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GAINING COMPETITIVE ADVANTAGEFROM ANALYTICS THROUGH THEMEDIATION OF DECISION-MAKINGEFFECTIVENESS: AN EMPIRICAL STUDYOF UK MANUFACTURING COMPANIESGuangming CaoUniversity of Bedfordshire, Luton, United Kingdom, guangming.cao@beds.ac.uk

Yanqing DuanUniversity of Bedfordshire Luton, United Kingdom, yangqing.duan@beds.ac.uk

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Recommended CitationCao, Guangming and Duan, Yanqing, "GAINING COMPETITIVE ADVANTAGE FROM ANALYTICS THROUGH THEMEDIATION OF DECISION-MAKING EFFECTIVENESS: AN EMPIRICAL STUDY OF UK MANUFACTURINGCOMPANIES" (2014). PACIS 2014 Proceedings. 377.http://aisel.aisnet.org/pacis2014/377

GAINING COMPETITIVE ADVANTAGE FROM ANALYTICS

THROUGH THE MEDIATION OF DECISION-MAKING

EFFECTIVENESS: AN EMPIRICAL STUDY OF UK

MANUFACTURING COMPANIES

Cao, Guangming, University of Bedfordshire, Luton, UK, guangming.cao@beds.ac.uk

Duan, Yanqing, University of Bedfordshire, Luton, UK, yangqing.duan@beds.ac.uk

Abstract

While it is widely believed that analytics capabilities enable a company to identify valuable insights

from big data thereby to improve the effectiveness of decision-making and to gain competitive

advantage, little empirical research has been undertaken to investigate the mechanisms through

which analytics capabilities improve decision-making effectiveness and organisational

competitiveness. This paper aims to reduce this research gap in the literature. Drawing on the

resource-based view (RBV), it develops a research model to specify the interrelationships among

information processing capabilities, resource heterogeneity, decision-making effectiveness, and

competitive advantage. It then empirically tests the proposed model using structural equation

modelling based on 232 responses collected from UK manufacturing companies. This paper has made

several contributions to the research and practice of business analytics and decision-making. First, it

advances the literatures by conceptually developing and empirically testing a path model linking

information processing capabilities to decision-making effectiveness and competitive advantage.

Second, it advances our knowledge by clarifying and testing the mediating role of decision-making

effectiveness in affecting the relationship between information processing capabilities and competitive

advantage. Third, it extends the RBV and decision-making literatures by explicating and testing the

mediating role of resource heterogeneity in affecting the relationship between information processing

capabilities and competitive advantage, and between information processing capabilities and

decision-making effectiveness. Finally, this paper contributes to managers’ and business analytics

practitioners’ knowledge by demonstrating the importance of improving decision-making

effectiveness in gaining competitive advantage.

Keywords: Information processing capability, Decision making effectiveness, Resource heterogeneity,

Competitive advantage, Mediating role, Questionnaire survey

1 INTRODUCTION

In order to compete successfully in the context of digitalisation, it is essential for contemporary

business organisations to be able to address the unprecedented challenges posed by big data that is

characterised by high volume, high velocity of generation and transmission, and a large variety of data

and file type. While a number of business analytics (BA) studies (Davenport et al. 2001, Davenport et

al. 2010, Davenport 2013, Kiron and Shockley 2011, Kiron et al. 2012, Lavalle et al. 2011) suggested

that companies with advanced analytic capabilities can derive valuable insights from big data, thereby

to make effective decisions and to gain competitive advantage; “many companies are still struggling

to figure out how, where and when to use analytics” (Kiron et al. 2012, p.17), or “unsure how to

proceed” (Barton and Court 2012, p.97), or “struggling to achieve a worthwhile return” (Marchand

and Peppard 2013, p.105).

Since BA is still emerging as an important area of research, there are only a few empirical studies in

the literature. To help business organisations understand the challenges and opportunities associated

with the use of BA, MIT Sloan Management Review conducted three large scale questionnaire surveys

consecutively; two of them were jointly with IBM Institute for Business Value and one with SAA

Institute Inc. (Kiron and Shockley 2011, Kiron et al. 2012, Lavalle et al. 2011). These surveys were

typically based on responses collected from more than 2,500 executives, managers, and analysts

working across more than 30 industries and 100 countries. The finding from the 2010 survey

indicated that there is an emerging performance gap between companies that use BA and those that do

not (Lavalle et al. 2011), and this divide becomes larger and rapidly widening based on the findings of

the 2011 survey (Kiron and Shockley 2011). The findings from the latest survey suggested that

companies using BA to compete and innovate are characterised by believing data being a core asset to

enhance business operations, effective use of data for faster results, and supports for analytics by

senior managers; consequently, they are much more likely to be able to make effective decisions and

create a competitive advantage (Kiron et al. 2012).

These studies have highlighted the importance of using BA to compete and innovate, together with a

number of recommendations made for leveraging analytic capabilities to improve organisational

performance. However, they did not reveal the specific mechanisms through which for example

analytic capabilities are related to effective decision-making and competitive advantage as no

inferential analyse was conducted; nor did other BA studies since little empirical research exists in

this emerging research area based on our literature review.

This paper therefore aims to reduce this research gap by focusing on examining the interrelationships

among analytics capabilities, decision-making effectiveness, and competitive advantage. Drawing on

the resource-based view (RBV) and relevant empirical studies in other research areas, this paper will

develop a path model to specify the links among these variables. It then empirically tests the proposed

path model using structural equation modelling based on data collected from 232 UK manufacturing

companies. The structure of the paper is as follows. The next section presents the conceptual research

model and hypotheses. The subsequent section describes the instrument development and the data

collection processes and reports on the empirical results. The final section discusses the results and

implications.

2 THEORETICAL DEVELOPMENT

2.1 Key Concepts Defined

Before developing our theoretical model, we clarify two key concepts to be used in this research:

information processing capabilities and decision-making effectiveness. Information processing

capabilities/capacities were used by Galbraith (1974) without definition in 1974 to outline the

information processing view of organisational design. These terms were adopted by Tushman and

Nadler (1978, p.614) in 1978 to further develop the information processing view of organisational

design, while information processing was defined as “the gathering, interpreting, and synthesis of

information in the context of organizational decision making”. Building upon the information

processing view (Galbraith 1974), Premkumar et al. (2005, p.266), defined information processing

capability as “the level of IT support for various activities” to refer to similar notions when they

examined the fit between information processing needs and information processing capability and its

effect on performance in an inter-organisational supply chain context.

BA studies, on the other hand, have used different terms to refer to similar meaning conveyed by

information processing capabilities. Davenport et al. (2001, p.117) used analytic capabilities to refer

to “the capability to aggregate, analyze, and use data to make informed decisions”. Lavalle et al.

(2011) used data management to refer to information capturing, aggregating, and analysing, and

disseminating information and insights. Kiron and Shockley (2011, p.3-9) defined analytics as “the

use of data and related insights developed through applied analytics disciples to drive fact-based

planning, decisions, execution, management, measurement, and learning. Analytics may be

descriptive, predictive or prescriptive”; and defined information management competency as “the use

of methodologies, techniques and technologies that address data architecture, extraction,

transformation movement, storage, integration and governance of enterprise information and master

data management”. Kiron et al. (2012), while adopted the same analytics definition used by Kiron

and Shockley (2011), used the concept of information value chain to refer to capabilities to capture,

aggregate, and analyse information, and to use insights to guide future strategies. Although BA

studies used different terms to delineate analytic capabilities and related concepts, the essence of these

definitions appear to focus on information capturing, integrating, and analysing, and utilising insights

derived from that information.

Based on these studies, we define information processing capabilities of an organisation as its

capacities to capture, integrate, and analyse big data, and utilise insights derived from that big data to

make informed decisions that generate real business value. We believe that this concept grasps the

essence of analytic capabilities in the context of big data and BA while it is conceptually rooted in the

information processing view. BA studies suggested that information processing capabilities are the

combined result of BA and a data-oriented culture that is characterised by an explicit organisational

strategy to guide analytic activities, and well-defined organisational structure and business processes

that enables analytic activities to be well coordinated (Davenport et al. 2001, Kiron et al. 2012,

Davenport 2006). For example, Davenport (2006, p.103) suggested that for an analytic effort to create

real business value in an organisation, “changes in culture, processes, behaviour, and skills” will be

necessary. Lavalle et al. (2011, p.22) stated that “for analytics-driven insights…to trigger new actions

across the organisation, they must be closely linked to business strategy, easy for end-users to

understand and embedded into organisational processes”; while Kiron and Shockley (2011, p.11)

characterise data-oriented culture as “a pattern of behaviours and practices by a group of people who

share a belief that having, understanding and using certain kinds of data and information plays a

critical role in the success of their organisation”.

The second key concept we use in this research is decision-making effectiveness. In order to describe

this concept, we first define data-driven decision-making as the extent to which a company has data

needs, making decisions relying on insights derived from big data for the creation of new service or

product, based on BA studies (Davenport et al. 2001, Davenport 2006, 2013, Kiron et al. 2012,

Lavalle et al. 2011). Having clarified the notion of data-driven decision-making, then we will be able

to define decision-making effectiveness. In decision-making literature, decision-making effectiveness,

referring to the extent to which a decision results in desired outcomes (Eisenhardt and Zbaracki 1992),

has been measured by various indicators at different levels, such as organisational performance at the

organisational level (Goll and Rasheed 1997, Elbanna and Child 2007), decision success for example

in terms of realisation, propitiousness, non-disturbance and perceived success at decision level

(Rodrigues and Hickson 1995), and decision quality in terms of the overall quality of the decision, the

quality of the decision relative to its original intent, and the quality of the decision given its effect on

organisational performance at both decision and organisational level (Amason 1996).

BA studies, though having extensively discussed how to improve decision-making by deriving

valuable insights from big data, have not provided a clear definition of decision-making effectiveness.

Instead, they have used terms such as better or best decisions (Davenport 2006, 2013), better

decisions or making-decisions effectively (Marchand and Peppard 2013), better decisions (Barton and

Court 2012), or best possible decisions (Kiron et al. 2012) to indicate various degrees of the desired

outcomes of a decision (Eisenhardt and Zbaracki 1992). Nevertheless, BA studies have indicated that

data-driven decision-making will help a company understand customers and serve them better and

increase customer loyalty (Davenport et al. 2001, Davenport 2013, 2006, Kiron and Shockley 2011,

Lavalle et al. 2011) or customer understanding (Kiron et al. 2012), “make decisions faster than ever

before” (Kiron and Shockley 2011, p.5) or “make decisions that are timely” (Davenport et al. 2001,

p.137) or “make real-time decisions” (Kiron et al. 2012, p.9), and “empower employees to act

confidently and decisively in a fast-paced marketplace” (Kiron and Shockley 2011, p.12) or “act more

quickly” (Kiron et al. 2012, p.11).

Underpinned by these studies, we define decision-making effectiveness as the extent to which a data-

driven decision enables a company to be more effective at understanding customers, making real-time

decisions, and responding more quickly to change. Our definition is also seen to be compatible with

the concept used in decision-making literature in that it refers to the desired outcomes of a decision

(Eisenhardt and Zbaracki 1992).

2.2 Theoretical Development

Prior BA studies (e.g., McAfee and Brynjolfsson 2012, Kiron and Shockley 2011, Kiron et al. 2012,

Lavalle et al. 2011) suggested that information processing capabilities enable a company to identify

data-based insights from big data thereby to improve decision-making effectiveness and to gain

competitive advantage. BA research believed this is possible because information processing

capabilities allow managers to measure and hence know more about their businesses, and making

decisions based on data-driven insights accordingly. This belief is seen to be compatible with

knowledge accumulated in decision-making studies, which for example suggested that companies

having complete and accurate information about the likely relationship between choices and outcomes

enable them to make successful decisions (Rodrigues and Hickson 1995), to generate viable

organisational strategies (Dean Jr and Sharfman 1996) and to improve organisational performance

(Goll and Rasheed 1997, Mueller et al. 2007).

Although prior BA studies indicated that decision-making effectiveness is positively associated with

competitive advantage (Kiron and Shockley 2011, Kiron et al. 2012, Lavalle et al. 2011), these

studies revealed little about the specific mechanisms through which decision-making effectiveness is

related to competitive advantage, as no inferential analyse has been conducted to our knowledge.

There is also a dearth of empirical studies investigating the impact of effective decision-making on

organisational performance (Driouchi and Bennett 2012, Rajagopalan et al. 1993).

In order to understand the interrelationships among information processing capabilities, decision-

making effectiveness, and competitive advantage, we turn to the RBV because it has been widely used

in various studies to understand the relationship between the resources or capabilities of a firm and its

competitive advantage. From the RBV, in order for a firm to gain competitive advantage, its resources

need to be simultaneously valuable, rare, imperfectly imitable, and non-substitutable (VRIN) (Barney

1991), based on the assumptions that resources are and continue to be heterogeneous across

organisations (Ambrosini et al. 2009). RBV has been widely used to understand the competitive effect

of various types of organisational resources in many disciplines; however, we found no empirical BA

research that we can draw on in the literature. The closest and most relevant research area we can turn

to is IT business value research because information processing capabilities are similar to various IT-

enabled capabilities (e.g., Peppard and Ward 2004, Lim et al. 2011, Barua et al. 2004, Bharadwaj et al.

2007) in that they are all generated from the integration of IT (BA in this research) and other

organisational factors.

Empirical IT business value studies showed that IT alone has no inherent strategic value; but when IT

is integrated with organisational factors such as strategy and structure, together they will meet the

VRIN conditions (or resource heterogeneity) to be a source of sustainable competitive advantage (e.g.,

Bhatt and Grover 2005, Powell and Dent-Micallef 1997, Rai et al. 2006, Ray et al. 2005). Similarly,

we argue that while BA has no inbuilt strategic value, information processing capabilities, which are

created when BA is supported by a data-driven culture (Kiron and Shockley 2011, Kiron et al. 2012,

Lavalle et al. 2011), can meet the VRIN conditions and thus become a source of competitive

advantage. We believe this is plausible because of the following reasons. Information processing

capabilities tend to be valuable because they are the combined result of BA and a data-driven culture,

and the joint use of assets in a company is value enhancing (Teece 2007) and synergistic (Eisenhardt

and Martin 2000). Information processing capabilities are rare since few companies have figured out

how to use BA for performance improvement (Barton and Court 2012, Kiron et al. 2012, Marchand

and Peppard 2013). They are also likely to be inimitable, since prior IT studies suggested that IT

combined with a firm’s innate cultural factors is socially complex and thus difficult to be duplicated

(Bharadwaj 2000, Lado and Wilson 1994, Miller 2003, Ray et al. 2004, Teece 2007), and that unique

capabilities created from the interdependence among various organisational factors are impossible to

be copied (Miller 1996). Along these lines, we argue that information processing capabilities are

complex, causal-ambiguous, and difficult to be imitated by other firms.

Having argued that information processing capabilities are likely to meet the VRIN conditions to be a

source of competitive advantage, the question that remains to be answered is how they specifically

relate to one other? According to Markman et al. (2004) and Newbert (2007), in most empirical

studies that are underpinned by the RBV to examine the connection between a company’s resources

and performance, specific resource or capability is claimed to meet the VRIN conditions and then the

amount of that resource or capability is correlated with competitive advantage or performance; the

VRIN conditions are rarely operationalised and tested. However, a few studies (e.g., Markman et al.

2004, Nevo and Wade 2011) had indeed tested the VRIN conditions or part of them. Markman et al.

(2004) for example tested whether inimitable and non-substitutable patents are positively related

superior performance in the context of pharmaceutical industry. They demonstrated that inimitability

is significantly related to firm profitability and new product introductions, while non-substitutability is

significantly related to new product introductions only. Nevo and Wade (2011), on the other hand,

tested all four VRIN conditions and demonstrated that IT-enabled resources are positively related to

the value, rarity, and inimitability conditions, which in turn have a positive and direct effect on

competitive advantage. They did not find any significant impact of non-substitutability on competitive

advantage.

In summary, while a few prior studies have demonstrated that resources are positively related to all or

part of VRIN conditions, which in turn have a positive effect on competitive advantage, most studies

showed that resources have a direct effect on competitive advantage (Newbert 2007). However, little

is known about the interrelationships among resources, resource heterogeneity, and competitive

advantage.

Inspired by and consistent with prior IT business value studies (e.g., Albadvi et al. 2007, Grover and

Teng 1998, Tanriverdi 2005) that frequently used a mediation model, we go one step further in this

research to conjecture that the value, rarity, and inimitability (VRI) conditions are likely to have an

intervening effect on the relationship between information processing capabilities and competitive

advantage. We believe this mediation relationship is conceivable because prior research has

demonstrated that there is a causal relationship between resources and competitive advantage, and

between VRI and competitive advantage. Thus, we extend prior research by assuming that

information processing capabilities may have an indirect effect on competitive advantage through the

mediation of the VRI conditions in the context of this research. It should be noted that we did not

include non-substitutability in this study and the reasons will be discussed in the following section.

In the same way, we argue that decision-making effectiveness is likely to have an intervening effect

on the relationship between information processing capabilities and competitive advantage. BA

studies have suggested that information processing capabilities will result in better decisions and

competitive advantage (e.g., Davenport 2006, Davenport 2013, e.g., Davenport et al. 2001, Davenport

et al. 2010, Kiron and Shockley 2011, Kiron et al. 2012, Lavalle et al. 2011), while decision-making

literature believed that decision-making effectiveness is the antecedent of competitive advantage (e.g.,

Dean Jr and Sharfman 1996, Rodrigues and Hickson 1995). Building upon these studies, we

conjecture that decsion-making effectiveness will have not only a direct effect on competitive

advantage but also a mediating effect on the relationship between information processing capabilities

and competitive advantage.

Having highlighted the central importance of decision-making effectiveness in linking information

processing capabilities to competitive advantage, another question that comes into view is the

interrelationship among resource heterogeneity, decision-making effectiveness, and competitive

advantage. This complex relationship has not been examined by prior research to date based on our

literature review; however, it is plausible to conjecture that the effect of resource heterogeneity on

competitive advantage is likely to be mediated by decision-making effectiveness based on the above

discussions. Essentially, we are suggesting an alternative possibility of linking resource heterogeneity

to competitive advantage: resource heterogeneity contributes to competitive advantage not only

directly but also indirectly through the mediation of decision-making effectiveness. Thus,

Hypothesis 1: Decsion-making effectiveness has a positive and direct effect on competitive

advantage.

Hypothesis 2: Decision-making effectiveness mediates the relationship between information

processing capabilities and competitive advantage.

Hypothesis 3: VRI mediates the relationship between information processing capabilities and

competitive advantage.

Hypothesis 4: Decision-making effectiveness mediates the relationship between VRI and competitive

advantage.

As a result, our research model is summarised and presented in Figure 1. The key variables include:

IPC- information processing capabilities, VRI-value, rarity, and inimitability, DME-decision-making

effectiveness, CA-competitive advantage.

Figure 1. Research model

3 RESEARCH METHODOLOGY

The hypotheses were tested empirically using partial least squares structural equation modelling

(PLS-SEM) based on survey data. PLS-SEM is recommended to be well-suited for research situations

where theory is less developed (Chung et al. 2003, Gefen et al. 2011, Hair et al. 2013b, Wetzels et al.

2009) and formatively constructs are part of the structural model (Becker et al. 2012, Hair et al.

2013b). Even though decision-making effectiveness and competitive advantage have been widely

discussed in literature, there are few empirical studies to shed light on the relationships among

information processing capabilities, the VRI of analytics resource, decision-making effectiveness, and

competitive advantage. Thus PLS-SEM is seen to be appropriate for the present study to conceptualise

and empirically test the interrelationship. PLS-SEM is also appropriate for the present study since it

can handle both reflective and formative constructs, both of which are included in the model.

Specifically, VRI and DME were modelled as formative on the premise that the indicators for each

construct are independent of one another, and most importantly the direction of causality is from the

indicators to the associated construct (Hair et al. 2013a). In the following section, we outline the

instrument development, validation, and dissemination processes.

VRI - value, rarity,

and inimitability

DME - decision-making

effectiveness

CA - competitive

advantage

IPC - information

processing capabilities

3.1 Research Model Constructs and Measures

To develop and test the theoretical model, a number of constructs and their associated measures were

identified and summarised in Table 1.

Constructs Indicators Supporting references

Information

processing

capabilities (IPC)

Capturing data/information (CD1)

Integrating data/information (ID1)

Analysing data/information (AD1)

Utilising data/information (UD1)

(Davenport et al. 2001,

Kiron et al. 2012, Lavalle et

al. 2011)

Value, rarity, and

inimitability

conditions (VRI)

VALUE

Usefulness of data-driven decisions (VALUE1)

Importance of data-driven decisions (VALUE2)

Data-driven decisions being valuable (VALUE3)

RARE

Others unlikely to have similar business analytics

(RARE1)

We have unique data-driven decisions (RARE2)

Inimitability (INIMI)

Our data-driven decisions cannot be easily replicated

(INIMI1)

Few can match our data-driven decision-making

(INIMI2)

(Nevo and Wade 2011)

Decision Making

Effectiveness

(DME)

More effective than its competitors at

Responding more quickly to change (CHA1)

Making real-time decisions (RDM1)

Understanding customers (CUS1)

(Davenport 2013, Kiron and

Shockley 2011, Kiron et al.

2012, Lavalle et al. 2011)

Perceived

Competitive

advantage (CA)

More effective than its competitors at

Increasing sales (SALE1)

Increasing revenue (REV1)

Generating profit (PROF1)

Providing product at a lower cost (COST1)

(Kiron and Shockley 2011,

Kiron et al. 2012, Lavalle et

al. 2011)

Table 1. Constructs and Indicators of the Study

Davenport et al. (2001), Kiron and Shockley (2011), Kiron et al. (2012) and Lavalle et al. (2011)

suggested that an organisation’s IPC has four key elements: its ability to capture data/information,

integrate data/information, analyse data/information, and use data-based insights, which had been

adopted in the current research.

We adopted the VRI measurement scales from Nevo and Wade (2011) into BA research. The non-

substitutability condition was initially included and operationalised using a single indicator, but it was

removed since its associated path coefficient was virtually zero.

Decision-making effectiveness has been measured by various indicators at different levels in decision-

making research (e.g., Amason 1996, Goll and Rasheed 1997, Elbanna and Child 2007). Compatible

with these measurements, BA research suggested that BA is likely to help a company to be more

effective than its competitors at understanding customers, making real-time decisions, and responding

quickly to change (Davenport 2013, Kiron and Shockley 2011, Kiron et al. 2012, Lavalle et al. 2011).

Based on BA research, we developed indicators to measure decision-making effectiveness as they are

more relevant to the current study.

Competitive advantage was measured based on manager’s perception of whether his/her company is

more effective than its competitors at reducing cost, increasing sales, generating revenue and profits,

and providing product at a lower cost. These scales were based on BA research (Kiron and Shockley

2011, Kiron et al. 2012, Lavalle et al. 2011). Perceived competitiveness has been commonly used by

prior studies (e.g., Armstrong and Sambamurthy 1999, Chan et al. 2006, Kearns and Sabherwal 2007,

Sabherwal and Chan 2001).

3.2 Data Collection

To test the hypotheses empirically, we selected both medium-sized (employees between 50 and 250)

and large UK manufacturing companies (more than 250 employees) based on UK Standard Industrial

Classification 2007 as our target population. The UK manufacturing sector had suffered significant

downturn in the last recession, but is now on the way to grow again. To compete in the current global

market, it is important for UK manufacturing companies to gain competitive advantages through

better information processing capabilities and effective decision making. Therefore, we decided to

focus our investigation on this particular sector. We generated a questionnaire survey using a five-

point Likert scale to provide responses to the model measurements of all constructs. The survey

instruments were piloted then delivered electronically through Qualtrics to managers, whose email

addresses were identified from the FAME database. The responding was voluntary without prior

consent. Three rounds, 4 weeks apart, of emails including a cover letter with a questionnaire were sent.

Each intended respondent was offered a summary of the results. While a total of 21,149 emails were

sent with the e-mail subject highlighted as questionnaire survey, the majority of them were never

opened. Of all sent emails, 782 were opened, representing a click-through rate of 3.7%; of these

opened, we received 232 usable responses, which represent a 29.7% response rate.

4 RESULTS

4.1 Respondents

Table 2 summarises the respondents’ characteristics in terms of their organisational positions and

years of experience in their current firms and industry.

Positions %

Years (x) of

experience

in the

firm %

in the

industry %

CEO/MD/Partner 26 x ≤ 5 22 4

Finance/Account director 13 5 < x ≤ 10 29 10

Operations director 13 10 < x ≤ 15 13 12

Marketing/Sales director 11 15 < x ≤ 20 12 15

CIO/IT Manager 8 20 < x ≤ 25 10 14

Other directors 29 x >25 14 45

Table 2. Respondent Profiles (N=232)

We used a key informant approach (Bagozzi et al. 1991) to collect data. The reported positions of the

respondents suggested that 26% of the respondents were in a senior managerial position and the rest

of them were in a middle managerial position. Based on their position within the firm, the respondents

were highly likely to participate in decision-making processes related to the topic of the survey

(Phillips and Bagozzi 1986). Of all respondents, 49% had been with their firms for more than 10 years,

whilst 86% had been in the industry for more than 10 years. Overall, the sample of respondents

seemed to be diverse, representing various managerial position and experience.

4.2 Common Method and Non-respondent Bias

Common method bias was assessed by conducting Harman’s single-factor test by entering all

independent and dependent variables (Podsakoff et al. 2003). As the first factor accounted for 20.74%

of the total variance, there is no evidence of a substantial respondent bias in this study.

Non-response bias was then assessed by comparing early and late respondents on all measures

through a t-test. The t-test results did not find significant differences between the two respondent

groups, suggesting an absence of non-response bias (Armstrong and Overton 1977).

4.3 Sample Size and Data Screening

In our structural model, the maximum number of arrows pointing at a construct is seven. In order to

detect minimum R2 value of 0.10 in any of the constructs for significant level of 1%, the minimum

sample size required is 228 (Hair et al. 2013a). Since we have 232 usable responses, the minimum

sample size requirement is thus met.

Data screening was performed using SPSS19. Missing data for an observation exceeding 10% had

been removed, and other missing values of an indicator were replaced by using the mean value

replacement. Of all 18 indicators, all of them were normal.

4.4 Evaluation of the Reflective Measurement Model

Our PLS-SEM includes both formative and reflective constructs. Following the recommendations

made by Hair et al. (2013a), the reflective measurement model was evaluated by considering the

internal consistency (composite reliability), indicator reliability, convergent validity and discriminant

validity.

Composite reliability (CR) scores summarised in Table 3 indicated that results based on these

constructs should be consistent, since all constructs met the recommended threshold value for

acceptable reliability, that is, both CR and Cronbach's α should be large than 0.70.

Table 3. Convergent Validity and Internal Consistency Reliability

Discriminant validity was satisfactory based on two tests conducted. The first test was to analyse

Fornell-Larcker criterion (Hair et al. 2013b) to evaluate if the square root of AVE value (diagonal

elements) for each construct was greater than the correlation of the construct with any other construct

(off-diagonal elements), which was true based on the comparison summarised in Table 4.

CA DME IPC VRI

CA 0.87

DME 0.80 NS

IPC 0.44 0.48 0.88

VRI 0.64 0.65 0.45 NS

NA-not applicable to formative measurement model

Table 4. Inter-construct correlations

The second test was to observe if each reflective indicator in Table 5 loaded highest on the construct it

was associated with, which was also true, thus demonstrating discriminant validity was satisfactory.

4.5 Assessment of Formative Measurement Model

The formative measurement model was evaluated in terms of assessing multicollinearity, the indicator

weights, significance of weights, and the indicator loadings, based on Hair et al. (2013a). To assess

the level of multicollinearity, the variance inflation of factor (VIF) values of all formative constructs

were evaluated in SPSS19 by conducting a linear regression with collinearity diagnostics included.

All VIF values were below 5, there were no collinearity issues.

Construct Indicator Loading

Indicator

Reliability

Composite

Reliability

Cronbach's

Alpha AVE

CA

PROF1 0.90 0.81

0.93 0.89 0.76 REV1 0.90 0.81

SALE1 0.86 0.74

COST1 0.82 0.67

IPC

AD1 0.83 0.69

0.93 0.90 0.77 ID1 0.93 0.87

CD1 0.88 0.77

UD1 0.88 0.77

Based on the Default Report of the bootstrapping process (5,000 samples), almost all indicators’ outer

weights were significant, except for Rare1 and Rare2, Value 1 and Value 2, and Inimt1 and Inimt2.

Nevertheless, these indicators were retained because their outer loadings were greater than 0.5. Thus,

the formative measurement model was valid.

Table 5. Cross Loading Analysis

4.6 Evaluation of the Structural Model

SmartPLS 2.0 M3 was used for testing the hypotheses and assessing the predictive power of the

research model. A bootstrapping procedure (5,000 samples) was used to assess the significance of the

hypothesised paths and the amount of variance in the dependent variables attributed to the explanatory

variables (Hair et al. 2013a). The results of the analysis are presented in Figure 2.

Figure 2. Final Research Model and Path Analysis Results

Following the recommendations made by Hair et al. (2013a), the structural model was assessed in

terms of collinearity and the significance and relevance of the structural model relationships. To

assess collinearity issues, two sets of predictor constructs were evaluated in SPSS19 based on the

latent variable scores from the Default Report of SmartPLS 2.0 M3. The VIF values were gained by

CA DME IPC VRI

COST1 0.82 0.63 0.32 0.50

PROF1 0.90 0.73 0.39 0.60

REV1 0.90 0.74 0.42 0.55

SALE1 0.86 0.69 0.39 0.56

CHA1 0.74 0.92 0.44 0.59

CUS1 0.70 0.89 0.42 0.60

RTD1 0.76 0.93 0.44 0.59

AD1 0.32 0.34 0.83 0.34

CD1 0.40 0.43 0.88 0.38

ID1 0.41 0.47 0.93 0.42

UD1 0.41 0.41 0.88 0.42

INIMT1 0.36 0.41 0.41 0.67

INIMT2 0.44 0.44 0.33 0.70

Rare1 0.25 0.31 0.26 0.47

Rare2 0.45 0.44 0.41 0.74

Value1 0.43 0.47 0.20 0.65

Value2 0.47 0.48 0.23 0.70

Value3 0.55 0.55 0.27 0.81

0.235**

*

VRI - value, rarity,

and inimitability

(R2=0.20)

***p<0.001 **p<0.01 *p<0.05

0.038ns

0.194**

*

0.658***

0.445***

0.540***

DME - decision-making

effectiveness

(R2=0.46)

IPC - information

processing capabilities

CA - competitive

advantage

(R2=0.67)

conducting a linear regression with collinearity diagnostics included. As all VIF values were below 5,

there was no collinearity issue.

From Table 6, DME had the strongest total effect on CA, followed by VRI and IPC. VRI had the

strongest total effect on DME, followed by IPC.

Total effect on CA Total effect on DME

DME VRI IPC VRI IPC

0.66 0.55 0.44 0.54 0.48

Table 6. Total Effect

The predictive power of the model can be assessed by observing the amount of variance attributed to

the latent variables (i.e., R2) and the value of the predictive relevance Q

2. All Q

2 values in Table 7 are

above zero, thus providing support for the model’s predictive relevance regarding the latent variables.

The model’s predictive accuracy is reflected by the variables’ R2 values, which are also seen to be

satisfactory.

Table 7. Results of R2 and Q

2 Values

4.7 Hypotheses Testing and Mediation Analysis

Table 8 summarises the testing results of the hypotheses, all of which were supported. Hypotheses

testing will be further evaluated in the conclusion section.

Hypothesised Path Standard path

coefficient

p-Values Empirical

evidence

Hypothesis 1 DMC -> CA 0.665 0.0000***

Yes

Hypothesis 2 IPC -> DME -> CA (full mediation) n/a Yes

Hypothesis 3 IPC -> VRI-> CA (partial mediation) n/a Yes

Hypothesis 4 VRI -> DME-> CA (partial mediation) n/a Yes ***p<0.001, **p<0.01, *p<0.05 n/a-not applicable

Table 8. Summary results of hypotheses testing

To verify Hypothesis 2, the mediating effect of DME on the relationship between IPC and CA was

analysed, following the recommendations made by Baron and Kenny (1986). The result of the

analysis is based on a bootstrapping procedure (5,000 samples) (Hair et al. 2013a) and summarised in

Table 10. To begin the analysis, the direct relationship between IPC and CA was estimated, which

was significant. Then the mediator, DME, was included to analysing whether the indirect effect of

IPC via DME on CA is significant. The evaluation indicated that the significance of the relationship

between IPC and DME (0.476), as well as between DME and CA (0.768). Thus, the indirect effect of

IPC via DME on CA was 0.366 (0.476×0.768), and its significance was confirmed by calculating the

empirical p value of the indirect effect based on the 5,000 bootstrapping results.

Similarly, Hypotheses 3 and 4 were validated and summarised in Table 9 by analysing the mediating

effects of VRI on the relationship between IPC and CA and of DME on the relationship between VRI

and CA accordingly. The relative size of the mediating effect was decided by calculating the variance

accounted for (VAF) based on Shrout and Bolger (2002). The VAF value suggested that DME fully

mediated the effect of IPC on CA; VRI partially mediated the effect of IPC on CA; and DME partially

and strongly mediated the effect of VRI on CA.

CA DME VRI

R2 Value 0.67 0.46 0.20

Q2 Value 0.50 0.38 0.09

Hypothesis Direct effect

without mediation

Direct effect

with mediation

Indirect

effect VAF

Mediation type

observed

Hypothesis 2

IPC -> DME -> CA 0.439

*** 0.073

ns 0.366

*** 0.834 Full

Hypothesis 3

IPC -> VRI-> CA 0.439***

0.189* 0.249

*** 0.569 Partial

Hypothesis 4

VRI -> DME-> CA 0.644*** 0.209

*** 0.431*** 0.673 Partial

***p<0.001, **p<0.01, *p<0.05 VAF>0.80 full mediation, 0.20 ≤ VAF ≤ 0.80 partial mediation, VAF < 0.20 no mediation

Table 9. The mediating role of DME and VRI

5 DISCUSSION AND CONCLUSION

The motivation of our study was to determine the interrelationship among information processing

capabilities, decision-making effectiveness, resource heterogeneity, and competitive advantage. A

conceptual path model was proposed and empirically tested using structural equation modelling.

5.1 Contributions to research

This paper has made several contributions to BA and decision-making literatures by conceptually

developing and empirically testing a model linking information processing capabilities to decision-

making effectiveness and competitive advantage from the RBV. To the best of our knowledge, this

path model has not been conceptually specified and empirically tested so far.

First, our findings indicated that decision-making effectiveness would have a direct and positive effect

on competitive advantage (Hypothesis 1). Prior BA research (e.g., Davenport et al. 2010, Davenport

2013, Kiron and Shockley 2011, Kiron et al. 2012, Lavalle et al. 2011) and decision-making literature

(e.g., Driouchi and Bennett 2012, Rodrigues and Hickson 1995) have suggested that this relationship

may exist, but there is no inferential analysis to verify this to date. Our research provided strong

empirical support for explicitly linking decision-making effectiveness to competitive advantage,

contributing to BA and decision-making literatures by adding an empirical analysis, thus advancing

our understanding of the effect of decision-making effectiveness on competitive advantage.

Second, our findings demonstrated that decision-making effectiveness would have a full mediating

effect on the relationship between information processing capabilities and competitive advantage

(Hypothesis 2), considering the interrelationship among information processing capabilities, decision-

making effectiveness, and competitive advantage. We were fully aware that caution needs to be

exercised in interpreting this full mediation effect since its statistical significance can be affected by

sample size, smaller total effect (Little et al. 2007), or significant discrepancy between direct effect

without mediation and the sum of direct and indirect effects with mediation (Gelfand et al. 2009). The

statistical significance of the mediation effect of decision-making effectiveness in this study was not

an issue since our sample size was satisfactory based on the requirement suggested by Hair et al.

(2013a), the magnitude of the total effect was strong with a direct effect of 0.439 without mediation

(see Table 10), and there was only small discrepancy caused by rounding. This mediating effect

showed that decision-making effectiveness would be the necessary condition for information

processing capabilities to impact on competitive advantage since information processing capabilities

would result in competitive advantage only indirectly through improving decision-making

effectiveness in the context of BA in UK manufacturing companies. Our findings revealed that in the

current research context the paths from information processing capabilities to competitive advantage

are more complex than the direct relationship between capabilities and competitive advantage

demonstrated in other research contexts in the literature (Newbert 2007). Thus, our findings would

make an original contribution to knowledge by providing an alternative explanation for and new

insight into how information processing capabilities, or other capabilities, may create strategic value

for a company.

Third, our findings provided empirical evidence that resource heterogeneity would partially mediate

the relationship between information processing capability and competitive advantage (Hypothesis 3).

This finding contributed novel insight into understanding the interrelationships among resources

(information processing capabilities in this study), resource heterogeneity (VRI conditions), and

competitive advantage, since prior studies only demonstrated that there is a direct relationship

between resources and competitive advantage and between resource heterogeneity and competitive

advantage (Markman et al. 2004, Newbert 2007). This finding also provided an additional empirical

example of systematically testing resource heterogeneity, which has rarely been operationalised and

tested by others (Markman et al. 2004, Newbert 2007).

Finally, the research findings demonstrated that decision-making effectiveness would significantly

mediate the positive impact of resource heterogeneity on competitive advantage (Hypothesis 4). As

mentioned above, resource heterogeneity has rarely been operationalised and tested in prior studies

that are underpinned by the RBV to examine the connection between a company’s resources and

performance (Markman et al. 2004, Newbert 2007); when it is tested, a direct relationship between

resource heterogeneity and competitive advantage has been assumed (e.g., Markman et al. 2004,

Nevo and Wade 2011). Thus our findings extended prior research by explicating that resource

heterogeneity would affect competitive advantage indirectly through the mediation of decision-

making effectiveness, in addition to its direct impact on competitive advantage (Markman et al. 2004,

Nevo and Wade 2011, Newbert 2007).

5.2 Implications for Practice

Our findings have important practical implications for maximising the strategic value of BA in

organisations. The research demonstrated that decision-making effectiveness would be of central

importance for gaining competitive advantage from BA applications and information processing

capabilities. Consequently, managerial activities wishing to improve competitive advantage should

focus on responding to change quickly, making real-time decisions, and understanding customers

better than competitors by exploiting information processing capabilities derived from implementing

BA applications. This finding suggested that without improving decision-making effectiveness,

companies would be less likely to gain competitive advantage from information processing

capabilities.

5.3 Limitations and future research

The study has several limitations, which could be opportunities for further research. While it has

demonstrated that information processing capabilities through decision-making effectiveness had a

significant impact on competitive advantage, we believe this understanding could be further advanced

by including more decision-making variables such as organisational factors, decision-specific factors,

decision-process characteristics, and decision process outcomes (Rajagopalan et al. 1993). In

particular, the role of environment, which may have a significant effect on shaping the outcomes of

decisions (Dean Jr and Sharfman 1996, Goll and Rasheed 1997), should be considered in future BA

research. Our findings were based on data collected in the UK manufacturing sector, thus future

research can test the model and hypothesis in other sectors and countries. In addition, we used

perceived competitiveness to measure a company’s competitive advantage, future research can also

use objective measures such as market share of the company to complement perceived measures.

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