) using empirical data of an international commercial bank. Results of the case study show that cost benefits of the integrated approach vary by factor levels and may in practice be smaller than suggest by previous literature.

Keywords: Inventory, Routing, Sensitivity, ANOVA, ATM.

1. Introduction

Inventory and transportation costs have a significant impact on the operating cost of the firm. Hence, researchers and practitioners alike seek ever new and better ways to gain competitive advantages in distribution networks over competitors. Integration and coordination of various aspects of distribution has become critical to lower costs and increase efficiency. Inventory routing is one model that has been adopted by the oil and gas, retail, textile and automotive industry (Campbell and Savelsbergh, 2004). The approach integrates inventory allocation and vehicle routing that have been initially regarded separately. Hence, the objective is to minimize costs based on three joint distribution decisions: (1) when to deliver to a customer, (2) how much to deliver to a customer, and (3) how to route deliveries to customers.

Inventory routing models make various assumptions about planning horizon, demand, service levels, vehicle fleet and related working constraints. The reader is referred to Moin and Salhi (2006), Kleywegt, Nori and Savelbergh (2002) and Campbell and Savelsbergh (2004) for topic overview, applications and developments in solving inventory routing problems. Clearly, integrating routing and inventory allocation enables efficiency gains and cost savings. As such, the potential of inventory routing is readily and intuitively supported by academics and

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practitioners alike. However, empirical evidence on the size of the effect and the underlying set-dependent cost structure is limited. Earlier literature suggests cost reductions to fall between six and ten percent (Bell, Dalberto, Fisher, Greenfield, Jaikumar, Kedia, Mack and Prutzman, 1983; Federgruen and Zipkin, 1984; Golden, Aassad and Dahl, 1984) offering no insight into the impact of factor levels and interaction effects. In a more recent study, Raa and Aghezzaf (2009) pioneer the analysis of cost trade-offs governing inventory routing problems, but the study does not extend the analysis to an evaluation of the effect of set-dependent cost structures and subadditive transaction costs. As such, the present study considers the impact of factor levels and factor interactions by isolating the effect of set-dependent cost-structures.

Following the rational outline above, this study investigates set-dependent cost structures of inventory routing problems and evaluates potential efficiency gains from optimization, particularly comparing a hierarchical approach and an integrated approach where replenishments and routing are considered jointly. The approach combines mixed-integer inventory and routing models to derive the optimal replenishment schedule. The methodology and analysis are applied to cash supply chains and illustrated for a network of ATMs, where cash has properties of both, currency and product. Effects are evaluated in a time-discrete, deterministic framework based on total replenishment costs using simulation and empirical data of an international commercial bank. Detailed analysis of the case study provides insight into the role of set-dependent cost structures. The paper is organized as follows. §2 motivates and describes the background to the case study that illustrates the proposed methodology. §3 presents the research design including the basic framework, mathematical formulation, data and design of experiments. The problem formulation is stated as a mixed-integer inventory routing problem, contrasting an integrated and a hierarchical approach. §4 presents the results of the nominal range sensitivity analysis and statistical analysis, followed by the discussion and conclusion in §5.

2. Background to the Case Study

Cash supply chains provide a new and potentially fruitful area for inventory routing. Currency is moved between different stages, incurring transaction and holding costs (Rajamani, Geismar and Sriskandarajah, 2006). Cash management decisions at each stage, such as central bank, cash in transit (CIT) provider, bank branch, off-premises and on-premises automated teller machine (ATM) aim to minimize total costs while ensuring the supply of currency. Integrating cash management decisions at various stages of the cash supply chain allows to balance cost trade-offs and to increase the efficiency of banking systems. Hence, providing cash management as a sharable service increases efficiency and lowers total costs across the cash supply chain. The underlying set-dependent cost structure is captured by subadditive transaction costs in combining and coordinating multiple replenishments.

Historically, cash management has received widespread attention from academics and practitioners alike (for reviews see Gentry, 1988; Gregory, 1976; Srinivasan and Kim, 1986). A good deal of research effort has been devoted to developing inventory cash management models that match various cash balance

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requirements and hypothesized cash flow patterns. More recent studies have focused on randomly varying environments (Hinderer and Waldmann, 2001) and stochastic programming (Castro, 2009). However, all cash management models implicitly assume additivity in transaction costs, which, viewed through the lens of set-dependent costs structures, provides limited insight into the overall economies of cash supply chains.

Although research in cash management and inventory management have traditionally taken a similar path, recent developments in cash management have failed to consider an integrated supply chain perspective that has lead to advances in modeling set-dependent cost structures in inventory management and logistics. Particularly, the inventory routing problem (IRP) is a prominent example. In essence, the model is an attempt to gain additional cost efficiency over simplified cost structures and hierarchical planning models by exploiting the subadditive nature of the integrated model characterized by set-dependent cost structures. This paper considers the properties of the complex decision problem arising from an integrated cash replenishment approach by investigating the effect of set-dependent cost structures in cash supply chains. The goal is to assess set-dependent cost structures exhibiting subadditive transaction costs not captured by simple fixed, linear or affine cost functions. In particular, a network of ATMs is considered that illustrates the effect of set-dependent cost structures and provides a new application for inventory routing.

3. Research Design and Data

The study is based on normative empirical quantitative research and discrete event simulation to determine the effects of set-dependent cost structures. Borrowing from the logical positivist approach, the phenomenon of set-dependent cost structures is isolated from the context for logical analysis, so to assess the impact of factor levels (i.e. cost parameters and demand). The formulated mixed-integer model is based on prior axiomatic quantitative research in the area of supply chain management and inventory management outlined earlier. However, the inventory routing model considered in this study focuses on the properties of set-dependent cost structures. Consequently, complicating capacity and working constraints in inventory routing are not considered (for example see Chandra, 1993).

The impact of set-dependent cost structures is determined by comparing total costs of an integrated approach with total costs of a hierarchical approach. Relative cost savings are measured using simulation and empirical data from an international commercial bank. Nominal range sensitivity analysis is conducted to determine the impact of factor levels on cost savings. The factorial design and simulation enable statistical analysis of cost savings. Discrete probability distributions of cash demand are obtained from historic data of the case company. Repeated measure analysis of variance (i.e. One-Way RM ANOVA, Two-Way RM ANOVA) is conducted to test for differences in the impact of factor levels and interaction effects.

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3.1 Basic Framework

For simplicity and illustration purpose, the effect of set-dependent cost structures in inventory routing is captured by a one-warehouse multi-retailer system in a multi-period, finite horizon framework. The case study contains a central vault supplying cash in form of a single currency to several cash dispensers (ATMs). Demand for cash is assumed to be time-varying, ATM specific, deterministic, and time-discrete. No cash shortages and backlogs are permitted. Cash is routed from the central vault to ATMs via a single uncapacitated vehicle at the beginning of each period. Routing costs are represented by an undirected graph, where each arc is associated with a known weight capturing the distance between two ATMs. The following notation is used in formulating the mathematical models: Sets

| 0,1, . . . , Set of N cash dispensers, atm0 is the central vault

| 1, . . . , Set of T days

Parameters Cf : fixed replenishment cost coefficient Cr : routing cost coefficient Di,j : arc weight (atmi, atmj) I : holding cost coefficient L : large number N : number of ATMs T : number of periods Wi,t : cash amount dispensed by ATM i in period t

Decision Variables dt : set-dependent routing in period t si,t : cash amount held by cash ATM i in period t ui,t ; uj,t : arbitrary real numbers xi,t : amount of cash supplied to ATM i in period t ωi,j,t : binary indicator of traversing from ATM i to ATM j in period t ωi,t : binary indicator for replenishing ATM i in period t

Total costs of inventory routing, as stated in Equation (1), are comprised of

three cost components: linear holding costs, fixed replenishment costs and set-dependent routing costs. Holding costs are a linear function of the aggregated amount of undispensed cash, whereas replenishment costs represent a fixed amount incurred every time cash is supplied. Routing costs are dependent on the set of replenishment decisions in a given time period and are determined by the aggregated length of the respective optimal routes, scaled by a routing cost coefficient.

  ⋅ ∑ ∑ ,∈∈   ⋅ ∑ ∑ ,∈∈ ⋅ ∑ ∈ (1)

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3.2 Integrated Approach

The here presented mathematical formulation of the integrated approach captures the effect of set-dependent cost structures by considering inventory and routing decisions jointly. Minimize

⋅ ∑ ∑ ,∈∈   ⋅ ∑ ∑ ,∈∈ ⋅ ∑ ∑ ∑ , ⋅ , ,∈∈∈ (2)

Subject to

, 1 , , , ∀ ∈ N; ∀ ∈ T (3)

,0 0 ∀ ∈ N (4)

, 0 ∀ ∈ N (5)

,, 0 ∀ ∈ N; ∀ ∈ T (6)

∑ , ,∈N , 0 ∀ ∈ T (7)

∑ , ,∈N ∑ , ,∈N 0 ∀ ∈ N;  ∀ ∈ T (8)

, , ⋅ , , 1  0 ∀ , ∈  N 0 , ;    ∀ ∈ T (9)

Constraint (3) is required to balance inventory for each ATM. Constraints (4)

and (5) set starting cash balances to zero and ensure the non-negativity of cash balance for each ATM. Replenishment indicator constraint (6) ensures that no cash is supplied without incurring fixed replenishment costs. Constraint (7) ensures that every route contains the central vault. Constraints (8) ensure that an optimal route that minimizes the objective (2) includes each ATM being replenished in a particular period. Constraint (9) is similar to the well known sub-tour elimination constraint for the travelling salesman problem.

3.3 Hierachical Approach

In order to isolate the effect of set-dependent cost structures a hierarchical approach is proposed, where replenishment decisions are determined independently (Subproblem 1) and to be accounted for in the subsequent routing problem (Subproblem 2). Subproblem 1 (Inventory): Minimize ⋅ ∑ ∑ ,∈∈   ⋅ ∑ ∑ ,∈∈ (11)

Subject to Constraints (3), (4), (5) and (6).

Subproblem 2 (Routing):

Minimize ⋅ ∑ ∑ ∑ , ⋅ , ,∈T∈N∈N (12)

Subject to Constraints (7), (8), and (9).

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The integrated approach as well as the hierarchical approach are computationally non-trivial with both subproblems being NP-hard. In fact, the routing subproblem turns out to comprise T distinct traveling salesman problems, each being NP-hard.

3.4 Data

The case study is based on data obtained from an international commercial bank covering a period of 200 days. The financial institution agreed to provide detailed cash dispense records for 107 ATMs in a particular region, comprising locations such as shopping malls, hotels, hospitals and airports (see Figure 1). The exact locations of all 107 ATMs and the central vault were provided, which allowed the distance matrix to be derived.

Figure 1. The ATM Network has 107 ATMs covering a region of approximately 625km².

3.5 Design of Experiments

In measuring the impact of set-dependent cost structures, four important design factors were identified: demand, fixed replenishment costs, holding costs, and routing costs. The factor demand is modeled as a multiplier that scales demand linearly and has quadratic impact on demand variance. The remaining three factors are cost coefficients. Factor levels resemble the situation faced by the bank and provide realistic upper and lower bounds. The full factorial design and respective factor levels are depicted in Table 3.

0

5

10

15

20

25

0 5 10 15 20 25

Depot

ATMs

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Table 1. Factorial Design

Factor Level I Level II Level III Demand Multiplier 0.5 1 2 Fixed Replenishment Cost Coefficient 10 30 90 Holding Cost Coefficient 0.00008 0.00016 0.00032 Routing Cost Coefficient 0.25 1 4 The factorial design assumes each factor to take one of three possible values, where the full design matrix is 34=81 trials. Each trial contains 100 replications of simulated 200 days; each based on the observed discrete probability distribution of cash withdrawals per ATM. Hence, the computational complexity is 81× 100 runs requiring 200 × 8 × 100 daily cash withdrawals.

4. Tests and Results

This section presents the results of the nominal range sensitivity analysis and the statistical analysis regarding the impact of individual design factors, factor levels as well as interaction effects. The response variable is relative cost savings of the integrated approach compared to the hierarchal approach.

4.1 Nominal Range Sensitivity Analysis

Results presented in this subsection are based on the orginal factor level (Level II) and historic demand series faced by the international commercial bank. It is noted that the original replenishment schedule exercised by the bank resulted in 27.00% higher total costs compared to a hierarchical approach. The effect of the set-dependent cost structure is captured by the integrated approach and amounts to total cost saving of 3.02% compared to the hierarchical approach (see Table 4).

Investigation of individual cost components reveals the impact of set-dependent costs structures on total costs and shows how individual cost components are balanced using an integrated approach. Fixed replenishment costs were unaffected and cost savings entirely attributable to the hierarchical approach. In the case of routing costs, relative cost saving were attributable to the integrated approach. For holding costs, the integrated approach resulted in relatively higher costs compared to the hierarchical approach, marking the trade-off in balancing individual cost components. Consequently, cost savings with respect to holding costs were attributable to the hierarchical approach.

Table 2. Relative Cost Savings

Component Original Integrated Approach Fixed Replenishment Costs -15.83% 0.00% Routing Costs 6.88% 22.05% Holding Costs -55.92% -2.26% Total Costs -27.00% 3.02% This table shows relative cost savings of the original cash deployment strategy and the integrated approach compared to the hierarchical approach for 107 ATMs in the base line scenario with all factors being set to Level II and covering a period of 200 days.

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In particular, analyzing the nominal range of each factor revealed differences in the impact of individual factor levels on relative cost savings (see Figure 1). The factor demand and the factor holding costs did not indicate trends or higher order powers contrary to the factor routing costs and the factor fixed replenishment costs. The importance measure, defined as the difference of cost savings within the variability range, indicated that the factor routing costs (10.98% - 0.48% =10.50%) has the largest impact on cost savings, followed by the factor fixed replenishment costs (8.01% - 0.65% = 7.36%). Conversely, the importance measure suggested only limited impact on relative cost savings for the factor demand (3.19% - 1.72% = 1.47%) as well as for the factor holding costs (3.42% - 2.39% =1.03%).

Fig. 2. Nominal range sensitivity analysis shows relative cost savings of each factor covering the variability range from Level I (0%) to Level III (100%). Relative cost savings are computed by varying one factor at a time with the remaining three factors being set to Level II.

4.2 Main Effects

One-way repeated measure ANOVA was performed to test the significance of factor levels for each of the four main factors: fixed replenishment costs, routing costs, holding costs, and demand. Normality of sampling distributions, relative cost savings, was met given the large sample size for each factor level (n=100), no outlying score (|z|>3.3) was detected. The assumption of homogeneity of variance was met, with the number of observations for each level being equal. The Huynh-Feldt adjustment corrected for the lack of sphericity in all four main factors detected by Mauchly’s test of sphericity (p<0.05).

Cost saving means for each factor level are tabulated together with standard errors, 95% confidence levels in Table 5. Results for one-way repeated measure ANOVA are presented in Table 6. Statistically significant effects included all four main factors. Interpretation, therefore, is focused on the differences in factor levels. Increase in cost savings as a function of an increasing routing cost coefficient was statistically significant, F(1, 1.12) = 13781.28, p<0.01, ηp² = 0.99. Similarly, an increase in the fixed replenishment cost coefficient was statistically significant, F(1, 1.12) = 8629.86, p<0.5, ηp² = 0.99. Both suggest a strong impact on cost savings. Although an increase in the holding cost coefficient F(1, 1.12) = 65.20, p<0.01, ηp² = 0.40, and an increase in the demand multiplier F(1, 1.12) = 309.75, p<0.01, ηp² = 0.76, were statistically significant, the effect sizes of the two factors were substantially smaller. Additionally, the Tukey HSD test for pairwise

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post-hoc comparison of level means was statistically significant (p<0.01) except the factor demand, where the pairwise comparison of Level I and Level II was not statistically significant.

Table 3. Descriptive Statistics of Cost Savings

Factor Level M SE 95% LCL 95% UCLFixed Replenishment Costs Level I 0.08339 0.00065 0.08211 0.08468 Level II 0.03145 0.00034 0.03078 0.03212 Level III 0.00796 0.00016 0.00765 0.00826Routing Costs Level I 0.00391 0.00009 0.00374 0.00408 Level II 0.03145 0.00034 0.03078 0.03212 Level III 0.11281 0.00094 0.11095 0.11467Holding Costs Level I 0.03355 0.00071 0.03213 0.03497 Level II 0.03145 0.00034 0.03078 0.03212 Level III 0.02600 0.00029 0.02543 0.02657Demand Level I 0.03282 0.00044 0.03194 0.03370 Level II 0.03145 0.00034 0.03078 0.03212 Level III 0.04446 0.00054 0.04338 0.04554This table shows descriptive statistics for each of the four main factors and the three factor levels.

Table 4. One-Way Repeated Measure ANOVA

Factor SS DF MS F ηp² Between-Subject Effects

Fixed Replenishment Costs 0.50261 1.00000 0.50261 23658.52546** 0.99584Routing Costs 0.73177 1.00000 0.73177 13661.34053** 0.99281Holding Costs 0.27601 1.00000 0.27601 11341.64507** 0.99134Demand 0.39407 1.00000 0.39407 14288.43231** 0.99312

Within-Subject Effects Fixed Replenishment Costs 0.29802 1.50576 0.19792 8629.85847** 0.98865Routing Costs 0.64126 1.12224 0.57141 13781.28117** 0.99286Holding Costs 0.00303 1.57041 0.00193 65.19542** 0.39660Demand 0.01023 1.88613 0.00542 309.74793** 0.75778

This table shows F-test and effect size for all four main factors: fixed replenishment costs, routing costs, holding costs and demand. Degrees of freedom are Huynh-Feldt adjusted for the averaged tests of significance. **p<0.01.

4.3 Interaction Effects

Two-way repeated measure ANOVA was performed to test the significance of the levels of interaction between the four factors: fixed replenishment costs, routing costs, holding costs, and demand. The assumptions of normality of sampling distributions and the assumption of homogeneity of variance were met, as with one-way repeated measure ANOVA. Likewise, degrees of freedom for the averaged tests of significance were Huynh-Feldt adjusted to account for the lack of sphericity indicated by Mauchly’s test of sphericity (p<0.05).

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Table 5. Two-Way Repeated Measure ANOVA

Factor SS DF MS F ηp² Demand×Fixed 0.13732 2.69344 0.05098 945.04972** 0.90518Demand×Routing 0.02902 1.76442 0.01645 518.17219** 0.83959Demand×Routing×Fixed 0.02276 3.48458 0.00653 199.91124** 0.66880Holding×Demand 0.12021 2.52555 0.04760 340.36967** 0.77468Holding×Demand×Fixed 0.07047 4.35787 0.01617 100.91872** 0.50480Holding×Demand×Routing 0.09215 3.07914 0.02993 349.96010** 0.77949Holding×Fixed 0.14889 2.76272 0.05389 998.66307** 0.90981Holding×Routing 0.03470 1.86480 0.01861 638.75881** 0.86581Routing×Fixed 4.35216 2.31585 1.87930 23020.64386** 0.99572Routing×Fixed×Holding 0.01921 3.24482 0.00592 173.04563** 0.63609Routing×Fixed×Holding × Demand

0.05496 5.46237 0.01006 98.40961** 0.49850

This table shows F-test and effect size for the factor interactions: fixed replenishment costs, routing costs, holding costs and demand. The degrees of freedom are Huynh-Feldt adjusted for the averaged tests of significance. **p<0.01.

Results for two-way repeated measure ANOVA are presented in Table 7. Factor interactions, including two, three and four factor interactions, were statistically significant (p<0.01). Interpretation, therefore, is focused on the differences in effect size. The interaction of the factor routing costs and the factor fixed replenishment costs was statistically significant, F(1, 2.32) = 23020.64, p<0.01, ηp² = 0.99, confirming a strong impact of routing costs and fixed replenishment costs on cost savings. Other two factor interactions were statistically significant, yet effect sizes were smaller. Three and four factor interactions were statistically significant, but the effect sizes were substantially smaller.

5. Conclusions

In this paper, a methodology for investigating set-dependent cost structures in inventory routing is presented. The set-dependent cost structures originated from coordinating multiple replenishments and was illustrated for a network of ATMs. The impact of set-dependent cost structures was demonstrated by contrasting total costs of an integrated approach with total costs of a hierarchical approach. In both, the nominal range sensitivity analysis and the statistical analysis, the integrated approach resulted in considerable lower total costs compared to the hierarchical.

Four statistically significant factors were identified: fixed replenishment costs, routing costs, holding costs and demand. Effect size was largest for fixed replenishment costs and routing costs. However, both factors had a contrary impact on cost savings. Larger fixed replenishment cost coefficients reduced cost savings, while larger routing cost coefficients increased cost savings. Somewhat surprising is that the effect size of holding cost coefficients and demand multipliers was substantially smaller indicating no clear trend or higher order powers, despite differences in cost savings being statistically significant.

Practical implications of this study concern the feasibility of inventory routing for cash supply chains. Results showed that set-dependent cost structures yield cost savings from coordinating multiple replenishments. However, the study also

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demonstrated that cost saving vary with factor levels and are likely to fall between two and four percent. Hence, cost savings between six to ten percent, as suggested by the literature, may in practice be not achieved.

Limitations of the case study are due to the single-case design. Nevertheless, using detailed empirical data distinguishes the study from exclusively normative quantitative research. The employed normative quantitative models allow for an assumptive generalizability, which a descriptive qualitative methodology does not provide (Eldabi, Irani, Paul and Love, 2002). Moreover, simulation based results of inventory studies tend to duplicate well in reality and thus reassure a certain generalizability which most single case settings otherwise would lack (Meredith, 1998; Voss, Tsikriktsis and Frohlich, 2002).

Further research will have to address the design of set-dependent cost structures to reduce computational complexity for large-scale optimization problems. Time series analysis may allow the specification of scenarios and provide the link between a deterministic and stochastic setting. More advanced models may additionally take the likelihood of scenarios into account and establish the link to risk models.

References

Bell, W. J., Dalberto, L. M., Fisher, M. L., Greenfield, A. J., Jaikumar, R., Kedia, P., et al. (1983). Improving the Distribution of Industrial Gases with an On-line Computerized Routing and Scheduling Optimizer. Interfaces, 13(6), 4-23. .

Campbell, A. M., & Savelsbergh, M. W. P. (2004). A Decomposition Approach for the Inventory-Routing Problem. Transportation Science, 38(4), 488–502.

Castro, J. (2009). A stochastic programming approach to cash management in banking. European Journal of Operational Research, 192(3), 963-974.

Chandra, P. (1993). A Dynamic Distribution Model with Warehouse and Customer Replenishment Requirements. The Journal of the Operational Research Society, 44(7), 681-692.

Eldabi, T., Irani, Z., Paul, P. J., & Love, P. E. D. (2002). Quantitative and qualitative decision-making methods in simulation modelling. Management Decision, 40(1/2), 64-73.

Federgruen, A., & Zipkin, P. (1984). A Combined Vehicle Routing and Inventory Allocation Problem. Operations Research, 32(5), 1019-1037.

Gentry, J. A. (1988). State of the Art of Short-Run Financial Management. Financial Management, 17(2), 41-57.

Golden, B., Aassad, A., & Dahl, R. (1984). Analysis of a large scale vehicle routing problem with an inventory component. Large Scale Systems 7, 181-190.

Gregory, G. (1976). Cash flow models: A review. Omega, 4(6), 643-656.

Hinderer, K., & Waldmann, K. H. (2001). Cash management in a randomly varying environment. European Journal of Operational Research, 130(3), 468-485.

Kleywegt, A. J., Nori, V. S., & Savelsbergh, M. W. P. (2002). The stochastic inventory routing problem with direct deliveries. Transportation Science, 36(1), 94118.

Meredith, J. (1998). Building operations management theory through case and field research. Journal of Operations Management, 16(4), 441-454.

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Moin, N. H., & Salhi, S. (2006). Inventory routing problems: a logistical overview. Journal of the Operational Research Society, 58(9), 1185-1194.

Raa, B., & Aghezzaf, E.-H. (2009). A practical solution approach for the cyclic inventory routing problem. European Journal of Operational Research, 192(2), 429-441.

Rajamani, D., Geismar, H. N., & Sriskandarajah, C. (2006). A Framework to Analyze Cash Supply Chains. Production and Operations Management, 15(4), 544-552.

Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145-166.

Voss, C., Tsikriktsis, N., & Frohlich, M. (2002). Case research in operations management. International Journal of Operations & Production Management, 22(2), 195-219.

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Exploring the link between job motivation, work stress and job satisfaction. Evidence from the banking industry.

Dr. Nikos Kakkos*

Marketing Lecturer, Department of Management, TEI of Larissa, Greece [email protected]

Dr. Panagiotis Trivellas

Ass. Professor, Department of Logistics Management, TEI of Chalkida, Greece [email protected]

Kaliopi Fillipou

MBA graduate, TEI of Larissa, Greece Abstract This empirical paper focuses on employee perceptions regarding job Motivation and work

Stress in order to study the relationship with the perceived employee Job Satisfaction in the

banking industry. Based on a sample of 143 employees drawn from both a private (n=71)

and a public bank (n=72) in Greece, the paper uses Alderfer’s (1967) theory of motivation

(ERG) to examine the impact of multiple needs’ satisfaction (namely, Existence needs-pay,

Existence needs-fringe benefits, Relatedness needs-supervisors, Relatedness needs-peers

and Growth needs) on employees’ job satisfaction. More, it examines the impact of work

stress and such variables as gender, age, work experience, work position, educational level

on job Satisfaction. Unlike the Existence needs-fringe benefits, four (out of five) Motivation

dimensions are found to have a positive influence on employee job Satisfaction. The analysis

also supports a negative relationship between Stress experienced at work and employee job

Satisfaction while the latter seems to be influenced by employees’ age, too. The findings

shed light into the determinants of job satisfaction and may contribute to the development of

HR policies, bank practices or behaviours aiming at enhancing the human capital potentials,

assisting individual performance and ultimately facilitating banks’ competitiveness.

Keywords: Job motivation, work stress, job satisfaction, Banking, Greece.

* Contact Author

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1. Introduction

Competitive advantages stemming from human resources are vital for firms’ success in the contemporary, dynamic, business environment (Vouzas, 2006). Moreover, it seems that problems encountered by manufacturing and service firms nowadays could be attributed to an extent, to the inadequate alignment between a firm’s strategies and its human resources thereby resulting into an in-effective response to the significant changes taking place in the complex, international environment where firms nowadays operate (Kotabe and Helsen, 2001). By implication, the strategic management of human resources is considered crucial for the achievement of the various organizational and individual outcomes including employee job satisfaction (Ooi et al., 2007). While the attainment of organizational outcomes has been researched extensively across the multidisciplinary business literature (as shown by the various studies focusing on the broad construct of “performance”), employee job satisfaction is considered to be an important individual outcome and a driver of performance in a given context including the financial and banking services context (Gabbott and Hogg, 1997). The notion of employee job satisfaction becomes even more vital in a services context particularly because the quality of the services offered by any given organization cannot be easily standardised across all its customers (Kotler and Keller, 2006). By implication, the notion of human capital’s “well being” needs to be paid particular attention. However, it is not clear whether management practices used by banking service organizations to achieve their short- and/or long-term goals have an effect on their human capital’s overall well-being; more specifically, their effect on employee work stress levels, motivation and employee job satisfaction experienced. While it has to be acknowledged that stress is an inseparable part of everyday life (Certo, 2003), common management practice often assumes the need of a reasonable amount of pressure, anxiety or fear in the environment to motivate employees to achieve even higher performance; besides motivating employees, pressure may have side effects such as the creation of employee dissatisfaction or even mental alienation (Sdrolias et al, 2005) which in turn, may compromise individual and/or organizational performance and affect customer satisfaction, too. Indeed, the level of service delivered to customers reflects to an extent the level of employee job satisfaction; high quality customer service is more likely to be achieved if employees are viewed and treated as internal customers (Gummesson, 1991). Understanding the link between job motivation, work stress imposed and employee job satisfaction becomes vital particularly for firms experiencing the adverse effects of the economic crisis affecting recently various industries in the world. For example, given the fact that many countries (including Greece) have been lately subjected to the effects of this recent crisis, employees in general (and those currently working in the Greek banking sector in particular), may well be required to achieve more demanding performance standards and company goals to remain competitive while faced with feelings of job insecurity for the future and/or forced to watch their own income shrink due to the harsh economic measures taken in a period of economic recession and uncertainty. In light of the above, this empirical study aspires to shed light into the relationship between stress employees experience at work, job motivation (as conceptualized by

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Alderfer, 1967) and employee job satisfaction in the banking sector. To be specific, it seeks to generate evidence on the determinants of employee job satisfaction in Greek banks and more specifically on the link between: (1) employee job motivation and job satisfaction and (2) the perceived work-stress and job satisfaction. Also, this paper explores the impact of various demographic and organizational characteristics on employees’ perceptions about work-stress, job motivation and job satisfaction. Furthermore, it brings evidence on (3) the role of age, gender, work experience, educational level and working position in the perceived amount of total stress, job motivation and employee job satisfaction. To serve the above aim, a sample of 143 employees drawn from two Greek banks (one public and one private) was surveyed (see more in section 3.1). The findings identify the level of work-related stress and job motivation in the banking sector’s work environment and the study’s theoretical contribution derives from understanding better the link between job satisfaction and the aforementioned two antecedents (see (1), (2) (3) above). Such empirical knowledge can be useful from a practitioner’s point of view, in terms of offering guidance towards the development of HR strategies, practices or behaviours aiming to enhance employee job satisfaction and improve individual (and ultimately Greek banks’) performance outcomes.

2. Theoretical Background

The following review of the literature focuses on job motivation, work stress and employee job satisfaction of which, the latter constitutes the dependent variable and the former two, the independent variables of this study (see more in section 2.3).

2.1 Motivation

The numerous benefits gained from a job such as income, resources, social status, structure, moral satisfaction, self-esteem and social support help individuals achieve balance in their life whereas their absence can damage their mental health (Murphy and Athanasou, 1999). Indeed, it is generally better for people’s mental health to work (rather than not) because unemployment has been linked to depression, anxiety and even suicide (Blakely et al., 2003). However, to succeed in any goal(s) set, organisations need motivated employees, too; motivated employees are more productive and help organizations to survive and prosper (Smith, 1994). In this context, the notion of motivation can be described as a psychological process that gives behaviour purpose and direction (Kreitner, 1995), or as an internal drive to satisfy an unsatisfied need (Higgins, 1994), or as “internal processes and external forces that direct behaviour” (Naylor, 1999, p.538). It is actually one of the management’s key tasks to constantly motivate their employees, something difficult at times, as what motivates one person may not motivate another and certainly such what motivates one do not necessarily remain static over time. For example, it has been argued that as income increases money becomes less of a motivator, or when employees get older, interesting work becomes more of a motivator (Kovach, 1987).

The notion of motivation and the factors that determine it has long been an issue of concern in the literature and several theories have been advanced in this respect. These have been grouped into three main categories namely, Content, Process and

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Reinforcement theories of motivation (Naylor, 1999). Tables 1 summarises the first two categories of motivation theories while the third category is discussed below. Table 1: Summary of Content and Process theories of motivation

Content Theories

Name Summary

Maslow's need-hierarchy theory (1943)

Ranks human needs hierarchically in a five level pyramid where people seek to satisfy their needs from bottom to top starting from Physiological, Safety, Belongingness, Esteem, and moving to Self-Actualization needs

Alderfer’s modified hierarchy of needs ERG

(1967)

Groups Maslow’s needs into 3 levels: (E) existence, (R) relatedness and (G) growth. Satisfaction at one level leads to progression to the next level, while dissatisfaction at one level drives regression to the previous one.

Herzberg's two- factor theory (1959)

Distinguishes between factors preventing dissatisfaction and affecting motivation: the hygiene and the motivation factors. Hygiene factors are needed to ensure an employee is not dissatisfied. Motivation factors are needed to motivate one to higher performance.

Process theories of motivation

Name Summary

Equity theory

(Adams, 1960)

Motivation is influenced by the sense of “fairness” that workers possess, after comparing their efforts and rewards ratio with others, which are in the same situation. Adams used the terms “inputs” and “outputs” instead of efforts and rewards to describe as “inputs” the qualifications, effort and competence and as “outputs” the pay, prospects, benefits and recognition.

Expectancy theory

(Vroom, 1964)

It advances that motivation is the product of an individual’s “expectancy” that a certain effort will lead to the intended performance, the “instrumentality” of this performance to achieve a certain result and “valence” which is the desirability to achieve that result.

Job characteristics

(Hackman and Oldham, 1980)

A theory based on the assumption that a job’s characteristics are independent of individuals - employees. These key job characteristics that are likely to influence the motivating potential of a job are: skill variety, task identity, task significance, autonomy and feedback.

Goal-Setting Theory

(Locke, 1984)

According to this theory, individuals’ goals must be aligned with the overall goals of the organization. Individual goals must be realistic and mutually agreed. The more dificult a goal is the more motivation it produces. Motivation through goals is influenced by such factors as: Clarity, Challenge, Commitment, Feedback, Task complexity.

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More specifically: (i) Content theories (see table 1) facilitate managers’ thinking about how to understand and satisfy employees’ innate needs through employment. An example is Alderfer’s ERG theory discussed below; (ii) Process theories (see table 1) place more emphasis on the process of motivation rather than its content and tend to view employees as conscious individuals seeking to maximise benefits from work; (iii) Reinforcement theories link desired behaviours to rewards thereby encouraging employees to act in a way that can benefit the organization (Naylor, 1999). A prominent exampIe is Skinner’s (1969) theory. Skinner’s term “reinforcer” refers to any stimulus that, when contingent to a response, serves to increase the rate of responding. A “reinforcer” controls behaviour. Skinner’s theory downplays the role of punishment in changing behaviour and states that a desired behaviour increases with either a positive or a negative type of reinforcement. Specifically, a Positive reinforcer relates with a reward after a desired behaviour, which increases the possibility for that behaviour to reoccur. The desired behaviour can also increase by using negative reinforcement, which involves steps designed to lead someone to the correct action in order to avoid an unwanted consequence. Negative reinforcement should not be confused with Punishment, an event that decreases (undesired) behaviour either by an aversive stimulus contingent on a response or taking away a rewarding stimulus contingent on a response. Extinction is a form of punishment (e.g. a withdrawal of intrinsic rewards or pay), pressing one to avoid undesired behaviours (Naylor, 1999).

This study however, places emphasis on Content theories and more specifically on Alderfer’s (1967) modified hierarchy of needs (ERG) theory. ERG expands further Maslow's widely acknowledged hierarchy of needs theory by categorizing needs into three groups, Existence, Relatedness and Growth needs. The lower order needs (physiological and safety) are grouped into the Existence category, the levels of love and esteem needs into the Relatedness category, while the Growth category contains the self-actualization and self-esteem needs. (i) Existence needs include physiological and safety needs such as hunger, thirst, sex; (ii) Relatedness needs include social and external esteem relating to family, co-workers, friends and employers; (iii) Growth, include internal (or self) esteem and self-actualization needs (see more in section 2.4). ERG states that human needs cannot be easily depicted in terms of a five-level hierarchy as Maslow suggests. Needs may vary from one employee to another and may differ for the same person over time; moreover, each employee has different needs to satisfy (simultaneously) and if a manager focuses on any one need at a time, employees will not be motivated enough in their tasks. ERG suggests that need satisfaction at a given level (or category) of needs drives progression to the next level. In contrast, if needs at a given level are not met then an employee regresses to the lower order needs. For example, if a need for self actualization or internal (self) esteem is not satisfied then one will invest effort into the relatedness category hoping thus, to achieve the higher order needs pursued. This is known as the frustration-regression principle the signs of which, a manager should identify to minimize frustration and motivate an employee to progress further. The adoption of ERG here stems from certain advantages: (a) ERG accommodates for the view that employees strive to satisfy multiple needs simultaneously, (b) ERG has a job-specific orientation its dimensions reflect (e.g. pay, fringe benefits, relatedness needs from co-workers/superiors), (c) unlike Maslow’s theory, Alderfer’s conceptualisation is empirically based (e.g. see Arnolds and Boshoff, 2002).

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2.2 Work-related stress

Following the brief review on motivation, the emphasis now shifts to the all-important issue of stress employees may experience at work. Selye (1956), as cited in Le Fevre et al. (2003), first introduced the term ‘stress’ to describe physical and psychological responses to severe conditions or influences. He used the word ‘stress’ which is an engineering term, to describe the responses to a force that when is implemented in bodies, causes deformation. While there is an agreement among researchers on the stress related terminology adopted (Maslach, 1998), Cummings and Cooper (1998) point out the difficulty to develop a coherent theory on stress, as different disciplines (medicine, psychology, sociology, management) and research methodologies have looked into this area. Nevertheless, a constellation of key stress related conceptualisations are presented in Table 2 below. Stress can be defined as “the non-specific response of the body to any demand placed upon it” (Selye, 1987, p.17). Selye (1956) used also the term ‘stressor’ to describe the force that when acting to a body, creates stress. Le Fevre et al. (2003) maintains that the term ‘stress’ describes a force that causes deformation, while the notion of response is better described by the term ‘strain’ referring to the manifestation of stress in a body. Selye (1964) also, used the term eustress (or good stress); while eustress and distress are regarded as being dinstinct elements of total stress, it is only the individual who can perceive stress as eustress or distress, according to whether one perceives the demand stimulus as pleasant or not (Selye, 1987). Having acknowledged that employers would rather maintain an amount of pressure or stress at “optimal” levels to boost employee performance (Certo, 2003), it is not easy to maintain a fine line between stress levels, motivation and one’s well being. Internal competition may for example, exert an amount of pressure or stress that at times can act as a motivator. In this respect, Papasolomou-Doukakis et al. (2004) suggest that a competitive internal environment can motivate staff to improve performance; such competition among a firm’s salesmen is facilitated by focusing on the achievement of sales targets, linking the achievement of sales targets to monetary rewards and using league tables involving sales performance. Too much pressure however, may have stress related implications such as employee mental alienation and/or impact on individual performance (Sdrolias et al, 2005); also, destruction of team spirit, dissatisfaction with or absence or even resignation from work can be manifestations of work stress (McCarty et al, 2007; Strahan et al, 2008). Moreover, occupational stress may cause a loss of talent and an increase of training cost, due to high turnover (Cartwright and Boyes, 2000). While stress is inevitable, it constitutes a real threat to the quality of life for employees (Dyck, 2001) not to mention its link to an increased risk of morbidity and mortality (Siegrist, 1998). Also (and related to the above), it is the widely studied effect called ‘burnout’. The term burnout syndrome was first introduced in the 1970s and defined as a response to chronic work stress (Freudenberger, 1974). It is a three-dimensional syndrome involving emotional exhaustion, depersonalization and decreased personal accomplishment (Byrne, 1993) dimensions that do not necessarily appear in the same order (e.g. see Golembiewski et al., 1986; Leiter and Maslach, 1988).

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TABLE 2: Stress models

Yerkes and Dodson’s experiements with mice (as cited in Certo, 2003) led to an inverted U-shaped function between arousal of stress and performance; there is in fact, an optimal level of arousal of stress that can be a motivator for learning, while

STRESS MODELS Theory Summary

Role Stressors Kahn et al (1964)

Stress is defined as the result of stressors, which are, Role ambiquity, Role overload and Role conflict. The bigger the amount of stressors is the bigger is the stress.

Job Demand-Control Model (DCM) Karasek (1979)

Job stress arises when high job demands (especially work overload and time pressure) are combined with low job control. Job control is defined as the ability employees have, to take decisions themselves about how they will achieve their job demands

Conservation of Resources (COR) Hobfoll (1989)

Stress is defined as a produced tension, which derives from the imbalance between what is demanded and offered to satisfy this demand. Job resources are related to: the organization’s offerings, interpersonal relationships, working structures and task structures.

Job characteristics Ganster and Schaubroeck (1991)

There is strong indirect evidence that stressors (job characteristics) affect human health. It is also assumed that there is a strong relationship between working experiences and psychological and emotional responses.

Effort Reward Imbalance (ERI) (Siegrist, 1996)

Job strain is the outcome of the imbalance between high effort and rewards both intrinsic and extrinsic (salary, esteem reward, career opportunities like promotion prospects, job security and status consistency).

Person-environment P-E fit theory (Edwards et al., 1998)

According to this theory, when there is a misfit (P-E fit) between the person and the environment, then this becomes a stressor and stress results. Three basic relationships between stressors and stress: The demand-ability dichotomy, the needs-supplies dichotomy, a combination of the demand-ability and the needs-supplies.

The Cybernetic theory (Cummings and Cooper, 1998)

This theory considers that stressor and stress development is a temporal situation. What is perceived as distressful situation for someone maybe eustressful for someone else.

Control theory (Spector, 1998)

The more control someone has on the factors that can create stress, the less is the possibility to experience stress. This theory also determines the range of control, from complete autonomy, personal control over schedule and workload to no personal control over schedule or workload.

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too much or too little will work against learning. Nevrtehless, Le Fevre et al (2003) disagree with the implementation of the idea for an optimal level of stress in working environments. They support that managers being practically advised to impose stress, make a mistake because they can’t evaluate which amount of stress is optimal. In addition, individuals with a strong goal orientation can be more vulnerable to different kinds of work-related negative impacts (Burke 2000). Specifically, a high focus on work-related goals has been associated with a low interest in other goals, such as those pertaining to family, close relationships and leisure activities, which may buffer the negative impacts of work on individuals' well-being (Salmela-Aro and Nurmi, 2004). The cumulative outcome, as Cranwell-Ward (1998) describes the notion of stress, might be a physiological and/or psychological reaction which occurs when individuals meet a threat or challenge that is beyond their immediate capacity; extended exposure to such pressing may cause physical reaction (e.g. insomnia), emotional reaction (e.g. depression) and mental reaction (e.g. forgetfulness). In light of the above, it seems there is a clear link between stress and emotional reactions at an individual level. Occupational stress has been of increasing concern for employees and has also attracted employers’ interest because the risk of being persecuted for damages to stressed employees has increased (Midgley, 1997). The financial effect of employees’ ill health (or well being) on employers is certainly not negligent and Harris (2000) highlights how important it is for management to appreciate that maintaining motivated and satisfied employees is likely to lead to happy and satisfied customers, too. While employers have a moral (at least) obligation to protect their employees’ health and well being by providing a healthy (and possibly non-stressful) working environment (Patterson et al., 1997), there are frameworks such as the Management Standards (Health and Safety Executive, 2009) aiming to identify, isolate and control the primary risk factors coming from six main sources of work stress: (i) Demands–workload, work patterns and work environment, (ii) Control– whether employees have a say in the way they do their work, (iii) Support– referring to the encouragement, sponsorship, resources provided by the organisation, the supervisors or the colleagues, (iv) Relationships–promoting positive work to avoid conflict and unacceptable behaviour, (v) Role– whether an organisation ensures that employees roles do not conflict and that employees understand their roles and (vi) Change– how organisational change is managed and communicated within the organisation. Having acknowledged that non-properly managed stressors can be linked to poor employee well-being, lower productivity and increased absence from work, the former management standards determine an organisation’s culture characteristics where key stressors are isolated so as to manage and control their stress effects on employees. In the relevant literature there are other relevant models namely, the Transactional model (Lazarus and Folkman, 1984) and Sedgeman’s (2005) Health Realization -Innate health model, whose implementation is claimed to foster a healthy and productive working environment. Evidently, the above models imply how important stress can be in terms of influencing such individual outcomes as job dissatisfaction and/or performance. The consensus among the former models is that, individuals need to possess the necessary coping strategies to deal with stress. Such

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competences derive from one’s mental training to sustain positive thinking and/or the organization itself that helps stressed individuals to cope with stress’ adverse effects.

2.3 Job Satisfaction

The emphasis is now placed on employee job satisfaction (the dependent variable of this study). Job satisfaction refers to “the pleasurable emotional state resulting from the appraisal of one's job as achieving or facilitating the achievement of one's job values” (Locke, 1969, p.316). Job satisfaction is a multidimensional construct the conceptualization and measurement of which has long been of interest in the industrial and organisational psychology literatures (Bodur, 2002) where according to (Bowling and Hammond, 2008), it has been the most widely studied topic. This is reflected in the various operationalisations proposed involving for example, perceptions about work itself, relationships with co-workers and supervisors, job control, job security and feelings such as self-accomplishment and self-advancement (Weis et al., 1967). Similarly, Rutherford’s et al., (1998) approach include aspects such as satisfaction with supervision, satisfaction with overall job, satisfaction with policy and support, promotion and advancement, satisfaction with pay, co-workers and customers. While it is evident from the above that various factors can influence job satisfaction, a misfit between what one expected and what one received, may cause a decrease in job satisfaction (Robinson and Rousseau, 1994). In fact, Job satisfaction, has been linked to individual outcomes or work related behaviours such as job performance (Arnolds and Boshoff, 2002), absenteeism and turnover (Griffeth et al., 2000). Regarding the link between motivation and job satisfaction all content theories link the former to the latter (see table 1). More specifically, Herzberg’s et al. (1959) motivation theory highlights two kinds of factors in working environments, hygiene factors and motivators. The hygiene factors’ presence (e.g. working conditions, job security, pay, company policies and administration, level and quality of supervision, interpersonal relationships) cannot motivate employees but prevent dissatisfaction. Unlike hygiene factors, motivators are intrinsic to a job and create employee satisfaction (e.g. the work itself, responsibility, sense of achievement, recognition by others, advancement, personal growth). This is in line with Hackman and Oldham’s (1980) view arguing that job characteristics can motivate employees and the motivating potential of a job is expected to affect the resulting job satisfaction, too. Regarding the relationship between work stress and job satisfaction, it seems that the constructs of stress and job satisfaction are inversely related (Babin and Boles, 1998). More specifically, they found that employees experiencing higher job-related stress, also experience lower job satisfaction and higher turnover intention, compared to those feeling less stress (Bhuian et al., 2005). Stress may have a stronger effect on job satisfaction for certain employee types (e.g. due to differences in gender), Sullivan and Baghat (1992) provide empirical support for a negative relationship between work stress and satisfaction. They also add that some variables can moderate this relationship namely, the sense of competence, the perceived control and locus of control. In contrast, Tuten and Neidermeyer (2004), found a positive relationship between work stress and employee job satisfaction where employees’ stress was self-imposed to achieve higher performance and receive

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higher pay (bonuses) for that performance. In other words, the relationship between stress and satisfaction became a function of the stress-performance relationship. Babin and Boles (1998) argue that stress and job satisfaction are negatively related; also stress has stronger effects on job satisfaction for certain types of employees than others, while demographic variables such as gender differences can moderate the relationship between stress and job satisfaction. Tuten and Neidermeyer (2004), found a positive direct relationship between stress and job satisfaction, in cases where employees impose stress on themselves to achieve higher performance and consequently a higher pay level. Sullivan and Baghat (1992) also confirmed the negative relationship between stress and satisfaction and added that several variables such as the sense of competence, the perceived control and locus of control can moderate this relationship.

2.4 Research Model and Hypotheses

The proposed conceptual framework shown in Figure 1 depicts the likely impact of job motivation and work-related stress on employee job satisfaction. The relevant hypotheses are presented below. Figure1: Conceptual Framework With respect to the independent variable of job motivation remember that this study uses Alderfer’s (1967) conceptualisation of motivation (see ERG modified hierarchy of needs theory in section 2.1.1); hence the emphasis is on employee perceptions about specific motivation factors including, (i) existence needs (pay), (ii) existence

-H2

+H1

WORK STRESS

JOB SATISFACTION

CONTROL VARIABLES

• Gender • Age • Working Experience • Educational Level • Work Position

MOTIVATION

ALDERFER’S NEEDS

(1) EXISTENCE (2) RELATEDNESS (3) GROWTH NEEDS

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needs (fringe benefits), (iii) relatedness needs (superiors), (iv) relatedness needs (peers), (v) growth needs and their influence on job satisfaction (i.e. the dependent variable shown in Figure 1). Regarding the independent variable of work stress, the focus is on the total sum of stress at work and involves employee perceptions about such stress factors as Demands, Control, Support, Inter-relationships and Role (see section 2.2) and their influence on employee job satisfaction. Next the research hypotheses are outlined. Following the discussion in sections 2.1 and 2.3 one can conclude that motivation and bank employee job satisfaction are positively related. To be more specific:

H1: The higher the job motivation, the higher the job satisfaction experienced. In light of Alderfer’s multidimensional conceptualisation adopted, a relationship should be also expected between each of the five need satisfaction dimensions and employees’ job satisfaction (Arnolds and Boshoff, 2002). By implication:

H1a: The higher the level of job motivation related with existence needs (pay), the higher the job satisfaction experienced.

H1b: The higher the level of job motivation related with existence needs (fringe benefits), the higher the job satisfaction experienced.

H1c: The higher the level of job motivation associated with relatedness needs (superiors), the higher the job satisfaction experienced.

H1d: The higher the level of job motivation associated with relatedness needs (peers), the higher the job satisfaction experienced.

H1e: The higher the level of job motivation related with growth needs, the higher the job satisfaction experienced. Following the relevant discussion involving work stress in sections 2.2 and 2.3, one is inclined to conclude that on average a negative relationship between stress and employee job satisfaction should be expected in banks. In fact:

H2: The higher the level of work-stress employees experience, the lower the job satisfaction expected. In addition to the hypotheses tested, this paper also explores the role of personal and organizational variables (i.e. bank type, age, gender, work experience, position and income) on bank employee perceptions about work stress, motivation and job satisfaction (see Figure 1).

3. Research Methodology

3.1 Sample

To test the research hypotheses, a survey was carried out in two well-known banks in Greece (one public and one private). The reason was to avoid any bias linked to likely differences between the private and the public sector; indeed, it may be the case that people who work within the private sector have higher rates of self-reported work-related stress than those in the public sector. More specifically, by using five branches of the public bank (named Bank A) and five branches of the private bank (named Bank B) at the region of Thessaly (Central Greece), the research focused on bank employees. A total of 220 questionnaires were administered using the “drop-off

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and collect” method. The final sample of 143 valid questionnaires (bank A: 72 and bank B: 71) corresponds to an overall 65% response. The respondents in the sample have different work positions (i.e. clerks, cashiers, supervisors, assistant managers, managers), are experienced (44% has more than 10 years of experience), 46% are less than 40 years old, 59% are female while the majority of them (66%) have one (at least) university degree.

3.2 Questionnaire Design, Variable Operationalisation and Measure Validation

A structured questionnaire was employed to carry out the survey. The measurement instrument was thoroughly evaluated before released. Ten branch managers of the banks involved examined it along with two experienced researchers; the instrument’s cognitive relevance to the banking sector was confirmed prior to data collection. The instrument was developed by adapting existing multi-dimensional scales to capture needs satisfaction, work stress and employee job satisfaction by providing respondents with 7-point scaled questions for each multi-item measure employed. Alderfer’s (1967) operationalization (needs satisfaction) was adopted to capture the construct of motivation. It is clear from Table 3 that all measures utilised have a solid academic foundation and other researchers have used similar items (e.g. Arnolds and Boshoff, 2002, Weiss, et al. 1967; Kahn, et al, 1964). Table 3. Basic references for all scales

Measures Items

Basic References

Independent Variables Alderfer needs satisfaction Existence needs-pay 4 Alderfer (1967) Existence needs-fringe benefits 4 Alderfer (1967) Relatedness needs-respect from superiors 4 Alderfer (1967) Relatedness needs-respect from peers 4 Alderfer (1967) Growth needs 4 Alderfer (1967) Work stress 15 Kahn, et al. (1964) JRTS

Dependent variable Job satisfaction 20 Weiss, et al. (1967)

Finally, reliability tests and principal components analyses were performed to confirm scale construct validity. In fact, all five ERG sub-scales proved to meet convergent and discriminant validation criteria prior to using multiple regression analyses to test the foregoing research hypotheses.

3.3 Principal Component Analysis

Principal Component Analysis (PCA) was conducted to identify latent factors within Alderfer needs scale. Five factors were extracted from the data, applying both Kaizer and Scree plot criteria. These principal components accounted for over 75.6% of the total variation. A cut-off of 0.50 was used for item scale selection and it was adopted a normalized varimax rotation to bring about simple and interpretable structure. According to Lewis-Beck (1994), this method is the most commonly used in order to

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reduce the number of items in a survey questionnaire. Following an inspection of the items’ loadings on each factor, five distinct principal components were identified, corresponding to: existence needs (pay), existence needs (fringe benefits), relatedness needs (respect from superiors), relatedness needs (respect from peers) and growth needs. Also, one principal component was extracted explaining approximately 48.7% of the overall variance for the work stress scale, as well as one component for the job satisfaction construct (49% of the total variation). Preceding PCA, the Bartlett sphericity testing on the degree of correlation between the variables (p<0.001) and the appropriateness of the sample according to Kaiser–Meyer–Olkin (KMO over 0.70) verified the appropriateness of the sample (Norusis 1990). Table 4. Descriptive statistics and internal reliability analysis of all scales Variables

Mean

S.D.

items

Cronbach’s

alpha

KMO*

Independent variables Alderfer needs 0.844

existence needs (pay) 4.09 1.310 4 0.856 existence needs (fringe benefits)

3.80 1.394 4 0.921 relatedness needs (superiors) 4.35 1.519 4 0.936 relatedness needs (peers) 4.85 1.358 4 0.914 growth needs 4.85 1.218 4 0.826

Work stress 3.61 1.032 15 0.887 0.894 Dependent variable

Job satisfaction 4.37 0.945 20 0.914 0.877 *The Kaiser–Meyer–Olkin (KMO) indicator was calculated to assess sample size adequacy. Bartlett's test of sphericity is significant at p<0.001 for all scales. (Valid N=90). Inter-item analysis used to assess internal consistency reliability for Alderfer needs, work stress and job satisfaction scales suggests that all sub-scales exhibit well over the minimum acceptable reliability level of 0.70 (Nunnally and Bernstein, 1994) as Cronbach’s alpha calculations (see Flynn et al., 1990) for each scale show in table 4.

4. Multivariate Statistical Analysis

Multiple regression analysis was undertaken for job satisfaction (dependent variable), to test the relationship between work stress and the need satisfaction dimensions (i.e. existence needs (pay), existence needs (fringe benefits), relatedness needs (respect from superiors), relatedness needs (respect from peers) and growth needs). Results show that the predictor variables have captured a significant proportion of change in the dependent variable, explaining 63.2% of job satisfaction’s variance. There are no serious problems of multi-collinearity between independent variables as the VIF is far below the 3 points limit suggested. The data were also examined for outliers, skewness, kurtosis and multivariate normality by using procedures and plots available by SPSS. The regression analysis results are shown in table 3.

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Table 5. Regression results pertaining to the relationship between employees’ Job Satisfaction, Motivation (Alderfer needs) and Work stress

Dependent variables Independent variables Job satisfaction Control Variables Gender 0.075 Age -0.013 Educational level 0.000 Working experience 0.110 Position -0.038 Alderfer needs existence needs (pay) 0.241*** existence needs (fringe benefits) -0.056 relatedness needs (superiors) 0.367*** relatedness needs (peers) 0.171**

growth needs 0.311***

Work stress -0.129*

Adjusted -R2 0.632*** * Significant at the 0.05 level, ** significant at the 0.01, *** significant at the 0.001 (Valid N=143)

5. Discussion and Conclusion

This paper investigates empirically the impact of motivation (as conceptualised by Alderfer’s multidimensional need satisfaction (ERG) theory) and work stress on job satisfaction among frontline employees in the banking sector. Based on the findings pertaining to Motivation, it seems that the most important driver of bank employees’ job satisfaction is the satisfaction of Relatedness needs referring to the respect of superiors, which is followed by Growth needs (see Table 5). Next, the Existence needs (pay) is found to be a significant determinant of employees’ job satisfaction unlike the existence needs (fringe benefits) that does not have any impact at all on job satisfaction. Last, relatedness needs involving respect and/or fellowship from peers is the least influential antecedent of employee job satisfaction. In light of the above, Hypotheses H1a, H1c, H1d, H1e are supported in the context of this study while H1b is not. With respect to the work-Stress variable, there is a significant negative relationship with job satisfaction and H2 has found support. The above findings pertaining to the relationship between bank employees’ job satisfaction and Motivation (i.e. Alderfer’s need-satisfaction or ERG theory) are generally consistent with Hackman and Oldham’s (1980) theory (see Table 1), linking employee job satisfaction to job specific characteristics. The finding on employees’ existence needs (fringe benefits) is consistent with Arnolds and Boshoff’s (2002) findings highlighting the non-significant role fringe benefits have in driving job performance. Instead, both studies seem to agree on the importance of satisfying employees’ existence needs (pay) as a driver of job satisfaction (and performance).

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This seems to be (partially) in line with Herzberg’s view (see table 1) that pay is considered to be a hygiene factor (not a motivator) that prevents employees from feeling dissatisfied (yet, it does not help increase performance or satisfaction). In addition, job satisfaction is influenced by satisfying employee relatedness needs with superiors (primarily) and also, peers (to a lesser degree). This contradicts with Arnolds and Boshoff’s (2002) finding; bank employees seem to care less about their peers’ acceptance and/or camaraderie and more about their superiors’ respect for them. In fact the latter is found to the most important driver of bank employee job satisfaction (see table 5) followed by the satisfaction of one’s personal growth needs. More, the evidence suggests that bank employees’ stress affects their job satisfaction while there is no significant difference between male and female employees or even bank type in terms of the level of stress experienced. In this context, it is worth mentioning the need to re-examine bank practices that may contribute to increasing work stress. For example, one of the two Greek banks studied uses MBO as a individual performance evaluation system. MBO was initially introduced by Drucker in 1955 as a system of management by objectives and self-control, which helps increase productivity if used colleboratively (D’Aveni, 1995). Although MBO has been very popular, it may prove to be more of a hindrance rather than a help if the original intent is not followed (Van Tassel, 1995). MBO may well fail in practice and end up creating stress for employees when applied as an individual performance appraisal system as opposed to a goal congruence and allignment system based on collaboration and self-control (Dinesh and Palmer, 1998). This paper shares the view that “enterprises owe their existence to the contemporary international economic environment. They are not only dependent on that, but are also forced to adapt their behaviour and policies to its complicated and usually unexpected processes” (Sdrolias et al., 2006, p.268). Acknowledging the economic crisis’ adverse effects industries face nowadays and also the recession period within which organisations operate, it is important that HR policies and practices should be used as a strategic tool, adapted to help organisations’ manage their human capital potentials to improve individual and organisational performance and competitiveness (Storey and Sisson 1993). In this context, the paper (at least partially) addresses a controversial issue between bank management and bank employee trade unions. It does so by examining for example, whether bank practices manage to motivate their employees while striving simultaneously, to keep stress at low levels to prevent employee dissatisfaction (see objectives in section 1). The empirically established causal links (see table 5) have managerial implications in terms of facilitating the development of relevant HR policies and/or practices. Bearing in mind the above findings and also the link between employee job satisfaction and job performance (see Bowling and Hammond, 2008), it seems that greater emphasis should be given in motivating employees rather than stressing them. Therefore, Banks should invest in programs aiming to (i) create a cohesive organisational culture where collaboration, group work and job enrichment are highly valued (ii) provide a healthier working environment and effectively control work stress, (iii) motivate employees by rewarding them based on a fair pay scheme (rather than a fringe benefits scheme) and offering appropriate training and development schemes to satisfy bank employees’ professional growth needs.

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6. Limitations and Further Research Directions. Despite the contribution of this paper in terms of better understanding the link between employees’ job satisfaction and its drivers in the banking sector, the sample is quite limited and does not allow the generalization of the findings. Different forms of stress such as eustress or distress or burnout (see section 2.2) and their impact on job satisfaction have not been considered which suggests another limitation and at the same time, a direction for further research. Also the relationship between work stress and bank employees job motivation has not been examined in the context of this study. Last, such variables as employees’ personality and customers’ demands may influence work stress and job satisfaction (Cummings and Cooper, 1998); this is also the case for other organisational and managerial characteristics that need to be considered in relevant conceptualisations in the future.

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Cummings, T.G., Cooper, C.L. (1998), "A cybernetic theory of organizational stress", in Cooper, C.L. (Eds), Theories of Organizational Stress, Oxford University Press, New York, NY, pp.101-121.

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