Management Perceptions, Industry Structure and Company Performance

Janine Wong BCom, MMktg

This thesis is presented for the degree of Doctor of Philosophy of

The University of Western Australia

Business School

Marketing

2011

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Abstract

Issues associated with competition, corporate strategic action and performance

have been addressed from three general academic perspectives – marketing,

business policy and strategic management, and industrial organisation

economics. Using the Structure-Conduct-Performance (SCP) paradigm from

industrial organisation economics, Porter (1980) developed the five forces

model to analyse industry structure which determined one of three generic

strategies a company should choose from to create a sustainable defendable

position and outperform competitors. Porter (1980) argues corporate response

to structure as the critical variable in determining industry and company

performance. Here, the unit of analysis is the industry and Porter (1980)

implicitly assumes managers within an industry define and observe the same

objective environment. In marketing, Hunt’s (2010) resource – advantage (R-A)

theory of competition asserts that the resources of firms within an industry are

heterogeneous and immobile and therefore, managers must make strategic

choices and these choices influence firm performance. Resources include

market and competitor intelligence and some firms will have a comparative

advantage in information that yields marketplace positions of competitive

advantage and thus, superior financial performance. The unit of analysis is the

manager and the manager or the top management team is central to the

evaluation of environmental conditions which form the basis of strategic action.

Consequently, managers develop strategies on the basis of imperfect

perception of information. This study investigated the degree of congruence of

individual perceptions of structure, conduct and performance within a company

and within an industry. It is also concerned with examining the degree of

congruence between the objective reality and individual perceptions (i.e.

subjective measures) of structure, conduct and performance. More importantly,

what is the best predictor of company performance – objective data as implied

by Porter’s (1980) five forces of competition model or individual perceptions of

structure as implied by Hunt’s R-A theory of competition?

This study is also concerned with investigating the theoretical relationships

between structure, conduct and performance as conceptualised by Porter

(1980). Porter (1980) does not clarify how the intensity of competition leads to

a better choice of strategy and therefore superior performance. The examples

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and brief case studies do not provide a rigorous basis for theory development

and testing. Further, the empirical evidence for Porter’s (1980) argument that a

company must choose one of the three generic strategies to create a

sustainable defendable position and outperform competitors or be stuck in the

middle is inconclusive.

I collected data on individual perceptions of structure, conduct and performance

through a mail survey of senior executives involved in top-level strategic

decision making for their organisations. I mailed out 754 questionnaires to top

management teams of private and publicly-listed Australian companies and

received 147 completed questionnaires, resulting in a response rate of 19.50%.

Data analysis began with an examination of the measurement issues

concerning Porter’s SCP model by using correlation coefficients to determine

the degree of congruence of individual perceptions of structure, conduct and

performance within companies and within industries. I found partial support for

congruent perceptions within a company but no support for congruent industry

perceptions. Then I used PLS (Partial Least Squares) to determine the degree

of congruence between the objective reality and individual perceptions of

structure, conduct and performance. Results showed individual perceptions of

structure, conduct and performance did not have a strong positive relationship

with the objective reality. It appears that the best predictor of company

performance is individual perceptions of structure, not the objective reality which

supports Hunt’s (2010) R-A theory as the better theory of competition compared

to Porter’s (1980) five forces model. Finally, I tested the theoretical issues in

Porter’s SCP model using PLS. While I did not find a positive relationship

between the intensity of industry competition and targeted strategic action, I did

find support for a positive relationship between targeted strategic action and

company performance.

This research should not be viewed as a final definitive evaluation of Porter’s

adaptation of the SCP paradigm but rather as a preliminary, exploratory

assessment. The results of this study contribute to the growing body of

evidence that firm effects, not industry effects, account for the diversity in firm

performance. Thus, future studies should test Hunt’s R-A theory of competition.

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Table of contents

CHAPTER 1 ............................................................................................................................................... 1

INTRODUCTION ....................................................................................................................................... 1

STATEMENT OF PROBLEM ...................................................................................................................... 4

FOCUS OF STUDY .................................................................................................................................... 7

VALUE OF STUDY .................................................................................................................................... 8

CHAPTER 2 ............................................................................................................................................. 13

CONGRUENCE OF PERCEPTIONS ............................................................................................................. 13

Marketing and Strategic Management theory .................................................................................. 14

Social Psychology theory .................................................................................................................. 21

Organisational Behaviour theory ..................................................................................................... 22

Managerial Cognition theory ........................................................................................................... 24

MANAGEMENT PERCEPTIONS VERSUS OBJECTIVE REALITY ................................................................... 31

THE BEST PREDICTOR OF PERFORMANCE ............................................................................................... 41

THEORETICAL RELATIONSHIPS BETWEEN STRUCTURE-CONDUCT-PERFORMANCE ......................... 51

Industry structure ............................................................................................................................. 58

Conduct ............................................................................................................................................ 64

Performance ..................................................................................................................................... 70

CHAPTER 3 ............................................................................................................................................. 77

SAMPLE ................................................................................................................................................. 77

APPARATUS ........................................................................................................................................... 82

INSTRUMENTATION ................................................................................................................................ 83

DESIGN ................................................................................................................................................... 88

PROCEDURE .......................................................................................................................................... 90

CHAPTER 4 ............................................................................................................................................. 95

PRELIMINARY DATA ANALYSIS............................................................................................................... 95

PRELIMINARY RELIABILITY TEST .......................................................................................................... 100

HYPOTHESIS TESTING ........................................................................................................................... 102

Congruence of perceptions ............................................................................................................. 102

Management Perceptions versus Objective Reality ........................................................................ 113

The Best Predictor of Performance ................................................................................................ 121

Theoretical Relationships between Structure-Conduct-Performance............................................. 139

CHAPTER 5 ........................................................................................................................................... 149

CONCLUSIONS ...................................................................................................................................... 149

LIMITATIONS ........................................................................................................................................ 156

DIRECTIONS FOR FUTURE RESEARCH .................................................................................................... 157

REFERENCES ....................................................................................................................................... 159

APPENDIX 1 .......................................................................................................................................... 171

APPENDIX 2 .......................................................................................................................................... 175

APPENDIX 3 .......................................................................................................................................... 180

APPENDIX 4 .......................................................................................................................................... 181

APPENDIX 5 .......................................................................................................................................... 183

APPENDIX 6 .......................................................................................................................................... 194

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Acknowledgements

There are many people and organisations who I wish to thank for their support

and encouragement throughout my PhD candidature.

First, thank you to the University of Western Australia for awarding me a

University / Australian Postgraduate Award which allowed me to commence the

PhD. Also, my sincere thanks to Professor Ram Ramaseshan, Head of School,

for the opportunity to work as a lecturer at the Curtin University School of

Marketing while completing the dissertation.

My eternal gratitude goes to my supervisor, Dr Anthony Pecotich, for his

unwavering commitment, invaluable guidance and compassion that he showed

during my candidature. I truly appreciate the knowledge and skills you have so

generously shared with me – I have learnt much more than ‘research’! Thank

you also to Jan Pecotich for your encouragement and the lovely afternoon teas

which helped lighten the intense meetings with Tony.

Finally, I am very grateful to my husband Hy, my parents Stephen and Joyce,

my siblings Sheryl and Marie and my friends. It has been wonderful to have

your support and words of encouragement, particularly during the more

demanding periods of my study.

Never regard study as a duty, but as the enviable opportunity to learn to know

the liberating influence of beauty in the realm of the spirit for your own personal

joy and to the profit of the community to which your later work belongs.

Albert Einstein

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CHAPTER 1

Introduction

Since the collapse of the Eastern bloc, the vehemence of the ideological debate

between the advocates of collectivist versus market-based systems as the best

option for the optimum achievement of human welfare has declined and most

nations of the world appear to be moving toward some form of a market system.

The critical assumption for the efficient operation of the market system is that

prices are set through a competitive interplay without interference from

institutional forces. The market determines the nature of production as well as

the price and quantity of what is produced. Within marketing, Hunt (2010, p.

364) has defined competition as “the constant struggle among firms for

comparative advantage in resources that will yield marketplace positions of

competitive advantage for some market segments and, thereby, superior

financial performance.” It seems that competition is a kind of behaviour

involving “structure” (the environment in which the firm must compete) and

processes. It involves the conditions prevailing in the market in which rival

sellers try to increase their profits at one another's expense. Although the

market system may well be an unreachable ideal, it is a goal of U.S. antitrust

laws as well as the philosophical basis of the reforms in much of the

transforming and developing world (Koves & Marer, 1991; Lane, 1991, 2000;

Lindblom, 2001). Issues associated with the market and competition are

therefore, both at the macro and micro levels, of critical interest to the business

related disciplines. Within the business related disciplines, the market,

competition and corporate strategic action have been addressed from three

general academic perspectives – marketing, business policy and strategic

management, and industrial organisation.

In marketing, the traditional marketing management paradigm proposes that

strategic action is the result of alignment between environmental opportunities

and threats with company strengths and weaknesses (Cravens & Piercy, 2009;

Jain & Haley, 2009; Kotler, Keller, & Burton, 2009). Within marketing, Hunt’s

(2010) resource – advantage (R-A) theory of competition proposed that the

resources of firms within an industry are heterogeneous and immobile (i.e. not

easily imitated or acquired) and therefore, managers must make strategic

2

choices and these choices influence firm performance. Resources include

informational (e.g. market and competitor intelligence), financial, physical, legal,

human, organisational and relational assets. Some firms will have a

comparative advantage in resources while others will have a comparative

disadvantage in resources. Specifically, some firms will have better intelligence

or information than others that will yield marketplace positions of competitive

advantage for some market segments and thereby, superior financial

performance. Therefore, managers develop strategies on the basis of the

resources the firm possess, including imperfect perception of information.

Within business policy and strategic management, a successful firm matches

company capabilities and resources to the external environment to maximise

firm performance. This is achieved when there is synergy across strategies

within a division and strategies across all divisions (Hanson, Hitt, Ireland, &

Hoskisson, 2011; Hubbard & Beamish, 2011; Jauch & Glueck, 1988; Lynch,

2009; Wheelen & Hunger, 2010). Although the perspectives and some of the

specifics of the marketing and strategic management disciplines may be

different, the unit of analysis is the manager. Here, human agency, the

manager or the management team, is central to the evaluation of environmental

conditions which form the basis of strategic action.

The third view on business strategy emerged from industrial organisation

economics where characteristics of the industry environment determines the

strategies that companies choose whose joint conduct determines the

performance of the industry, the Structure-Conduct-Performance (SCP)

paradigm illustrated in Figure 1 (Bain, 1956, 1968; Caves, 1980; Mason, 1939;

Porter, 1980; Scherer, 1970, 1980; Scherer & Ross, 1990). In this formulation

the role of human agency, even if implicit, is ambivalent at best. The unit of

analysis is the industry and the focus is on objective external criteria where the

executive’s role is largely non-existent.

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Figure 1 The Industrial Organisation Structure-Conduct-Performance Paradigm

Note. Based on (Mason, 1939; Scherer, 1970, 1980; Scherer & Ross, 1990)

It was not until Porter (1979, 1980, 1981, 1985, 1990, 1991) integrated research

across these three disciplines to formulate a model for assessing the structure

of competition in an industry and a new classification basis for generic

corporate-level strategies that the implicit role of the executive became clear.

Using the SCP paradigm from industrial organisation economics, Porter (1980)

developed the five forces model to analyse industry structure which determined

one of three generic strategies a company should choose from to create a

sustainable defendable position and outperform competitors (Figure 2).

Figure 2 Porter’s Structure-Conduct-Performance Paradigm

Porter (1980) proposed that industry structure is shaped by five forces – rivalry

among existing companies, the threat of new entrants, the threat of substitute

products/services, the bargaining power of buyers, and the bargaining power of

suppliers. The five forces determine the intensity of competition and hence

industry profitability, measured as long run return on invested capital. While

economic and social factors affect all companies, Porter emphasised corporate

response to industry structure as the critical variable. Specifically, if superior

financial performance results primarily from industry conditions, choosing the

industries to compete in and/or altering industry structure to increase monopoly

power should be the focus of strategy.

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Statement of Problem

Porter’s theory was quickly embraced within both marketing (Cravens & Piercy,

2009; Jain & Haley, 2009; Kotler et al., 2009) and strategic management

(Hanson et al., 2011; Hubbard & Beamish, 2011; Jauch & Glueck, 1988; Lynch,

2009; Wheelen & Hunger, 2010) as it provided a model for analysing

competition in an industry and a new perspective on generic corporate-level

strategies. In particular, Porter’s “five forces”, “generic strategy” and “value

chain” models remain at the heart of most business school strategy courses to

this day (Stonehouse & Snowdon, 2007).

The two major issues concerning Porter’s model are measurement-related and

theoretical. The first major issue concerning Porter’s model is measurement-

related. Porter (1980) implicitly assumes managers define and observe the

same objective environment. On this basis, perception of structure as

described by Porter’s five forces model should be identical for all managers

operating in the same industry. However, companies within an industry may

have different perceptions of the same environment and these perceptions may

not correspond to the objective reality. Further, individuals within a company

may have different perceptions of the same environment and their perceptions

may not correspond to the objective reality. Perception is influenced by many

variables including the decision maker’s personality, internal politics and

company objectives. Consequently, the organisation becomes a victim of

perceptions which ignore or distort environmental elements (Barrett, Balloun, &

Weinstein, 2009; Cyert & March, 1963; Nadkarni & Barr, 2008; Panagiotou,

2006; Snow, 1976; Snow & Hrebiniak, 1980; Weick, 1979) and management

perceptions of structure determine strategy, not the objective reality.

The notion that management perceptions of structure drive strategy rather than

strategy being driven by objective reality is important because Searle (1998, p.

10) argues “there is a real world that exists independently of us, independently

of our experiences, our thoughts, our language.” Objects have properties and

people have perceptions about the extent to which objects possess these

properties and feelings about whether this is good or bad. In the applied

strategic marketing context, the industry may be viewed as the object and its

properties are the five competitive forces. Managers within an organisation can

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observe the same industry but their perception of its structural properties and

their feelings about whether this is good or bad may vary. For example, within

an organisation the marketing manager believes that competition from current

rivals is a significant threat but the sales manager perceives that the bargaining

power of buyers poses a more serious one. The sales manager can decrease

prices in response to their perception that the bargaining power of buyers poses

a serious threat resulting in lower short term profitability. Thus, it is possible

that corporate strategic action is determined by management perceptions of

industry structure rather than the objective reality (Downey, Hellriegel, & Slocum

Jr, 1975; Hambrick, 1981; Mezias & Starbuck, 2003; Pecotich, Hattie, & Low,

1999; Pecotich, Laczniak, & Inderreiden, 1985; Pecotich, Laczniak, Lusch, &

Carroll, 1992; Pecotich, Purdie, & Hattie, 2003; Shortell & Zajac, 1990; Tosi,

Aldag, & Storey, 1973). This study is concerned with the degree of congruence

between the objective reality (as measured by archival data) and individual

perceptions (i.e. subjective measures) of structure, conduct and performance.

More importantly, the critical question is: what is the best predictor of

performance – objective data or individual perceptions of structure? According

to Porter’s adaptation of the SCP model, industry profitability is dependent on

the structural features of industry (i.e. the five forces). Firms are viewed as

combiners of homogeneous, perfectly mobile resources and intra-industry

demand is viewed as homogeneous. Porter (1980) assumes that managers

develop strategy after an objective analysis of structure and therefore, “industry

effects” or objective data should explain most of the variance in firms’

performance. In contrast, Hunt’s R-A theory proposes that demand within

industries is heterogeneous and resource heterogeneity and immobility imply

strategic choices must be made and that these choices influence firm

performance. It is the role of managers to develop strategies on the basis of

the resources the firm possess, including imperfect perception of information

(Hunt, 2000a, 2000b, 2001, 2002a, 2002b, 2010; Hunt & Arnett, 2001; Hunt &

Derozier, 2004; Hunt & Duhan, 2002; Hunt & Morgan, 1995). If this is the case,

then “firm effects” or individual perceptions of structure determine conduct and it

may not correspond to the objective reality. If individual perceptions of structure

correspond to the objective reality, this supports Porter’s paradigm which states

response to industry structure is the critical determinant of company

6

performance. If individual perceptions of structure do not correspond with the

objective reality, then management perceptions of structure determine conduct,

which supports Hunt’s R-A theory as the case for the general theory of

competition. This study will determine the best predictor of company

performance – industry effects (i.e. objective data) or firm effects (i.e. individual

perceptions).

The second major issue concerning Porter’s model is the theoretical

relationships between industry structure, conduct and performance. The

evidence for Porter’s conceptualisation of structure and of strategy is “anecdotal

based on a series of case studies and examples” and “do not allow for a strong

scientific evaluation of the true content of the theoretical typology and the

inferred relationships” (Pecotich et al., 1999, p. 419). Sound theory must be

empirically testable and a theory is capable of being empirically testable when it

can be used to generate hypotheses that are agreeable to verification by real-

world data (Hunt, 2002a, 2010). The empirical evidence on whether Porter’s

generic strategies lead to a superior return on investment has been

inconclusive. Campbell-Hunt (2000) employed meta analysis to examine 17

empirical studies on Porter’s generic strategies and results did not support

Porter’s proposition that companies must pursue one of the three generic

strategies or get stuck in the middle and suffer low profitability.

There is a lack of strong empirical evidence on the degree to which individual

perceptions of the five forces of competition impact a manager’s choice of one

of the three generic strategies and thus, the impact on company and industry

performance. While separate studies have demonstrated management

perceptions of structure and conduct conform to Porter’s formulation (Pecotich

et al., 1999; Pecotich et al., 2003), the logical next step of a research program

should be evaluating Porter’s adaptation of the SCP paradigm at the top

executive perception level (Pecotich et al., 1999; Pecotich et al., 1985; Pecotich

et al., 1992; Pecotich et al., 2003). The view that executive perceptions should

be the focus of research in marketing, management and industrial organisation

has been advocated, especially at the strategic management / business policy

level (Anderson & Paine, 1975; Dess & Davis, 1984; Fombrun & Zajac, 1987;

7

Hambrick & Mason, 1984; Kotha & Vadlami, 1995; Pecotich et al., 1985; Snow,

1976; Walton, 1986; Wind & Robertson, 1983).

Focus of Study

It is our purpose to provide an integration of the literature and to develop a

conceptual framework that may form the basis for the evaluation of Porter’s

theory with the correspondence between objective conditions and the nature of

management perceptions accounted for (Figure 3).

Figure 3 The Conceptual Model

The objectives of this study are:

1. To determine the degree of congruence of individual perceptions of

structure, conduct and performance within a company.

2. To determine the degree of congruence of company perceptions of

structure, conduct and performance within an industry.

3. To determine the degree of congruence between the objective reality

and individual perceptions (i.e. subjective measures) of structure,

conduct and performance.

4. To determine the best predictor of company performance – the

objective reality or individual perceptions of structure.

5. To determine if there is a positive association between the:

8

a. Intensity of industry competition (i.e. five forces) and targeted

strategic action (i.e. cost leadership, differentiation and focus)

and

b. Targeted strategic action (i.e. cost leadership, differentiation

and focus) and industry and firm performance.

Value of Study

In contestable markets where companies are under continuous pressure to

increase profits, understanding the relationship between management

perceptions of the environment and its impact on company strategy and

performance is valuable for both practitioners and scholars. The academic and

managerial contributions of this study are discussed in the following section.

Academic applied

This study hopes to provide empirical evidence for Porter’s (1980)

conceptualisation of a positive relationship between the intensity of industry

competition (i.e. five forces) and targeted strategic action (i.e. cost leadership,

differentiation and focus). It will also contribute to the debate regarding Porter’s

(1980) hypothesised positive relationship between targeted strategic action (i.e.

cost leadership, differentiation and focus) and industry performance because

the current evidence is inconclusive. Thus, I will resolve the issue of “stuck in

the middle” poor performing organisations.

Porter (1980) assumes that managers develop strategy after an objective

analysis of structure and therefore, “industry effects” or objective data should

explain most of the variance in firms’ performance. If individual perceptions of

structure correspond to objective reality, this supports Porter’s paradigm which

states response to industry structure is the critical determinant of company

performance. If individual perceptions of structure do not correspond with the

objective reality, then management perceptions of structure determine conduct,

which supports Hunt’s (2010) R-A theory as the case for the general theory of

competition. This contributes to explaining observed differences in quality,

innovativeness and productivity between the market-based and command-

based economies of the world. Support for Hunt’s R-A theory also adds

impetus to including this alternative theory of competition in strategic marketing

9

and management texts and provide an incentive for further empirical research

to test its validity. Finally, evidence in favour of Hunt’s R-A theory brings into

question the validity of findings from previous studies within marketing and

strategic management that have relied on self-administered questionnaires and

a single respondent per firm.

While separate studies have demonstrated individual perceptions of industry

structure and conduct conform to Porter’s formulation, we wish to further

validate that Australian top management team perceptions conform to Porter’s

five forces of competition and three generic strategies. Previous empirical

studies examining the influence of individual perceptions of structure have been

conducted in the US. This study aims to improve our knowledge and

understanding of the influence of management perceptions of industry structure

on strategic action and performance in an Australian context.

Managerial applied

If the top management team within a company has different perceptions of the

same objective environment, this affects the firm’s strategy and therefore

performance because it affects the nature and duration of decision making and

implementation. Disagreement within the top management team concerning

the environment can prompt or delay information gathering and scanning

processes, increase or decrease information sharing and processing, delay

strategic decisions and subsequent actions, which can lead to either higher or

lower organisational performance (Bourgeois, 1978, 1980, 1985; Dess & Keats,

1987; Kotha & Nair, 1995). This suggests the need for a corporate market

intelligence system to systematically capture objective data about the

environment that can inform strategic decision making. In particular, it will

establish communication / sharing of perceptions between those interacting with

customers (e.g. Sales Manager) and top management (e.g. Chief Executive

Officer / CEO). Further, if companies within an industry have different

perceptions of the same objective environment, this suggests the need to

develop industry maps perhaps on the basis of building a database similar to

the PIMS (Profit Impact of Market Strategy) project. This would allow

information to be shared between competitors and act as a benchmark in

performance comparisons.

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If firm effects (i.e. individual perceptions) dominate industry effects (i.e.

objective reality) in explaining company performance, then policy makers should

support formal and informal institutions that promote R-A competition (i.e. the

constant struggle among firms for comparative advantage in resources that will

yield marketplace positions of competitive advantage for some market

segments and, thereby, superior financial performance). R-A competition

promotes innovations that create resources that ultimately results in productivity

and economic growth. Vigorous competition requires institutions that protect

the property rights that firms and individuals have in the innovations they create

(e.g. trade secrets, copyrights, trademarks). Therefore, to the extent that the

goal of public policy is wealth creation, productivity and economic growth, policy

makers should promote formal and informal institutions that promote R-A

competition. Important formal institutions are those that protect property rights

and promote economic freedom. Important informal institutions are those that

promote social trust. Policy makers should also endorse institutions that

promote the link between performance and rewards. Therefore low marginal

tax rates for both organisations and individuals promote the linkage between

performance and rewards which, in turn, promote R-A competition and thus

productivity and economic growth (Hunt, 2010).

If the objective reality is the best predictor of company performance then top

managers could be thought of as power brokers, responding to demands and

constraints imposed by stakeholders in contrast to leaders who drive corporate

performance. Such a finding would lend support to ecology theory which

suggests the environment determines the survival of organisations and

therefore, managers have little influence on performance (Hannan & Freeman,

1977). On the contrary, if individual perceptions of structure determine

company performance, then managers play an important role in aligning

external opportunities and threats with company strengths and weaknesses

(Andrews, 1971; Chandler, 1962).

Many business school courses include Porter’s “five forces” and “generic

strategies” models but there is only anecdotal evidence for the relationship

between structure and strategy and inconclusive evidence for the relationship

between targeted strategic action and industry performance. If the evidence

11

suggests that individual perceptions of competition, as opposed to the objective

reality, determine strategy and thus performance, then Hunt’s R-A model should

be given more prominence in the pedagogy of both undergraduate and

postgraduate courses in competitive strategy.

The application of INDUSTRUCT, a measure of industry structure based on

Porter’s (1980) five competitive forces, provides managers with an initial

checklist for identifying the structural variable(s) that determine competitive

intensity. Understanding the five forces of competition can highlight the

strengths and weaknesses of an organisation relative to its competitors. This

would assist in developing strategies to alter industry structure to achieve

superior financial performance. The measurement of Porter’s generic strategic

typology provides executives with a comprehensive and convenient list of

strategic action statements with which to communicate and measure business

strategy.

Knowledge yielded from investigating whether management characteristics

influence perception of structure and thus, conduct can help predict both

corporate performance and competitors’ conduct. Knowing the influence of

management characteristics on strategic action can assist in recruiting and

managing senior executives. The company can match top management

characteristics with the external environment, particularly to cope with

environmental uncertainties. Further, understanding the constraints facing top

management is useful for strategic problem solving, rather than changing Chief

Executive Officers in the hope that problems will be solved.

Porter (1980) proposed five forces (i.e. rivalry among existing companies, the

threat of new entrants, the threat of substitute products/services, the bargaining

power of buyers, and the bargaining power of suppliers) comprise industry

structure and together, determine the intensity of competition and hence

industry and company performance. While economic and social factors affect

all companies, Porter emphasised corporate response to industry structure as

the critical variable. Porter’s theory was quickly embraced within both

marketing (Cravens & Piercy, 2009; Jain & Haley, 2009; Kotler et al., 2009) and

strategic management (Hanson et al., 2011; Hubbard & Beamish, 2011; Jauch

12

& Glueck, 1988; Lynch, 2009; Wheelen & Hunger, 2010). However, this study

will examine two major issues concerning Porter’s (1980) adaptation of the SCP

paradigm from industrial organisation. The first issue is the implicit assumption

that managers choose one of the three generic strategies after an objective

analysis of the five forces of competition. Perceptions of competition within a

company and within an industry may vary and further, not strictly correspond to

the objective reality. Therefore, what is the best predictor of company

performance – objective data (i.e. industry effects) or individual perceptions (i.e.

firm effects) of structure? The second major issue is the lack of conclusive

empirical evidence for the proposed theoretical relationships between the

intensity of industry competition and targeted strategic action and between

targeted strategic action and industry/firm performance. The next chapter

provides a detailed examination of these two issues.

13

CHAPTER 2

In this chapter, I begin with a discussion on the congruence of individual

perceptions of structure, conduct and performance within a company and within

an industry as well as the variation between individual perceptions and the

objective reality. This leads to a discussion on which is the better predictor of

firm performance and thus, the superior theory of competition – objective reality

as implicitly suggested by Porter’s five forces model or individual perceptions as

proposed by Hunt’s R-A theory of competition? I conclude by discussing the

theoretical issues in Porter’s adaptation of the SCP paradigm.

Congruence of Perceptions

Within the marketing and strategic management disciplines, it is managers who

observe and interpret the environment to develop the most appropriate strategy.

The unit of analysis is the manager who is central to the evaluation of

environmental conditions which form the basis of strategic action (Cravens &

Piercy, 2009; Jain & Haley, 2009; Jauch & Glueck, 1988). In contrast, industrial

organisation economists implicitly assume all managers within an industry

define and observe the same environment (Bain, 1956, 1968; Caves, 1980;

Mason, 1939; Porter, 1980; Scherer, 1970, 1980; Scherer & Ross, 1990). The

unit of analysis is the industry and the focus is on objective external criteria

where the executive’s role is largely non-existent. On this basis, perception of

the five forces of competition should be identical for all managers operating in

the same industry. However, what if companies competing in the same industry

have different perceptions of the same objective environment? Further, what if

members of the top management team within a company have different

perceptions of the same objective environment? The stimuli that one executive

perceives may be the same stimuli that another executive fails to perceive or

filters out. Moreover, executives who notice the same stimuli may use different

frameworks to interpret these stimuli and as a result, disagree about their

meanings, causes or effects. Therefore, perceptual filtering is affected by the

manager’s habits, beliefs, experiences and work settings(Starbuck & Milliken,

1988). There are several theories that seek to explain this phenomenon and

while there is some overlap between them, I classified these theories into

14

marketing and strategic management, social psychology, organisational

behaviour and managerial cognition.

Marketing and Strategic Management theory

The view that executive perceptions should be the focus of research has been

advocated especially at the marketing and strategic management / business

policy level (Anderson & Paine, 1975; Dess & Davis, 1984; Fombrun & Zajac,

1987; Hambrick & Mason, 1984; Kotha & Vadlami, 1995; Mintzberg, 1978;

Pecotich et al., 1985; Walton, 1986; Wind & Robertson, 1983). Scholars from

both these disciplines have asserted that it is managers who observe and

interpret the environment and on that basis, develop the most fitting strategy.

Anderson and Paine (1975) assumed that management perception of the

environment determines strategic action and this explains why companies

pursue different strategies in the same objective environment. They proposed

that strategy is influenced by executive perceptions of environmental

uncertainty and the need for organisational change in response to

environmental trends. Mintzberg (1978) asserted it is the CEO’s role to

maintain a stable organisation while simultaneously ensuring it adapts to a

dynamic environment, characterised by change that is irregular in its frequency

and rate of change. Wind and Robertson (1983) advocated the involvement of

top management in their integrated framework for strategic marketing. They

found top management involvement is critical to the success of their process

when applied to a large division in a Fortune 500 company. Dess and Davis

(1984) measured strategic orientation within an industry by surveying top

management perceptions of strategic action. Pecotich et al. (1985) assessed

executives’ perceptions of strategic action in their study of the influence of

environmental conditions on management choice of growth/expansion and

retrenchment strategies as well as corporate performance. Walton (1986)

investigated how top managers classify organisations in their own industries on

the basis that meanings assigned to environmental conditions by individuals

influence strategic action. Moreover, organisations respond to employee

perceptions and these perceptions do not necessarily reflect objective realities.

Fombrun and Zajac (1987) argued top managers form strategies taking into

15

consideration environmental threats and opportunities and therefore managers’

perceptions of the environment should influence strategic groupings.

Hambrick and Mason (1984) argued that top management team characteristics

affect their interpretation of the objective environment, which in turn affects a

company’s strategies and performance. This is illustrated in Figure 4.

Figure 4 An Upper Echelons Perspective of Organisations

Note. From “Upper Echelons: The Organization as a Reflection of Its Top Management.” by D.C. Hambrick and P.A. Mason, 1984, Academy of Management Review, 9(2), p.198.

These management characteristics form a screen between the objective

situation and their eventual perception of it and can be classified into

psychological and observable attributes. Psychological attributes are

comprised of the manager’s cognitive base and values. The cognitive base

forms the manager’s knowledge or assumptions about future events,

alternatives and consequences attached to the alternatives. The manager also

brings a set of values to the decision – principles for ordering consequences or

alternatives according to preference. Observable characteristics such as the

manager’s age, career experience, and education can also influence their

choice of strategy. Kotha and Vadlamani (1995) assessed the validity of two

generic strategic typologies – Mintzberg (1988) and Porter (1980) – based on

the assumption that strategic action is the result of the manager’s perceptions of

the competitive environment.

There have been some empirical studies investigating the congruence of

individual perceptions of structure, conduct and performance within a company

16

(Barrett et al., 2009; Bourgeois, 1978; Pelham & Lieb, 2004; Snow & Hrebiniak,

1980) and within companies competing in the same industry (Clark &

Montgomery, 1996; Ng, Westgren, & Sonka, 2009; Snow, 1976; Wilson,

Dahlgran, Conklin, Armstrong, & Luginsland, 1993). At the company-level,

Bourgeois (1978) investigated the effect of top management team perceptions

of goals and strategies on organisational performance. Given the first step in

strategic decision making is setting goals and then developing strategies to

achieve those goals, the author hypothesised that companies whose top

management teams agree on both the goals and the strategies should

experience higher economic performance than those who do not.

Questionnaires were mailed to CEOs and their top management teams of 12

non-diversified publicly-listed companies. The standard deviation for each

strategy was computed for each top management team to attain a firm-level

score. Thus, variance in perception within each top management team was

computed by summing standard deviations for each strategy. Firm

performance was measured using return on total assets, capital growth, net

earnings growth, EPS growth and return on sales growth averaged over a five-

year period from 1971 to 1976. Results showed that some top management

teams shared similar perceptions of both goals and strategies while other top

management teams did not agree on either the goals or the strategies or both.

Moreover, shared perception of strategies was more important to firm

performance than shared perception of goals. Therefore, consensus on

strategy within the top management team is critical to firm performance.

Snow and Hrebiniak (1980) examined the relationships between strategy,

organisational competence and performance within and across industries. They

argued that several strategies are potentially feasible within a particular

industry, but in order to achieve high performance, each strategy must be

supported with appropriate distinctive competences. Strategy was measured

according to the Miles and Snow (1978) typology of Defender, Prospector,

Analyzer and Reactor. Questionnaires were mailed to 721 top managers in 100

organisations generating a sample of 247 usable questionnaires (34% response

rate) from managers in 88 companies (an average of 2.8 respondents per

organisation). Each organisation's strategy was determined by calculating the

mode of the top managers' evaluations of strategy for their organisation. Since

17

strategy was measured by a nominal scale, the mode was considered the most

appropriate means of classifying the sample organisations based on the

strategic typology. One way analysis of variance revealed that top managers

within a company shared similar perceptions of strategy. Snow and Hrebiniak

(1980) concluded top managers deliberately develop strategies and competitive

advantages that are distinct from their rivals even though the environmental

situation faced by companies within an industry may be generally similar.

Pelham and Lieb (2004) investigated differences in perception of the

environment and strategy between presidents and national sales managers in

small and medium-sized industrial firms. Questionnaires were mailed to 1,200

industrial manufacturing firms but only 148 firms sent complete responses from

both the president and the national sales manager (12.3% response rate). This

is testament to the difficulty of collecting information from top managers within a

company. Respondents were asked to rate their satisfaction with their firm’s

performance compared to the competition on two measures of performance -

marketing/sales effectiveness (relative product quality, new product sales and

customer retention) and profitability (return on equity, return on investment,

gross profit margin).

Pelham and Lieb (2004) found substantial differences in perception of the

environment between the CEO and the national sales manager had a negative

impact on profit. They surmised that it could be due to incorrect environmental

analysis by either manager, poor communication between the managers or the

lack of formal environmental feedback procedures such as sales force call

reports. Results also showed substantial differences in perception of strategy

between the CEO and the national sales manager had a negative impact on the

organisation’s marketing/sales effectiveness. If the CEO pursues a strategy of

low cost, resources are allocated toward increasing production efficiency and

cost cutting measures. Meanwhile, the sales manager maybe focused on

product differentiation and encouraging the sales force to emphasise product

feature superiority to customers but resources are not available to substantiate

these claims. Consequently, different perceptions of customer needs (i.e.

environment) within a firm can lead to different strategies being pursued that

ultimately affect the firm’s performance. The authors recommended that future

18

studies should use archival (i.e. objective) performance data as the subjective

performance measures used in the study are vulnerable to biases.

Barrett et al. (2009) examined the variation in top management team

perceptions of four variables (entrepreneurial management, market orientation,

organisational flexibility and learning orientation) and its effect on performance.

The authors proposed that each executive has a different perception of reality

and developed the term ‘interpretative ambiguity’ to describe a management

team that perceives reality in different ways because of their cognitive diversity.

A non-probabilistic, convenience sampling procedure was used that involved

soliciting employees of several organisations, contacting members of personal

networks, and targeting particular firms to build industries. This resulted in a

sample of 696 managers from 60 organisations representing a wide variety of

industries including education, banking, healthcare and manufacturing.

Performance was measured using management perceptions given the

difficulties in obtaining correct (i.e. objective) financial information that is of

similar nature and time period among respondents, as well as the outright

refusal by many to release such information. Managers were asked to

qualitatively assess (1) how well the organisation did this year versus last year,

and (2) how well it did versus leading competitors or similar organisations (for

businesses and non-profits, respectively). Results showed highly significant

variation in top management team perceptions of the four factors and that this

had a negative effect on performance. The authors state that executives and

researchers should be concerned because many studies into organisations

utilised a single respondent raising questions on the validity of the findings.

Therefore, future studies should examine the top management team within a

company. Further, management team diversity (in terms of race, age, and

gender) contributes to variance in perceptions and while this can aid innovative

problem-solving, it also means information on strategies and performance must

be shared within a company.

At the industry-level, Snow (1976) examined management perceptions of, and

organisational response to, generally similar environmental conditions in the

college textbook publishing industry. Snow (1976, p.249) argued that firms act

upon and respond to an environment that their top managers have observed

19

and perceived: “That is, management responds only to what it perceives; those

environmental conditions that are not noticed do not affect management's

decisions and actions. This focus on enactment means that the same ‘objective’

environment may appear differently to different organizations, and, as a result,

these organizations may respond in a variety of ways.” This explains why firms

facing similar conditions pursue different strategies and by implication, achieve

different performance levels. Data was collected through in-depth interviews

concerning various organisation-environment relations with 62 high-level

college textbook publishing executives (e.g. College Division Director, Editor-in-

Chief, and National Sales Manager) in 16 organisations. Snow (1976) found at

a general level and in the short run that managerial perceptions of the

environment did not differ significantly. However, managerial perceptions of

future environmental trends and the organisation’s ability to cope with the

changes were quite different. Although managers' perceptions may vary

substantially across organisations in a similar environment, Snow (1976) argued

each organisation could be effective provided that it is properly designed to

pursue its chosen strategy.

In a study of the Arizona dairy industry, Wilson et al. (1993) found that the

personal characteristics of the manager and the characteristics of the business

influenced perceptions of the environment. The authors noted that it is

managers who observe, perceive and act on their interpretation of the

environment and thus, different individuals can have different perceptions of the

same industry. Questionnaires were mailed to 97 commercial dairy operations

but only 59 were used in the data analysis. Milk producers were asked to rank

the six most important sources of variability in their operations from a list of 19

potential sources of uncertainty. The respondents were also asked to rank the

six most important responses for managing this uncertainty. Results showed

milk producers did not share similar perceptions of the six most important

sources of uncertainty and that firm size, ownership structure and age

influenced managers’ perceptions of the environment and the strategies they

developed.

Clark and Montgomery (1996) examined management perceptions and

responses to threats from competitors in a hypothetical consumer durable

20

goods industry. The authors argued noisy environments make it difficult for

managers to perceive competitor actions. Participants comprised of MBA

students and senior executives of a European multinational who were placed in

a simulation game. In this simulation, there were five teams in charge of the

marketing and research and development strategy of competing companies.

Accuracy was counted by comparing perceived reactions in period t to reported

reactions in period t - 1 and calculating true positives, true negatives, false

positives, and false negatives accordingly. Results showed that teams did not

perceive some competitive actions – teams missed 79% of their competitor’s

reactions to their strategic decisions. In addition, both the MBA students and

the senior executives had inaccurate perceptions of competitive actions - when

correct perceptions were calculated as a percentage of total observed

reactions, teams were accurate only 35% of the time. Clark and Montgomery

(1996) concluded that executives often misinterpret competitors’ actions and

even correct interpretations do not necessarily help performance.

Ng et al. (2009) studied perceptions of competition among the value chain

members of the swine genetics industry. While previous research examined

perceptions of competition among rival firms, this research investigated

perceptions among members of the value chain, including direct and end

customers. Ng et al. (2009) argued it is important to study the perceptions of

customers because customers define competition in terms of satisfaction of

particular customer needs whereas managers are likely to define competition in

terms of a perceived competitive advantage. Such differences are likely to be

missed by managers because they are subject to competitive ‘blind spots’

brought about by overconfidence. Overconfidence limits managers’ ability to

question their perceptions, which can blind them from understanding their

competition. The authors studied perceptions of the attributes and groupings of

competition for three members of the swine genetics value chain: the top

management team, veterinarians and hog producers. A questionnaire was

mailed and to examine differences in perceptions, the intersection of firms that

was common to all respondents was sought. Attributes studied included firm

size, price, quality of service, technical support, responsiveness to customers

and promotional budget. Results indicated that each of the three respondent

group’s perceptions about the competitive attributes and competitive groupings

21

of the swine genetics market were significantly different. Further, the greater

the distance from the top management team, the greater the variance in

perceptions and therefore, the greater the number of blind spots. Therefore,

managers can mitigate their blind spots by broadening their perceptions of

competition to include those of its value chain customers.

Although the present study is concerned with executive perceptions of the SCP

paradigm as conceptualised by Porter (1980), previous studies have examined

the congruence of top management team perceptions of the organisation’s

internal environment (e.g. culture) (Stevenson, 1976; Ward, Lankau, Amason,

Sonnenfeld & Agle, 2007). Stevenson (1976) sampled 50 managers from six

firms to investigate their perceptions of company strengths and weaknesses.

Results showed managers’ perceptions of their company strengths and

weaknesses varied. However, Stevenson's findings must be considered as

exploratory because the sample size was small and the data from these case

studies did not permit quantitative testing of hypotheses. Ward et al. (2007)

found managers have different views on the organisation’s values (objectives

that an individual or group believes are important in running a business) but

concluded the success of a company depends, to a large degree, on its top

management team.

Social Psychology theory

Social psychology’s micro view of the organisation asserts that corporate action

can be traced to the decisions and behaviour of individual employees (Cyert &

March, 1963, 1992; Robey, 1982; Weick, 1979). Here, the company is viewed

as a collection of individuals. Weick (1979) argued that organisations respond

to an environment which has been enacted or created through a process of

managerial attention. That is, management responds only to what it perceives;

those environmental conditions that are not noticed do not affect management's

decisions and actions. The enacted environment is artificial in that it is affected

by the manager’s preferences, purposes, idiosyncratic punctuations, desires,

selective perceptions and designs. This focus on enactment means that the

same "objective" environment may appear differently to different organisations

and consequently these organisations respond in different ways. Robey (1982)

22

acknowledged that the perceived environment is affected by the manager’s

characteristics (e.g. age, experience, personality) and the environment itself.

Further, managers cannot scan the entire environment and may misinterpret the

environment. Cyert and March (1992) argue any decision process involves a

group of individuals who are simultaneously involved with other activities and as

a result, the manager only perceives a portion of the environment, not all of it.

Consequently, understanding decisions requires an understanding of how those

decisions fit into the lives of the decision makers.

Organisational Behaviour theory

Decision makers selectively perceive the environment because they cannot

process all the information relevant to their environment. This theory originated

from Simon’s (1945) Administrative Behaviour where he proposed the concept

of bounded rationality – the idea that organisational decision makers strive to be

rational within the limits of their cognitive capacities and information availability.

Managers attempt to make rational decisions but due to the limited information

available and limited cognitive capacity to process information, they use rules to

simplify their decision making.

Dearborn and Simon (1958) studied the effect of selective perception within an

organisation. They proposed that managers perceive aspects of the

environment that are relevant to the activities of their department. This

construct is called selective perception: in a complex environment, the

manager perceives in it what he is “ready” to perceive; the more complex the

environment, the more the perception is determined by what is familiar to the

manager and the less by what is in the stimulus. The authors sampled 23

executives from a single large manufacturing company. The executives were

presented with a case study and asked to write what they believed to be the

most important problem facing the company in the case study. Dearborn and

Simon (1958) found a significant relationship between the most important

problem mentioned and the department the manager belonged to. It was

concluded that managers perceive those aspects of the environment that relate

specifically to the activities of their department.

23

During the 1970s, several researchers investigated the influence of bounded

rationality on the congruence of individual perceptions of the environment.

Duncan (1972) indirectly examined the congruence of company perceptions as

part of his study on the characteristics of the environment that contribute to

managers experiencing uncertainty in decision making. Twenty-two decision

units were studied in three manufacturing organisations (ten decision units) and

in three research and development organisations (twelve decision units) A

decision unit is defined as a formal work group within the organisation with a

designated leader charged with a formally defined set of responsibilities

directed toward achieving the firm’s goals. Responses obtained from decision

unit members on all the items on a variable were pooled to reflect the degree of

the given variable experienced by the unit as a whole. To assess the

homogeneity of a decision unit’s perception of a particular variable, one way

analysis of variance was calculated. Results showed no significant differences

across members of a decision unit, that is, there were no significant differences

in company-level perceptions of the environment. However, Duncan (1972,

p.134) noted it is possible that individual perceptions of the environment within a

company can vary depending on the individual’s threshold level for uncertainty:

“Some individuals may have a very high tolerance for ambiguity and uncertainty

so they may perceive situations as less uncertain than others with lower

tolerances.” Lawrence and Lorsch (1973) investigated the congruence of

management perceptions of the environment across six organisations from the

plastics industry. The authors interviewed senior executives and asked them to

complete questionnaires that measured environmental attributes as perceived

by them. Results showed that management perceptions of the plastics industry

environment varied within firms. Specifically, managers in the research and

development departments perceived their environment to be highly uncertain,

while production managers perceived their environment to be largely

predictable. Downey, Hellriegel and Slocum (1977) studied the influence of

environmental characteristics and individual differences on perception of the

environment. For them, “the view that an organisation is for the most part what

people perceive it to be suggests the need to identify the potential role of

individual differences in the perceptions of organisational properties”. The

authors proposed three sources of individual differences explain the variation in

managers’ perceptions of uncertainty: individual cognitive processes, individual

24

experience and organisation expectations. They sampled 51 division managers

of a large US conglomerate and results showed cognitive process variables

were more consistently related to a manager’s perceived uncertainty than

perceived environmental variables. Downey et al. (1977) concluded that

individual characteristics influence perception of the environment and future

research should investigate the process by which the objective environment is

altered by an individual’s perceptual process.

Managerial Cognition theory

Another theory that seeks to explain why individual perceptions of the

environment vary within an industry is the theory of managerial cognition

(Ashforth & Fried, 1988; Daniels, Johnson, & de Chernatony, 2002;

Hodgkinson, 1997; Johnson & Hoopes, 2003; Kaplan, 2008; Nadkarni & Barr,

2008; Porac & Porac, 1995; Porac & Thomas, 1990, 1994; Porac, Thomas, &

Baden-Fuller, 1989; Schwenk, 1984), Hodgkinson’s (1997) review on

competition from a cognitive perspective highlighted that the literature on

strategy is based on the assumption that environments are objective entities

waiting to be discovered through formal analysis. However, it is managers’

perceptions of the environment filtered through existing mental models which

form the basis for strategy development. That is, structure is determined by

manager’s perceptions of the environment filtered through existing mental

models. Mental models can be considered managers’ mental representations

of the competitive environment, although there is considerable disagreement

concerning its definition and usage ranging from conceptions as temporary

dynamic models in working memory to knowledge structures in long-term

memory (Hodgkinson & Healey, 2008).

Schwenk (1984), drawing upon Simon’s (1947) bounded rationality concept,

argued that both limited cognitive capacities and information availability affect

managers’ perceptions. Specifically, managers employ cognitive simplification

processes to assist in environmental analysis. Two processes that may affect

manager’s perceptions during the environmental scanning stage are prior

hypothesis bias and anchoring. According to the prior hypothesis bias,

individuals who form mistaken beliefs tend to make decisions on the basis of

25

those beliefs despite evidence to the contrary. Further, these individuals seek

and use information consistent with their beliefs. Thus, decision makers who

believe that the company’s current strategy is successful may ignore

information suggesting gaps between performance and expectation. Under the

anchoring process, managers make insufficient changes to initial judgments

about the environment as new data emerges. This results in final estimates

being biased toward their initial values. Therefore, manager’s revisions to

strategies may be smaller than justified by the new information.

Ashforth and Fried (1988) proposed that executives use event schemas or

scripts in making decisions, resulting in “mindless behaviour”. An event schema

or script is defined as a cognitive structure that specifies a typical sequence of

events in a given situation. Event schemas regulate both the process and

content of decision making, leading to blinkered perceptions. Vital information

may be missed because it does not originate from a recognised source or

conform to an existing cue category, it may be distorted to fit a cue category or

it may cue a script that is no longer valid.

Empirical evidence on the congruence of company-level and industry-level

perceptions of structure, conduct and performance from the managerial

cognition discipline is conflicting. Some studies have found similar managerial

perceptions of the environment (Johnson & Hoopes, 2003; Panagiotou, 2006;

Porac et al., 1989) while other studies have revealed variances in executive

perceptions (Daniels et al., 2002; Hodgkinson & Johnson, 1994; Kaplan, 2008;

Nadkarni & Barr, 2008).

Porac and his colleagues conducted several studies in executive cognition.

Porac et al. (1989) studied the mental models of managers in the Scottish

knitwear manufacturing industry. The authors proposed that managers form

mental models of the environment from material/technical cues (e.g. entry

barriers, cross-elasticity of demand, product differentiation, pricing). These

mental models represent their beliefs about the company, competitors,

customers and suppliers as well as causal beliefs about the strategies required

to outperform competitors. Over time, managers in an industry share core

beliefs about the industry through mutual enactment processes in which

26

subjective perception of external information are objectified via behaviour.

However, managers cannot notice all cues and therefore mental models are

partial representations of the environment. Further, mental models are affected

by exogenous factors such as personal histories of managers. The authors

chose Scottish knitwear manufacturers for studying the influence of shared

beliefs because of their small size, cultural homogeneity, geographical

characteristics, and long-standing traditions. Extensive semi-structured

interviews were conducted with top managers from approximately 35 per cent of

these companies over a six-month period. Results from these interviews were

combined with detailed analyses of secondary industry data. Results showed

managers in the Scottish knitwear manufacturing industry share similar

perceptions of the environment. Specifically, top managers cited only other

Scottish firms as competitors even though there were other producers from

around the world. This caused them to follow a similar strategy leading to a

limited range of strategic actions for individual firms within the group.

In subsequent studies, Porac and his colleagues examined the notion that

managers subjectively identify competitors using simplified mental models. This

explains why individual perceptions of structure vary within a company and

within an industry. Porac and Thomas (1990) argued that the complex

information-processing demands on decision makers to identify competitors and

develop an appropriate strategic response caused them to use simplified mental

models to define rivals. Later, Porac and Thomas (1994) conducted two

studies to measure the cognitive structures of competitor definitions among

retailing firms based on the assumption that managers subjectively define

competitors. Porac, Thomas, Wilson, Paton and Kanfer (1995) examined how

firms defined a reference group of competitors by surveying managers to

identify their competitors based on their perception of competitors’ similarity to

their organisation.

Johnson and Hoopes (2003) found that sunk costs and bounded rationality

forced firms to focus their attention on nearby competitors. Although

managerial cognition affects perceptions of industry structure, the economics of

an industry may force firms to accept a reality they might not have created on

their own. To analyse the relationship between managerial cognition, industry

27

economics and industry structure, the authors carried out a series of

simulations. Specifically, they simulated competition in a spatial differentiation

game in which each player (competitor) possessed unique beliefs about the

distribution of consumer tastes. Results demonstrated focused attention meant

that firms did not consider all the potential competitors and this caused them to

develop biased estimates of their competitive environment. Thus, because they

observed each other, clusters of firms shared similar beliefs. However, as the

costs of strategic change decreased, managerial beliefs converged and

companies were free to change strategies to adapt to new opportunities.

Panagiotou (2006) studied the influence of managerial cognition on perceptions

of structure. Human judgment is required to analyse data and thus subjective

judgments by managers are a major component in the strategic planning

process. However, cost, time, cognitive abilities and information availability

affect management perceptions of structure, conduct and performance. Face to

face interviews with semi-structured questionnaires were conducted with one

manager from firms in the UK packaged holidays industry. The firms were

divided into two groups: (1) the Big Four representing the large incumbents and

(2) the Dotcoms comprised of internet-based new entrants. Results showed

that 86.29% of managers from the Big Four shared similar perceptions of the

industry. For the Dotcoms, 77.13% of managers shared similar perceptions of

the industry. For both the Big Four and the Dotcoms, 89.6% agreed managerial

cognitions drive firm performance and profitability.

In contrast, other studies in managerial cognition have revealed variances in

executive perceptions (Daniels et al., 2002; Hodgkinson & Johnson, 1994;

Kaplan, 2008; McNamara, Luce, & Tompson, 2002; Nadkarni & Barr, 2008).

Hodgkinson and Johnson (1994) examined managers’ mental models of the

competitive environment in the UK grocery retailing industry. Twenty-three

managers from two organisations were each interviewed using a variant of the

cognitive taxonomic interview procedures devised by Porac et al. (1989). The

study revealed differences in the cognitive categories identified by the

managers both within and between the organisations. However, the study also

revealed consensus within organisations regarding the categories which

28

describe the self-identity of the research participants' organisations and their

major competitors.

McNamara, Luce and Tompson (2002) investigated the relationship between

the complexity of a top management team’s knowledge structures and firm

performance. Specifically, the authors examined the complexity of top

management team’s knowledge structures regarding their competition using a

sample of 76 top management teams from banks in three U.S. cities. Using

hierarchical regression, results showed a significant relationship between the

complexity of cognitive knowledge structures and firm performance. They found

that the best performing firms identified the fewest number of strategies in their

industry. Results also showed that the degree of complexity in top

management team’s mental models varied significantly within the industry and

thus, managers within an industry do not share homogeneous beliefs regarding

the competitive structure of their industry. McNamara et al. (2002) concluded

that top managers should focus on the general positioning of their competitors

because too much segmentation may lead to inferior performance as the

organisation overlooks threats from rivals they have placed into other

competitive niches or possibly ignored market opportunities that are perceived

to be outside of their current market niche.

Daniels et al. (2002) examined the influence of the task and institutional

environmental on manager’s mental models of competition. In a task

environment, managers seek a competitive advantage over rivals and this

implies that companies within an industry will not share similar perceptions of

the environment. In an institutional environment, forces such as regulatory

changes, can force companies within an industry to share similar perceptions of

the environment. The authors sampled 32 managers from six firms in the UK

personal financial services industry. Data was collected through semi-

structured interviews and respondents were required to identify their

competitors and how they classified their competitors into strategic groups with

regards to one product – home loans for first time home owners. Results

showed firms within the personal financial services industry did not have similar

mental models of competition. However, the study showed that managers

within a firm shared similar mental models of competition. The authors

29

concluded that both the task and institutional environments influence managers’

perceptions of competition.

Kaplan (2008) proposed that managers’ inability to determine the meaning of

environmental changes leads to ambiguity. According to managerial cognition

theory, frames are the means by which managers make sense of ambiguous

information from their environment. This study adopts the view that strategic

action is determined by how managers observe and interpret change using

cognitive frames. The author employed ethnographic techniques to study the

strategy making process of a business unit within a manufacturer of

communications technologies. Specifically, Kaplan (2008) followed the

development and execution of two technology strategy initiatives within the

business unit by observing everyday activities and collecting other sources of

data to clarify and support her insights. This involved observing activities

associated with the two strategic initiatives, conducting 80 formal unstructured

interviews, observing 33 meetings and collecting documentation for each

project (e.g. e-mails, spreadsheets, PowerPoint documents, agendas, meeting

minutes). Results showed that frames about environmental events and

strategic action differed substantially across employees. Of the six decisions

studied, five were highly contested and one was not. Where perceptions about

a strategic choice were not congruent, employees engaged in highly political

framing practices to make their frames resonate and achieve the desired action.

According to Nadkarni and Barr (2008), there are two schools of thought

regarding the drivers of strategic action: industry structure and managerial

cognition. Under the industry structure view, managers are rational and

industry structure influences the effectiveness of strategic action. In contrast,

the managerial cognition view asserts that bounded rationality prevents top

managers from developing a complete understanding of their environments and

thus, top managers develop subjective representations of the environment

which determines strategic action. The authors integrated both perspectives to

investigate if industry velocity (i.e. industry structure) affects managerial

cognition (i.e. attention focus and causal logics) about environments and if

managerial cognition mediates the relationship between industry structure and

strategic action.

30

Nadkarni and Barr (2008) hypothesised that industry velocity affects attention

focus and causal logics, which in turn, determine speed of strategic response.

Specifically, as top managers observe and perceive challenges in the velocity of

their environment (i.e. the frequency of changes and time between these

changes), they develop specific attention focus (i.e. direct attention to those

parts of the environment they believe to be relevant while selectively ignoring

others) and causal logics (i.e. beliefs regarding the causal relationship between

environment and strategy) about their environments. Further, top managers’

attention focus and causal logics determine how they observe and respond to

environmental challenges. Firms will not respond to raw environmental

challenges unless they notice these variables and interpret how these variables

affect their firm. Thus, industry velocity will not affect strategic action directly.

Managerial cognition was measured using letters to shareholders in company

annual reports. Nadkarni and Barr (2008) chose not to use questionnaires on

the basis that cognitive structures cannot be measured directly and the very act

of asking individuals to reveal their beliefs can change them. Also, it becomes

more difficult if managers are asked to recall beliefs held in previous time

periods because memories are often incomplete, misinterpreted or mistakenly

reports because of the outcomes later achieved. Results revealed that industry

velocity significantly and directly influenced attention focus and causal logics

that top managers develop about their environment, which in turn, influenced

the speed of response to changes in the environment. This supports the

managerial cognition theory of strategic action which suggests that firms enact

their environments.

In conclusion, the empirical evidence from various disciplines suggests that top

management teams within a company may have different perceptions of the

same objective environment (e.g. Bourgeois, 1978; Pelham & Lieb, 2004;

Barrett, et al., 2009; Lawrence & Lorsch, 1973; Downey, et al., 1977; Kaplan,

2008; Nadkarni & Barr, 2008). Further, companies competing in the same

industry may have different perceptions of the same objective environment (e.g.

Snow, 1976; Wilson, et al., 1993; Clark & Montgomery, 1996; Hodgkinson &

Johnson, 1994; Daniels, et al., 2002). Despite such evidence, Porter (1980)

31

argues corporate response to structure as the critical variable in determining

industry and company performance. Here, the unit of analysis is the industry

and Porter (1980) implicitly assumes managers within an industry define and

observe the same objective environment. On this basis, perception of structure

as described by Porter’s five forces model should be identical for all managers

operating in the same industry. Within marketing, Hunt’s (2010) R-A theory of

competition proposed that the resources of firms within an industry are

heterogeneous and immobile and therefore, managers must make strategic

choices and these choices influence firm performance. Resources include

market and competitor intelligence and some firms will have a comparative

advantage in resources while others will have a comparative disadvantage in

resources. Specifically, some firms will have better intelligence or information

than others. The unit of analysis is the manager and it is manager who

develops strategies on the basis of the resources the firm possess, including

imperfect perception of information. The purpose of this research is to test

Porter’s theory which implicitly assumes managers within an industry define and

observe the same objective environment and in doing so, juxtapose it against

the current empirical evidence and Hunt’s (2010) R-A theory of competition.

This leads to the study’s first and second hypotheses.

H1: Individual perceptions of structure, conduct and performance within a

company will have a strong positive relationship.

H2: Company perceptions of structure, conduct and performance within an

industry will have a strong positive relationship.

Management Perceptions versus Objective Reality

I have discussed that managers within a company may have different

perceptions of the same objective environment. Further, companies competing

in the same industry may have different perceptions of the same objective

environment. Several theories have been proposed to account for this

phenomenon including the enacted environment concept from social

psychology and the bounded rationality concept from organisational behaviour.

Managerial cognition theory argues managers’ mental models can also

32

influence the congruence of individual perceptions. This raises the next issue –

to what extent do individual perceptions of structure correspond to facts/reality?

(Walsh, 1995). Hunt (1993, p. 84) argues that it is possible human perceptions

are ‘truthful’ because the fact that humans have survived implies early humans

were capable of veridically distinguishing alligators from logs, solid earth from

quicksand, tigers from domestic cats, wolves from dogs, and human friend from

human foe. Thus, while our perceptions may not be completely accurate, it has

assisted in the survival of the human race unless our cognitively held theories of

the world warn us of an illusion.

Within the business discipline, Zalkind and Costello (1962, p. 219) developed

the term “naive realism” to refer to a condition whereby managers assume that

their perceptions correspond with what is “out there”. The notion that

management perceptions of industry structure drives strategy rather than

strategy being driven by objective reality is important because Searle (1998, p.

10) argues “there is a real world that exists independently of us, independently

of our experiences, our thoughts, our language.” The concept that there is a

real world that exists independent of human beings and of what they say or

think about it is called realism. To determine if statements about objects are

true or false depending on whether things in the world really are the way we say

they are is called the correspondence theory of truth (Searle, 1998). For

example, phenomena which are independent of human beings’ opinions and

hence, can be verified as true or false are hydrogen atoms, tectonic plates,

viruses, trees and galaxies. In contrast, phenomena which depend on human

beings’ consciousness for their existence are money, property, marriage, wars,

football games and cocktail parties.

In the real world there are objects and there are people who are observers that

perceive, evaluate and act. Objects have properties and people have

perceptions about the extent to which objects possess these properties

(intensity) and feelings about whether this is good or bad (Pecotich & Ward,

2007). In the applied strategic marketing context, the industry may be viewed

as the object and its properties are the five forces – rivalry among existing

companies, the bargaining power of buyers, the bargaining power of suppliers,

the threat of new entrants, and the threat of substitute products/services. The

33

top management team within an organisation can observe the same industry

but their perception of its structural properties and their feelings about whether

this is good or bad may vary. For example, the marketing manager perceives

that competition from current rivals is a greater threat than the sales manager

who believes the bargaining power of buyers poses a more serious one. Their

perception of the environment can affect company performance. The sales

manager can reduce prices in response to their perception that the bargaining

power of buyers poses a serious threat resulting in lower short term profitability.

Also, information from sales personnel is often used in the strategic planning

process so inaccurate perceptions will affect the quality of long term plans.

Therefore, while perceptions of the five forces may vary within a company,

these five forces do exist independently of management perception and can be

verified by objective (archival) data.

Porter (1980) assumes managers within an industry define and observe the

same environment. The issue is that a manager’s perceptions of industry

structure may not correspond to the objective reality (e.g. number of competing

firms). Perception is influenced by many variables including the decision

maker’s personality, internal politics and company objectives. Therefore

management perceptions of structure determine conduct, not the objective

reality, and the organisation becomes a victim of perceptions which ignore or

distort environmental elements (Miles, Snow, & Pfeffer, 1974). For example,

identification of competitors is required to analyse the five competitive forces.

This depends on how managers perceive and define both current and potential

competitors as well as substitute products and thus, analysis of industry

structure depends on a manager’s perceptions of industry boundaries. In

another example, the focus strategy is a demand-driven concept and the

concern is with a particular market, buyer or segment (Mintzberg, 1988). This

strategy depends on how managers perceive, define and disaggregate the

market and so strategic action is driven by subjective perceptions of the nature

of the market (Pecotich et al., 2003).

Not many studies have attempted to compare company perceptions with

objective data. In a review of past research on the accuracy of management

perceptions, Starbuck and Mezias (1996) did not find any studies that compared

34

managers’ perceptions with objective data. The authors examined the

Abstracted Business Index (ABI) database from 1986 – 1992 and 1988 – 1994

and highlighted a study by Dess and Robinson (1984) which claimed to

compare top management team perceptions with objective measures of return

on total assets and sales growth. However, “objective” data was, in fact, CEOs

perceptions and hence, the study compared two sets of perceptions. Starbuck

and Mezias (1996) concluded it is very difficult to compare managers’

perceptions with objective data due to the methodological challenges in

measuring the accuracy of subjective data include designing good

questionnaires, obtaining good objective data, and securing a sufficient number

of appropriate respondents. They also pointed out that many studies have

reported finding large errors and biases in perceptions and if this is the case,

published research that relied on managers as primary data sources describe

errors or shared myths. Further, if managers have erroneous perceptions and

make inaccurate forecasts, strategic planning could result in the organisation

pursing the wrong goals and missing opportunities.

Some studies have attempted to compare company perceptions with objective

data and did not find strong positive relationships (Bourgeois, 1985; Downey et

al., 1977; Hambrick, 1981; Mezias & Starbuck, 2003; Payne & Pugh, 1976;

Shortell & Zajac, 1990; Tosi et al., 1973). Tosi, Aldag and Storey (1973)

compared middle and top management perceptions of environmental

uncertainty with volatility indices, assuming that volatility is highly correlated

with uncertainty. Data was collected from 102 middle and top managers from

22 diverse firms in 13 industries and volatility indices were calculated from the

firms’ financial reports and industry statistics. Correlations between the volatility

indices and managers’ perceptions of environmental uncertainty varied from -

0.29 to 0.07. Downey, Hellriegel and Slocum (1975) replicated the Tosi et al.

(1973) study and found similar results. Data was collected from 51 heads of the

divisions of one large conglomerate. They calculated three kinds of volatility

indices and one index of competitiveness, and compared these with the

managers’ perceptions. The correlations ranged from -0.24 to 0.21. Both

studies found no relationship between individual perceptions and objective data.

However Tosi et al. (1973) did not purposely set out to compare management

perceptions with objective measures and Downey et al. (1975) were doubtful

35

about the appropriateness of the objective measures. Thus, the low and

negative correlations may have been due to poor objective measures, poor

questionnaires or inaccurate perceptions.

Whereas the preceding studies highlight errors in managers’ perceptions of

their competitive environments, Payne and Pugh (1976) raised the possibility of

similar errors in perceptions of organisational properties. They reviewed studies

in which researchers asked employees to describe their firms’ structures and

cultures and found that most employees held inaccurate perceptions. Payne

and Pugh (1976) concluded that employees within a firm have such significant

differences in perceptions that perceptions could not be averaged because the

mean scores were uninterpretable. Further, except for organisational size,

employees’ perceptions of their firms’ properties correlated weakly with

objective measures. Finally, differences among employees’ perceptions of their

firms’ properties corresponded with their jobs and hierarchical position. For

example, senior managers generally held more favourable views of their

organisations.

Hambrick (1981) investigated strategic awareness within top management

teams and its relationship to an executive’s hierarchical position in the firm.

Strategic awareness was conceptualised as the degree to which an executive's

perception of the organisation's strategy is congruent with the organisation’s

realised strategy (as externally measured) and the CEO’s perception. It was

hypothesised that the CEO’s perception of their organisation’s strategy would

be most congruent with the external measure of strategy. Second-level

executives would exhibit a greater gap between their perceptions of strategy

and the external measure of strategy and third-level executives would exhibit

the largest gap.

The sample comprised of 20 organisations from three industries – private liberal

arts colleges, voluntary general hospitals and life insurance firms. Objective

data on strategy was collected from two sources: published data on recent

product/market additions and expert panel assessments. The published data

was sourced from the state office of education. The opinion of experts cannot

be considered ‘objective’ but the correlations between the published objective

36

data and expert panel assessments were 0.56, 0.46, and 0.41 for colleges,

hospitals and insurance firms, respectively (all significant at the .01 level).

Hambrick (1981) also conducted interviews with the CEOs to verify that current

strategy aligned with the published data based on the rationale that published

data may be historical and not reflect current realised strategy. Top

management team perceptions of strategy (i.e. subjective data) was collected

through questionnaires mailed to 218 executives, of whom 209 (96%)

responded (this was conducted as part of a broader study, thus accounting for

the high response rate). The strategy construct was operationalised using the

Miles and Snow (1978) typology of Defenders and Prospectors.

Results demonstrated the higher an executive’s level, the greater the

agreement between his perception of the organisation’s strategy and the

external measure of realised strategy. There was also support to show the

higher the executive's position in the organisation, the greater the agreement

between his or her perception and the CEO’s perception. Those closest to the

top of the organisation are most aware of its strategy (either its realised strategy

or the chief executive's vision of strategy). Hambrick (1981) concluded these

results were expected for two reasons. First, strategy is usually developed by

the senior management team. The more organisational layers a strategy must

be communicated, the less likely they are to be perceived. Second, executives

closest to the top of organisations generally tend to have the organisation-wide

and industry-wide perspectives necessary for accurately assessing strategies,

compared to more specialised perspectives of middle level executives. These

findings suggest that future research examining management perceptions of

strategy must sample the CEO or risk receiving inaccurate information.

Bourgeois (1985) compared the top management team’s perceptions of

environmental uncertainty with objective data on environmental volatility and its

effect on firm performance. This study integrated two perspectives on the

strategic decision making process: strategic management and empirical

organisation. According to the strategic management view, managers make

decisions based on accurate assessments of their external environments but

under the organisation theory view, managers are subject to high levels of

perceived environmental uncertainty that is detrimental to performance. The

author hypothesised the higher the congruence between top management

37

perceptions of volatility with objective data, the higher the firm performance.

This is based on the rationale that strategic action is based on an accurate

perception of the environment during the scanning process. The author also

predicted the greater the homogeneity of perceived environmental uncertainty

within a top management team, the greater the economic performance of a firm.

Here, performance is viewed as a function of perceptual homogeneity within

firms, regardless of the degree of environmental uncertainty. If top managers

work together to develop strategy, they must share differing information,

opinions and perceptions. Working together leads to consensus and thus

coordinated and effective strategies. This results in higher economic

performance and low variance in perceptions of environmental uncertainty

within the top managements.

The sample comprised of 20 public corporations with a non-diversified business

to ensure respondents’ answers within a firm were comparable. Data was

collected through questionnaires mailed to CEOs and their teams – 106 were

mailed and 99 usable responses were returned (93% response rate). Objective

data was collected from secondary data sources (industry statistics and annual

reports). Results demonstrated strong support for the first hypothesis, that is,

the higher the congruence between top management team perceptions of

volatility with objective data, the higher the firm performance. Congruence

between volatility (objective measure) and perceived environmental uncertainty

(subjective measure) explained over 30% of the variance in economic

performance. However, the second hypothesis regarding homogeneity of top

management team perceptions and firm performance was rejected. Variance in

perceptions of environmental uncertainty was positively correlated with

performance. Therefore, as variance in perceptions within the top management

team increased, so did the level of economic performance. Bourgeois (1985)

reasoned this may be that when the top management team share similar

perceptions this leads to “insulation, arrogance, tunnel vision, blindness,

Watergate-style feelings of moral omnipotence” whereas diversity in perception

removes blinders. However, the author concluded that large variation in

perception within the top management team is only good when the mean

perceived environmental uncertainty (subjective measure) is congruent with

volatility (objective measure). This study was criticised by Mezias and Starbuck

38

(2003) for averaging subjective perceptions. Averaging individual perceptions

misrepresents the accuracy of their individual perceptions because averaged

perceptions can be quite accurate, even though most individuals have

inaccurate perceptions.

Shortell and Zajac (1990) found congruence between management perceptions

of strategic orientation and objective data in the hospital industry although the

main purpose of their study was to provide evidence on the reliability and

validity of Miles and Snow's typology of strategic orientations. Two types of

data were collected: subjective (i.e. perceptual) and objective (i.e. archival).

Subjective data was gathered through a self-administered questionnaire on

strategic planning that was mailed to each hospital's chief executive officer

(CEO). Additional subjective data was gathered through interviews with

hospital management including board chairmen, CEOs, and vice presidents for

strategic planning, finance, marketing, human resources, and related functional

areas. Objective data on services provided by each hospital was collected from

an industry body – the American Hospital Association. Results showed

congruence between manager’s perceptions of their firm’s strategic orientation

and archival data. Shortell and Zajac (1990) concluded that future researchers

should use self-typing data with archival data to obtain a more accurate

description of a given firm's strategy.

Mezias and Starbuck (2003) examined two decades of research into the

accuracy of managerial perceptions and conducted two empirical studies.

Strategic planning requires managers to match an organisation’s strengths and

weaknesses to environmental opportunities and threats. Thus, perceptual

accuracy regarding the environment is required for successful planning. The

first study collected data from managers in executive MBA courses on their

perceptions of organisational properties and environmental properties. The

questions about their organisations concerned properties such as number of

employees, number of rules, use of formal versus informal communications,

emphasis on numerical or non-numerical information, processes used for

evaluating strategies and policies and stability of strategies. The questions

about their environments concerned properties such as sales growth, industry

concentration, industry homogeneity, industry growth, fluctuations in sales and

39

their industries’ SIC codes. Objective data on organisational properties was

collected from the respondents colleagues because the authors deemed the

organisational properties were socially constructed and hence, not amenable to

objective measurement. Objective data on environmental properties was

collected from company annual reports and government statistics. Results

demonstrated managers’ perceptions of the environment did not match

objective data. Managers greatly understate rates of change over time as well

as period-to period fluctuations. For some variables, about 40% of managers

had accurate perceptions but managers with very inaccurate perceptions were

more prevalent. In the second study, respondents were senior managers from

four major divisions within a large firm. However, this study was restricted to

examining management perceptions of the firm’s quality improvement

programme as part of the firm’s agreement to participate in the study. Objective

data was collected from internal quarterly reports issued to employees

regarding the performance of the quality improvement programme. Each

manager was required to rate six numeric measures of quality performance on

quantitative and qualitative scales. Alarmingly, 52 – 91% of respondents gave a

qualitative response even after stating “I don’t know” about the corresponding

quantitative measure. Further, respondents within departments gave very

different answers to the actual/objective measures. The authors concluded

firms can take the following actions to increase the accuracy of manager’s

perceptions: using education and training to inform managers about

organisational and environmental properties, exploiting improved technology,

helping organisations to identify and correct misperceptions and designing

robust organisations that can tolerate misperceptions. However, Mezias and

Starbuck (2003) argued that most problem solving does not require accurate

perceptions because managers can act effectively without having accurate

perceptions; they need only pursue general, long term goals.

The empirical evidence on the accuracy of management perceptions suggests

that the knowledge managers possess is erroneous and therefore, what

managers believe to be true is often quite different from reality (Pillai, 2010).

However, how important is it for individual perceptions of structure to

correspond with objective reality? According to Starbuck and Milliken (1988, p.

38): “one thing an intelligent executive does not need is totally accurate

40

perception.” Completely accurate perceptions require the manager to notice all

stimuli and as a result, he or she is unable to focus attention on the relevant

stimuli. Perceptual filters amplify some stimuli and attenuate others, resulting in

the distortion of raw data and focusing attention. Effective perceptual filtering

amplifies relevant information and attenuates irrelevant information – the filtered

information is less accurate but if the filtering is effective, it is more

understandable. Winter (2003) asserts the importance of accurate

management perceptions depends on the manager’s role and identified four

roles: (1) as useful informants in academic research; (2) to be competent in

their normal work; (3) to be effective problem solvers in non-strategic, novel

situations and (4) to be effective in making strategic decisions. Managers, as

informants in academic research, can be unreliable because they are asked

things they have no reason to know (i.e. outside their normal work), they may

not understand the questions, they are too busy to respond fully, or they have

secrets to protect.

In summary, while the majority of the empirical evidence suggests that

individual perceptions may not correspond to the objective reality, Porter (1980)

implicitly assumes managers within an industry define and observe the same

objective environment. He argues corporate response to industry structure as

the critical variable in determining industry and company performance and

therefore, it is imperative for companies to choose the right industries to

compete in and/or alter industry structure to increase monopoly power. Hunt’s

(2010) R-A theory of competition asserts that heterogeneous and immobile

resources within an industry require managers to make strategic choices and

these choices influence firm performance. Resources include market and

competitor intelligence and some firms will have a comparative advantage in

intelligence while others will have a comparative disadvantage. Therefore,

managers develop strategies on the basis of the resources the firm possess,

including imperfect perception of information. The purpose of this research is

to test Porter’s theory which implicitly assumes managers within an industry

define and observe the same objective environment but the current empirical

evidence and Hunt’s (2010) R-A theory suggests that perceptions may not

correspond to the objective reality (Figure 5). This leads to the following

hypothesis.

41

H3: Individual perceptions of structure, conduct and performance will have a

strong positive relationship with the objective reality.

Figure 5 The Conceptual Model

The Best Predictor of Performance

I have discussed that executive perceptions of the environment may not

correspond to the objective reality and therefore, it is possible that management

perceptions of industry structure drives strategy rather than strategy being

driven by objective reality. The organisation becomes a victim of perceptions

which ignore or distort environmental elements (Miles & Snow, 1978; Miles et

al., 1974). This raises the critical question as to what is the best predictor of

performance – objective data or management perceptions of industry structure.

Under the SCP model in industrial organisation economics, firm performance is

dependent on the structural features of industry. Characteristics of the industry

environment determine the strategies that companies choose whose joint

conduct determines the performance of the industry. In this formulation the role

of human agency, even if implicit, is ambivalent at best. The unit of analysis is

the industry and the focus is on objective external criteria where the executive’s

role is largely non-existent. Porter (1980) emphasised corporate response to

industry structure as the critical variable in determining financial performance.

“The essence of formulating competitive strategy is relating a company to its

environment” (p. 3) and the key to success is to “find a position in the industry

where the company can best defend itself against these competitive forces or

can influence them in its favour” (p. 4). Empirically, therefore, Porter’s theory

42

predicts that “industry effects” should explain most of the variance in firms’

performance and “firm effects” should explain little, if any.

This view is supported by Schmalensee’s (1985) study into industry effects

versus firm effects using variance components analysis. Schmalensee (1985)

used this technique to assess the independent importance of nested business

unit, corporate and industry effects where “the firm” is conceptually closest to

the business unit. Data was collected from the Federal Trade Commission’s

(FTC) line of business data and 1975 return on assets was used as the

measure of financial performance. The FTC database contained only

manufacturing businesses and data was reported at the business unit level.

Results showed industry effects accounted for 19.5% of the variance of

business unit return on assets and corporate effects to be not significant. He

concludes: “This supports the classical focus on industry level analysis as

against the revisionist tendency to downplay industry differences” (p. 349).

McGahan and Porter (1999) studied a large sample of U.S companies from

Compustat Business-Segment reports for 1981 to 1994 to examine the

persistence of incremental industry, corporate-parent and business-specific

effects on profitability. They concluded that changes in industry structure had a

more persistent impact on profitability than changes in firm structure.

In contrast, the marketing and strategic management disciplines view firm

effects as the major driving force behind firm performance. It is implicitly

assumed that a manager’s decisions and actions determine firm performance.

The unit of analysis is the manager and human agency, the manager or the

management team is central to the evaluation of environmental conditions

which form the basis of strategic action. Rumelt, Schendel and Teece (1991, p.

6) asserted “firms have choices to make if they are to survive...the selection of

goals, the choice of products and services to offer; the design and configuration

of policies determining how the firm positions itself to compete in product-

markets (e.g. competitive strategy)...It is a basic proposition of the strategy field

that these choices have critical influences on the success or failure of the

enterprise...”. In marketing, the traditional marketing management paradigm

proposes that strategic action is the result of managers aligning environmental

opportunities and threats with company strengths and weaknesses (e.g. Aaker,

43

2001; Cravens, 1999; Kotler, 1994; Wind and Robertson, 1983). Specifically, it

is the manager who makes decisions and takes action in response to

environmental conditions. This view has dominated marketing from its earliest

conceptual foundations (McCarthy, 1960; McKitterick, 1957; Shaw, 1916; Wind

& Robertson, 1983). Accordingly, firm diversity should have a greater impact on

financial performance than the industry.

Within marketing, Hunt (1997a, 1997b, 1999, 2000a, 2000b, 2001, 2002a,

2002b, 2010; Hunt & Arnett, 2001, 2006; Hunt & Derozier, 2004; Hunt & Duhan,

2002; Hunt & Lambe, 2000; Hunt & Morgan, 1995, 1996, 1997) offered an

alternative theory of competition to explain why the firm has a stronger influence

on performance than the industry. R-A theory can be used to explain and

predict the micro phenomenon of the significant heterogeneity of firms

throughout the world’s market-based economies. Specifically, firms differ in

size, scope, methods of operations and financial performance across industries

and within industries in the market-based economies around the world. For

example:

1. Some firms are so large that their sales exceed the GDP of many

countries, whereas others sell flowers on a single street corner

2. Some produce hundreds of products and others sell only one

3. Some are vertically integrated" hierarchies and others specialise in one

activity

4. Some are profitable and others are unprofitable

5. Some consistently maintain relatively high profits and others "fall back

into the pack”

For Hunt (2010), competition is an evolutionary, disequilibrium-provoking

process because of the constant struggle among firms for comparative

advantages in resources that will yield marketplace positions of competitive

advantage for some market segments and thereby, superior financial

performance. This process is illustrated in Figure 6.

44

Figure 6 Hunt’s Resource-Advantage Theory of Competition

Note. From Marketing theory: foundations, controversy, strategy, resource-advantage theory, S. Hunt, 2010, New York: M.E. Sharpe, Inc.

Rivals will attempt to neutralise or take over the advantaged firm through

acquisition, imitation, substitution or major innovation. Thus R-A theory is

dynamic and disequilibrium is the norm. The implication is that though market

based economies are moving, they are not moving toward some final state or

equilibrium. The success of this process is influenced by five factors: societal

resources, societal institutions that form the “rules of the game”, actions of

competitors, behaviour of consumers and suppliers, and public policy decisions.

R-A theory combines two central concepts to explain why firms differ in size,

scope, methods of operations and financial performance across industries and

within industries in the market-based economies of the world – heterogeneous

intra-industry demand and heterogeneous, imperfectly mobile resources (Hunt,

2010).

R-A theory adopts marketing’s heterogeneous demand theory to propose that

consumers within an industry have different tastes and preferences so firms

have to develop different offers for different segments within the same industry.

Market segments are defined as intra-industry groups of consumers whose

tastes and preferences with regard to an industry’s output are relatively

homogenous. A group of firms that make up the shoe industry do not

collectively face a single, downward sloping demand curve for such an industry

demand curve implies consumers have the same tastes and preferences. The

45

prolific variety of shoe styles available suggest otherwise – if we examine the

market for ladies’ shoes there are work shoes, evening shoes, sneakers,

sandals, boots, etc. Therefore Porter’s (1980) competitive strategies for what

he considers to be homogeneous industry environments – industries that are

fragmented, emerging, maturing, declining or global – cannot work. For

example, he recommends offensive strategies for organisations in a fragmented

industry to overcome fragmentation. Porter (1980) assumes that homogeneous

industry environments exist but Hunt (2010) argues that generic industry

environments do not exist (with the exception of commodities) as demand within

and across industries is heterogeneous. The fact that intra-industry demand is

heterogeneous in most industries supports R-A theory’s ability (and

neoclassical theory’s inability) to correctly predict diversity in business unit

financial performance.

R-A theory also adopts a resource-based theory of the firm whereby the firm

combines heterogeneous, imperfectly mobile resources. Resources are

tangible and intangible entities available to the firm that enable it to produce

efficiently and/or effectively a market offering that has value for some market

segments. Resources can be financial (e.g. cash resources, access to

financial markets), physical (e.g. plant, equipment), legal (e.g. trademarks,

licenses), human (e.g. skills and knowledge of employees), organisational (e.g.

competences, controls, policies, culture), informational (e.g. consumer and

competitor intelligence) and relational (e.g. relationships with suppliers and

customers). One of the assumptions of R-A theory is that the firm’s information

is imperfect and costly. Further, Hunt (2000b, p. 188) argues “because of

differences in the histories of firms with respect to investments in information

capital, the knowledge capital of firms in the same industry will be

heterogeneous and asymmetrically distributed.” Therefore, a comparative

advantage in information yields a marketplace position of competitive

advantage which leads to superior financial performance. Resource

heterogeneity and immobility imply strategic choices must be made and that

these choices influence performance. It is the role of managers to recognise,

understand, create, select, implement and modify strategies.

46

While R-A theory posits firm effects over industry effects as the main driver of

diversity in financial performance, it does share three similarities with industrial

organisation economics. First, the firm’s objective is superior financial

performance and the proximate cause of superior financial performance is

marketplace position. Second, R-A theory agrees that a “stress on resources

must complement, not substitute for, stress on market positions” (Porter 1991,

p. 108). R-A theory integrates marketplace position view with the resource view

by positing that it is a comparative advantage in resources that results in

marketplace positions of competitive advantage and thus superior financial

performance. Therefore R-A theory provides an explanation for Porter’s (1991)

claim that some firms are superior to others in performing value-chain activities:

such superior-performing firms have a comparative advantage in resources e.g.

specific competences related to specific value-producing activities. Finally, R-A

theory agrees that competitors, suppliers and customers influence the process

of competition and firm performance but disagrees with “Bain-type” thinking that

industry structure entirely determines performance. Bain (1956, 1968)

emphasised that industry structure, rather than firm strategies, determined

performance.

Hunt’s (2010) theory of competition contributes to explaining observed

differences in quality, innovativeness and productivity between market-based

and command-based economies. Historically, Eastern-bloc products have been

of lower quality and unless consumers in these command economies desire

lower-quality products (which is not supported by the higher prices commanded

by Western goods in such economies), the theory of perfect competition does

not satisfactorily explain these lower-quality products.

Therefore, R-A theory rejects the notion that “choosing industry” is the key to

success. It is firm effects; not industry effects, that explains variation in firm

performance. Empirical research on financial performance clearly shows that

“firm effects” dominate “industry effects” and competition is market segment by

market segment (Brush, Bromiley, & Hendrickx, 1999; Chang & Singh, 2000;

Cubbin & Geroski, 1987; Galbreath & Galvin, 2008; Hansen & Wernerfelt, 1989;

Hawawini, Subramanian, & Verdin, 2003; Mauri & Michaels, 1998; McGahan &

Porter, 1997; Powell, 1996; Roquebert, Phillips, & Westfall, 1996; Rumelt, 1991;

47

Short, Ketchen, Palmer, & Hult, 2007). Cubbin and Geroski (1987) found

company profits were more affected by firm than industry variables in their study

of 217 large UK firms from 1951 – 1977. They concluded that market dynamics

within industries were heterogeneous with differences between firms persisting

for long periods of time. Hansen and Wernerfelt (1989) used the Compustat 5-

year average rate of return on assets as the measure of financial performance

in a sample of 60 Fortune 1000 companies. They found that industry explained

19% of the variance in return on assets but that organisational factors explained

about twice as much variance in performance.

Rumelt (1991) highlighted that Schmalensee’s (1985) use of only one year of

the FTC data did not take into account transient annual fluctuations but also did

not separate the effects of the overall corporation from those of the individual

business unit. The author supplemented Schmalensee’s (1985) 1975 data with

the FTC data for 1974, 1976, and 1977 and found that corporate and business

unit effects explained 2% and 44% of the variance and industry effects

explained only 8% respectively. Therefore, Rumelt (1991) found “total firm”

effects of 46% (2% + 44%) was six times stronger than industry effects and

concluded the most important determinant of long term rates of return are

resources or market positions specific to business units rather than corporate

resources or membership in an industry.

Supporting Rumelt’s findings, Roquebert et al. (1996) found industry, corporate

and business unit effects to be 10%, 18% and 37% respectively (giving “total

firm” effects of 18% + 37% = 55%). Roquebert et al. (1996) used the

COMPUSTAT database which, in comparison to the FTC database, was much

larger (over 6,800 manufacturing firms), from a more recent time period (1985 –

1991), used a longer time period (7 years vs. 4), included service businesses

and both large and small corporations. Similarly, McGahan and Porter (1997)

using the COMPUSTAT database, found industry, corporate and business unit

effects accounted for 19%, 4% and 32% respectively for their sample of 7,003

corporations during the time period 1982 – 1988 (resulting in “total firm” effects

of 4% + 32% = 36%). Brush et al. (1999) used structural equation modelling

instead of variance components analysis to examine the effect of industry,

corporate, and business unit on business unit profitability. Using the

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COMPUSTAT database, they found industry, corporate and business segment

effects to be 15%, 15% and 25% respectively of return on assets (resulting in

“total firm” effects of 15% + 25% = 40%).

Powell (1996) also studied the influence of industry factors on firm performance

but used executives’ perceptions of industry structure and the firm’s

performance rather than objective data. Despite the methodological

differences, Powell (1996) found industry factors explained about 20% of overall

performance variance which was consistent with earlier studies (Rumelt, 1991;

Schmalensee, 1985).

Mauri and Michaels (1998) use a sample of 264 single-business companies

from 69 4-digit SIC code industries from 1988 – 1992 and found industry and

firm effects to be 5% and 30% respectively. For the time period 1978 – 1992,

Mauri and Michaels (1998) found industry effects of 4% and firm effects of 19%.

Chang and Singh (2000) used the Trinet database and market share as the

measure of firm performance to investigate the effect of industry, corporate and

business unit effects on firm performance. They argue the FTC and Compustat

databases used in earlier studies were plagued with several issues. First, the

FTC database is limited to large companies. Second, both databases are

biased in how they define business units and each is based on self-reporting.

Finally, both databases use broad, arbitrary definitions of industry that may not

reveal the true strength of industry effects. However, since the Trinet database

does not provide line of business profitability measures, the authors used

market share as the measure of performance. Using variance components

analysis, results showed business unit effects accounted for the greatest

variance in market share (28.0%) followed by industry effects (17.0%) and then

corporate effects (6.3%) for lines of business defined at the 3-digit SIC level.

Similar results for lines of business defined at the 4-digit SIC level with business

unit effects of 48.7%, industry effects of 17.5% and corporate effects of 11.0%.

Once the sample was broken down by firm size, business unit effects (47.6%)

accounted for the greatest variance in market share for large firms (sales

between $121b - $893m total sales in 1989), followed by industry effects

49

(19.3%) and corporate effects (9.5%). However, industry effects accounted for

the greatest variance in market share at 40.6% and 54.2% for medium firms

($893m - $171m total sales in 1989) and for small firms ($170m - $2m total

sales in 1989) respectively. This finding is consistent with Roquebert et al.

(1996), who found that corporate effects increase as firms have a smaller

number of business units. Many business units within medium-sized

companies may depend on corporate-level resources, such as transferring skills

and sharing activities with other business units.

Hawawini et al. (2003) also studied industry effects versus firm effects on

performance but departed from earlier studies in a number of ways. First, they

used value-based measures of performance (EP/CE or economic profit per

dollar of capital employed and TMV/CE or total market value per dollar of capital

employed) instead of accounting ratios (e.g. return on assets). Second, they

employed ANOVA instead of variance components analysis. Lastly, they did

not use the FTC or COMPUSTAT databases but instead sampled companies

from a consulting firm’s database. Results showed firm effects dominated

industry effects as the main driver of performance regardless of whether

performance was measured as EP/CE, TMV/CE or ROA. Stable firm effects

accounted for 27.1%, 32.5% and 35.8% of EP/CE, TMV/CE and ROA

respectively. In comparison, the corresponding figures for total industry effects

accounted for 10.7%, 14.3%, and 11.2%. Thus, industry factors had little

impact on performance.

However, the results were reversed once the authors examined strategic

groups within an industry. To study firm effects versus industry effects in

strategic groups, the authors examined firms who were average or ‘stuck in the

middle’ by taking out the top two leaders and bottom two losers from each

industry according to the performance measures. When performance was

measured using TMV/CE, total industry effects explained 35.2% in variation

compared to only 17.0% for firm effects. In the case of EP/CE it was 18.2% for

industry effects compared to 17.6% for firm effects and for ROA it was 20.1%

against 16.7%. In general, industry effects seemed to dominate firm effects in

explaining variation in performance for the majority of the industry’s firms when

the industry’s value leaders and losers were taken out of the sample.

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Therefore, the smaller the number of value leaders and value losers and the

larger the number of firms ‘stuck in the middle’, the greater will be the

importance of structural factors. Hawawini et al. (2003) concluded that for value

leaders and value losers, firm factors matter more than industry factors perhaps

because superior management leads to superior firm performance, regardless

of industry structure. For the rest of the firms ‘stuck in the middle’, average

managerial capabilities and performance means industry effects are more

important to performance than firm factors.

Short et al. (2007) used hierarchical liner modelling to simultaneously estimate

the influence of firm, strategic groups and industry on short and long term

performance. However, this study examined firm effects within strategic

groups. They found managers matter – firm effects had the strongest influence

on performance. Short et al. (2007) concluded that strategic choices generally

offer greater explanatory power than ecology when it comes to long term

survival. Profit, however, was dependent on both the firm and its strategic

group/industry.

Galbreath and Galvin (2008) examined firm effects versus industry effects,

departing from previous studies by using Australian data, testing specific

hypotheses, measuring specific resource and industry structure constructs and

comparing manufacturing versus services firms. The authors used the resource

based view of the firm (RBV) to support their hypothesis that firm resources will

have a greater effect on performance variation than industry effects. Further,

they hypothesised that firm effects, relative to industry effects, will have a

greater effect on performance in service-based firms than in manufacturing

firms. Under RBV, there are a variety of tangible and intangible resources (e.g.

reputation, interorganisational relationships) which determine firm performance.

Firms need to develop and deploy resources that competitors cannot imitate or

directly purchase.

Results showed that resources were 2.23 times as important as industry

structure in explaining firm performance. Therefore, firm effects have a greater

impact on performance than industry effects. In services firms, resources were

4.17 times as important as industry structure in explaining firm performance.

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therefore, firm effects explain firm performance better than industry effects for

service-based firms. However, this study was conducted at the corporate level;

not the business level and used management perceptions of performance

instead of objective (i.e. archival) data. The study should have been conducted

at the business unit level, as each business unit within a firm may face different

environmental challenges (i.e. industry structure). Management perceptions of

performance are subjective and as previously discussed, perceptions are

distorted by: (1) characteristics of the perceiver; (2) characteristics of the

perceived; (3) situational factors and (4) organisational factors (Zalkind and

Costello, 1962).

Many studies have tested firm effects versus industry effects on firm

performance. Depending on the database used, industry effects account for 4%

to 19% of the variance in performance (as measured by return on assets)

whereas firm effects account for 19% to 55%. The empirical evidence supports

Hunt’s (2010) R-A theory as the general case of the process of competition.

Resources are heterogeneous and immobile and thus, some firms will have a

comparative advantage in resources (and others a comparative disadvantage)

in efficiently/effectively producing particular market offerings that yield

marketplace positions of competitive advantage for some market segments and

thereby, superior financial performance. If industry structure is not a major

determinant of financial performance, it implies that the neoclassical SCP

paradigm of competition is an inadequate theory of competition. The argument

that strategy is anticompetitive and antisocial because superior financial

performance must result from industry factors is empirically false; it is firm

factors (i.e. individual perceptions) that determine most of the variance in

financial performance. It would seem that industry is the “tail” of competition;

the firm is the “dog” (Hunt, 2000b, p. 155).

Theoretical Relationships between Structure-Conduct-Performance

Economists working within the industrial organisation discipline are concerned

with the workings of markets and industries, in particular with corporate

competition. Industrial organisation economics is focused on corporate

strategies in an oligopoly environment and on this basis, could be described as

the “economics of imperfect competition” (Cabral, 2000).

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The main premise of industrial organisation economics is the Structure-

Conduct-Performance paradigm (SCP) illustrated in Figure 7.

Figure 7 The Industrial Organisation Structure-Conduct-Performance Paradigm

Note. Based on (Mason, 1939; Scherer, 1970, 1980; Scherer & Ross, 1990)

According to this paradigm, characteristics of the industry environment

determine the conduct of firms whose joint conduct determines industry

profitability. For example, in an industry with very few competitors, each

company is likely to charge higher prices. Higher prices lead to increased

profits for both the company and the industry in which they operate in. In this

case, public policy should be aimed at decreasing monopoly power by

restricting mergers, breaking up large corporations and reducing barriers to

entry (Hunt, 2000). Since structure determines firms’ conduct, which jointly

determines performance, conduct can be ignored and industry structure

explains performance. Conduct is just a result of the environment the firm

operates in. These relationships between industry structure, company conduct

and performance were conceived by Edward Mason at Harvard Business

School during the 1930s and extended by numerous scholars (Bain, 1956,

1968; Caves, 1980; Mason, 1939; Porter, 1980; Scherer, 1970, 1980; Scherer &

Ross, 1990) .

Theoretical development of industrial organisation economics occurred during a

time of the large reach of communism, government-imposed trade restrictions,

national protectionism, growing industry concentration, manufacturing as the

dominant industry in most developed countries and relatively stable competitive

environments. Monopolistic competition was growing and Porter’s model

focused on building barriers to entry which, today, is considered anti-

competitive. However, the competitive landscape changed during the 1980s

and 1990s brought about by the rapid development and diffusion of technology,

53

the free flow and ease of access to capital and globalisation. Significant events

including the collapse of communism in Eastern Europe, the increasing

privatisation of state owned utilities, and the emergence of East Asian

economies has given rise to the need for a better theory on the process of

competition (Galbreath & Galvin, 2008).

Mason (1939) asserted the size of an organisation influences its choice of

strategy in two ways:

1. The more it purchases and sells, the more power it has over suppliers

and buyers;

2. The absolute size of a firm (measured by assets, employees, and sales)

determines its reaction to market forces.

Mason also acknowledged the role of managers in determining company

strategy noting that “organisations make men as well as the reverse, and in the

making of men policies are also made “(1939, p. 67).

Bain (1956; 1968) expanded Mason’s (1939) work in his book Industrial

Organisation. According to Bain (1956; 1968), market structure is determined

by the:

1. Degree of seller concentration – the number and size distribution of

sellers.

2. Degree of buyer concentration – the number and size distribution of

buyers.

3. Degree of product differentiation – the extent to which goods and

services are perceived as unique by buyers in terms of quality,

design, packaging or brand.

4. Condition of entry – the extent to which existing companies have

advantages over potential entrants.

Although market structure can also include psychological, technological,

geographical, and institutional factors, Bain (1956; 1968) stressed the four

structural determinants above. These structural determinants shaped market

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conduct, or the company’s behaviour in response to market situations. The

company’s behaviour is demonstrated by their price, product and promotion

strategies as well as any predatory tactics directed at current and potential

competitors. Conduct, in turn, determined market performance measured along

several dimensions:

1. Size of profits determined by the difference between price and

average cost of production

2. Production efficiency

3. Size of promotion costs relative to production costs

4. Character of the product e.g. design, quality and variety

5. Progress in developing product and production techniques

While Bain’s (1956; 1968) work emphasised the relationship between market

structure and performance, Scherer (1970) advocated examining the conduct

linkages by developing richer independent variables that predict conduct from

structure and performance from conduct. Bain (1956; 1968) believed that using

market structure as an independent variable to predict performance would be

sufficiently accurate because companies facing similar conditions in the same

market can pursue different strategies and even companies pursuing the same

strategies may experience different performance levels. Thus, Bain (1956;

1968) is predominantly a structuralist while Scherer is a behaviourist (Scherer,

1970).

Scherer (1970) proposed successful performance requires achievement of the

following goals as illustrated in Figure 8:

1. Production and allocative efficiency whereby scarce resources are not

wasted and production decisions factor in quantitative and qualitative

needs.

2. Production operations are progressive to achieve increases in output per

unit of input and constantly produce improved products.

3. Producers facilitate stable full employment of resources especially

labour.

4. The distribution of income should be equitable.

55

Figure 8 Scherer’s Model of Industrial Organisation

Note. From Industrial Market Structure and Economic Performance p.5, by F.M. Scherer & D. Ross, 1990, Boston: Houghton Mifflin Company.

Performance depends on the conduct of buyers and sellers in areas such as

pricing policies, open and discrete collusion among firms, product strategies,

R&D budgets, promotion strategies, and legal tactics (e.g. enforcing patent

rights). Conduct, in turn, depends on the structure of the market such as the

number and size distribution of sellers, degree of product differentiation, barriers

to entry for new competitors, the ratio of fixed to total costs in the short run, the

degree of vertical integration (from manufacturing to retail) and the diversity of a

company’s product lines. Both market structure and conduct are also

influenced by supply and demand conditions (Scherer, 1970). On the supply

side, factors such as the location and ownership of raw materials, the character

of available technology (e.g. batch vs. process production), the durability of

56

product, the value-weight characteristics of the product, etc. can shape market

structure and company conduct. Basic demand conditions include the price

elasticity of demand, the rate of growth of demand, the availability of

substitutes, the marketing characteristics of the product (e.g. specialty vs.

convenience), the purchase method (e.g. acceptance of list prices vs. bidding

vs. haggling) and the time pattern of production / sales (e.g. are good produced

to order or delivered from inventory). Scherer (1970) also claimed that a

company’s strategies could alter market structure and supply / demand

conditions. For example, a company’s R&D strategy may change an industry’s

technology and hence its cost structure and degree of product differentiation.

Other contributions to the industrial organisation discipline have come from

Caves and Porter (1977) with their concepts of mobility barriers and strategic

groups. Caves and Porter (1977) proposed that entry barriers should be

renamed mobility barriers based on the existence of a strategic group – a group

of firms within an industry following the same or a similar strategy. Entry

barriers protect firms in a strategic group from entry by firms outside the

industry but also provide barriers to shifting strategic position from one strategic

group to another. Later, Caves’ (1980) asserted that top management’s

perceptions of market structure and the company’s strengths and weaknesses

jointly determine choice of corporate strategy and organisational structure. Both

corporate strategy and organisational structure, in turn, influence the

performance of the company and the industry.

Porter (1980) adapted the Bain/Mason SCP paradigm to develop the “five

forces” and “generic strategy” models depicted in Figure 9.

Figure 9 Porter’s Structure-Conduct-Performance Paradigm

According to Porter (1980), five forces determine the intensity of competition

which, in turn, determines one of three generic strategies a company should

57

choose from to create a sustainable defendable position and outperform

competitors. While economic and social factors affect all companies, Porter

emphasised corporate response to industry structure as the critical variable.

Specifically, if superior financial performance results primarily from industry

conditions, choosing the industries to compete in and/or altering industry

structure to increase monopoly power should be the focus of strategy.

Porter’s theory turned industrial organisation economics “upside down” (Barney

& Ouchi, 1986, p. 374) because what was considered anticompetitive and

socially undesirable under the Bain/Mason paradigm forms the basis for a

normative theory of competitive strategy. Under Porter’s view, choosing the

industries to compete in and/or altering the structure of chosen industries

should be the focus of strategy because industry structure is the most

significant predictor of company performance. Altering industry structure to

generate a superior return on investment involves creating high barriers to

entry, reducing the number of firms in the industry, increasing product

differentiation or modifying demand elasticity (Porter, 1980).

The evidence for Porter’s conceptualisation of structure and of strategy is

“anecdotal based on a series of case studies and examples” and “does not

allow for a strong scientific evaluation of the true content of the theoretical

typology and the inferred relationships” (Pecotich et al., 1999). Other studies

have examined the traditional SCP paradigm from industrial organisation

(Calem & Carlino, 1991; Delorme, Kamerschen, Klein, & Voeks, 2002;

Liebenberg & Kamerschen, 2008). Liebenberg and Kamerschen (2008) applied

the SCP paradigm to predict conduct and/or performance of the South African

auto insurance market from knowledge of its structure. Results demonstrated

that knowledge of industry structure could not predict conduct and/or

performance. Delorme et al. (2002) employed a simultaneous equations

framework to study the relationship between structure, conduct and

performance in US manufacturing in the 1980s and 1990s. The study

expanded on earlier SCP studies by using a lag structure to signify that

structure, conduct and performance do not affect one another

contemporaneously. Findings supported some aspects of the traditional SCP

model, but challenged others. There was little evidence that industry conduct,

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proxied by advertising, is affected by industry structure. Also, the authors found

that industry performance did not depend on industry conduct, though it is

sensitive to industry structure. Calem and Carlino (1991) examined the SCP

paradigm in the retail bank deposit market. The authors wanted to determine

whether banks behave competitively or strategically, and whether their conduct

is influenced by market concentration. Competitive behaviour was evidenced

by collusion while strategic behaviour was evidenced by conduct such as lower

operating costs and lower retail deposit rates (prices). They found strategic

conduct, rather than competitive conduct, was the norm in MMDA (money

market deposit accounts) and in 3- and 6-month CD (certificates of deposit)

markets. Market concentration (i.e. industry structure) had a statistically

significant but small effect on short term retail deposit rates. Other significant

structural variables included local income growth and the age distribution of the

local population (these two variables could be interpreted as a proxy for market-

size effects).

Industry structure

Porter (1980) proposed that industry structure is shaped by five forces – rivalry

among existing companies, the threat of new entrants, the threat of substitute

products/services, the bargaining power of buyers, and the bargaining power of

suppliers (Figure 10).

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Figure 10 Five Forces that Determine Industry Structure

Note. From Competitive Strategy: Techniques for Analyzing Industries and Competitors p.4, by M. Porter, 1980, New York: The Free Press.

These structural features of industries determine the intensity of competition

and hence industry and firm profitability. According to Porter (1980), analysis of

the five forces “highlights the critical strengths and weaknesses of the company,

animates its positioning in its industry, clarifies the areas where strategic

changes may yield the greatest payoff and highlights the areas where industry

trends promise to hold the greatest significance as either opportunities or

threats.” The next section discusses the five forces in detail.

Intensity of rivalry among existing competitors

Competitors engage in tactics like price wars, aggressive advertising, new

products and better customer service to improve their market position. In some

cases, one organisation’s tactics can generate counter moves by rivals which

impact the profitability of all organisations in the industry. The intensity of rivalry

among existing competitors depends on a number of factors:

• The number of competitors – when there are many competitors in an

industry, some companies believe their actions may go unnoticed. Or

when there are few competitors but they are equally balanced in terms of

size or resources, they are likely to engage in a lengthy battle.

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• Rate of industry growth – in an industry experiencing slow growth,

competitors seek increases in market share as a way to expand.

• High fixed or storage costs – companies facing high fixed costs are

under pressure to fill excess capacity which may result in discount

pricing.

• Lack of differentiation – in commoditised markets, purchase decisions

are based on price whereas differentiated products can command a price

premium from loyal customers.

• Capacity increases on a large scale – in industries where capacity must

be increased on a significant basis to gain economies of scale, this can

lead to excess capacity and pricing wars.

• Diverse competitors – when competitors in an industry have different

strategies, histories, and personalities they have different ways of

competing and creating continuous pressure.

• High strategic stakes – companies may sacrifice profitability to secure a

highly prized strategic position.

• High exit barriers – there are economic, strategic and emotional factors

that keep companies in the industry even when profits are low. For

example, a company may have specialised assets difficult to get rid of,

strategic interrelationships between business units or management are

unwilling to let go.

Porter (1980, p.21) attempts to establish a relationship between the intensity of

rivalry (i.e. structure) and strategy (i.e. conduct) by suggesting an organisation

“may try to raise buyers’ switching costs by providing engineering assistance to

customers to design its product into their operations or to make them dependent

for technical advice. Or the firm can try to raise differentiation through new

kinds of services, marketing innovations, or product changes. Focusing seller

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efforts on the fastest growing segment of the industry or on market areas with

the lowest fixed costs may reduce the impact of industry rivalry”. These

anecdotal case studies and examples do not provide sound theory that is

empirically testable and can be used to generate hypotheses that are agreeable

to verification by real-world data (Hunt, 2002a, 2010).

Threat of new entrants

Potential competitors bring new capacity, the desire to gain market share and

sometimes significant resources. In their desire to gain market share, new

entrants can drive down prices reducing industry profitability. The threat of

entry depends on the barriers to entry and the expected reaction from existing

competitors.

There are several sources of barriers to entry.

• Economies of scale make it difficult for new entrants who are forced to

enter an industry at large scale and face retaliation from existing players

or come in at small scale but suffer a cost disadvantage.

• Established companies with a strong brand or customer loyalty require

potential rivals to invest heavily in product differentiation to win market

share.

• Significant financial resources to establish research and development

facilities, production facilities, customer credit or inventories are another

barrier to entry.

• The one-off costs facing the buyer to switch from one supplier’s product

to another such as employee retraining acts as a deterrent to new

entrants.

• Securing distribution channels that are serving established players can

represent a barrier to entry.

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• Cost advantages independent of scale include proprietary technology,

favourable access to raw materials, favourable locations, government

subsidies, and a learning curve.

• Government policy can limit entry to industries in the form of license

requirements or access to raw materials.

A strong reaction from existing rivals may also deter entrants. Signals to look

for that suggest a high probability of retaliation to entry are a history of vigorous

retaliation, established companies with substantial resources to fight back or

highly illiquid assets, and a mature industry characterised by slow industry

growth.

Threat of substitute products

All companies in an industry compete against industries offering products or

services that perform a similar function. Substitutes restrict the prices

companies in an industry can charge therefore limiting industry profitability.

Substitutes to monitor closely are those that are subject to price-performance

improvements compared to the industry’s product or produced by industries

earning high profits.

Using the security guard industry as an example where electronic alarm

systems represent a substitute, Porter (1980, p. 24) recommends “the

appropriate response of security guard firms is probably to offer packages of

guards and electronic systems, based on a redefinition of the security guard as

a skilled operator, rather than to try to outcompete electronic systems across

the board”. Again, the evidence for Porter’s conceptualisation of structure and

of strategy do not allow for a strong scientific evaluation of the theory and the

inferred relationships.

Bargaining power of buyers and suppliers

Buyers can affect industry profitability by bargaining for higher quality or forcing

down prices as they play competitors against each other. A buyer group is

powerful if:

1. It is concentrated or purchases large volumes relative to seller sales

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2. The products it purchases represent a significant portion of the buyer’s

costs because they are more likely to shop around for the best prices

3. The products it purchases from industry are standard or undifferentiated

4. It faces few switching costs

5. It earns low profits

6. Buyers represent a credible threat of backward integration.

7. The industry’s product is not important to the quality of the buyers’

products

8. The buyer has full information

Porter (1980) attempts to establish a relationship between the bargaining power

of buyers (i.e. structure) and conduct by using the ready-to-wear clothing

manufacturing industry as an example. Here, clothing manufacturers have

suffered from falling margins as the buyers (department stores and clothing

chains) have become more concentrated and thus, more powerful. The clothing

manufacturers should engage in product differentiation or increase switching

costs to reduce buyer power. Again, such anecdotal case studies do not allow

for a strong scientific evaluation of the theory and the inferred relationships.

Suppliers affect industry profitability by their ability to raise prices or reduce the

quality of purchased goods and services. The conditions making suppliers

powerful are similar to those that make buyers powerful. Porter (1980) provides

an example of contract aerosol packagers who have experienced price rises

from suppliers (i.e. chemical companies) but are unable to raise prices to

buyers (i.e. aerosol resellers) because these buyers have some in-house

manufacturing.

Once a company has analysed the cause of the five forces affecting competition

in an industry, it can formulate a strategy to create a defendable position. This

may involve (Porter, 1980):

1. Positioning the company so its capabilities provide the best defence

against existing forces. The manager assumes that industry structure is

a given and matches company’s strengths and weaknesses to it.

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2. Changing industry structure to improve the company’s relative position.

This requires the company to adopt an offensive strategy. Examples

include innovation in marketing to differentiate a product, capital

investments in large scale facilities or vertical integration to increase

entry barriers.

3. Forecasting and responding to changes in industry structure before

competitors. For example, aggregation tends to occur in maturing

industries. This raises economies of scale and the capital required to

compete in the industry thus raising entry barriers.

Conduct

After choosing industries to compete in and/or altering their structure, Porter

(1980) argues there are three potentially successful generic strategic

approaches to outperforming competitors in an industry: (1) Overall cost

leadership, (2) Differentiation and (3) Focus. The differences between the three

generic strategies based on the target market and competitive advantage are

illustrated in Figure 11.

Figure 11 Porter’s Three Generic Strategies

Note. From Competitive Strategy: Techniques for Analyzing Industries and Competitors p.39, by M. Porter, 1980, New York: The Free Press.

65

Overall Cost Leadership

A company achieves cost leadership in an industry through functional policies.

It requires efficient-scale facilities, pursuit of cost reductions from experience,

tight cost and overhead control, avoidance of marginal customer accounts, and

cost minimisation in areas like R&D, service, sales.

Achieving a low cost position requires either high relative market share or other

advantages such as favourable access to raw materials. For new entrants,

implementing a low cost strategy requires significant up front capital investment,

aggressive pricing and initial losses to build market share. High market share

allow economies in purchasing which lower costs and allow the company to

reinvest in new equipment to maintain cost leadership.

While the cost leadership strategy provides a defendable position against

competitors, there is the risk of first, changes in consumer needs which nullify

past capital investments, second, new entrants imitating, and finally, the cost

leader fails to detect the need for product or marketing change because of the

focus on cost.

Porter (1980, p. 36) provides some anecdotal examples of companies that

appear to have successfully executed a cost leadership strategy. “The cost

leadership strategy seems to be the cornerstone of Briggs and Stratton’s

success in small horsepower gasoline engines..., and Lincoln Electric’s success

in arc welding equipment and supplies. Other firms known for successful

application of cost leadership strategies to a number of businesses are

Emerson Electric, Texas Instruments, Black and Decker and Dupont.”

Differentiation

Under the differentiation strategy, a company’s product is perceived industry

wide as unique. Although costs should not be ignored, differentiating the

product is the primary objective. Differentiation may be achieved through brand

image, technology, features, customer service or dealer network. Ideally a

company should differentiate itself along several dimensions. Porter (1980, p.

37) discusses Caterpillar Tractor as an example which “is known not only for its

dealer network and excellent spare parts availability but also for its extremely

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high quality durable products, all of which are crucial in heavy equipment where

downtime is very expensive.”

The differentiation strategy carries its own risks. Sometimes, the difference in

price between low cost rivals and differentiated companies becomes too great

and buyers switch to low cost products. Otherwise it may be that the buyer’s

need for a unique factor falls or imitation by competitors narrows the perceived

differentiation.

Focus

There are several ways a company could adopt a focus strategy including

emphasis on a particular buyer group, a segment of the product line, or a

geographic market. Porter (1980 p. 39) provides Martin-Brower, a food

distributor, as an example of a focus strategy that achieves a low cost position

in serving its particular target. Martin –Brower focuses on serving eight leading

fast food chains...”stocking only their narrow product lines, order taking

procedures geared to their purchasing cycles, locating warehouses based on

their locations, and intensely controlling and computerizing record keeping.”

The focus strategy delivers a superior return on investment because a company

is able to serve a narrow strategic target more effectively or efficiently than

competitors who are competing more broadly. Thus the company achieves

either differentiation from better meeting the needs of a strategic target or lower

costs in serving this strategic target or both. However, due to the narrow

strategic target, a company must trade off between profitability and sales

volume.

There is the risk that the cost between broad-range competitors and a focused

company increase to reduce the cost savings of serving a narrow target or

offsets the differentiation achieved by focus. Consumer demand for a focused

product may fall or competitors identify a submarket within the narrow target.

After the firm chooses one of the three generic strategies, then firm effects or

internal factors come into play (Porter, 1985). To execute its chosen strategy,

the firm must manage the activities in its value chain to generate a competitive

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advantage. For Porter, “the basic unit of competitive advantage...is the discrete

activity” and “competitive advantage results from a firm’s ability to perform the

required activities at a collectively lower cost than rivals, or perform some

activities in unique ways that create buyer value and hence allow the firm to

command a price premium.” (1991, p. 102). These activities are captured in the

value chain and value system displayed in Figure 12 where value refers to

customer value.

Figure 12 Porter’s Value Chain and Value System

Note. From Competitive Advantage: Creating and Sustaining Superior Performance p.37, by M. Porter, 1985, New York: The Free Press.

The value chain separates activities that directly produce (inbound logistics,

operations, etc) from support activities (firm infrastructure, human resources,

etc). It shows that the cost of one activity can be affected by the way others are

performed e.g. the cost of after sales service is linked to product design,

inspection and installation. These linkages extend outside the firm to include

the activities of suppliers, channels and buyers. The mix of activities carried out

by the company is determined by scope, that is, whether the company is

focused on a particular buyer group, segment of the product line, geographic

market, etc. However, the value chain model has limited applicability beyond

manufacturing firms. Service firms and knowledge-based firms are poorly

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represented by linear, input-output chains of activities (Hunt & Derozier, 2004).

The value chain concept is outside the scope of this study.

In subsequent years, Porter (1996) refined his theory of competition to propose

that the three generic corporate-level strategies represented strategic positions

at the simplest and highest level. Within these strategic positions, Porter (1996)

proposed three bases for positioning that are not mutually exclusive:

1. Customer needs

2. Customer accessibility

3. Variety of company’s products / services

Customer needs positioning is about serving most or all the needs of a target

market. Ikea is an example of cost-based focus that aims to satisfy the entire

home furnishing needs of its target market. Customer accessibility is about

serving customers who can be reached in different ways. Access can be

related to geography (e.g. rural vs. urban) or customer scale. One example is

WIN Television, Australia’s largest regional television network which reaches

more than 5.2 million viewers across six states of Australia and the nation’s

capital. Variety-based positioning is about producing a subset of an industry’s

products or services. This form of positioning is feasible if the company can

best produce a particular product or service using a distinctive set of activities.

Apia, an insurance company that only serves people over 50 and not working

full-time, is an example.

Stuck in the middle

Porter (1980) then suggests that a company must choose one of the three

generic strategies to create a sustainable defendable position and outperform

competitors or be stuck in the middle. Companies stuck in the middle will suffer

from low profitability because it will lose customers looking for the lowest cost or

a unique product. Such situations may be the result of management failing to

make choices or tradeoffs.

A sustainable strategic position requires tradeoffs because competitors are

likely to imitate in one of two ways (Porter, 1996). First, a rival can reposition

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itself to match the superior performer. Second, competitors may imitate as

demonstrated by Qantas’ creation of Jet Star to compete against Virgin Blue in

the Australian domestic travel industry. Trade offs are essential to strategy

because adhering to one strategic position builds a consistent brand in

customers’ minds, creates efficiencies in carrying out a tailored set of activities

that support the strategic position and clarifies a company’s organisational

priorities.

Therefore the company stuck in the middle must choose to be the industry cost

leader, concentrate on a particular target (focus) or achieve industry-wide

uniqueness. The choice will depend, in part, on the company’s resources and

capabilities. Successful execution of each generic strategy requires different

resources, strengths, organisational structures and management style (Porter,

1980).

Porter (1980) does not clarify how the intensity of competition leads to a better

choice of strategy and therefore superior performance. The examples and brief

case studies do not provide a rigorous basis for theory development and

testing. According to Hunt (2002), sound theory must be empirically testable so

it may be:

a. Intersubjectively certifiable: capable of being verified by various

investigators with differing attitudes, opinions and beliefs.

b. Capable of explaining and predicting phenomena

c. Capable of being verified as true or false by examining real-world facts.

A theory is capable of being empirically testable when it can be used to

generate hypotheses that can be verified by real-world data. However, it seems

reasonable to postulate a general positive relationship between structure and

conduct. The economic and business literature strongly implies the tougher the

competition, the more likely inefficient competitors will be forced out and the

better the choice of strategy by existing companies (or the less likely they will be

stuck in the middle) leading to superior performance (Figure 13). For example,

under the product life cycle concept, as industries mature and their growth rates

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decline, this results in intense rivalry between incumbents, declining profits and

weaker players being forced out (Jain & Haley, 2009).

Figure 13 The Conceptual Model

Further, Porter (1980) assumes his generic strategies of cost leadership,

differentiation and focus will succeed because industry environments are

homogeneous. According to Hunt’s R-A theory of competition, demand within

and across industries is heterogeneous (with the exception of commodities). As

a result, consumers within an industry have different tastes and preferences so

firms have to develop different offers for different segments within the same

industry. The fact that intra-industry demand is heterogeneous in most

industries supports R-A theory’s ability (and neoclassical theory’s inability) to

correctly predict diversity in business unit financial performance. Therefore

Porter’s (1980) generic strategies of cost leadership, differentiation and focus

for what he considers to be homogeneous industry environments may not work.

However, Porter (1980) assumes that homogeneous industry environments

exist. This leads to our hypothesis to determine if a positive relationship exists

between structure and conduct.

H4: There is a positive association between intensity of industry competition

(i.e. five forces) and targeted strategic action (i.e. cost leadership, differentiation

and focus).

Performance

Cost leadership protects a company from existing competitors because it can

still earn profits after competitors have cut prices and or profits through rivalry.

Buyers can only drive down prices to the level of the next most efficient

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competitor and there is more flexibility to cope with input cost increases from

suppliers. New entrants are forced to enter an industry at large scale and face

retaliation from existing players or come in at small scale but suffer a cost

disadvantage. The cost leadership strategy places the company’s product in a

favourable position against substitutes.

The differentiation strategy protects a company from existing competitors

because the product commands customer loyalty. Customer loyalty results in

less price sensitivity and higher margins. Buyers do not have a similar product

to choose from and are therefore less price sensitive while higher margins

allows the company to deal with powerful suppliers. Customer loyalty and the

need for a competitor to overcome uniqueness are an entry to barrier and also

create a favourable position against substitutes.

A company that is focused on a particular target can achieve a superior return

on investment because it is able to serve a narrow strategic target more

effectively or efficiently than competitors who are competing more broadly.

Thus the company achieves either differentiation from better meeting the needs

of a strategic target or lower costs in serving this strategic target or both.

The benefits of following one of Porter’s generic strategies suggest that smaller

(focused or differentiated) companies and the largest (cost leadership)

companies are the most profitable while medium-sized companies are least

profitable (Porter, 1980). This results in a u-shaped relationship between

profitability and market share is shown in Figure 14. However, Porter (1980, p.

44) argues that there is ‘no single relationship between profitability and market

share’ and this explains the high return on investment of firms who have

achieved differentiation industry wide and yet have lower market shares than

the industry leader. Porter (1980) does not clearly specify the relationship

between market share and return on investment (i.e. firm performance) but it

would appear from the proposed u-shaped relationship in Figure 14 that market

share precedes performance.

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Figure 14 Stuck In the Middle

Return on investment

Market share

Note. From Competitive Strategy: Techniques for Analyzing Industries and Competitors p.43, by M. Porter, 1980, New York: The Free Press.

Porter (1980) advocates that achieving cost leadership and differentiation

typically does not work because a company cannot be all things to all people

and differentiation is costly. However, there are three conditions where a

company can achieve both cost leadership and differentiation but these

conditions are temporary because existing competitors or new entrants will

imitate.

1. Competitors are stuck in the middle

Rivals are not choosing between cost leadership, differentiation or focus

and thus, are stuck in the middle. However, there is the risk of a capable

competitor entering the industry or existing rivals realising their position

and choosing a generic strategy. Therefore the company must choose

the competitive advantage it intends to sustain in the long run.

2. Cost is strongly affected by market share or interrelationships

A company can achieve both cost leadership and differentiation when its

cost position is determined by market share rather than product design,

level of technology, service provided, etc. In this case, cost savings from

being the market share leader can be used to differentiate its product.

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3. A company comes up with a major innovation

This position holds true if the company is the only organisation with the

new innovation. Once competitors imitate then the company will have to

make a trade off.

The empirical evidence on whether Porter’s generic strategies lead to a superior

return on investment has been inconclusive (Campbell-Hunt, 2000). Campbell-

Hunt (2000) employed meta analysis to examine 17 empirical studies on

Porter’s generic strategies including Hambrick (1983), Galbraith and Schendel

(1983), Dess and Davis (1984), Robinson and Pearce (1988), Miller and

Friesen (1986), and Kotha and Vadlamani (1995). Results did not support

Porter’s proposition that companies must pursue one of the three generic

strategies or get stuck in the middle and suffer low profitability - any generic

strategy, including stuck in the middle, can produce above-average

performance. Campbell-Hunt (2000) suggests that stuck in the middle may be

superior to specialising (i.e. cost leadership or differentiation) and described it

as an all rounder strategy well adapted to a specified set of conditions. There

is also evidence that companies such as Toyota can follow both cost leadership

and differentiation and still be successful (Miller, 1992). Mixed strategies are

useful when customers are relatively insensitive to price such as the luxury

automobiles market where Toyota has been successful with the Lexus brand by

equalling the quality of German firms while beating them on cost and price.

Cronshaw, Davis and Kay (1994) suggest stuck in the middle is best used as a

classification scheme of strategic outcomes – a company that fails to distinguish

itself from competitors by lower costs or differentiated products perform poorly.

The authors proposed Porter’s stuck in the middle could be applied to a

product’s positioning (narrow), company positioning (broad) or as a scheme for

classifying companies by strategic outcomes. The success of Sainsbury’s

contradicts the narrow definition and the PIMS (Profit Impact of Market

Strategy) data shows intermediate positions are profitable. Therefore,

Cronshaw et al. (1994) conclude that ‘stuck in the middle’ is best used as a

classification scheme of strategic outcomes. More recently, Goll, Johnson and

Rasheed (2008) examined the relationships between business strategy and firm

performance of major US airlines before and after industry deregulation.

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Business strategy was measured as low cost, differentiation and scope. Using

secondary data as proxies, they found airlines that differentiated their service

experienced better firm performance under deregulation. However, results also

showed airlines that spent more on flying operations and maintenance

expenses (a proxy for low cost strategy) also had better performance. The

authors suggested that because flying operations expenses included flight crew

wages, paying them more may contribute to better customer service.

Since Campbell-Hunt’s (2000) meta analysis of 17 empirical studies on Porter’s

generic strategies, there has been evidence to support Porter’s proposition that

companies stuck in the middle will suffer from low profitability because it will

lose customers looking for the lowest cost or a unique product. Pecotich et al.

(2003) found support for Porter’s hypothesis that those who do not implement a

focussed strategic thrust (the “stuck in the middles”) suffered from low

performance. A mail survey was administered to senior executives involved in

high level strategic decision making and corporate performance was measured

using a three-item subjective performance index adapted from Pearce et al.

(1987) who asked executives to state the extent to which their corporate return

on total assets, total sales and overall business performance was poor –

excellent on a five-point scale. Knudsen, Randal and Rugholm (2005) found

that premium and no-frills offerings were squeezing middle-of-the-road products

and services in a trend they named market polarisation. In their study of 25

industries and product categories spanning the globe, the authors found the

growth rate of revenues for midtier products and services was below market

average by nearly 6% a year from 1999 to 2004. During that same period,

companies competing in the value-oriented segment of the market such as Dell

and Wal-mart experienced 4.2% growth on average. A critical success factor

was driving down costs because competitors were constantly looking for

opportunities to enter the market. Companies operating in the premium end of

the market also achieved above average growth of 8.7% from 1999 to 2004.

Their higher prices were justified by a focus on innovation that added value and

built an emotional connection with customers. Torgovicky et al. (2005) found

support for Porter’s proposition that stuck in the middle is dangerous territory in

a study of competitive strategies in the Israeli ambulatory health care system.

The authors compared managerial perceptions of business strategies in two

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Israeli sick funds. They found that the sick fund with superior performance

used differentiation and focus strategies extensively while the inferior fund was

characterised by an extensive use of stuck-in-the-middle strategy.

In summary, the empirical evidence for Porter’s (1980) argument that a

company must choose one of the three generic strategies to create a

sustainable defendable position and outperform competitors or be stuck in the

middle is inconclusive. However, it is Porter’s (1980) argument that companies

stuck in the middle will suffer from low profitability because they will lose

customers looking for the lowest cost or a unique product (Figure 15).

Figure 15 The Conceptual Model

Trade offs are essential to strategy because adhering to one strategic position

builds a consistent brand in customers minds, creates efficiencies in carrying

out a tailored set of activities that support the strategic position and clarifies a

company’s organisational priorities. These arguments lead to the following

theoretical hypothesis.

H5: There is a positive association between targeted strategic action (conduct -

cost leadership, differentiation and focus) and performance.

Porter (1980) proposed five forces (i.e. rivalry among existing companies, the

threat of new entrants, the threat of substitute products/services, the bargaining

power of buyers, and the bargaining power of suppliers) comprise industry

structure and together, determine the intensity of competition and hence

industry and company performance. While economic and social factors affect

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all companies, Porter (1980) emphasised corporate response to industry

structure as the critical variable. Here, the unit of analysis is the industry and

Porter (1980) implicitly assumes managers within an industry define and

observe the same objective environment. On this basis, perception of structure

as described by Porter’s five forces model should be identical for all managers

operating in the same industry. However, this study will examine two major

issues concerning Porter’s adaptation of the SCP paradigm from industrial

organisation economics. The first issue is the implicit assumption that

managers choose one of the three generic strategies after an objective analysis

of the five forces of competition. Within marketing, Hunt’s (2010) R-A theory of

competition proposed that the resources of firms within an industry are

heterogeneous and immobile and therefore, managers must make strategic

choices and these choices influence firm performance. Resources include

market and competitor intelligence and some firms will have a comparative

advantage in intelligence while others will have a comparative disadvantage.

Consequently, managers develop strategies on the basis of imperfect

perception of information. Therefore, what is the better predictor of

performance – objective data as implied by Porter’s (1980) five forces model or

individual perceptions of structure as implied by Hunt’s R-A theory? The

second major issue is the lack of conclusive empirical evidence for the

proposed theoretical relationships between the intensity of industry competition

and targeted strategic action and between targeted strategic action and

industry/firm performance. The next chapter discusses the methodology

employed in the present study to examine the hypotheses.

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CHAPTER 3

Theorising and research on corporate strategy in management, economics and

marketing has faced serious problems in conceptualisation, operationalisation

and research implementation. The unsatisfactory recognition of these problems

may vitiate the strength of the conclusions and compromise any research study.

While all research faces variations of these problems and it is often impossible

to find a complete resolution, it is imperative that the problems be recognised,

its nature clearly described, and the exact tactics used for its resolution be

described.

Sample

Level and unit of analysis

While all research faces sample selection and definition problems, in this study

the difficulties are of particular relevance. Pecotich et al. (2003) observed that a

problem throughout the strategic marketing and management literature is the

explication of the hierarchy of strategy levels and the unit of analysis. This may

be due to the treatment of the terms “unit of analysis” and “level of analysis” as

interchangeable concepts. According to Pecotich et al (2003), unit of analysis

refers to the object, event or other entity whose properties are being

investigated and that is of primary interest to the researcher. The level of the

analysis refers to the hierarchical position of the object, event or other entity

within the particular system of research interest. A hierarchy consists of units

that may be grouped at different levels (Doty & Glick, 1994; Foss, Husted, &

Michailova, 2010; Goldstein, 1995; Judge, 2011; Klein, Dansereau, & Hall,

1994; Lenski, 1994; Sánchez-Hernández, Martínez-Tur, Peiró, & Ramos, 2009;

Scherbaum & Ferreter, 2009; Singer, 1961; Wetzels, Odekerken-Schröder, &

Van Oppen, 2009; Williams & Naumann, 2011; Yammarino & Dansereau, 2008)

so for example, individuals may be grouped in organisations and they, in turn, in

industry or national groupings. Research may be conducted within the same

level or across levels. It is of critical importance that researchers clearly specify

both the unit and the level of analysis.

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In this research, the unit of analysis is the business unit. The business unit as

defined by the PIMS (Profit Impact of Market Strategy) project, is a division,

product line or other profit centre of a company that:

• Produces and markets a well-defined set of related products and/or

services

• Serves a clearly defined set of customers, in a reasonably self-contained

geographic area and

• Competes with a well-defined set of competitors (Buzzell and Gale,

1987)

We chose the PIMS definition because it represents the “smallest subdivision of

a company for which it would be sensible to develop a distinct, separate

strategy” (Buzzell and Gale, 1987 p.32). To investigate the degree of

congruence of individual perceptions of a business unit’s structure, conduct and

performance within a company and companies within the same industry,

respondents must answer questions regarding the same business unit.

Therefore, the business unit was pre-determined and instructions in the

questionnaire requested respondents to answer questions in relation to the

business unit’s operations in Australia.

There are three levels of analysis: the individual, the company and the industry.

Specifically, this study is investigating a business unit’s structure, conduct and

performance as perceived by individuals (i.e. senior managers) within a

company. These perceptions will be compared against other senior managers

from companies competing in the same industry. For example, in a hypothetical

telecommunications industry comprised of three companies, this study would

investigate the perceptions of the senior managers across all three companies

with regards to a specific business unit. This study differs from previous studies

because I am examining several industries; not a single industry. It is critical to

accurately define the industry in which the business unit operates to correctly

measure the effect of structure on performance (Ali, Klasa, & Yeung, 2009;

Caves, 1987; Galbreath & Galvin, 2008; Pecotich et al., 1999). Commenting on

Rumelt’s (1991) finding that the competitive environment has a weak direct

effect on performance, Brooks (1995) states it is “the inappropriate definition of

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competitive environments which limits the measured influence of competitive

conditions on firm performance”. Buzzell and Gale (1987) offer additional

reasons why it is important to identify the industry in which the business unit

competes:

• A business unit’s market share is measured in relation to its industry

• Market growth rates are measured for each unit’s industry.

• The identity and market shares of major competitors are determined by

the scope of the industry.

According to Porter (1980, p. 32), an industry is the group of firms producing

products that are close substitutes for each other and “any definition of an

industry is essentially a choice of where to draw the line between established

competitors and substitute products, between existing firms and potential

entrants, and between existing firms and suppliers and buyers.” Porter’s

definition is broad and given the importance of accurately defining the industry, I

asked respondents to identify the business unit’s industry from a pre-

determined list.

The pre-determined list of industries was generated from the Australian and

New Zealand Standard Industrial Classification (ANZSIC) system developed by

the Australian Bureau of Statistics (ABS) (Australian Bureau of Statistics, 2006).

This classification system groups business units carrying out similar productive

activities with the purpose of organising data and producing reports. There are

18 divisions within ANZSIC, each identified by an alphabetical character

displayed in Table 1. In those instances where respondents chose “Other”, I

classified the industry into one of the 17 divisions based on my knowledge of

the business unit’s operations, answers from respondents in the same

organisation and answers from respondents in organisations in the same

industry.

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Table 1

ANZSIC Classification of Industries

A Agriculture, Forestry and Fishing

B Mining

C Manufacturing

D Electricity, Gas and Water Supply

E Construction

F Wholesale Trade

G Retail Trade

H Accommodation, Cafes and Restaurants

I Transport and Storage

J Communication Services

K Finance and Insurance

L Property and Business Services

M Government Administration and Defence

N Education

O Health and Community Services

P Cultural and Recreational Services

Q Personal and Other Services

R Other (please specify)

Define sample

Data for the present study was collected from the senior management team

because the CEO’s workload and to some extent, power, is shared with senior

executives. Specifically, I focused on executives who are familiar with the

overall strategic direction of the business unit and have direct input into the

strategic decision making process. Data on strategy gathered from middle and

lower managers have questionable validity because these managers typically

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do not have access to information about how the total system operates (Kotha

and Vadlami, 1995). Further, studies that look at executive perceptions should

examine top management perceptions because major decisions are made at

higher management levels and studies which do not focus directly on these

managers' perceptions are not likely to provide insights into the responses

which organisations make to perceived conditions in the environment (Snow,

1976). Finally, one should examine the top management team because few

decisions which affect the entire organisation and its relationship to the

environment are made by a single manager. Such decisions normally reflect an

integration of the perceptions, opinions, and recommendations of the top

management team. Over time, the decisions and actions of this group form an

organisational strategy that both guides and reflects individual managers'

perceptions of the environment and their beliefs about how the organisation

ought to respond. Most studies examining management perceptions collect

data from one respondent per firm but studies relying on multiple respondents

within an organisation are rare because the target organisation's commitment to

the research needs to be considerable and patient (James & Hatten, 1995).

The sample was selected from Dun and Bradstreet’s Business Who's Who of

Australia, an online database of 40,000 publicly-listed and private Australian

companies. The database contained information such as company contact

details, the names of key decision makers, annual revenue and their SIC code

(Dun and Bradstreet, 2009). Given this study is examining the degree of

congruence of individual perceptions within an industry, I used judgment

sampling, a nonprobability sampling technique in which several companies

representing one industry were chosen (Zikmund, Ward, Lowe, & Winzar,

2007). Barrett et al. (2009) relied on a non-probabilistic, convenience sample in

their study of top management team perceptions because they recognised the

difficulty in collecting data from the top management team. The authors

solicited members of several organisations, contacted members of personal

networks, and targeted particular firms to build sectors and industries.

Define sample size

In this study we mailed out 754 questionnaires to top management teams of

private and publicly-listed companies of which 102 respondents refused to

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participate and 55 were no contacts or returned to the sender. The refusals

provided a number of reasons for declining to participate including company

policy, confidentiality of data and lack of knowledge to participate as they were

relatively new to their current position.

We received 147 completed questionnaires, resulting in a response rate of

21.03%. This response rate is disappointing but considered satisfactory given

the high management level of the respondents and was similar to comparable

surveys (Dillman, 2000, 2007; Dillman, Smyth, & Christian, 2009b; Groves,

1989; Kotha & Vadlami, 1995). For example, Pecotich et al. (2003) contacted

700 organisations in their investigation into top management perceptions of

strategic action in Australia. Of 700 questionnaires distributed, 255 were

returned giving a response rate of 36.43%. In a study of management

perceptions of industry structure as conceptualised by Porter’s five forces

model, Pecotich et al. (1999) generated a 25.17% response rate. In an earlier

study of top management perceptions of future competitive structure, Pecotich

et al. (1992) generated 17.6% from 1000 questionnaires. This was also

considered satisfactory given the questionnaires were addresses specifically to

the chief executive officer. Galbreath and Galvin (2008) sampled Australian

firms in both the manufacturing and services industries in their study of firm

effects versus industry effects. They mailed questionnaires to CEOs and

generated a 14.3% response rate. Pelham and Lieb (2004) sampled North

American small and medium-sized industrial manufacturing firms in their study

of senior management team perceptions and generated a 12.3% response rate.

Bourgeois (1978) sampled only 12 North American non-diversified public

companies in his study of the congruence of top management team perceptions

of goals and strategies.

Apparatus

Data was collected via a mail survey which involved mailing questionnaires to

potential respondents, who completed and returned them by mail. Collecting

data on management perceptions can be problematic because the act of asking

individuals to reveal their beliefs can change them. Additional problems may

arise when asking managers to recall beliefs held in previous time periods

83

because memories are often incomplete, misinterpreted or mistakenly reported

because of the outcomes later achieved (Nadkarni and Barr, 2008).

Using mail surveys to collect data has its advantages and disadvantages

(Churchill & Iacobucci, 2005; Malhotra, 2007; Zikmund et al., 2007). For

example, mail surveys allow the respondent to complete the questionnaire at

his or her own pace and no interviewer is present to bias the responses. In

addition, with clear instructions, complex scales can be used to gather

responses. Mail surveys also allow the researcher to receive the responses

directly, thereby reducing the chance of interviewer cheating. However, the

problem with mail surveys is that there is no one present to encourage the

respondent to complete the questionnaire, which ultimately leads to low

response rates. There is also no one available to help interpret instructions or

questions, which can cause both confusion and frustration on the part of the

respondent. If the mailing list is not constantly updated, many potential

respondents may have moved, and hence will be unreachable. Further, the

slow speed of response delays the study and may make the responses

vulnerable to external events taking place during the study. There is also the

chance that mail questionnaires may be treated as junk mail and duly

discarded. Finally, don’t know/blank responses occur more frequently on self-

administered questionnaires than with phone or personal interviews. The cover

page provided respondents with the internet address of an online version of the

questionnaire for those who found this method more convenient.

The questionnaire was structured and undisguised to ensure that all

respondents replied to the same question. This also made it simple to

administer and easy to tabulate and analyse.

Instrumentation

The questionnaire was accompanied by a cover letter that explained why a

response was important. In the cover letter, we described the purpose of the

questionnaire, provided a contact number in case respondents needed further

clarification and guaranteed anonymity.

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The instructions asked the respondent to distribute the enclosed additional

questionnaires to their senior management team, specifically those familiar with

the strategic direction of the business unit and have direct input into the

strategic decision making process. The remaining instructions informed

respondents the questionnaire was divided into three parts (Part A, B, C), to

answer all questions and that the term “product” also included services.

Following the instructions, there were four questions that asked respondents to

state the name of the organisation, identify the business unit’s principal industry

(from the pre-determined ANZSIC list), specify the major product of the

business unit and list the three major competitors. I asked respondents to list

the three major competitors of their business unit operating in the same industry

and in Australia to assist respondents to answer the subsequent section on

industry structure (i.e. Part A) with reference to its major competitors.

This study is concerned with determining the degree of congruence of individual

perceptions of structure, conduct and performance within a company and within

an industry. In order to obtain valid and reliable measures of the various

constructs, previously validated scales were used. To measure individual

perceptions of structure, I used 42 scale items from INDUSTRUCT as listed in

Appendix 1 (Pecotich et al. 1999). Pecotich et al. (1999) developed and

validated a measure of industry structure, INDUSTRUCT, encompassing

Porter’s five competitive forces on the basis that there was little empirical

evidence to support Porter’s model (e.g. it is possible there may be more or less

than five forces) and no psychometrically validated measurement scale. The

authors utilised a seven-step procedure for scale development and assessment

including administering the scale items to a sample of senior executives

involved in top-level strategic decision making for their organisations. To

assess the construct validity of the scales, a multitrait-multimethod analyses

(MTMM) was designed. The chi-square/degrees of freedom ratio was 1.64

indicating this model provided satisfactory fit to the data. The Tucker-Lewis

index was .94, the Bentler-Bonnet index was .92 and the McDonald index was

.10, which all indicated excellent fit of the data to the model. Akaike’s

information was 1.10 and cross-validation index was 1.10. Estimates of

reliability were reasonably high (with alphas from 0.72 to 0.84), which

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suggested it is meaningful to use these scales to investigate industry clusters

and to dependably assess the key competitive force of each industry. Findings

demonstrated that executive perceptions of industry structure corresponded to

Porter’s five forces. Thus, Part A of the questionnaire measured individual

perceptions of structure using scale items developed by Pecotich et al. (1999).

Market share was included as a measure of industry structure. Although Porter

(1980, p. 44) argued that there is ‘no single relationship between profitability

and market share’, he proposed a u-shaped relationship between profitability

and market share whereby smaller (focused or differentiated) companies and

the largest (cost leadership) companies are the most profitable while medium-

sized companies are least profitable. Porter (1980) does not clearly specify the

relationship between market share and return on investment (i.e. firm

performance) but it would appear from the proposed u-shaped relationship in

Figure 14 that market share precedes performance. This study measured both

absolute and relative market share. Respondents were asked to estimate the

business unit’s sales as a percentage of total market sales for the calendar year

of 2008 (i.e. absolute market share) and to rank their business unit in terms of

market share in calendar 2008 (i.e. relative market share). Both items were

taken from Pecotich et al. (1999) and included in Part C of the questionnaire.

Additional scale items to measure individual perceptions of structure based on

the structural characteristics identified by Bain (1968) and Scherer (1970) were

included in Part C of the questionnaire. This was carried out because the

INDUSTRUCT scale items (Appendix 1) were not amenable to comparison with

objective data. For example, one of the INDUSTRUCT scale items asks the

respondent to indicate the extent to which “Firms in our industry compete

intensely to hold and/or increase their market share”, which is not amenable to

comparison with objective data. Therefore, additional scale items measuring

perceptions of structure as identified by Bain (1968) and Scherer (1970) were

taken from PIMS for this study (Buzzell & Gale, 1987). A list of these items can

be found in Appendix 4. Respondents’ answers to these structural questions

were compared to objective data to determine the degree of congruence

between the objective reality (as measured by archival data) and individual

perceptions.

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To measure individual perceptions of conduct, I used 49 scale items developed

by Pecotich et al. (2003) as displayed in Appendix 2. Pecotich et al. (2003)

investigated top management perceptions on the content of strategic action in

Australia. Four typologies were identified for testing:

1. Retrenchment versus growth: retrenchment refers to the withdrawal of a

firm from a particular strategic position while growth refers to an

increase, expansion or entry to a particular strategic position.

2. Product/market matrix: this refers to Ansoff’s framework of market

penetration, product development, market development and

diversification. This has been extended in recent times to include a

harvesting strategy, market consolidation, product rationalisation and

withdrawal.

3. Four grand strategic alternatives of stability, internal growth, external

growth and retrenchment

4. Porter’s three generic competitive strategies: differentiation, focus and

cost leadership.

The authors proposed that executive strategic perceptions would be structured

according to the four typologies but one would prove to be best fit to the data. A

list of possible strategic options used at business unit level was developed

based on the four typologies. Results supported typology 4 or Porter’s generic

strategy formulation. The scale means were close to the midpoint of the scale

and the scale range suggested that there was sufficient variability in the

pursued strategies. An examination of the plots, and skewness and kurtosis

statistics showed no serious deviation from normality. Further, the scale met

reliability criteria with all coefficient alphas above 0.7. Thus, Part B of the

questionnaire measured individual perceptions of conduct using scale items

developed by Pecotich et al. (2003).

To measure individual perceptions of business unit and industry performance,

scale items were taken from several studies and are listed in Appendix 3. The

first item asked respondents to list the business unit’s sales/revenue (external

and internal), earnings before interest and tax (EBIT), total assets and total

liabilities for the 2007/2008 financial year. This was followed by four items that

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asked respondents to rate the performance of their business unit from poor to

excellent on a five-point scale for return on sales, return on investment, return

on total assets and overall business unit performance. Pecotich et al. (2003)

measured corporate performance using a three-item subjective performance

index which asked executives to state the extent to which their return on sales,

return on total assets and overall business performance was poor to excellent

on a five-point scale. The coefficient alpha for this instrument was 0.79 and

therefore we used the same three items in the present study. Return on

investment has been used as a measure of company performance by several

studies investigating the relationship between Porter’s generic strategies and

company performance (Buzzell, Gale, & Sultan, 1975; Galbraith & Schendel,

1983; Hambrick, 1983; Miller & Friesen, 1986; Wright, Kroll, Tu, & Helms,

1991). The next item was taken from Pecotich et al. (1992) and asked

respondents to indicate their business unit’s net profit on a seven-point scale

from “1 = less than $100,000” to “7 = greater than $100,000,000”. This was

followed by another item taken from Pecotich et al. (1992) which asked

respondents to rate the industry performance of their business unit on a nine-

point scale ranging from “0 = very poor” to “9 = excellent”. Requesting

managers to rate the performance of their business unit relative to industry

performance is complicated because “it is difficult to ensure that members of the

top management team within a given firm as well as across firms have a similar

‘referent’ or ‘peer’ set of organisations” (Dess & Robinson, 1984). To partly

overcome this issue, the questionnaire instructions asked respondents to

complete the questionnaire in relation to the pre-determined business unit

operating in Australia to enable comparison between companies in the same

industry.

This study also seeks to determine the degree of congruence between the

objective reality and individual perceptions (i.e. subjective measures) of

structure, conduct and performance. Starbuck and Mezias (1996) highlighted

that the scarcity of studies comparing management perceptions with the

objective reality maybe due to the difficulty in obtaining good objective data to

verify the accuracy of management perceptions. For example, obtaining

reliable and valid financial data for the business unit is difficult because the data

is often confidential and accounting procedures for allocating a firm’s assets

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and sales among its business units may vary (Dess & Robinson, 1984). Barrett

et al. (2009) also highlighted the difficultly of obtaining objective performance

data that is of a similar nature and time period among respondents, as well as

the outright refusal by many to release such information. Nevertheless, I

collected objective data on each business unit’s structure, conduct (i.e.

strategies) and performance from government reports as well as any

independent published material (e.g. newspapers, trade magazines and

consultant reports). For example, I used a report published by the ABS

(Australian Bureau of Statistics) to collect objective data on industry

performance (Australian Bureau of Statistics, 2008-2009).

Finally, this study wishes to test Porter’s conceptualisation of the SCP

paradigm. Specifically, this research will test if there is a positive association

between the intensity of industry competition and targeted strategic action (i.e.

cost leadership, differentiation and focus) and between targeted strategic action

and industry/firm performance. Intensity of industry competition, targeted

strategic action, and industry/firm performance were measured using the scale

items for subjective measures of structure (Appendix 1), conduct (Appendix 2)

and industry/firm performance (Appendix 3) respectively.

Finally, the questionnaire sought to gather factual information relating to the

respondent such as their position in the organisation, level of education, age,

number of years in a managerial position in the organisation, number of years

spent in the current industry and number of hours per week discussing strategy

with the senior management team.

Design

The questionnaire was designed to reduce nonresponse error which occurs

when people who respond to a survey are different from sampled individuals

who did not respond. To reduce nonresponse error, this study employed the

Tailored Design (TD) method (Dillman, 2000, 2007; Dillman et al., 2009b). This

method is based on the concept of social exchange theory and thus, seeks to

create respondent trust and perceptions of increased rewards and reduced

costs for being a respondent. I employed the TD method in designing the

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questionnaire and during the data collection procedure (to be discussed in

“Procedure”).

To establish trust, I provided a token of appreciation. I chose an incentive that

did not have a direct personal benefit for the respondent but would benefit the

organisation as a whole. Thus, a summary of results was promised to

participating companies upon completion of the study. Other means of creating

trust include sponsorship by a legitimate authority, making the task appear

important and invoking exchange relationships. Who sponsors a survey

influences how a questionnaire is viewed by the recipient and the probability of

responding. The University of Western Australia (UWA) was identified as the

sponsor of the survey in the hope that this would not present the threat of a

competitor asking about the organisation’s business. To make the task of

completing the questionnaire appear important, I personalised correspondence

by using real names instead of a pre-printed generic salutation of “Dear Sir /

Madam” and a replacement questionnaire with the message “To the best of our

knowledge you have not yet responded”. I also used a cover letter on UWA

letterhead stationery because some respondents may have attended UWA and

feel they want to repay a favour.

There are a number of ways to create perceptions of increased rewards for

completing the questionnaire such as showing positive regard to the respondent

(Dillman, 2000; 2007; 2009). This was achieved by giving respondents reasons

that this survey was being done and personally addressing correspondence.

Saying thank you with phrases such as “Thank you very much for helping” was

included in the cover letter of the questionnaire. I also used a follow up

postcard designed as a thank you for the prompt return of the first wave of

questionnaires (see “Procedure”). Asking people for advice provides a sense of

reward and subordinates the sponsor to the respondent and so I used the cover

letter to explain that the purpose of the questionnaire was to seek their

knowledge on the organisation’s competitive environment, strategy and

performance. Giving social validation is another means of increasing perceived

rewards which involves informing people in later contacts that many others have

already responded in the hope that this will encourage them to act in a similar

way. In the second wave of questionnaires, I stated that some of their

colleagues and other executives in their industry had already responded. I also

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communicated the scarcity of response opportunities by including a deadline

date for returning the questionnaire. This technique has been used in

telephone surveys of businesses after 20 plus unsuccessful attempts where

“gatekeepers” were informed that all calling had to be completed by the end of

the week.

To reduce the perceived social costs of participating in the study, I avoided

subordinating language and made the questionnaire appear short and easy. I

used language which implied the writer was dependent on the respondent as

people do not like to be subordinated to others and may avoid responding. I

used words such as “We are writing to seek your assistance” rather than “For

us to assist your organisation, it is necessary for you to complete this

questionnaire”. Making the questionnaire appear short and easy was achieved

by indicating in the cover letter the length of time to complete the questionnaire

and carefully organised questions in easy-to-answer formats. For example,

categorical answers were provided for some performance scale items instead of

requesting absolute numbers so respondents did not have to consult records.

Procedure

Pretesting the questionnaire

A vital step in the research process is pretesting the questionnaire. Pretests

allow the researcher to identify issues with question content, wording,

sequence, form and layout, question difficulty and instructions. For this study,

the questionnaire was pretested on executive MBA students who were involved

in strategic decision making in their companies. Their primary task was to

complete the questionnaire and critically evaluate its content. On the basis of

this evaluation, the cover letter was reworded; some items measuring industry

structure and strategy were reworded for simplification. Performance measures

which requested managers to recall financial data over a period of four years

were changed because pretest results revealed that it was difficult for managers

to recall such data. Therefore, I deleted items requesting respondents to

calculate return on investment, return on sales and gross margin as it increased

the perceived social costs of completing the questionnaire (Dillman, 2007;

Dillman et al., 2009b). Instead, I asked respondents to provide data for sales,

EBIT, total assets and total liabilities for the most recent financial year (i.e.

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2007/2008) so I could calculate return on investment and return on sales

figures.

Increasing Response Rate

All research is plagued with compromises and imperfections. The critical issue

is that these compromises and imperfections are recognised and that an

attempt is made to reduce their impact on the major purposes of the study. Two

major non-response problems occur in surveys: unit nonresponse and item

nonresponse (Baruch & Holtom, 2008; Dillman, 1978, 2000, 2007; Dillman,

Christensen, Carpenter, & Brooks, 1974; Dillman et al., 2009a; Groves, 1989;

Nordholt, 1998; Tourangeau, Rips, & Rasinski, 2000). Unit nonresponse refers

to the noncompletion of a complete questionnaire due to such factors as:

refusals, not at homes, deaths and other uncontrollable factors. The critical

issue is whether the complete unit nonresponse is selective and therefore

different from the respondents and the nonrespondents. Unfortunately, this

study is aimed at very important people within organisations who, whatever the

ethical implications, are more prone to refuse. This is despite the ethical

principle of reciprocity which suggests that those who ask people to complete

surveys should complete them themselves. In dealing with this problem, the

key to achieving satisfactory response rates to self-administered surveys is

multiple attempts (Dillman, 1978, 2000, 2007; Dillman et al., 1974; Dillman et

al., 2009a)‘.

Using social exchange theory, Dillman (2007) explains people are “more likely

to complete and return self-administered questionnaires if they trust that the

rewards of doing so will, in the long run, outweigh the costs they expect to incur”

(p. 26). In this study, I followed five steps recommended by Dillman (2009) to

increase response rates during implementation.

1. A brief prenotice letter was mailed to the potential respondents a few

days before the questionnaire. It noted that a questionnaire for an

important survey will arrive in a few days and that the person’s response

would be greatly appreciated. This was mailed on 27 May 2009 to 754

respondents.

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2. A questionnaire mailing that included a detailed cover letter explaining

why a response is important. A cover letter accompanied the

questionnaire that explained the purpose of the questionnaire, provided a

contact number in case respondents needed further clarification and

guaranteed anonymity. Each respondent was given additional

questionnaires for distribution to the senior management team.

Therefore the envelope included one questionnaire addressed to the

respondent and several questionnaires with a generic salutation of “Dear

Sir / Madam”. The questionnaires were mailed two days after the

prenotice letter on the 29 May 2009.

3. A thank you postcard mailed a few days after the questionnaire. The

purpose of this postcard was to express appreciation for responding and

indicates that if the completed questionnaire has not been mailed it is

hoped that it will be returned soon. A glossy printed postcard was

mailed to each respondent on the 10 June 2009.

4. A replacement questionnaire was mailed to respondents 2 – 4 weeks

after the previous questionnaire mailing. It indicated that the person’s

completed questionnaire has not been received and asks the recipient to

respond. We mailed out a second wave of questionnaires to those who

had not yet responded by 21 July 2009.

5. A final contact by telephone a week or so after the fourth contact. The

different mode of contact distinguished the final contact from regular mail

delivery. Each of these delivery modes was built upon past research

showing that a “special” contact of this type improves response to mail

surveys.

Research in competition and strategic action has come across issues in

conceptualisation, operationalisation and research implementation. This

chapter has identified and discussed the nature of these problems and

described solutions to tackle these issues.

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Pecotich et al. (2003) observed that a problem throughout the strategic

marketing and management literature is the explication of the hierarchy of

strategy levels and the unit of analysis. This may be due to the treatment of the

terms “unit of analysis” and “level of analysis” as interchangeable concepts. In

this research, the unit of analysis is the business unit because it is the smallest

subdivision of a company for which it would be sensible to develop a distinct,

separate strategy. The business unit was pre-determined to permit comparison

of perceptions within a company and within an industry. There are three levels

of analysis: the individual, the company and the industry. Specifically, this study

is investigating individual perceptions of the business unit’s structure, conduct

and performance within a company and within an industry. I asked respondents

to identify the business unit’s industry from a pre-determined list generated from

the ANZSIC (Australian and New Zealand Standard Industrial Classification)

system (Australian Bureau of Statistics, 2006). Data for the present study was

collected from the senior management team because the chief executive

officer’s workload and to some extent, power, is shared with senior executives.

Data were collected via a mail survey. The instructions asked the respondent to

distribute the enclosed additional questionnaires to their senior management

team, specifically those familiar with the strategic direction of the business unit

and have direct input into the strategic decision making process.

The questionnaire was pretested with executive MBA students who were

involved in strategic decision making in their companies. On the basis of this

evaluation, the cover letter was reworded; some items measuring industry

structure and strategy were reworded for simplification and performance

measures changed. I also deleted items requesting respondents to calculate

return on investment, return on sales and gross margin for the previous four

years because it was difficult for managers to recall financial data over a period

of four years and thus, increased the perceived social costs of completing the

questionnaire (Dillman et al., 2009b).

I employed the TD method in designing the questionnaire to reduce survey

errors and in the data collection procedure to increase response rates (Dillman,

2000, 2007; Dillman et al., 2009b). Accordingly, the procedures were aimed at

94

creating respondent trust and perceptions of increased rewards and reduced

costs for being a respondent. This involved mailing a pre-notice letter to inform

respondents that a questionnaire would be arriving in a few days. After the

questionnaire was mailed out, a thank you postcard arrived several days later to

thank respondents who had completed the questionnaire and to encourage

those who had not yet participated. A replacement questionnaire was mailed

to respondents 2 to 4 weeks after the first questionnaire and final contact by

telephone a week or so after the fourth contact.

In this study we mailed out 754 questionnaires to top management teams of

private and publicly-listed companies of which 102 respondents refused to

participate and 55 were no contacts or returned to the sender. We received 147

completed questionnaires, resulting in a response rate of 19.50%. This

response rate is disappointing but considered satisfactory given the high

management level of the respondents and was similar to comparable surveys

(e.g. Bourgeois, 1978; Galbreath & Galvin, 2008; Pecotich et al.; 2003; Pelham

& Lieb, 2004). The next chapter provides a discussion of the data analysis

procedure and the results.

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CHAPTER 4

I begin by discussing the results of the preliminary analysis, the treatment of

missing values and the outcome of a preliminary reliability test. This is followed

by an examination of the measurement issues in Porter’s SCP model by using

correlation coefficients to determine the degree of congruence of individual

perceptions of structure, conduct and performance within a company and within

an industry. Then I use Partial Least Squares (PLS) (Abdi, 2003; Chin, 1998;

Chin & Fry, 2003; Fornell & Cha, 1994; Fornell & Larcker, 1981; Henseler,

Ringle, & Sinkovics, 2009; Lohmoeller, 1981; Vinzi, Chin, Henseler, & Wang,

2010; Wetzels et al., 2009; Wold, 1981) to determine the degree of congruence

between the objective reality and individual perceptions of structure, conduct

and performance and thus determine the best predictor of company

performance. Finally, I examine the theoretical issues in Porter’s SCP model by

using PLS to determine if there is a positive association between the intensity of

industry competition (i.e. five forces), targeted strategic action (i.e. cost

leadership, differentiation and focus) and industry/firm performance.

Preliminary data analysis

The final number of respondents was 147 managers who represented 43

business units from 66 organisations. The average age of these executives

was 44 years with respondents reporting an average of eight years in a

management position in their current organisation. These executives spend an

average of 10 hours per week discussing the strategy of their business unit with

their colleagues. These results are presented in Table 2.

Table 2

Summary of Sample Characteristics

Characteristic Mean Std dev.

Age 44.44 9.49

Management experience (in years) 8.45 7.21

Industry experience (in years) 15.12 10.65

Strategy discussions (hours per week) 10.30 12.27

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The respondents occupied senior positions in their company with the titles of

their positions ranging from Chief Executive Officer/Managing Director (14%) to

Manager of a functional area/business unit (29%). About 37% of respondents

reported holding a postgraduate degree as their highest completed level of

education. A summary of these sample characteristics is shown in Table 3.

Table 3

Organisational Position and Highest Completed Level of Education of Sample

Characteristic no. %

Position in current organisation

Chief Executive Officer/Managing Director 21 14.3

Chief Operating Officer 5 3.4

Chief Financial Officer 11 7.5

Director of functional area / business unit 20 13.6

Associate Director 3 2.0

Manager of a functional area / business unit 42 28.6

Other 28 19.0

Missing 16 10.9

Total 147 100.0

Highest completed level of education

Middle school or below 7 4.8

High school 13 8.8

Post-secondary training 14 9.5

Bachelor degree 43 29.3

Postgraduate degree 54 36.7

Missing 16 10.9

Total 147 100.0

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Of the 147 respondents representing 13 industries, 21% were from the

Communication Services industry, 16% from Retail Trade and 14% from

Finance and Insurance. Table 4 shows the breakdown of respondents by the

ABS (Australian Bureau of Statistics) ANZSIC (Australian and New Zealand

Standard Industrial Classification) system. The major product for each business

unit included accommodation, engineering consulting, general insurance, media

publishing, residential construction, telecommunications and transport.

Table 4

Industry Classification of Sample

Industry no. %

Agriculture, Forestry and Fishing 2 1.4

Mining 1 0.7

Manufacturing 16 10.9

Electricity, Gas and Water Supply 1 0.7

Construction 11 7.5

Retail Trade 24 16.3

Accommodation, Cafes and Restaurants 7 4.8

Transport and Storage 15 10.2

Communication Services 31 21.1

Finance and Insurance 21 14.3

Property and Business Services 14 9.5

Education 1 0.7

Health and Community Services 3 2.0

Estimation of missing values

Nonresponse can pose a significant problem for any survey research and

occurs at the unit level and the item level. Unit nonresponse refers to the failure

of individuals in the sample to participate in the study. The nature of the unit

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nonresponse has been discussed in the previous chapter, so my purpose here

is to discuss the item nonresponse or missing values. Item nonresponse refers

to the absence of answers to specific questions in the survey after the

respondents agreed to participate in the study. The literature on missing values

is extensive (Baraldi & Enders, 2010; Donders, van der Heijden, Stijnen, &

Moons, 2006; Enders, 2010; Graham, 2009; Little, 1976; Little & Rubin, 1987;

Marcantonio & Pechnyo, 2002; Nordholt, 1998; Raghunathan, 2004; Rubin,

1976; Schafer & Graham, 2002; Scheffer, 2002; Schlomer, Bauman, & Card,

2010; West, 2001). Missing values has many possible causes, for example,

inadvertent oversight, confusion, lack of understanding, confidentiality or even

impatience due to disinterest. However, whatever the cause and whatever the

effort made to minimise missing values, they occur and must be dealt with. In

my case, they occurred despite adopting Dillman’s (2000, 2007; 2009b) TD

method and additional call backs to achieve completion. My examination of the

literature revealed that Rubin's (1976) theoretical classification of: (1) Missing

completely at random (MCAR); (2) Missing at random (MAR), and (3) Missing

not at random (MNAR) may be the most useful point of departure for this study.

These are statistical assumptions attempting to describe the nature of the

relationship of the data with the missing values. Data can be classified as

missing completely at random (MCAR) if the observed values of a variable are

truly a random sample of that variable’s values. Data that is missing at random

(MAR) suggests that whatever events caused the data to be missed does not

depend upon the missing data itself such as when a respondent accidentally

misses a question in a survey. Data that is not missing at random (NMAR) is

data that is missed for a specific reason such as the respondent purposely

skipping a question in the survey.

Commonly used techniques to treat missing data include Listwise deletion,

Pairwise deletion, Mean imputation and Regression imputation. These

methods, although commonly used, may be badly biased. Listwise and

pairwise deletion methods were considered unsuitable because of the study’s

small sample size and large number of variables. Unconditional mean

imputation was also considered inappropriate because it underestimates the

variances and covariances for the variables. Regression imputation cannot

capture covariances between jointly missing data, nor does it lead to maximum

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likelihood estimates based on observed data. The recent literature

recommends two missing data handling approaches – maximum likelihood

estimation and multiple imputation (Enders, 2010; Graham, 2009; Little & Rubin,

1987; Marcantonio & Pechnyo, 2002; Schafer & Graham, 2002; Schlomer et al.,

2010). These methods require weaker than the MAR assumptions and tend to

produce similar results (Enders, 2010). I chose the EM (expectation

maximisation) method which uses the maximum likelihood method to compute

the estimates. This procedure defines a model for the partially missing data

and bases inferences on the likelihood under that model. It is an iterative model

that consists of an E step and an M step. The E stage estimates the missing

data by finding the conditional expectation for the log likelihood based on

complete data, with respect to the missing data model, given the observed

values and current estimates of the parameters. The M stage uses maximum

likelihood estimation to make estimates of the parameters (means, standard

deviations and correlations) assuming the missing data were replaced. This

process continues through the two stages until the change in the estimated

values is negligible and they replace the missing data(Marcantonio & Pechnyo,

2002).

It has been suggested that the procedure for handling missing values is no

longer inconsequential if the proportion of missing data is greater than 5%,

(Enders, 2006, 2010; Graham, 2009; Little & Rubin, 1987, 1989; Marcantonio &

Pechnyo, 2002; Raghunathan, 2004; Schlomer et al., 2010; Tabachnick &

Fidell, 2007). This was the case for three variables in this study – Number of

suppliers, Percentage of external purchases from three largest suppliers and

Number of substitute products. I used SYSTAT, a statistical analysis and

graphical software, to calculate the mean and standard deviation of each

variable for both the data with missing values and the estimated EM

(expectation maximisation) values (Wilkinson, 2007). A comparison of both

datasets indicated that the estimated EM values did not differ greatly to the data

with missing values. Therefore, the effect of estimated missing values was not

likely to confound the results.

After estimating missing values, it is important to test variables for the violations

of statistical assumptions such as normality. Normality of variables is assessed

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by testing for skewness and kurtosis. Skewness is concerned with the

symmetry of the distribution. A distribution is skewed if one tail extends farther

than the other. Negative values imply negative/left skew while positive values

indicate positive/right skew. Kurtosis refers to how sharply peaked a distribution

is and a value of zero indicates normally peaked data. Negative values indicate

a distribution that is flatter than normal whereas positive values indicate a

distribution with a sharper than normal peak (Kutner, Nachtsheim, Neter, & Li,

2005; Neter, Wasserman, & Kutner, 1990).

Descriptive statistics and histograms were generated for each variable to

assess skewness and kurtosis. Four variables exceeded the skewness or

kurtosis cut-off rule of +/- 2 standard deviations from the mean (Tabachnik and

Fidell, 1996): Sales, EBIT, Total Assets and Objective number of substitute

products. I considered performing log linear transformation but this was

deemed unnecessary because of the exploratory nature of this study and thus, I

will be applying non-parametric techniques such as PLS and bootstrapping.

Nonetheless, the nature of the data was considered satisfactory for further

analysis.

Preliminary reliability test

Preliminary data analysis is required to identify scale items that do not

contribute to the reliability of the constructs in this study. Two constructs were

tested: (1) the five forces of competition that were measured using 42 scale

items from INDUSTRUCT (Pecotich et al. 1999) and (2) the three generic

strategies that were measured using 49 scale items developed by Pecotich et

al. (2003).

I conducted exploratory factor analysis to test for unidimensionality, that is,

scale items are strongly associated with each other and represent a single

construct (Hair, Black, & Babin, 2010; Hattie, 1985; Tabachnick & Fidell, 2007).

Exploratory factor analysis identifies the number of factors and the loadings of

each variable on the factor(s). By examining the correlation matrices,

eigenvalues and scree plots for each block of items, the results indicated that

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the INDUSTRUCT scale items represented five factors and the strategy scale

items represented three factors (Table 5).

I then assessed the reliability of the INDUSTRUCT and strategy measures to

determine the extent to which measures are free from random errors that affect

the observed score in different ways each time the measurement is made. This

is an important test because random error produces inconsistency, leading to

lower reliability (Hair et al., 2010; Tabachnick & Fidell, 2007). I chose to test for

internal consistency reliability using the coefficient alpha or Cronbach’s alpha,

which can be defined as the average of all possible split-half coefficients

resulting from different splittings of the scale items. This coefficient varies from

zero to one and a value of 0.60 or higher is considered acceptable (Hair et al.,

2010). Cronbach’s alpha for each of the five forces and each of the three

generic strategies exceeded 0.70, indicating high internal consistency reliability.

The results are shown in Table 5.

Table 5

Results of preliminary reliability tests

Construct Cronbach’s alpha Factor analysis for unidimensionality

INDUSTRUCT

Intensity of rivalry .85 Yes

Bargaining power of suppliers .85 Yes

Threat of new entrants .85 Yes

Threat of substitute products .74 Yes

Bargaining power of buyers .86 Yes

Generic Strategies

Cost leadership .87 Yes

Differentiation .85 Yes

Focus .82 Yes

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Hypothesis testing

Congruence of perceptions

Within marketing, Hunt’s (2010) R-A theory of competition asserts that the

resources of firms within an industry are heterogeneous and immobile and

therefore managers must make strategic choices and these choices influence

firm performance. Resources include information (e.g. market and competitor

intelligence) and some firms will have a comparative advantage in resources

while others will have a comparative disadvantage in resources. Specifically,

some firms will have better information than others and therefore, managers

develop strategies on the basis of imperfect perception of information.

Empirical research has shown that individual perceptions of structure, conduct

and performance vary within a company including studies from marketing and

strategic management (Barrett et al., 2009; Bourgeois, 1978; Pelham & Lieb,

2004), organisational behaviour (Dearborn & Simon, 1958; Downey et al., 1977;

Lawrence & Lorsch, 1973) and managerial cognition (Kaplan, 2008; McNamara

et al., 2002; Nadkarni & Barr, 2008).

In contrast, it is the view of industrial organisation economists that corporate

response to industry structure is the critical variable in determining industry and

thus, company performance. Porter’s (1980) adaption of the SCP paradigm

from industrial organisation economics implicitly assumes all managers should

define and observe the same objective environment. Here, the unit of analysis

is the industry and companies must choose the industries to compete in and/or

alter industry structure to increase monopoly power. On this basis, perception

of the five forces of competition should be identical for all managers operating in

the same industry. Some studies have shown individuals within a company

share similar perceptions of the environment (Duncan, 1972; Johnson &

Hoopes, 2003; Porac et al., 1989; Snow & Hrebiniak, 1980). On this basis, I

expect to find individual perceptions of structure, conduct and performance

within a company will have a strong positive relationship.

To test my hypothesis, I created an index of individuals from the same company

and labelled this variable Company Repeat (COMPRPT). I assigned missing

values to companies which only had one respondent and these were eliminated

103

from the analysis. This resulted in 31 companies with multiple respondents.

First, I created a scatterplot for each company to check for outlying data points.

Then I generated a correlation matrix to test the strength of the linear

relationship between respondents from the same company (i.e. the degree to

which one manager’s perception of structure, conduct and performance is

related to another manager’s perception of the same variables).

Many rules of thumb and guidelines for the interpretation of correlation

coefficients exist (Cohen, 1992; Cooper, 1982; Nunnally, 2004; Nunnally &

Bernstein, 1994). However, there is a degree of arbitrariness of many such

rules and the interpretation of correlation coefficients depends on the situation.

A correlation coefficient of 0.30 may be considered very low in the very precise

measurement context of the hard sciences but quite high in the social sciences

where the measurement may be relatively imprecise. In the circumstances of

this study, the procedure is multi-faceted and gradual. Given that I am

evaluating measurement correspondence, the correlation coefficients are

expected to be uniformly very high. I considered correlation coefficients of 0.70

and above to indicate substantive agreement and be in support of the

conjectures. This is consistent with Nunnally (1978) and Nunnally and

Bernstein (1994) and follows the power guidelines provided by Cohen (1992) in

that it explains 49% of the variance. The highest and lowest correlation

coefficient values for each company and the summary of significance are

presented in Table 6. The full range of correlation coefficient values for each

company is in Appendix 5.

Of the 31 companies, 11 companies showed their top management team

perceptions of structure, conduct and performance had a strong positive

relationship (Company 1, Company 4, Company 5, Company 6, Company 7,

Company 11, Company 17, Company 19, Company 22, Company 25, and

Company 26) while six companies indicated their top management teams did

not share similar perceptions of the environment (Company 3, Company 14,

Company 23, Company 24, Company 29, Company 31). For the remaining 14

companies, the correlation ranged from weak negative correlations to strong

104

positive correlations which demonstrated partial support for Hypothesis 1. For

example, within Company 2, only one of six correlations was greater than 0 .70.

Within Company 8, one of three correlations was large and highly significant.

Overall, the results show, at best, mixed support for Porter’s argument that

managers within a company observe the same environment. Perhaps, as

Duncan (1972, p.134) noted, it is possible that individual perceptions of the

environment within a company can vary depending on the individual’s threshold

level for uncertainty: “Some individuals may have a very high tolerance for

ambiguity and uncertainty so they may perceive situations as less uncertain

than others with lower tolerances.” The implications of this finding will be

discussed in the next chapter.

10

5

Tab

le 6

Degree of Congruence of Individual Perceptions of Structure, Conduct and Performance within a Company

Co

mpa

ny

no

. re

spo

nd

en

ts

Co

rre

latio

n r

an

ge

S

ign

ific

an

ce o

f co

rre

latio

n*

Hyp

oth

esis

a

1

3

.78

- .9

8

All

larg

e a

nd

hig

hly

sig

nific

an

t S

2

4

-.2

2 -

.97

On

e o

f six

co

rre

latio

ns w

as la

rge

an

d h

igh

ly s

ign

ific

ant

PS

3

3

.01

- .3

9

No

ne

N

S

4

2

.77

La

rge

and

hig

hly

sig

nific

an

t S

5

3

.93

- .9

6

All

larg

e a

nd

hig

hly

sig

nific

an

t S

6

2

.99

La

rge

and

hig

hly

sig

nific

an

t S

7

2

.75

La

rge

and

hig

hly

sig

nific

an

t S

10

6

Co

mpa

ny

no

. re

spo

nd

en

ts

Co

rre

latio

n r

an

ge

S

ign

ific

an

ce o

f co

rre

latio

n*

Hyp

oth

esis

a

8

3

.45

- .7

9

On

e o

f th

ree

co

rre

lation

s w

as la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

9

9

-.1

8 -

1.0

0

12

of

36

co

rre

lation

s w

ere

la

rge

and

hig

hly

sig

nific

ant

PS

10

4

.50

- .9

6

Th

ree

of

six

co

rre

latio

ns w

ere

la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

11

3

.94

- .9

9

All

larg

e a

nd

hig

hly

sig

nific

an

t S

12

6

-.0

6 -

.92

Se

ven

of

15

co

rre

lation

s w

ere

la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

13

4

.00

5 -

.9

3

On

e o

f six

co

rre

latio

ns w

as la

rge

an

d h

igh

ly s

ign

ific

ant

PS

14

2

.12

No

ne

N

S

15

3

.17

- .9

1

On

e o

f th

ree

co

rre

lation

s w

as la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

16

3

-.0

02

- .7

3

On

e o

f th

ree

co

rre

lation

s w

as la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

10

7

Co

mpa

ny

no

. re

spo

nd

en

ts

Co

rre

latio

n r

an

ge

S

ign

ific

an

ce o

f co

rre

latio

n*

Hyp

oth

esis

a

17

3

.88

- .9

7

All

larg

e a

nd

hig

hly

sig

nific

an

t S

18

7

-.0

5 -

1.0

0

Tw

o o

f 10

co

rre

latio

ns w

ere

la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

19

2

.74

La

rge

and

hig

hly

sig

nific

an

t S

20

3

.44

- .9

3

On

e o

f th

ree

co

rre

lation

s w

as la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

21

3

.46

- .7

3

On

e o

f th

ree

co

rre

lation

s w

as la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

22

5

.77

- 1

.00

All

larg

e a

nd

hig

hly

sig

nific

an

t S

23

2

.06

No

ne

NS

24

2

.55

No

ne

N

S

25

6

.90

- .9

9

All

larg

e a

nd

hig

hly

sig

nific

an

t S

10

8

Co

mpa

ny

no

. re

spo

nd

en

ts

Co

rre

latio

n r

an

ge

S

ign

ific

an

ce o

f co

rre

latio

n*

Hyp

oth

esis

a

26

2

1.0

0

La

rge

and

hig

hly

sig

nific

an

t S

27

4

-.1

8 -

.98

On

e o

f six

co

rre

latio

ns w

as la

rge

an

d h

igh

ly s

ign

ific

ant

PS

28

6

-.1

9 -

.98

Six

of

15

co

rre

latio

ns w

ere

la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

29

4

-.0

7 -

.42

No

ne

N

S

30

4

.17

- .8

6

Tw

o o

f six

co

rre

latio

ns w

ere

la

rge

an

d h

igh

ly s

ign

ific

an

t P

S

31

2

.67

No

ne

NS

*Corr

ela

tion c

oeff

icie

nts

ove

r .7

0 c

onsid

ere

d larg

e a

nd h

ighly

sig

nific

ant

aS

= S

upport

ed, P

S =

Part

ially

support

ed, N

S =

Not

support

ed

109

The next step is to test if companies competing within an industry have similar

perceptions of the same objective environment. Hunt’s (2010) R-A theory

suggests managers make strategic decisions on the basis of imperfect

perception of information. Research within marketing and strategic

management (Clark & Montgomery, 1996; Ng et al., 2009; Wilson et al., 1993)

and managerial cognition (Daniels et al., 2002; Hodgkinson & Johnson, 1994)

have demonstrated company perceptions vary within an industry.

However, Porter (1980) argues corporate response to industry structure as the

critical variable in determining industry and thus, company performance. For

Porter (1980), perception of the five forces of competition should be identical for

all managers operating in the same industry. The unit of analysis is the industry

and companies must choose the industries to compete in and/or alter industry

structure to increase monopoly power. Some studies in managerial cognition

have found similar perceptions within an industry (Johnson & Hoopes, 2003;

Panagiotou, 2006; Porac et al., 1989). Porter (1980) implicitly assumes

managers within a company define and observe the same objective

environment. On this basis, I expect to find company perceptions of structure,

conduct and performance within an industry will have a strong positive

relationship.

I created an index of companies from the same industry to test this hypothesis

and labelled this variable Industry Repeat (INDUSRPT). Then I assigned

missing values to industries which only had one company and these were

eliminated from the analysis. This resulted in 13 industries with multiple

respondents. I performed a correlation matrix to test the strength of the linear

relationship between companies from the same industry. The highest and

lowest correlation coefficient values for each industry and the summary of

significance are presented in Table 7. The full range of correlation coefficient

values for each industry is in Appendix 6. Of the 13 industries, 11 industries

reported correlations ranging from weak negative correlations to strong positive

correlations, indicating some companies within an industry share similar

perceptions of the environment while others do not. For example, within the

Transport & Storage industry, 24 of 105 correlations exceeded 0.70 indicating a

positive and highly significant relationship in company perceptions of structure,

110

conduct and performance within this industry. Only two industries reported

highly significant correlations indicating a strong positive relationship in

company perceptions of structure, conduct and performance within each

industry: Agriculture, Forestry & Fishing and Health & Community Services.

Overall, the results do not support Porter’s theory that managers define and

observe the same objective environment and therefore Hypothesis 2 is rejected.

This finding is similar to previous studies in marketing, strategic management

and managerial cognition (Clark & Montgomery, 1996; Daniels et al., 2002;

Hodgkinson & Johnson, 1994; Ng et al., 2009; Wilson et al., 1993). The weak

negative correlations to strong positive correlations of company perceptions of

structure, conduct and performance within an industry provides support for

Hunt’s (2010) R-A theory of competition whereby resource heterogeneity

requires managers to make strategic choices and these choices influence

performance. Resources include consumer and competitor intelligence and

some companies will have better intelligence than others, resulting in imperfect

perception of information. Therefore, it is the role of managers to develop

strategies on the basis of the resources they possess, including imperfect

perception of information. Snow and Hrebiniak (1980) concluded top

managers deliberately develop strategies and competitive advantages that are

distinct from their rivals even though the environmental situation faced by

companies within an industry may be generally similar.

11

1

Tab

le 7

Degree of Congruence of Company Perceptions of Structure, Conduct and Performance Within an Industry

Ind

ust

ry

no

. co

mpa

nie

s C

orr

ela

tio

n r

an

ge

S

ign

ific

an

ce o

f co

rre

latio

n*

Hyp

oth

esis

a

1 A

gricu

ltu

re,

Fo

rest

ry,

Fis

hin

g

2

.91

La

rge

a

nd

hig

hly

sig

nific

an

t S

2 M

an

ufa

ctu

rin

g

16

-.1

4 –

.9

3

7 o

f 1

20

co

rre

lation

s w

ere

la

rge

and

h

igh

ly s

ign

ific

an

t P

S

3 C

on

str

uct

ion

11

-.0

9 –

.9

96

1

1 o

f 55

co

rre

lation

s w

ere

la

rge

and

h

igh

ly s

ign

ific

an

t P

S

4 R

eta

il tr

ad

e

24

-.2

4 –

.9

96

69

of

27

6 c

orr

ela

tio

ns w

ere

la

rge

an

d

hig

hly

sig

nific

an

t P

S

5 A

ccom

mo

da

tion

, cafe

s,

resta

ura

nts

7

.1

3 –

.99

5

15

of

21

co

rre

lation

s w

ere

la

rge

and

h

igh

ly s

ign

ific

an

t P

S

11

2

Ind

ust

ry

no

. co

mpa

nie

s C

orr

ela

tio

n r

an

ge

S

ign

ific

an

ce o

f co

rre

latio

n*

Hyp

oth

esis

a

6 T

ran

spo

rt a

nd

Sto

rage

1

5

-.1

8 –

1.0

0

24

of

10

5 c

orr

ela

tio

ns w

ere

la

rge

an

d

hig

hly

sig

nific

an

t P

S

7 C

om

mu

nic

ation

se

rvic

es

31

-.3

9 –

.9

8

29

of

46

5 c

orr

ela

tio

ns w

ere

la

rge

an

d

hig

hly

sig

nific

an

t

PS

8 F

ina

nce

an

d In

su

rance

2

1

-.3

1 –

1.0

0

50

of

21

0 c

orr

ela

tio

ns w

ere

la

rge

an

d

hig

hly

sig

nific

an

t P

S

9 P

rope

rty

an

d B

usin

ess S

erv

ice

s

13

-.2

7 –

.9

96

17

of

78

co

rre

lation

s w

ere

la

rge

and

h

igh

ly s

ign

ific

an

t P

S

10

Hea

lth

an

d C

om

mun

ity

Se

rvic

es

3

.93

– .

96

La

rge

a

nd

hig

hly

sig

nific

an

t S

*Corr

ela

tion c

oeff

icie

nts

ove

r 0.7

consid

ere

d larg

e a

nd h

ighly

sig

nific

ant

aS

= S

upport

ed, P

S =

Part

ially

support

ed, N

S =

Not

support

ed

113

Management Perceptions versus Objective Reality

So far, the results have demonstrated partial support for my expectation that

individual perceptions of structure, conduct and performance within a company

would show a strong positive relationship. However, the results failed to

support my hypothesis that company perceptions within an industry would have

a strong positive relationship. In fact, 11 of the 13 industries reported

correlations ranging from weak negative correlations to strong positive

correlations.

The next step is to determine if individual perceptions of structure, conduct and

performance will have a strong positive relationship with the objective reality.

Within marketing, Hunt’s (2010) R-A theory of competition proposed that it is the

role of managers to develop strategies on the basis of the resources they

possess, including imperfect perception of information. Some studies have

investigated the correspondence between management perceptions and

objective data and did not find strong positive relationships (Bourgeois, 1985;

Downey et al., 1975; Hambrick, 1981; Mezias & Starbuck, 2003; Tosi et al.,

1973). However, Porter (1980) argues corporate response to industry structure

as the critical variable in determining superior financial performance for the firm.

It is imperative for companies to choose the right industries to compete in

and/or alter industry structure to increase monopoly power. The unit of analysis

is the industry and Porter (1980) implicitly assumes managers develop

strategies after an objective analysis of industry structure. Therefore, I expect

to find that individual perceptions of structure, conduct and performance will

have a strong positive relationship with objective reality.

To test my hypothesis, I used the Partial Least Squares (PLS) estimation

procedure (Abdi, 2003; Chin, 1998; Chin & Fry, 2003; Fornell & Cha, 1994;

Fornell & Larcker, 1981; Henseler et al., 2009; Lohmoeller, 1981; Vinzi et al.,

2010; Wetzels et al., 2009; Wold, 1981). The small sample size and the

stringent distributional assumptions precluded the use of a more well-known

method such as LISREL. PLS was developed by Wold (1981) for estimating

path models involving latent constructs indirectly observed by multiple

indicators. This technique does not require the “hard” assumptions of normality

114

and large sample sizes. Thus, PLS is sometimes referred to as a form of “soft

modelling” (Falk & Miller, 1992) and provides benefits for non-experimental data

(Kroonenberg, 1990).

The present study differs from previous studies that examined firm effects

versus industry effects using variance component analysis (VCA) (Galbreath &

Galvin, 2008). VCA has been questioned because it can produce highly non-

linear indicators of performance and the basic assumptions of this statistical

technique have also been questioned. In addition, this stream of research is

limited as it offers “no information about the basic drivers of business

performance or about the mechanisms by which the performance is generated”

(McGahan & Porter, 2005, p. 873). Galbreath and Galvin (2008) suggested that

research in this area can be improved by using alternative methodologies and

thus, the present study chose PLS.

A PLS model is formally specified by three sets of relations. The first set is the

outer (measurement) model which specifies the relationship between a latent

variable (e.g. Structure) and its associated observed or manifest variables (e.g.

indicators of Structure such as the intensity of rivalry), and where the

interpretation is similar to that of principal component loadings. The second set

is the inner (structural) model which specifies the relationships between the

latent variables (e.g. Structure, Conduct and Performance) and whose

interpretation is as for standardised regression coefficients. The third set

specifies the weight relations where the case value for each latent variable is

estimated. The separation into three parts allows explicit least-squares iterative

estimation of latent variable scores as a weighted aggregate of its indicators

(Abdi, 2003; Chin, 1998; Chin & Fry, 2003; Fornell & Cha, 1994; Fornell &

Larcker, 1981; Henseler et al., 2009; Lohmoeller, 1981; Vinzi et al., 2010;

Wetzels et al., 2009; Wold, 1981).

I used the PLS Graph 3.0 software, as developed and refined by Chin (2002), to

evaluate the outer (measurement) model and in so doing, test my hypothesis

that individual perceptions of structure, conduct and performance will have a

strong positive relationship with the objective reality. Evaluation of the outer

115

model requires the use of multiple indices which are characterised by “many

aspects regarding their quality, sufficiency to explain the data, congruence with

substantive expectations, precision and confidentiality” (Lohmoeller, 1981 p.

49). Hence, I used a number of fit indices to determine the predictive relevance

of the model such as factor loadings, composite reliability (CR), average

variance extracted (AVE) and t-values. As no distributional assumptions are

made, these indices provide evidence for the existence of the relationships

rather than a definitive statistical test which may be contrary to the philosophy of

soft modelling (Falk and Miller, 1992).

Factor loadings measure how well the latent variable predicts each indicator in

its block better than indicators from other blocks and should exceed 0.40 as

recommended by Falk and Miller (1992). For example, I expect to find factor

loadings greater than 0.40 for both the subjective and objective indicators of the

latent variable, Structure, which would support my hypothesis for a strong

positive relationship between perceptions of structure and objective reality. I

also assessed the reliability and validity of each construct by calculating the

composite reliability (CR) and average variance extracted (AVE) respectively

(Chin, 1998; Fornell & Larcker, 1981). CR assesses internal consistency

reliability. PLS prioritises indicators according to their reliability, resulting in a

more reliable composite and it can be interpreted in the same way as

Cronbach’s alpha. Thus, a CR value above 0.7 in exploratory research or

above 0.8 in more advanced stages of research is regarded as satisfactory

(Nunnally & Bernstein, 1994). AVE measures convergent validity, that is, a set

of indicators represent a single underlying construct. It should exceed the cut-

off value of 0.50 proposed by Fornell and Larcker (1981), meaning that a latent

variable is able to explain more than half of the variance of its indicators on

average. Since PLS does not make assumptions about distribution, resampling

procedures such as blindfolding, jackknifing and bootstrapping are used to

obtain information about the variability of the parameter estimates. Derivation

of valid standard errors of t-values by bootstrapping is superior to the other two

resampling methods and critical ratios should exceed 1.96 (Temme et al, 2006).

The factor loadings and t-stat for each indicator of the latent variable, Structure,

as well as the CR and AVE for the overall Structure construct are reported in

116

Table 8. The indicators of Structure measured individual perceptions of Porter’s

(1980) five forces using the INDUSTRUCT scale items (Pecotich et al., 1999)

and perceptions of structure identified by Bain (1968) and Scherer (1970).

Therefore, the indicators measuring perceptions of structure identified by Bain

(1968) and Scherer (1970) are not pure measures of Porter’s (1980) five forces.

As previously explained, this is because the INDUSTRUCT scale items

(Appendix 1) were not amenable to comparison with objective data. For

example, one of the INDUSTRUCT scale items asks the respondent to indicate

the extent to which “Firms in our industry compete intensely to hold and/or

increase their market share”, which is not amenable to comparison with

objective data. Therefore, additional scale items (Appendix 4) measuring

perceptions of structure as identified by Bain (1968) and Scherer (1970) were

taken from PIMS for this study (Buzzell & Gale, 1987). For each of the

indicators measuring structure as conceptualised by Bain (1968) and Scherer

(1970), there was a corresponding indicator measuring the objective reality.

I expected to find factor loadings greater than 0.40 for both the subjective and

objective indicators of the latent variable, Structure, which would support my

hypothesis for a strong positive relationship between individual perceptions of

structure and the objective reality. However, the factor loadings varied from -

0.57 (Objective market share rank) to 0.82 (Objective number of competitors

that have exited the industry during the past five years) which suggests that

both the subjective and objective indicators of Structure are measuring different

constructs. However, this finding should be interpreted with caution because

some of the indicators are not pure measures of Porter’s five forces of

competition. The AVE of 0.16, which is below the cut-off value of 0.50

proposed by Fornell and Larcker (1981), suggests a lack of convergent validity.

However, a CR of 0.76 indicates satisfactory scale reliability in this exploratory

study (Nunnally, 2004). This finding is consistent with previous empirical

studies that found no congruence between individual perceptions of structure

and objective data (Bourgeois, 1985; Downey et al., 1975; Mezias & Starbuck,

2003; Tosi et al., 1973). Therefore, management perceptions of structure

determine conduct, not the objective reality, and the organisation becomes a

victim of perceptions which ignore or distort environmental elements (Miles et

al., 1974).

117

Table 8

Psychometric properties for the overall Structure construct

Items Loading t-stata

Structure (CRb = 0.76, AVEc = 0.16)

(Subjective) Intensity of rivalry 0.20 0.67

(Subjective) Bargaining power of suppliers 0.08 0.39

(Subjective) Threat of new entrants 0.25 1.07

(Subjective) Threat of substitute products 0.26 0.75

(Subjective) Bargaining power of buyers 0.17 0.50

(Subjective) Number of competing businesses in the market actively served by the business unit in 2008

0.34 2.20*

(Subjective) Number of competitors that have entered the industry during the past five years

0.63 1.66

(Subjective) Number of competitors that have exited the industry during the past five years

0.80 2.45*

(Subjective) Market share in 2008 -0.36 2.40*

(Subjective) Market share rank in 2008 -0.41 2.71*

(Subjective) Number of customers -0.06 0.50

(Subjective) Percentage of customers purchased 50% of products/services

-0.25 1.78

(Subjective) Number of suppliers -0.04 0.21

118

Items Loading t-stata

(Subjective) Percentage of purchases from three (3) largest suppliers

-0.16 1.30

(Subjective) Number of substitute products customers can switch to

0.78 1.65

Objective number of competing businesses in the market actively served by the business unit in 2008

0.48 2.51*

Objective number of competitors that have entered the industry during the past five years

0.47 3.34*

Objective number of competitors that have exited the industry during the past five years

0.82 3.37*

Objective market share in 2008 -0.42 2.46*

Objective market share rank in 2008 -0.57 4.13*

Objective number of customers -0.09 0.62

Objective percentage of customers purchased 50% of products/services

0.10 0.92

Objective number of suppliers -0.001 0.004

Objective percentage of purchases from three (3) largest suppliers

-0.18 1.58

Objective number of substitute products customers can switch to

-0.17 1.34

a Bootstrapping estimates calculation based on Chin (1998a,b) *Significant at P <.05 b Composite Reliability c Average Variance Extracted

119

The next step was to assess the degree of congruence between individual

perceptions of conduct and the objective reality. After choosing industries to

compete in and/or altering their structure, Porter (1980) argues there are three

potentially successful generic strategic approaches to outperforming

competitors in an industry: (1) Overall cost leadership, (2) Differentiation and (3)

Focus. There are three indicators for subjective measures of conduct and one

indicator for the objective measurement of conduct.

Results in Table 9 show the indicators generated factor loadings from -0.10

(Objective Strategy) to 0.88 (Differentiation). Objective Strategy was the only

indicator that did not meet the critical ratio of 1.96. It appears that the

subjective and objective indicators of Conduct are measuring different

constructs and thus individual perceptions of conduct do not have a strong

positive relationship with the objective reality. This finding is similar to the

results of Hambrick’s (1981) study. The CR of 0.75 suggests sufficient reliability

for this exploratory study. The AVE of 0.49 is just below the cut-off value of

0.50 proposed by Fornell and Larcker (1981) for convergent validity.

Table 9

Psychometric properties for the overall Conduct construct

Items Loading t-stata

Conduct (CRb = 0.75, AVEc = 0.49)

Objective Strategy -0.10 0.68

(Subjective) Cost Leadership 0.82 25.50*

(Subjective) Differentiation 0.88 39.18*

(Subjective) Focus 0.70 9.66*

a Bootstrapping estimates calculation based on Chin (1998a,b) *Significant at P <.05 b Composite Reliability c Average Variance Extracted

The final step was to assess the degree of congruence between individual

perceptions of performance and the objective reality. According to Porter

120

(1980), the five forces determine the intensity of competition and hence industry

profitability, measured as long run return on invested capital. I expected to find

factor loadings greater than 0.40 for both the subjective and objective indicators

of return on investment, which would support my hypothesis for a strong

positive relationship between individual perceptions of performance and the

objective reality. As shown in Table 10, the factor loadings of 0.93 and 0.02 for

subjective and objective return on investment respectively, suggests individual

perceptions of performance do not correspond with the objective reality.

Further, the factor loadings for additional indicators of Performance range from -

0.22 (Objective Earnings Before Interest and Tax) to 0.93 (Subjective return on

investment) which implies these indicators are measuring different constructs.

Four of the 13 indicators did not pass the critical ratio of 1.96. This finding

supports Mezias and Starbuck (2003) who asked managers about six numeric

measures of quality performance and found management perceptions of quality

performance measures did not match objective data even though these

managers received quarterly reports about these measures. The CR of 0.79

suggests sufficient reliability for this exploratory study. The AVE of 0.31 is

below the cut-off value of 0.50 proposed by Fornell and Larcker (1981) for

convergent validity.

Table 10

Psychometric properties for the overall Performance construct

Items Loading t-stata

Performance(CRb = 0.79, AVEc = 0.31)

(Subjective) Return on sales 0.81 21.80*

(Subjective) Return on investment 0.93 52.30*

(Subjective) Return on total assets 0.90 40.67*

(Subjective) Overall business unit performance 0.85 20.85*

(Subjective) Net profit 0.71 13.50*

(Subjective) Industry performance of business unit 0.49 5.26*

121

Items Loading t-stata

Objective industry performance of business unit 0.02 0.19

Objective Return on sales 0.09 0.66

Objective Return on investment 0.02 0.20

Objective Sales / Revenue (External and Internal) -0.21 2.13*

Objective Earnings Before Interest and Tax (EBIT) -0.22 2.20*

Objective Total Assets -0.18 1.82

Objective Total Liabilities 0.28 3.60*

a Bootstrapping estimates calculation based on Chin (1998a,b) *Significant at P <.05 b Composite Reliability c Average Variance Extracted

In conclusion, my findings reject the notion that individual perceptions of

structure, conduct and performance will have a strong positive relationship with

the objective reality and therefore, Hypothesis 3 is rejected. This supports

Hunt’s R-A theory of competition which suggests managers make strategic

decisions on the basis of imperfect perception of information. I emphasise that

this research is not to be seen as a final definitive evaluation of Porter’s

adaptation of the SCP paradigm but rather as a preliminary, exploratory

assessment.

The Best Predictor of Performance

My hypothesis that individual perceptions of structure, conduct and performance

would show a strong positive relationship with objective reality was not

supported. Therefore, management perceptions of structure determine

conduct, not the objective reality, and the organisation becomes a victim of

perceptions which ignore or distort environmental elements (Miles & Snow,

1978; Miles et al., 1974).

This supports Hunt’s R-A theory of competition which suggests managers make

strategic decisions on the basis of imperfect perception of information. Under

122

Hunt’s R-A theory of competition, heterogeneous intra-industry demand and

heterogeneous, imperfectly mobile resources result in diversity in business unit

financial performance (Hunt, 1983, 2000a, 2000b, 2001, 2002b, 2010; Hunt &

Arnett, 2001, 2006; Hunt & Derozier, 2004; Hunt & Duhan, 2002; Hunt &

Morgan, 1995). Resource heterogeneity and immobility imply strategic choices

must be made and that these choices influence performance. It is the role of

managers to recognise, understand, create, select, implement and modify

strategies. If this is the case, then “firm effects” or management perceptions of

industry structure determine conduct and performance. Empirical research on

financial performance clearly shows that “firm effects” dominate “industry

effects” and competition is market segment by market segment (Brush et al.,

1999; Chang & Singh, 2000; Cubbin & Geroski, 1987; Galbreath & Galvin,

2008; Hansen & Wernerfelt, 1989; Hawawini et al., 2003; Mauri & Michaels,

1998; McGahan & Porter, 1997; Powell, 1996; Roquebert et al., 1996; Rumelt,

1991; Short et al., 2007).

In contrast to Hunt’s R-A theory of competition, industrial organisation

economists contend that firm performance is dependent on the structural

features of industry under the SCP model. Here, firms are viewed as combiners

of homogeneous, perfectly mobile resources and intra-industry demand is

viewed as homogeneous. Porter (1980) emphasised corporate response to

industry structure as the critical variable in determining financial performance.

“The essence of formulating competitive strategy is relating a company to its

environment” (p. 3) and the key to success is to “find a position in the industry

where the company can best defend itself against these competitive forces or

can influence them in its favour” (p. 4). Therefore, Porter’s theory predicts that

“industry effects” or objective data should explain most of the variance in firms’

performance and this has been supported by Schmalensee (1985) and

McGahan and Porter (1999).

This study showed individual perceptions of structure, conduct and performance

did not have a strong positive relationship with the objective reality and thus,

suggests that Hunt’s R-A theory of competition is the better predictor of firm

performance. Together with evidence from previous studies, there is

123

overwhelming support that the industry is the “tail” of competition; the firm is the

“dog” (Hunt, 2000b, p. 155).

Measurement Refinement – PLS regression

I began data analysis by investigating the measurement issues concerning

Porter’s SCP model. This involved using correlation coefficients to determine

the strength of the relationship of individual perceptions of structure, conduct

and performance within companies and within industries. I found partial support

for congruent company perceptions but no support for congruent industry

perceptions. Then, I tested the strength of the relationship between perceptions

of structure, conduct and performance with the objective reality. This involved

the use of PLS to evaluate the outer (measurement) model which specified the

relationships between latent variables (e.g. Structure) and their associated

observed or manifest variables (e.g. indicators of Structure such as the intensity

of rivalry). I found indicators for each of the three latent variables (i.e. Structure,

Conduct and Performance) did not measure their respective construct, that is,

individual perceptions of structure, conduct and performance did not correspond

with the objective reality. Thus, the next step is to use PLS to identify the inner

(structural) model to investigate the theoretical issues in Porter’s model. These

theoretical issues are associated with the nature of the relationship between the

intensity of competition (i.e. five forces), targeted strategic action (i.e. cost

leadership, differentiation and focus) and industry/firm performance.

The factor loadings for the 25 indicators of Structure did not all exceed 0.40 as

recommended by Falk and Miller (1992) which suggests that the indicators

represent different constructs. Only six of the 25 indicators reported factor

loadings that exceeded the recommended guide of 0.40 as shown in Table 8

(Falk and Miller, 1992). These six factors measured the subjective and

objective number of competitors that have entered and exited the industry, the

subjective number of substitutes and the objective number of competitors. I

grouped these six indicators into one factor which I labelled Entry-Exit (Table

11). The factor loadings ranged from 0.44 (the objective number of competitors

that have entered the industry during the past five years) to 0.90 (the number of

competitors that have exited the industry during the past five years) which

meets the cut-off rule of 0.40 as recommended by Falk and Miller (1992).

124

Further, each factor loading had a t-statistic exceeding 1.96. The CR of 0.87

indicates satisfactory internal consistency reliability and the AVE of 0.55

suggests sufficient convergent validity.

12

5

Tab

le 1

1

Psychometric Properties for the Structure variables

Co

nstr

uct

Item

L

oad

ing

t-sta

ta

CR

b

AV

Ec

En

try-

Exit

(Su

bje

ctive

) N

um

be

r of

co

mp

etito

rs t

ha

t ha

ve e

nte

red

th

e

ind

ustr

y d

urin

g t

he

pa

st f

ive

ye

ars

0

.75

2.0

9

0.8

7

0.5

5

(S

ub

jective

) N

um

be

r of

co

mp

etito

rs t

ha

t ha

ve e

xite

d

the

in

du

str

y d

urin

g t

he

pa

st f

ive

ye

ars

0

.90

2.9

3

(S

ub

jective

) N

um

be

r of

su

bstitu

te p

rodu

cts

custo

me

rs c

an

sw

itch

to

0

.89

1.8

9

O

bje

ctive

num

be

r of

com

pe

tin

g b

usin

esse

s in

the

ma

rke

t a

ctive

ly s

erv

ed

by

the

bu

sin

ess

un

it in

20

08

0.4

8

4.0

9

O

bje

ctive

num

be

r of

com

pe

tito

rs th

at

ha

ve e

nte

red t

he

in

du

str

y d

urin

g t

he

pa

st five

ye

ars

0

.44

2.1

7

O

bje

ctive

num

be

r of

com

pe

tito

rs th

at

ha

ve e

xite

d

the

ind

ust

ry

du

rin

g t

he

pa

st five

ye

ars

0

.85

13

.79

12

6

Co

nstr

uct

Item

L

oad

ing

t-sta

ta

CR

b

AV

Ec

Fiv

e

Fo

rce

s P

eco

tich

(Su

bje

ctive

) In

ten

sity

of

riva

lry

0

.71

16

.55

0.8

2

0.4

7

(S

ub

jective

) B

arg

ain

ing p

ow

er

of

sup

plie

rs

0.6

9

13

.86

(S

ub

jective

) T

hre

at of

ne

w e

ntr

an

ts

0.6

4

9.5

8

(S

ub

jective

) T

hre

at of

su

bstitu

te p

rod

uct

s 0

.73

12

.43

(S

ub

jective

) B

arg

ain

ing p

ow

er

of

bu

yers

0

.66

11

.48

Nu

mbe

r C

usto

me

rs-S

up

plie

rs

(Su

bje

ctive

) N

um

be

r of

cu

sto

me

rs

0.4

8

2.3

0

0.7

9

0.5

1

(S

ub

jective

) N

um

be

r o

f su

pp

liers

0

.87

3.7

0

O

bje

ctive

num

be

r of

custo

me

rs

0.5

0

2.8

7

O

bje

ctive

num

be

r o

f su

pp

liers

0

.88

2.7

4

12

7

Co

nstr

uct

Item

L

oad

ing

t-sta

ta

CR

b

AV

Ec

Po

we

r C

usto

me

rs-S

upp

liers

(S

ub

jective

) P

erc

enta

ge

of

custo

me

rs p

urc

hase

d 5

0%

of

pro

du

cts

/se

rvic

es

0.6

0

5.3

3

0.6

8

0.3

6

(S

ub

jective

) P

erc

enta

ge

of

pu

rcha

se

s f

rom

thre

e (

3)

larg

est

su

pp

liers

0

.64

8.8

3

O

bje

ctive

cu

sto

me

r p

ow

er

0

.53

1.7

3

O

bje

ctive

su

pp

lier

po

we

r 0

.62

8.7

2

a B

oots

trappin

g e

stim

ate

s c

alc

ula

tion b

ased o

n C

hin

(1998a,b

) b C

om

posite R

elia

bili

ty

c A

vera

ge V

ariance E

xtra

cte

d

128

The second block of indicators which were highly loaded onto a single latent

variable measured perceptions of Porter’s (1980) five forces of competition

based on the Pecotich et al. (1999) measure (Table 11). Consequently, I

labelled the second latent variable Five Forces Pecotich. The factor loadings

ranged from 0.64 (Threat of new entrants) to 0.73 (Threat of substitute

products). Also, indicators reported t-statistics greater than 1.96. The CR

of0.82 indicates sufficient reliability but the AVE of 0.47 was just below the

prescribed cut-off value of 0.50 (Fornell and Larcker, 1981) for convergent

validity.

The third block of indicators which were highly loaded onto a single latent

variable measured both the subjective and objective number of suppliers and

customers. This latent variable was labelled Number Customers-Suppliers

(Table 11). All indicators reported factor loadings that exceeded 0.40 and all t-

statistics were above 1.96. The CR of 0.77 indicates satisfactory reliability and

the AVE of 0.51indicates sufficient convergent validity.

The fourth block of indicators which were highly loaded onto a single latent

variable measured the power of customers and suppliers. This latent variable

was labelled Power Customers-Suppliers (Table 11). The indicators reported

factor loadings from 0.53 (Objective customer power) to 0.64 (Percentage of

purchases from three largest suppliers). Only one indicator, objective customer

power, generated a critical ratio below 1.96. The CR of 0.68 is sufficient for

reliability but the AVE of 0.36 indicates convergent validity maybe a problem.

Therefore, individual perceptions of Structure did not conform to Porter’s (1980)

five forces – rivalry among existing companies, the threat of new entrants, the

threat of substitute products/services, the bargaining power of buyers, and the

bargaining power of suppliers. In fact, Structure is comprised of four factors:

Entry-Exit, Five Forces Pecotich, Number Customers-Suppliers, and Power

Customers-Suppliers.

Next, I examined the indicators for the Conduct latent variable to identify the

factors that comprise Conduct and thus develop the inner (structural) model for

hypothesis testing. Factor loadings for the indicators of Conduct did not all

129

exceed 0.40 (Table 9) and this implies the indicators did not represent the

single construct – Conduct. Three of the four indicators reported factor loadings

that exceeded the recommended guide of 0.40 (Falk and Miller, 1992). These

three indicators measured individual perceptions of strategy based on the

Pecotich et al. (2003) measure. I grouped these three indicators into one factor

which I labelled Subjective Conduct (Table 12). The factor loadings ranged

from 0.71 (Focus) to 0.88 (Differentiation) and each factor loading had a t-

statistic exceeding 1.96. The CR of 0.85 denotes satisfactory reliability and the

AVE of 0.65 shows convergent validity.

13

0

Tab

le 1

2

Psychometric Properties for the Conduct variables

Co

nstr

uct

Item

L

oad

ing

t-sta

ta

CR

b

AV

Ec

Su

bje

ctive

Co

nd

uct

Co

st

lead

ers

hip

0

.82

27

.47

0.8

5

0.6

5

D

iffe

ren

tia

tion

0

.88

43

.06

F

ocu

s 0

.71

11

.45

a B

oots

tra

pp

ing e

stim

ate

s c

alc

ula

tio

n b

ased

on

Ch

in (

19

98a

,b)

b C

om

po

site

Re

liab

ility

c A

vera

ge

Va

rian

ce

Ext

racte

d

131

Finally, I identified the factors that comprise the Performance construct in order

to develop the inner (structural) model for hypothesis testing. The factor

loadings for the indicators of Performance (Table 10) suggest the indicators are

not measuring one Performance construct but multiple constructs. Six of the 13

indicators reported factor loadings that exceeded the recommended guide of

0.4 (Falk and Miller, 1992) and measured individual perceptions of return on

sales, return on investment, return on total assets, overall business unit

performance, net profit and overall industry performance. I grouped these six

indicators into one factor which I labelled Subjective Performance (Table 13).

The factor loadings ranged from 0.52 (Industry Performance) to 0.95 (Return on

Investment) which meets the cut-off rule of 0.40 as recommended by Falk and

Miller (1992). Further, each indicator had a t-statistic exceeding 1.96. The CR

of 0.92 and the AVE of 0.65 suggests satisfactory reliability and convergent

validity respectively.

The second block of indicators which were highly loaded onto a single latent

variable measured Objective sales, Objective EBIT and Objective total assets

and hence, was labelled Objective Performance (Table 13). All indicators

reported factor loadings greater than 0.40 and a t-statistic that exceeded 1.96.

The CR of 0.96 and the AVE of 0.89 indicates satisfactory reliability and

convergent validity respectively.

The third block of indicators which were highly loaded onto a single latent

variable measured Objective return on sales and Objective return on investment

so this was labelled Relative Performance (Table 13). All indicators reported

factor loadings which exceeded 0.40 and critical ratios greater than 1.96. The

CR of 0.68 and the AVE of 0.52 signify sufficient reliability and convergent

validity respectively.

The fourth block of indicators which were highly loaded onto a single latent

variable measured both subjective and objective market share and market

share rank. This latent variable was labelled Market Share and all indicators

reported factor loadings greater than 0.40 and t-statistics that exceeded 1.96

(Table 13). The CR of 0.86 and the AVE of 0.61 suggests sufficient reliability

132

and convergent validity respectively. Initially, market share was included as an

indicator of structure given Porter’s (1980) hypothesised u-shaped relationship

between market share and return on investment (i.e. firm performance).

However, factor loadings below the recommended 0.40 (Falk & Miller, 1992)

when market share was included as indicators of the latent variable Structure

suggested these indicators were measuring a different construct. This is not

surprising given the relationship between market share and return on

investment has been the subject of much controversy and research. Product

portfolio models such as the Boston Consulting Group (BCG) growth-share

matrix became very popular during the 1960s and 1970s because they provided

strategic recommendations based on the key concept of the business unit’s

market share. Empirical studies using PIMS data found companies with large

market shares enjoyed experience curve effects leading to lower per unit costs

and thus increased return on investment (Buzzell et al., 1975; Caves, Gale, &

Porter, 1977; Gale, 1972; Gale & Branch, 1982; Ravenscraft, 1983).

Therefore, a high market share through time will result in lower relative costs

per unit and higher relative return on investment. However, subsequent studies

using the same PIMS data and data analysis technique showed the relationship

between market share and return on investment to be spurious (Jacobson,

1988; Jacobson & Aaker, 1985). Market share and return on investment were

positively correlated because both were caused by some other factor(s) – not

because increases in market share cause increases in return on investment.

It was decided that the fourth latent variable Market Share would be included in

the revised structural model as a performance construct because market share

has been acknowledged as an important measure of business strategy success

(Clark, 1999; Davidson, 1999; Grewal, Iyer, Kamakura, Mehrotra, & Sharma,

2009; Gronholdt & Martensen, 2006; King, 1964; Venkatraman & Ramanujam,

1986). In addition, market share serves as a useful non-financial performance

measure when financial data is unavailable as in the case of private companies

and it is also more likely to be shared than confidential or sensitive financial

data (Venkatraman & Ramanujam, 1986). Studies reviewing performance

measures have highlighted the importance of market share as a valuable

measure of firm performance for both management and analysts (Davidson,

1999; Gronholdt & Martensen, 2006).

133

Therefore, individual perceptions of Performance did not conform to Porter’s

(1980) conceptualisation, that is, return on investment. Indeed, Performance is

comprised of four factors – Subjective Performance, Objective Performance,

Relative Performance and Market Share.

13

4

Tab

le 1

3

Psychometric Properties for the Performance variables

Co

nstr

uct

Item

L

oad

ing

t-sta

ta

CR

b

AV

Ec

Su

bje

ctive

Pe

rfo

rma

nce

(S

ub

jective

) R

etu

rn o

n s

ale

s 0

.84

20

.41

0.9

2

0.6

5

(S

ub

jective

) R

etu

rn o

n in

vestm

en

t 0

.95

11

9.9

0

(S

ub

jective

) R

etu

rn o

n to

tal a

sse

ts

0.9

2

80

.58

(S

ub

jective

) O

vera

ll b

usin

ess u

nit p

erf

orm

an

ce

0.8

7

28

.77

(S

ub

jective

) N

et

pro

fit

0.6

6

9.5

6

(S

ub

jective

) H

ow

wo

uld

yo

u c

lassify

the

ove

rall

ind

ustr

y p

erf

orm

an

ce o

f yo

ur

bu

sin

ess u

nit a

t th

is t

ime

0

.52

6.1

2

Ob

jective

Pe

rfo

rma

nce

O

bje

ctive

sa

les

0.9

5

53

.65

0.9

6

0.8

9

O

bje

ctive

EB

IT

0.9

9

14

6.7

4

O

bje

ctive

To

tal A

sse

ts

0.8

9

18

.65

13

5

Co

nstr

uct

Item

L

oad

ing

t-sta

ta

CR

b

AV

Ec

Re

lative

pe

rfo

rma

nce

Ob

jective

re

turn

on

sa

les

0.7

2

14

.37

0.6

8

0.5

2

O

bje

ctive

re

turn

on

in

vestm

en

t 0

.72

14

.37

Ma

rke

t S

ha

re

(Su

bje

ctive

) M

ark

et

sha

re in

20

08

0.7

4

16

.15

0.8

6

0.6

1

(S

ub

jective

) M

ark

et

sha

re r

an

k in

200

8

0.8

3

29

.84

O

bje

ctive

ma

rket

sha

re in

20

08

0.7

7

22

.25

O

bje

ctive

ma

rket

sha

re r

an

k in 2

00

8

0.7

8

27

.05

a B

oots

tra

pp

ing e

stim

ate

s c

alc

ula

tio

n b

ased

on

Ch

in (

19

98a

,b)

b C

om

po

site

Re

liab

ility

c A

vera

ge

Va

rian

ce

Ext

racte

d

136

I assessed the reliability and convergent validity of the outer model by

examining the CR and AVE respectively. Next, I assessed the discriminant

validity or the extent to which the latent variables were theoretically not related

to each other by examining the square root of the AVE and the between-blocks

correlation coefficients. As shown in Table 14, the square root of the AVE is

greater than the correlations among the constructs indicating discriminant

validity, with the exception of the correlation between Five Forces Pecotich and

Subjective Conduct (Chin, 1998; Fornell & Larcker, 1981). Although this needs

to be noted, it is not a major defect as the result indicates substantive support

and the two variables are, to a certain extent, expected to be related. They

were treated as distinct variables to form a revised structural model illustrated in

Figure 16.

13

7

Tab

le 1

4

Correlation and Discriminant Validity of Latent Variables*

E

ntr

y-E

xit

Fiv

e F

orc

es

Pecotich

Num

ber

Custo

mers

-S

upplie

rs

Pow

er

Custo

mers

-S

upplie

rs

Conduct

Subje

ctive

P

erf

orm

ance

Obje

ctive

P

erf

orm

ance

Rela

tive

P

erf

orm

ance

Mark

et

share

Entr

y-E

xit

0.65

Fiv

e F

orc

es

Pecotich

0.2

5

0.68

Num

ber

Custo

mers

-S

upplie

rs

0.1

3

0.2

1

0.59

Pow

er

Custo

mers

-S

upplie

rs

-0.0

6

-0.1

2

-0.1

7

0.57

Conduct

0.1

0

0.8

0

0.1

0

-0.0

4

0.80

Subje

ctive

P

erf

orm

ance

-0.2

2

0.1

1

0.0

1

-0.1

5

0.1

5

0.78

Obje

ctive

P

erf

orm

ance

-0.1

8

-0.3

0

-0.0

9

0.3

6

-0.2

8

-0.1

0

0.94

Rela

tive

P

erf

orm

ance

0.0

2

-0.0

08

0.1

9

0.0

2

-0.0

6

0.0

8

0.0

2

0.69

Mark

et share

-0

.11

0.1

2

0.1

1

0.1

7

0.1

2

0.0

8

-0.2

0

0.1

4

0.78

*Sq

uare

root of

the A

VE

on t

he d

iag

onal

13

8

Fig

ure

16

Th

e r

evi

se

d s

tru

ctu

ral m

ode

l

139

Theoretical Relationships between Structure-Conduct-Performance

Porter (1980) developed the five forces model to analyse industry structure

which determined one of three generic strategies a company should choose

from to create a sustainable defendable position and outperform competitors.

Porter’s theory was quickly embraced within both marketing (e.g. Cravens and

Piercy, 2009; Jain and Haley, 2009; Kotler et al. 2009) and strategic

management (e.g. Glueck and Jauch, 1988; Hanson et al., 2011; Hubbard and

Beamish, 2011) as it provided a model for analysing competition in an industry

and a new perspective on generic corporate-level strategies. However, results

of the present study have demonstrated individual perceptions of structure,

conduct and performance do not conform to Porter’s (1980) formulation.

Specifically I identified that Structure is comprised of four factors (i.e. Entry-Exit,

Five Forces Pecotich, Number Customers-Suppliers, and Power Customers-

Suppliers), Conduct is comprised of one factor (i.e. Subjective Conduct) and

Performance is comprised of four factors (i.e. Subjective Performance,

Objective Performance, Relative Performance, and Market Share).

Consequently, I have revised the original model as illustrated in Figure 16.

After choosing industries to compete in and/or altering their structure, Porter

(1980) argues there are three potentially successful generic strategic

approaches to outperforming competitors in an industry: (1) Overall cost

leadership, (2) Differentiation and (3) Focus. Porter (1980) does not clarify how

the intensity of competition leads to a better choice of strategy and therefore

superior performance. The examples and brief case studies do not provide a

rigorous basis for theory development and testing. According to Hunt (2002),

sound theory must be empirically testable. A theory is capable of being

empirically testable when it can be used to generate hypotheses that are

agreeable to verification by real-world data. Nonetheless, I expected to find a

positive association between intensity of industry competition (i.e. five forces)

and targeted strategic action (i.e. cost leadership, differentiation and focus).The

empirical evidence on whether Porter’s generic strategies lead to a superior

return on investment has been inconclusive. Campbell-Hunt (2000) employed

meta analysis to examine 17 empirical studies on Porter’s generic strategies

and results did not support Porter’s proposition that companies must pursue

140

one of the three generic strategies or get stuck in the middle and suffer low

profitability. Yet I expected to find a positive association between targeted

strategic action (i.e. cost leadership, differentiation and focus) and performance.

PLS results of the evaluation of the full theoretical model are shown in Table 15

as "Model A" and illustrated in Figure 17. The individual R2 of 0.65 was greater

than the recommended 0.10 for the predicted variable, Conduct (Falk and

Miller, 1992). Since the R2 estimate was larger than the recommended level, it

is appropriate and informative to examine the significance of the paths

associated with the Conduct variable. A reasonable criterion for evaluating the

significance of the individual path is the absolute value of the product of the

path coefficient and the appropriate correlation coefficient (Abdi, 2003; Chin,

1998; Falk & Miller, 1992; Henseler et al., 2009; Roy & Roy, 2008; Vinzi et al.,

2010). As paths are estimates of the standardised regression weights, this

produces an index of the variance in an endogenous variable explained by that

particular path and 1.5% of the variance is recommended as the cutoff point.

Only the path from Five Forces Pecotich exceeds this criterion and the

bootstrap critical ratio was also of the appropriate size (greater than 1.96). This

does not support our hypothesis which postulated a positive relationship

between the intensity of industry competition and targeted strategic action (i.e.

cost leadership, differentiation and focus). Results of the present study show

that as industry competition becomes more intense, companies are more likely

to follow all three generic strategies (i.e. get stuck in the middle) or perhaps,

practise all three strategies with equal targeted intensity.

The individual R2 for paths leading to the predicted Performance variables (i.e.

Subjective Performance, Objective Performance, Relative Performance, and

Market Share) were below the recommended 0.10 (Falk and Miller, 1992)

suggesting there is no association between targeted strategic action (i.e. cost

leadership, differentiation and focus) and performance. Porter (1980) proposed

that a company must choose one of the three generic strategies to create a

sustainable defendable position and outperform competitors or be stuck in the

middle. Within industrial organisation economics, it is assumed that industry

environments are homogeneous; however, according to Hunt’s R-A theory,

demand within and across industries is heterogeneous (with the exception of

141

commodities). As a result, consumers within an industry have different tastes

and preferences so firms have to develop different offers for different segments

within the same industry. The fact that intra-industry demand is heterogeneous

in most industries supports R-A theory’s ability (and neoclassical theory’s

inability) to correctly predict diversity in business unit financial performance.

Therefore Porter’s (1980) generic strategies of cost leadership, differentiation

and focus cannot work because homogeneous industry environments do not

exist. Results suggest that Porter’s generic strategies may not lead to superior

performance.

14

2

Tab

le 1

5

Pa

rtia

l le

ast squ

are

s r

esu

lts f

or

the

re

vise

d the

ore

tica

l M

od

el A

and

th

e t

rim

med

exp

an

ded

Mo

de

l B

M

od

el A

Mo

de

l B

P

red

icte

d

varia

ble

s

Pre

dic

tor

varia

ble

s H

ypo

the

sis

P

ath

V

aria

nce d

ue

to

pa

tha

R2

Critica

l ra

tio

b

P

ath

V

aria

nce d

ue

to

pa

tha

R2

Critica

l ra

tio

b

Co

nd

uct

N

um

be

r C

usto

me

rs-

Su

pp

liers

H

4

-0.0

6

0.6

9

P

ow

er

Cu

sto

me

rs-

Su

pp

liers

H

4

0.0

4

0.6

7

E

ntr

y-E

xit

H4

-0

.10

1.1

4

F

ive

Fo

rce

s

Pe

co

tich

H4

0

.84

0.6

8

.65

23

.12

0

.80

0.6

3

.63

35

.11

Su

bje

ctive

P

erf

orm

an

ce

Co

nd

uct

H

5

0.1

5

- .0

2

1.0

6

Ob

jective

P

erf

orm

an

ce

H

5

-0.2

8

.08

.08

3.5

9

-0

.18

0.1

1

.34

1.9

9

Re

lative

P

erf

orm

an

ce

H

5

-0.0

6

- .0

03

0.5

4

14

3

M

od

el A

Mo

de

l B

P

red

icte

d

varia

ble

s

Pre

dic

tor

varia

ble

s H

ypo

the

sis

P

ath

V

aria

nce d

ue

to

pa

tha

R2

Critica

l ra

tio

b

P

ath

V

aria

nce d

ue

to

pa

tha

R2

Critica

l ra

tio

b

Ma

rke

t S

ha

re

H

5

0.1

2

- .0

2

1.2

4

Ob

jective

P

erf

orm

an

ce

Po

we

r C

usto

me

rs-

Su

pp

liers

U

H1

0.5

0

0.2

6

0.5

2

8.4

1

a V

ariances d

ue t

o t

he p

ath

coeff

icie

nts

are

inte

rpre

ted o

nly

if the R

2 is

gre

ate

r th

an 0

.10.

b B

oots

trap e

stim

ate

div

ided b

y boots

trap s

tandard

err

or

c U

nhyp

oth

esis

ed d

iscovere

d e

ffect

s

d A

vera

ge v

ariance a

ccounte

d for

14

4

Fig

ure

17

PLS

Re

su

lts

for

the

re

vise

d s

tru

ctu

ral m

od

el

No

te:

*In

dic

ate

s th

e b

oo

tstr

ap

estim

ate

is s

ign

ific

an

t at

P=

0.0

5;

R2 in

squ

are

bra

cke

ts

145

As a further step in the evaluation of the quality of the theoretical model, the

non-significant measurements paths were trimmed, the correlations between

the latent variables were examined and the large ones were suitable for

inclusion in the new model given a theoretical rationale. The model was then

re-estimated. The results of this process are shown as "Model B" in Table 15

and illustrated in Figure 18. The R2 for Five Forces Pecotich hardly changed

from 0.65 in Model A to 0.63 in Model B. Again, where the R2 was greater than

0.10, I calculated the significance of the individual paths using 1.5% of the

variance as the recommended cutoff point (Abdi, 2003; Chin, 1998; Falk &

Miller, 1992; Henseler et al., 2009; Roy & Roy, 2008; Vinzi et al., 2010). The

variance due to the path for Conduct slightly decreased from 0.68 to 0.63.

However, the R2 for Objective Performance increased from 0.08 in Model A to

0.34 in Model B which exceeds the recommended 0.10 (Falk and Miller, 1992).

The variance due to the path for Objective Performance increased from 0.08

under Model A to 0.11 under Model B but the bootstrap critical ratio also

exceeded 1.96. Therefore, under Model B, Hypothesis 4 which postulated a

positive relationship between the intensity of industry competition and targeted

strategic action (i.e. cost leadership, differentiation and focus) remains rejected.

Under Model B, Hypothesis 5 is not rejected as there is a weak association

between targeted strategic action (i.e. cost leadership, differentiation and focus)

and performance.

The trimmed model (Model B) also revealed an unhypothesised but significant

relationship between Power Customers-Suppliers and Objective Performance

labelled UH1 in Table 15. The R2 of 0.52 exceeds the recommended 0.10 (Falk

and Miller, 1992) while the variance due to the path of 0.26 also met the

necessary criterion. The bootstrap critical ratio also exceeded 1.96. The

unhypothesised relationship suggests powerful customers and suppliers directly

affect the business unit’s performance. The implications of these results will

be further discussed in Chapter 5.

Hypothesis testing began with an examination of the measurement issues

concerning Porter’s model by using correlation coefficients to determine the

strength of the relationship of individual perceptions of structure, conduct and

performance within companies and within industries. I found partial support for

146

congruent perceptions within a company but no support for congruent industry

perceptions.

Then I used PLS to evaluate the outer (measurement) model and by doing so,

test the strength of the relationship between the subjective and objective

measures of structure, conduct and performance. Results showed individual

perceptions of structure, conduct and performance did not have a strong

positive relationship with the objective reality. Indeed, the indicators I had

proposed to measure each of the three constructs (i.e. Structure, Conduct, and

Performance) appeared to measure different constructs. For example,

individual perceptions of Structure did not conform to Porter’s (1980) five forces

– rivalry among existing companies, the threat of new entrants, the threat of

substitute products/services, the bargaining power of buyers, and the

bargaining power of suppliers. In fact, Structure was comprised of four factors:

Entry-Exit, Five Forces Pecotich, Number Customers-Suppliers, and Power

Customers-Suppliers. Similarly, Performance was comprised of one factor –

Subjective Conduct – and Performance was comprised of four factors –

Subjective Performance, Objective Performance, Relative Performance and

Market Share. As a result, the inner (structural) model was revised to reflect the

new factors (Figure 16) and the basis to test the theoretical issues in Porter’s

adaptation of the SCP model.

Again, using PLS, I tested the relationships between structure, conduct and

performance as conceptualised by Porter (1980). Under the revised structural

model, results showed that as industry competition becomes more intense,

companies are more likely to follow all three generic strategies (i.e. get stuck in

the middle) or perhaps, practise all three strategies with equal targeted

intensity. However, there was no association between targeted strategic action

and performance. As a further step in the evaluation of the quality of the

theoretical model, the non-significant measurements paths were trimmed, the

correlations between the latent variables were examined and the large ones

were suitable for inclusion in a new trimmed model given a theoretical rationale.

Under the trimmed model, I still did not find a significant relationship between

the intensity of industry competition and targeted strategic action. However, I

did find support for a weak but positive relationship between targeted strategic

147

action and company performance. Results also revealed an unhypothesised

but significant relationship between two latent variables: Power Customers-

Suppliers and Objective Performance. The final chapter provides a summary of

the conclusions from the results of the data analysis, limitations of the present

study and suggestions for future research.

14

8

Fig

ure

18

PLS

Re

su

lts

for

the

trim

med

mo

de

l

No

te:

*In

dic

ate

s th

e b

oo

tstr

ap

estim

ate

is s

ign

ific

an

t a

t P

=0.0

5;

R2 in

squ

are

bra

cke

ts

149

CHAPTER 5

In this chapter, I will discuss the implications of the results from the data

analysis, the limitations of the study and finally, provide suggestions for future

research.

Conclusions

Using the SCP paradigm from industrial organisation economics, Porter (1980)

developed the five forces model to analyse industry structure which determined

one of three generic strategies a company should choose from to create a

sustainable defendable position and outperform competitors. The two major

issues concerning Porter’s model are measurement-related and theoretical.

The first major issue concerning Porter’s model is measurement-related. Porter

(1980) implicitly assumes managers define and observe the same objective

environment. On this basis, perception of structure as described by Porter’s

five forces model should be identical for all managers operating in the same

industry. However, companies within an industry may have different

perceptions of the same environment and these perceptions may not

correspond to the objective reality. Further, individuals within a company may

have different perceptions of the same environment and their perceptions may

not correspond to the objective reality. Perception is influenced by many

variables including the decision maker’s personality, internal politics and

company objectives. Consequently, the organisation becomes a victim of

perceptions which ignore or distort environmental elements (Barrett et al., 2009;

Cyert & March, 1963; Nadkarni & Barr, 2008; Panagiotou, 2006; Snow, 1976;

Snow & Hrebiniak, 1980; Weick, 1979) and management perceptions of

structure determine strategy, not the objective reality. More importantly, the

critical question is: what is the best predictor of performance – objective data or

individual perceptions of structure? According to Porter’s adaptation of the SCP

model, industry profitability is dependent on the structural features of industry

(i.e. the five forces). Firms are viewed as combiners of homogeneous, perfectly

mobile resources and intra-industry demand is viewed as homogeneous. Porter

(1980) assumes that managers develop strategy after an objective analysis of

structure and therefore, “industry effects” or objective data should explain most

150

of the variance in firms’ performances. In contrast, Hunt’s R-A theory proposes

that demand within industries is heterogeneous and, resource heterogeneity

and immobility imply strategic choices must be made and that these choices

influence firm performance. It is the role of managers to develop strategies on

the basis of resources the firms possess, including imperfect perception of

information (Hunt, 2000a, 2000b, 2001, 2002a, 2002b, 2010; Hunt & Arnett,

2001; Hunt & Derozier, 2004; Hunt & Duhan, 2002; Hunt & Morgan, 1995). If

this is the case, then “firm effects” or individual perceptions of structure

determine conduct and it may not correspond to the objective reality.

The second major issue concerning Porter’s model is the theoretical

relationships between industry structure, conduct and performance. The

evidence for Porter’s conceptualisation of structure and of strategy is anecdotal

based on brief examples and case studies. The empirical evidence on whether

Porter’s generic strategies lead to a superior return on investment has been

inconclusive (Campbell-Hunt, 2000; Knudsen et al., 2005; Pecotich et al., 2003;

Torgovicky et al., 2005). There is a lack of strong empirical evidence on the

degree to which individual perceptions of the five forces of competition impact a

manager’s choice of one of the three generic strategies and thus, the impact on

company and industry performance. While separate studies have

demonstrated management perceptions of structure and conduct conform to

Porter’s formulation (Pecotich et al., 1999; Pecotich et al., 2003), this study

evaluated Porter’s adaptation of the SCP paradigm at the top executive

perception level.

The first hypothesis of this study was concerned with the degree of congruence

of individual perceptions of structure, conduct and performance within a

company. Of the 31 companies, 11 companies showed their top management

team perceptions of structure, conduct and performance had a strong positive

relationship while six companies indicated their top management teams did not

share similar perceptions. For the remaining 14 companies, the correlations

ranged from weak negative ones to strong positive ones. For example, within

one company, only one of six correlations was greater than 0.70. Overall, the

results show, at best, mixed support for Porter’s argument that managers within

a company define and observe the same environment. Therefore, there is only

151

partial support for Hypothesis 1. This finding is similar to that of Bourgeois

(1978) who investigated the effect of top management team perceptions of

goals and strategies on organisational performance in 12 non-diversified

publicly-listed companies. Bourgeois (1978) found some top management

teams shared similar perceptions of both goals and strategies while other top

management teams did not agree on either the goals or the strategies or both.

Further, shared perception of strategies was more important to firm

performance than shared perception of goals and Bourgeois (1978) concluded

that consensus on strategy within the top management team is critical to firm

performance. In a later study, Bourgeois (1985) found that as variance in

perceptions of environmental uncertainty within the top management team

increased, so too did the level of firm performance. The author reasoned that

diversity in perception removes blinders but this is only beneficial when

perceptions of environmental uncertainty are congruent with the objective

reality. Bourgeois’ (1985) study was criticised by Mezias and Starbuck (2003)

for averaging subjective perceptions. Averaging individual perceptions

misrepresents the accuracy of their individual perceptions because averaged

perceptions can be quite accurate, even though most individuals have

inaccurate perceptions. The mixed support for Hypothesis 1 may also be

explained by the manager’s threshold level for uncertainty: “Some individuals

may have a very high tolerance for ambiguity and uncertainty so they may

perceive situations as less uncertain than others with lower tolerances”

(Duncan, 1972) Top management team diversity in terms of race, age and

gender may also contribute to the variance in company-level perceptions

(Barrett et al., 2009).

The second hypothesis was to determine the degree of congruence of company

perceptions of structure, conduct and performance within an industry. Only two

of the 13 industries reported highly significant correlations indicating a strong

positive relationship in company perceptions of structure, conduct and

performance within each industry: Agriculture, Forestry & Fishing and Health &

Community Services. The remaining 11 industries reported widely ranging

correlations from weak negative correlations to strong positive correlations. For

example, within the Transport & Storage industry, 24 of 105 correlations

exceeded 0.70 indicating a positive and highly significant relationship in

152

company perceptions of structure, conduct and performance within this industry.

Overall, the results do not support Porter’s theory that managers within an

industry define and observe the same objective environment and therefore

Hypothesis 2 is rejected. This finding is consistent with earlier research from

marketing, strategic management and managerial cognition disciplines that

found companies within an industry have different perceptions of the same

environment (Daniels et al., 2002; Hodgkinson & Johnson, 1994; Kaplan, 2008;

McNamara et al., 2002; Nadkarni & Barr, 2008). Perhaps, top managers

deliberately develop strategies and competitive advantages that are distinct

from their rivals even though the environmental situation faced by companies

within an industry may be generally similar (Snow & Hrebiniak, 1980). This is a

view also supported by Daniels et al. (2002) who found that in a task

environment, managers seek a competitive advantage over rivals and this

implies that companies within an industry will not share similar perceptions of

the environment. In an institutional environment, factors such as regulatory

changes can force companies within an industry to share similar perceptions of

the environment. These results suggest that managers within an industry do

not define and observe the same objective environment as implicitly assumed

by Porter’s (1980) five forces model. It appears that Hunt’s (2010) R-A theory

of competition better explains why firms in the same industry pursue different

strategies. Hunt’s (2010) R-A theory of competition proposed that the

resources of firms within an industry are heterogeneous and immobile and

therefore, managers must make strategic choices and these choices influence

firm performance. Resources include market and competitor intelligence and

some firms will have better intelligence or information than others. The unit of

analysis is the manager and it is manager who develops strategies on the basis

of the resources the firm possess, including imperfect perception of information.

The third hypothesis was to determine the degree of congruence between the

objective reality and individual perceptions (i.e. subjective measures) of

structure, conduct and performance. I used PLS and given this technique did

not require the “hard” assumptions of normality and large sample sizes, the

results should be interpreted with caution. By examining the factor loadings, t-

stats, CR and AVE, the PLS results for each construct demonstrated that

individual perceptions of structure, conduct and performance did not have a

153

strong positive relationship with the objective reality. Therefore, Hypothesis 3

is rejected and I conclude that management perceptions of structure determine

conduct; not objective reality and the organisation becomes a victim of

perceptions which ignore or distort environmental elements (Miles et al., 1974).

This finding is similar to previous studies that have also found individual

perceptions of the environment did not correspond with the objective reality

(Bourgeois, 1985; Downey et al., 1975; Hambrick, 1981; Tosi et al., 1973). It

supports the strategic-choice perspective which proposes that managers play

an important role in aligning external opportunities and threats with company

strengths and weaknesses (Andrews, 1971; Chandler, 1962). This finding

conflicts with the ecological or natural selection model which suggests that the

environment determines the survival of organisations and therefore, managers

have little influence on performance (Hannan & Freeman, 1977).

If individual perceptions of structure determine conduct; not the objective reality;

then previously published research that relied on managers as primary data

sources describe errors or shared myths. Moreover, if managers hold

inaccurate perceptions of industry structure, this has serious consequences for

the strategic planning process. This suggests the need for organisations to

establish a market intelligence system that captures objective data about the

environment to inform strategic decision making. In particular, it will establish

communication / sharing of perceptions between those interacting with

customers (e.g. Sales Manager) and top management (e.g. CEO).

Given individual perceptions of structure do not correspond with the objective

reality, it is management perceptions of structure (i.e. firm effects) that

determine conduct; not the objective reality (i.e. industry effects). This result

supports Hunt’s R-A theory as the better theory of competition relative to

Porter’s (1980) five forces model. According to R-A theory, heterogeneous

intra-industry demand and heterogeneous, imperfectly mobile resources result

in diversity in business unit financial performance (Hunt, 1983, 2000a, 2000b,

2001, 2002b, 2010; Hunt & Arnett, 2001, 2006; Hunt & Derozier, 2004; Hunt &

Duhan, 2002; Hunt & Morgan, 1995). Resource heterogeneity and immobility

imply strategic choices must be made and that these choices influence

performance. It is the role of managers to develop strategies on the basis of the

154

resources the firm possess, including imperfect perception of information. If this

is the case, then “firm effects” or individual perceptions of industry structure

determine conduct and performance. Empirical research on financial

performance clearly shows that “firm effects” dominate “industry effects” and

competition is market segment by market segment (Cubbin and Geroski, 1987;

Galbreath and Galvin, 2008; Hansen and Wernerfelt, 1989; McGahan and

Porter, 1997; Mauri and Michaels, 1998; Rumelt, 1991; Short, Ketchen, Palmer

and Hult, 2007). R-A theory contributes to explaining observed differences in

quality, innovativeness and productivity between the market-based and

command-based economies of the world. Moreover, policy makers should

support formal and informal institutions that promote R-A competition. R-A

competition promotes innovations that create resources that ultimately results in

productivity and economic growth. Vigorous competition requires institutions

that protect the property rights that firms and individuals have in the innovations

they create (e.g. trade secrets, copyrights, trademarks). Therefore, to the

extent that the goal of public policy is wealth creation, productivity and

economic growth, policy makers should promote formal and informal institutions

that promote R-A competition. Important formal institutions are those that

protect property rights and promote economic freedom. Important informal

institutions are those that promote social trust. Policy makers should also

endorse institutions that promote the link between performance and rewards.

Therefore low marginal tax rates for both organisations and individuals promote

the linkage between performance and rewards which, in turn, promote R-A

competition and thus productivity and economic growth. Support for Hunt’s R-

A theory also adds impetus to including this alternative theory of competition in

strategic marketing and management texts. Many business school courses

include Porter’s “five forces” and “generic strategies” models but there is only

anecdotal evidence for the relationship between structure and strategy and

inconclusive evidence for the relationship between targeted strategic action and

industry performance. Hunt’s R-A model should be given more prominence in

the pedagogy of both undergraduate and postgraduate courses in competitive

strategy.

155

To examine the theoretical issues associated with Porter’s adaptation of the

SCP paradigm, I used PLS to determine if there is a positive association

between the intensity of industry competition (i.e. five forces) and targeted

strategic action (i.e. cost leadership, differentiation and focus) and between

targeted strategic action and performance. The PLS results for both the original

(Model A) and the trimmed (Model B) structural models showed that as the

intensity of competition increases, managers are more likely to get stuck in the

middle because they are engaged in all three generic strategies. Therefore

Hypothesis 4 is rejected as there was no positive relationship between the

intensity of industry competition and targeted strategic action. This is not

surprising given that Porter (1980) did not clarify how the intensity of

competition resulted in a better choice of strategy and therefore superior

performance. The examples and brief case studies do not provide a rigorous

basis for theory development and testing.

Under the original model (Model A), I did not find a positive relationship

between targeted strategic action and firm performance. However, under the

trimmed model (Model B), the PLS results suggest it is possible that firms which

follow one of the three generic strategies are likely to perform better as

measured by Objective Performance (Objective sales, Objective EBIT and

Objective total assets). Companies stuck in the middle will suffer from low

profitability because it will lose customers looking for the lowest cost or a unique

product. Such situations may be the result of management failing to make

choices or tradeoffs. Therefore, Hypothesis 5 is not unreservedly rejected but

the evidence shows some support for it. Many studies have sought to provide

empirical evidence regarding Porter’s (1980) proposition that a company must

choose one of the three generic strategies to create a sustainable defendable

position and outperform competitors or be stuck in the middle. Campbell-Hunt

(2000) employed meta analysis to examine 17 empirical studies on Porter’s

generic strategies and the results showed any generic strategy, including stuck

in the middle, could produce above-average performance. However, more

recent evidence and the findings of this present study support a positive

association between targeted strategic action and firm performance (Knudsen

et al., 2005; Pecotich et al., 2003; Torgovicky et al., 2005). Therefore, as

industry competition becomes more intense, companies are more likely to follow

156

all three generic strategies (i.e. get stuck in the middle) or perhaps, practise all

three strategies with equal targeted intensity. Further, it is possible that firms

which follow one of the three generic strategies are more likely to achieve

higher performance.

The trimmed model (Model B) also revealed an unhypothesised but significant

relationship between two latent variables: Power Customers-Suppliers and

Objective Performance. The construct Power Customers-Suppliers measured

the percentage of customers who purchased 50% of the business unit’s

products/services and the percentage of purchases from the business unit’s

three largest suppliers. The construct Objective Performance measured

objective sales, objective EBIT and objective total assets The unhypothesised

relationship suggests the higher the concentration of suppliers and customers,

the greater their bargaining power and this exercises a direct influence on firm

performance. This is not surprising as Porter (1980) proposed that buyers can

affect industry profitability by bargaining for higher quality or forcing down prices

as they play competitors against each other. Suppliers affect industry

profitability by their ability to raise prices or reduce the quality of purchased

goods and services.

Limitations

As this was an exploratory study in the evaluation of Porter’s theory with the

correspondence between objective conditions and the nature of management

perceptions accounted for, the results should be treated with caution. The

sample consisted of Australian senior managers and therefore it is possible that

the results cannot be generalised out of that context. This limitation is normal

and it is recommended that future studies be conducted in other countries

before some degree of general consensus can be found. While the sample size

maybe an issue, this study provides an important insight into the extent to which

top management team perceptions of industry structure impact strategic action

and hence industry and company performance. It should be noted that top

management team participation in questionnaires is difficult to obtain as well as

sampling several companies competing in the same industry. There is also the

157

possibility that a different statistical technique may have led to slightly different

results; however, this is an opportunity for future research to address.

Directions for future research

The data suggest that the trimmed model (Figure 18 p.152) should form the

basis for further research into the relationships between structure, conduct and

performance. The PLS results revealed a positive and significant relationship

between Power Customers-Suppliers and Objective Performance which was

not hypothesised and should be considered in future studies.

The present study examined top management team perceptions of structure

and strategy over the past 12 months but subjective performance was based on

the most recently published objective data available. Thus both the subjective

and objective measurement of performance lagged perceptions of structure and

conduct because such data are published on a six-monthly or annual basis.

This may have implications for the relationship between structure, conduct and

performance. Future studies should measure the relationships between

structure, strategy and performance within the same period.

The results of this study contribute to the growing body of evidence that ‘firm

effects’; not ‘industry effects’ accounts for the diversity in firm performance.

Thus, future studies should test Hunt’s R-A theory of competition. Specifically,

does a comparative advantage in resources yield marketplace positions of

competitive advantage for some market segments and, thereby, superior

financial performance?

It is worthwhile to examine if the relationship between structure and

performance varies by industry type – consumer, industrial, services. Future

research could also investigate the moderating effect of firm characteristics on

perception of the environment since Wilson et al. (1993) found firm size and

ownership structure influenced managers’ perceptions of the environment and

the strategies they developed. Another consideration for future researchers is

to consider conducting the same study but choosing the product-market as the

158

unit of analysis because the business unit level can result in more variation in

number and/or types of products accounted for which influences perceptions of

structure, conduct and performance.

In the future, it may also be useful to consider a longitudinal study of Porter’s

adaptation of the SCP paradigm with the correspondence between objective

conditions and the nature of management perceptions accounted for in the

Australian context. This would allow information to be shared between

competitors and act as a benchmark in performance comparisons. A study of

this scale would require substantial resources to establish and operate but

maybe worthwhile given the benefits of a similar project, PIMS in the USA

(Buzzell & Gale, 1987; Buzzell et al., 1975). Finally, the validity of scientific

conclusions can be improved through replication and the final decision as to the

extent of appropriateness of the measures used in this exploratory study

remains in the hands of the research community who will provide that

information in future studies.

159

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17

1

Appendix 1

Me

asu

rem

ent

ind

icato

rs f

or

Ind

ustr

y S

tru

ctu

re

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

n

am

e

So

urc

e

Inte

nsity

of

riva

lry

F

irm

s in

ou

r in

du

str

y co

mpe

te in

ten

se

ly t

o h

old

and

/or

incre

ase

th

eir m

ark

et

sha

re

A1

Pe

co

tich,

Ha

ttie

& L

ow

19

99

T

he

re is a

div

ers

ity

of

co

mpe

tito

rs in o

ur

ind

ustr

y (i.e

. co

mp

etito

rs m

ay

be

div

ers

e

in s

tra

tegie

s, o

rigin

s,

pe

rso

na

lity,

and

re

lationsh

ips t

o t

he

ir p

are

nt

com

pan

ies)

A2

In

ou

r in

du

stry

, co

mpe

titiv

e m

ove

s b

y o

ne

firm

ha

ve n

otice

ab

le e

ffe

cts

on

co

mpe

tito

rs a

nd t

hu

s in

cite

re

talia

tio

n a

nd

cou

nte

r m

ove

s A

3

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ou

r in

du

stry

, a

dve

rtis

ing b

att

les o

ccu

r fr

equ

en

tly

A4

In

ou

r in

du

stry

, p

rice

co

mpe

tition

is h

igh

ly in

ten

se

(i.e.

price

cu

ts a

re q

uic

kly

an

d

ea

sily

ma

tche

d)

A5

P

rice

cu

ttin

g is a

com

mo

n c

om

petitive

actio

n in

ou

r in

du

str

y A

6

A

pp

rop

ria

te te

rms u

sed

to

de

scrib

e c

om

petitio

n in

ou

r in

du

str

y a

re “

wa

rlik

e”,

”b

itte

r”,

or

“cu

tth

roa

t”

A7

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ou

r in

du

stry

, firm

s h

ave

th

e r

eso

urc

es fo

r vi

go

rou

s a

nd s

usta

ine

d c

om

pe

titive

a

ctio

n a

nd f

or

reta

liation

aga

inst

com

pe

tito

rs

A8

In

ou

r in

du

stry

, fo

reig

n f

irm

s p

lay

an

impo

rta

nt ro

le in

ind

ustr

y co

mp

etitio

n

A9

17

2

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

n

am

e

So

urc

e

Ba

rga

inin

g

po

we

r o

f su

pp

liers

Ou

r m

ajo

r sup

plie

rs' p

rod

uct(

s)

ca

n a

ffe

ct

the f

ina

l qu

alit

y of

this

in

du

str

y's

pro

du

ct

A1

0

O

ur

ma

jor

sup

plie

rs' p

rod

uct(

s)

is a

n im

po

rtan

t in

pu

t in

to o

ur

indu

str

y

A1

1

T

he

pro

du

cts

pro

vid

ed

by

ou

r su

pp

liers

an

d u

se

d in

ou

r p

rod

uctio

n p

roce

ss

ca

nno

t be

sto

red

fo

r an

ext

en

de

d len

gth

of

time

A1

2

S

up

plie

rs o

f p

rod

uct

s to

ou

r in

du

str

y co

uld

inte

gra

te o

ur

pro

du

ctio

n p

roce

ss in

to

the

ir o

pe

ration

s A

13

T

he

sup

plie

rs t

o o

ur

ind

ustr

y ca

n t

hre

ate

n to

ra

ise

th

eir p

rice

s o

r re

du

ce

th

e

qu

alit

y o

f th

eir p

rodu

cts

A

14

In

ou

r in

du

stry

, su

pp

lier

or

su

pp

lier

gro

up

s a

re p

ow

erf

ul

A1

5

T

he

sup

plie

rs o

f ra

w a

nd

oth

er

mate

ria

ls t

o o

ur

ind

ustr

y ca

n a

nd d

o d

em

and

, an

d

ga

in c

on

ce

ssio

ns

A1

6

F

irm

s in

ou

r in

du

str

y a

re n

ot

we

ll in

form

ed

abo

ut

the

ir s

up

plie

rs' d

em

and

/sa

les

figu

res, p

rofita

bili

ty a

nd

co

st

stru

ctu

res

A1

7

T

he

re e

xist

a s

ma

ll num

be

r of

sup

plie

rs w

ho

co

ntr

ibu

te to

a la

rge

pro

po

rtio

n o

f o

ur

indu

str

y's in

pu

ts

A1

8

Th

rea

t of

ne

w

en

tra

nts

In

ou

r in

du

stry

, n

ew

co

mp

etito

rs h

ave

to

en

ter

at

a h

igh

ly v

isib

le la

rge

sca

le a

nd

risk s

tro

ng r

ea

ctio

n f

rom

exi

stin

g f

irm

s A

19

E

sta

blis

he

d f

irm

s in

our

ind

ustr

y h

ave

su

bsta

ntia

l re

sou

rce

s w

hic

h m

ay

be

use

d

to p

reve

nt th

e e

ntr

y o

f n

ew

co

mpe

tito

rs

A2

0

17

3

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

n

am

e

So

urc

e

N

ew

firm

s e

nte

rin

g o

ur

ind

ustr

y m

ust

spe

nd

a la

rge

am

ou

nt of

ca

pita

l o

n r

isky

an

d u

nre

cove

rab

le u

p-f

ron

t ad

vert

isin

g a

nd

/or

for

Re

sea

rch

an

d D

eve

lopm

ent

A2

1

R

eta

liatio

n b

y e

sta

blis

he

d f

irm

s to

wa

rds n

ew

firm

s e

nte

rin

g o

ur

ind

ustr

y is

an

d

ha

s b

ee

n s

tro

ng

A2

2

N

ew

firm

s e

nte

rin

g o

ur

ind

ustr

y h

ave

to

sp

end

hea

vily

to

bu

ild u

p the

ir b

ran

d

na

me

s a

nd

to

ove

rco

me

exi

stin

g b

ran

d lo

yaltie

s

A2

3

N

ew

firm

s e

nte

rin

g o

ur

ind

ustr

y w

ill f

ind it

diff

icu

lt t

o p

ers

ua

de d

istr

ibu

tio

n

ch

ann

els

to a

ccep

t th

eir p

rod

ucts

A

24

N

ew

firm

s e

nte

rin

g o

ur

ind

ustr

y a

s s

ma

ll sca

le o

pe

rato

rs m

ust

acce

pt

a

co

nsid

era

ble

co

st

dis

ad

van

tage

A

25

L

arg

e c

ap

ital a

nd

/or

fina

ncia

l re

sou

rce

s a

re r

equ

ire

d f

or

entr

y in

to o

ur

ind

ustr

y

A2

6

N

ew

firm

s e

nte

rin

g o

ur

ind

ustr

y w

ill f

ace

co

st d

isa

dva

nta

ge

s if

th

ey

do

not

con

tro

l su

cce

ssiv

e s

tage

s in

th

e p

rod

uctio

n a

nd/o

r d

istr

ibu

tio

n o

f th

is in

du

str

y's p

rod

uct

A

27

Th

rea

t of

su

bstitu

te

pro

du

cts

In

ou

r in

du

stry

, th

ere

is

co

nsid

era

ble

pre

ssu

re f

rom

ch

ea

pe

r su

bstitu

tes

A2

8

It

is d

iffic

ult

to f

ind

su

bstitu

tes fo

r th

e s

elle

rs' p

rod

uct in

th

is in

du

str

y

A2

9

A

ll firm

s in

ou

r in

du

str

y a

re a

wa

re o

f th

e s

tro

ng c

om

petitio

n f

rom

su

bstitu

tes

A3

0

T

he

ava

ilab

ility

of

su

bst

itu

te p

rodu

cts

lim

its

the

pote

ntia

l re

turn

s in

ou

r in

du

str

y A

31

17

4

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

n

am

e

So

urc

e

O

ur

ind

ust

ry's

pro

du

cts

se

rve

fun

ction

s w

hic

h m

ay

be

ea

sily

se

rve

d b

y m

an

y o

the

r p

rod

uct

s

A3

2

T

he

pro

du

cts

of

the in

du

str

y in

wh

ich

we

co

mp

ete

ha

ve in

trin

sic

ch

ara

cte

ristics f

or

wh

ich

it

is d

ifficu

lt to

fin

d s

ub

stitu

tes

A3

3

O

ur

ind

ust

ry m

ake

s p

rod

ucts

fo

r w

hic

h t

he

re a

re a

la

rge

num

be

r of

su

bstitu

tes

A3

4

S

ub

stitu

te p

rodu

cts

lim

it t

he

pro

fita

bili

ty o

f o

ur

ind

ustr

y A

35

T

he

ne

ed

s w

hic

h o

ur

ind

ustr

y's p

rod

uct

s s

atis

fy m

ay

be e

asily

sa

tisf

ied

by

pro

du

cts

fro

m m

an

y o

the

r so

urc

es

A3

6

Ba

rga

inin

g

po

we

r o

f b

uye

rs

In o

ur

ind

ust

ry,

bu

yers

are

hig

hly

co

nce

ntr

ate

d (

i.e

. bu

yers

pu

rch

ase

la

rge

vo

lum

es r

ela

tive

to

se

llers

sa

les)

A3

7

T

he

pro

du

cts

fro

m o

ur

ind

ustr

y a

re s

old

to

buye

rs in

in

du

str

ies w

hic

h m

ake

lo

w

pro

fits

A

38

T

he

bu

yers

of

pro

du

cts

fro

m o

ur

ind

ustr

y a

re m

ain

ly w

ho

lesa

lers

and

re

taile

rs

wh

o c

an

influe

nce t

he

fin

al co

nsu

me

rs' p

urc

ha

se

de

cis

ion

s A

39

In

ou

r in

du

stry

, b

uye

rs o

r b

uye

r gro

up

s a

re p

ow

erf

ul

A4

0

T

he

bu

yers

of

ou

r in

dustr

y's p

rod

ucts

are

in a

po

sitio

n t

o d

em

an

d c

on

ce

ssio

ns

A4

1

T

he

re a

re a

sm

all

num

be

r of

bu

yers

wh

o f

orm

a la

rge

pro

po

rtio

n o

f th

is in

du

stry

's

sa

les

A4

2

17

5

Appendix 2

Me

asu

rem

ent

ind

icato

rs f

or

Co

nd

uct

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

nam

e

So

urc

e

Co

st

lead

ers

hip

E

mph

asis

on

eff

icie

ncy

an

d s

tan

da

rdis

ed

pro

du

ctio

n s

che

du

ling

B1

Pe

co

tich,

Pu

rdie

& H

att

ie

20

03

A

void

an

ce

or

elim

ina

tion

of

ma

rgin

al cu

sto

me

r a

cco

un

ts

B2

E

mph

asis

on

pro

ce

ss R

&D

B

3

C

on

scio

us a

ttem

pt

to r

ed

uce

the

le

vel of

accou

nts

re

ceiv

ab

les

B4

P

urs

uit o

f con

sta

nt

an

d h

igh

ca

pa

city

utilis

ation

of

reso

urc

es

B5

U

se

of

low

dis

trib

uto

r m

arg

ins

B6

C

on

tin

uo

usly

off

er

low

price

s t

o a

ttra

ct cu

sto

me

rs

B7

M

ain

tena

nce o

f a

sm

all

su

pp

ly o

f go

od

s a

nd

/or

wo

rk in

pro

gre

ss

B8

P

urs

uit o

f e

con

om

ies o

f sca

le w

he

reve

r p

ossib

le

B9

17

6

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

nam

e

So

urc

e

E

mph

asis

on

co

st c

uttin

g a

nd

inte

rna

l eff

icie

ncy

pro

gra

ms

B1

0

A

do

ptio

n o

f p

roce

du

res t

o e

ncou

rage

hig

h u

tilis

atio

n o

f a

ssets

B

11

A

void

an

ce

of

dis

eco

nom

ies o

f sca

le w

he

ne

ver

po

ssib

le

B1

2

E

mph

asis

on

in

tern

al e

ffic

ien

cy

an

d c

ost

cutt

ing w

ith

a v

iew

to

th

e o

vera

ll gro

wth

of

the

bu

sin

ess

B

13

U

se

of

an

“im

itative

” str

ate

gy

with

re

ga

rd t

o n

ew

pro

du

ct

de

velo

pm

en

t (

i.e

. fo

llow

co

mp

etito

rs)

B

14

A

ttem

pt to

ke

ep

kno

wle

dge

with

in t

he

bu

sin

ess t

o p

reve

nt it f

rom

“sp

illin

g o

ver”

to

oth

er

firm

s in

th

e indu

str

y

B1

5

E

mph

asis

on

ne

w p

roce

ss t

ech

no

logy

B1

6

A

ttem

pt to

loca

te p

rodu

ctio

n w

he

re lo

gis

tica

l co

sts

, ta

xes a

nd r

aw

ma

teria

ls a

re

co

mpa

rative

ly in

exp

en

siv

e

B1

7

A

ttem

pt to

clo

se

ly c

o-o

rdin

ate

all

bu

sin

ess a

ctiv

itie

s in

ord

er

to a

chie

ve

pe

rma

nen

t co

st a

dva

nta

ge

s

B1

8

O

ffe

r a lim

ited

mix

and v

arie

ty o

f p

rod

ucts

to

a w

ide

ra

nge

of

custo

me

rs

B1

9

P

rod

uce

and

ma

rke

t “n

o-f

rills

” p

rod

uct

s B

20

17

7

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

nam

e

So

urc

e

E

mph

asis

on

ob

tain

ing s

up

erio

r a

cce

ss to

lo

w c

ost

ra

w m

ate

ria

ls a

nd

co

mpo

ne

nts

B

21

E

mph

asis

on

fle

xib

ility

in

pro

du

ctio

n s

ch

ed

ulin

g

B2

2

Diffe

ren

tia

tion

E

mph

asis

on

pro

du

ct R

&D

B

23

C

on

scio

us a

nd d

elib

era

te e

ffo

rt t

o m

ain

tain

hig

h p

rice

s

B2

4

E

mph

asis

on

aft

er-

sa

les s

erv

ice

an

d c

ust

om

er

su

pp

ort

B

25

C

on

tro

l of

the

cha

nne

ls o

f d

istr

ibu

tio

n (

i.e

. str

on

g influ

en

ce

on

ou

tlets

th

at

dis

trib

ute

th

e p

rodu

ct/

s of

the

bu

sin

ess)

B

26

In

tro

du

ctio

n o

f n

ew

pro

du

cts

B

27

F

orw

ard

in

tegra

tio

n (

i.e

. a

ttem

pt to

acqu

ire

or

de

velo

p w

ho

lesa

lers

an

d/o

r re

taile

rs s

o th

at th

ey

form

pa

rt o

f th

e e

xistin

g b

usin

ess)

B

28

E

nco

ura

ge

men

t of

pro

du

ct o

bso

lesce

nce

B

29

U

se

of

hig

h d

istr

ibu

tor

ma

rgin

s

B3

0

M

ain

tena

nce o

f a

la

rge s

up

ply

of

go

od

s a

nd

/or

wo

rk in

pro

gre

ss

B3

1

17

8

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

nam

e

So

urc

e

E

mph

asis

on

pro

du

ct q

ua

lity

B

32

E

mph

asis

on

ad

vert

isin

g e

xpe

nd

itu

re a

nd

pro

motio

na

l eff

ort

B

33

U

se

of

an

inn

ova

tive

“firs

t to

ma

rke

t” s

trate

gy

with

re

ga

rd t

o n

ew

pro

du

cts

B

34

E

nga

ge

in

pro

du

ct

line

fo

rtific

atio

n (

i.e

. off

er

a w

ide

ra

nge

of

pro

du

cts

)

B3

5

U

se

of

ne

w t

ech

no

logy t

o p

rovi

de

ne

w p

rod

uct

s t

o c

on

sum

ers

B

36

In

tro

du

ctio

n o

f m

ino

r m

od

ific

ation

s t

o e

xistin

g p

rod

ucts

B

37

E

mph

asis

on

th

e e

nhan

ce

me

nt of

pro

du

ct

ima

ge

an

d b

usin

ess r

ep

uta

tio

n

B3

8

Fo

cu

s C

on

ce

ntr

atio

n o

n a

na

rro

w b

uye

r gro

up

ca

refu

lly s

ele

cte

d f

rom

th

e to

tal m

ark

et

B3

9

C

on

ce

ntr

atio

n o

n s

erv

ing a

lim

ited

ge

ogra

ph

ica

l a

rea

B4

0

C

on

ce

ntr

atio

n o

n o

ffe

rin

g a

na

rro

w r

an

ge

of

pro

du

cts

B

41

C

on

ce

ntr

atio

n o

n a

spe

cific

co

nsum

er

se

gm

en

t B

42

17

9

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

nam

e

So

urc

e

C

hie

f E

xecu

tive

Off

ice

r str

on

gly

in

flu

en

ce

s th

e o

pe

ratio

ns o

f th

e b

usin

ess

B4

3

E

mph

asis

on

th

e m

an

ufa

ctu

re o

f sp

ecia

lty

pro

du

cts

B

44

O

pe

rate

with

in a

ma

rke

t n

iche

or

spe

cia

lise

d s

egm

en

t, n

ota

bly

diffe

ren

t fr

om

th

e o

vera

ll m

ark

et

B4

5

A

ttem

pt to

cre

ate

an im

age

asso

cia

ted

with

a s

pe

cia

l a

nd

dis

tin

ct c

usto

me

r gro

up

B

46

N

o b

ackw

ard

or

forw

ard

in

tegra

tion

(i.e

. n

o a

tte

mpt

to a

cqu

ire

sup

plie

rs a

nd/o

r re

taile

rs s

o th

at th

ey

form

pa

rt o

f th

e e

xistin

g b

usin

ess)

B4

7

D

elib

era

tely

lim

it s

ale

s v

olu

me

go

als

B

48

C

on

ce

ntr

atio

n o

n a

pa

rtic

ula

r d

istr

ibu

tion

cha

nn

el ty

pe

to

re

ach

bu

yers

B

49

18

0

Appendix 3

Me

asu

rem

ent

ind

icato

rs f

or

Pe

rfo

rman

ce

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

nam

e

So

urc

e

Bu

sin

ess u

nit

pe

rfo

rma

nce

S

ale

s /

Re

ven

ue

(E

xtern

al a

nd

Inte

rna

l)

SA

LE

S

Bu

zze

ll a

nd

Ga

le 1

987

E

arn

ings B

efo

re In

tere

st

and

Ta

x (E

BIT

) E

BIT

B

uzz

ell

an

d G

ale

19

87

T

ota

l A

sse

ts

TA

B

uzz

ell

an

d G

ale

19

87

T

ota

l L

iab

ilitie

s T

L

Bu

zze

ll a

nd

Ga

le 1

987

R

etu

rn o

n s

ale

s R

OS

P

eco

tich,

Pu

rdie

an

d

Ha

ttie

20

03

R

etu

rn o

n in

vestm

en

t R

OI

Bu

zze

ll a

nd

Ga

le 1

987

R

etu

rn o

n to

tal a

sse

ts

RO

A

Pe

co

tich,

Pu

rdie

an

d

Ha

ttie

20

03

O

vera

ll b

usin

ess u

nit p

erf

orm

an

ce

BU

PE

RF

P

eco

tich,

Pu

rdie

an

d

Ha

ttie

20

03

N

et

pro

fit

BU

PR

OF

IT

Pe

co

tich,

La

czn

iak,

Lusch

a

nd

Ca

rro

ll 1

992

Ind

ust

ry

pe

rfo

rma

nce

H

ow

wo

uld

yo

u c

lassify

the

ove

rall

ind

ustr

y p

erf

orm

an

ce

of

you

r b

usin

ess

un

it a

t th

is t

ime

IND

PE

RF

P

eco

tich,

La

czn

iak,

Lusch

a

nd

Ca

rro

ll 1

992

18

1

Appendix 4

Me

asu

rem

ent

ind

icato

rs f

or

su

bje

ctive

In

du

str

y S

tru

ctu

re f

or

com

pa

riso

n to

ob

jective

da

ta

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

nam

e

So

urc

e

Inte

nsity

of

riva

lry

Ap

pro

xim

ate

ly h

ow

ma

ny

co

mp

etin

g b

usin

esse

s o

pe

rate

d in

th

e m

ark

et

active

ly s

erv

ed

by

this

bu

sin

ess

un

it in

20

08?

C

1C

om

p

Ba

in 1

968

S

ch

ere

r 19

70

Bu

zze

ll &

Ga

le

19

87

Th

rea

t of

ne

w e

ntr

an

ts

Du

rin

g t

he

pa

st five

(5

) ye

ars

, a

pp

roxi

ma

tely

ho

w m

an

y co

mp

etito

rs h

ave

e

nte

red

yo

ur

indu

str

y?

C2

En

try

D

urin

g t

he

pa

st five

(5

) ye

ars

, a

pp

roxi

ma

tely

ho

w m

an

y co

mp

etito

rs h

ave

le

ft y

ou

r in

du

stry

?

C3

Exi

t

Ma

rke

t sh

are

E

stim

ate

yo

ur

bu

sin

ess

un

it’s

ma

rke

t sh

are

in

ca

len

da

r 2

008

C4

Mkts

h

Bu

zze

ll &

Ga

le

19

87

R

an

k y

ou

r b

usin

ess u

nit in

te

rms o

f m

ark

et

sha

re in

ca

len

da

r 2

00

8

C5

Mkts

hr

Bu

zze

ll &

Ga

le

19

87

Ba

rga

inin

g p

ow

er

of

cu

sto

me

rs

Ap

pro

xim

ate

ly h

ow

ma

ny

imm

ed

iate

cu

sto

mers

are

the

re t

o w

ho

m y

ou

se

ll th

e b

usin

ess u

nit’s

pro

du

cts o

r se

rvic

es

C6

Cu

st

W

hat pe

rce

nta

ge

of

this

bu

sin

ess u

nit’s

imm

ed

iate

cu

sto

me

rs a

cco

un

t fo

r 5

0%

of

tota

l pu

rch

ase

s of

its p

rod

uct

s o

r se

rvic

es?

C7

Cu

stP

18

2

Ch

ara

cte

ristic

Ind

ica

tor

Va

ria

ble

nam

e

So

urc

e

Ba

rga

inin

g p

ow

er

of

su

pp

liers

A

pp

roxi

ma

tely

ho

w m

an

y su

pp

liers

are

th

ere

fo

r th

e r

aw

ma

teria

ls a

nd

o

the

r fa

cto

rs n

ee

ded

fo

r th

e o

utp

ut of

you

r bu

sin

ess u

nit’s

in

du

str

y?

C8

Su

pp

W

hat pe

rce

nta

ge

of

you

r b

usin

ess u

nit’s

to

tal e

xte

rna

l p

urc

ha

se

s a

re

ma

de

fro

m y

ou

r th

ree

(3

) la

rge

st

su

pp

liers

?

C9

Su

pp

P

Th

rea

t of

sub

stitu

te

pro

du

cts

In

yo

ur

bu

sin

ess u

nit’s

ma

rke

t, h

ow

ma

ny

su

bstitu

te p

rodu

cts

can

cu

sto

me

rs s

witch

to

?

C1

0S

ub

s

18

3

Appendix 5

Correlation Coefficients of Individual Perceptions of Structure, Conduct and Performance within a Company

Com

pany

1

Respondent

1

Respondent

2

Respondent

2

.78

Respondent

3

.81

.98

Com

pany

2

Respondent

1

Respondent

2

Respondent

3

Respondent

2

-.10

Respondent

3

.97

-.22

Respondent

4

.14

-.12

.35

Com

pany

3

Respondent

1

Respondent

2

Respondent

2

.01

Respondent

3

.39

.36

18

4

Com

pany

4

Respondent

1

Respondent

2

.77

Com

pany

5

Respondent

1

Respondent

2

Respondent

2

.96

Respondent

3

.93

.93

Com

pany

6

Respondent

1

Respondent

2

.996

Com

pany

7

Respondent

1

Respondent

2

.75

18

5

Com

pany

8

Respondent

1

Respondent

2

Respondent

2

.45

Respondent

3

.79

.63

Com

pany

9

Respondent

1

Respondent

2

Respondent

3

Respondent

4

Respondent

5

Respondent

6

Respondent

7

Respondent

8

Respondent

2

.83

Respondent

3

-.05

-0.0

4

Respondent

4

.83

.59

.16

Respondent

5

.56

.65

-.18

.60

Respondent

6

.006

.005

.998

.23

-.13

Respondent

7

.83

.79

-.18

.82

.84

-.06

Respondent

8

.07

.10

.88

.29

-.15

.88

-.11

Respondent

9

-.07

-.05

0.9

99

.16

-.18

.997

-.12

.88

18

6

Com

pany

10

Respondent

1

Respondent

2

Respondent

3

Respondent

2

.66

Respondent

3

.84

.55

Respondent

4

.96

.50

.83

Com

pany

11

Respondent

1

Respondent

2

Respondent

2

.94

Respondent

3

.99

.94

Com

pany

12

Respondent

1

Respondent

2

Respondent

3

Respondent

4

Respondent

5

Respondent

2

.71

Respondent

3

.92

.73

Respondent

4

.82

.64

.85

Respondent

5

-.06

.03

.22

.07

Respondent

6

.88

.63

.88

.55

.05

18

7

Com

pany

13

Respondent

1

Respondent

2

Respondent

3

Respondent

2

.19

Respondent

3

.11

.58

Respondent

4

.93

.09

.005

Com

pany

14

Respondent

1

Respondent

2

.12

Com

pany

15

Respondent

1

Respondent

2

Respondent

2

.17

Respondent

3

.91

.20

18

8

Com

pany

16

Respondent

1

Respondent

2

Respondent

2

-.05

Respondent

3

.73

-.002

Com

pany

17

Respondent

1

Respondent

2

Respondent

2

.97

Respondent

3

.95

.88

Com

pany

18

Respondent

1

Respondent

2

Respondent

3

Respondent

4

Respondent

2

1.0

0

Respondent

3

.65

.65

Respondent

4

-.05

-.05

.27

Respondent

5

-.03

-.03

.35

.80

18

9

Com

pany

19

Respondent

1

Respondent

2

.74

Com

pany

20

Respondent

1

Respondent

2

Respondent

2

.44

Respondent

3

.93

.57

Com

pany

21

Respondent

1

Respondent

2

Respondent

2

.73

Respondent

3

.46

.65

19

0

Com

pany

22

Respondent

1

Respondent

2

Respondent

3

Respondent

4

Respondent

2

.81

Respondent

3

.77

.996

Respondent

4

.81

.999

.996

Respondent

5

.88

.98

.97

.98

Com

pany

23

Respondent

1

Respondent

2

.06

Com

pany

24

Respondent

1

Respondent

2

.55

19

1

Com

pany

25

Respondent

1

Respondent

2

Respondent

3

Respondent

4

Respondent

2

.98

Respondent

3

.92

.94

Respondent

4

.90

.92

.99

Respondent

5

.99

.98

.95

.94

Com

pany

26

Respondent

1

Respondent

2

.999

Com

pany

27

Respondent

1

Respondent

2

Respondent

3

Respondent

2

-.18

Respondent

3

.009

.25

Respondent

4

-.17

.98

.19

19

2

Com

pany

28

Respondent

1

Respondent

2

Respondent

3

Respondent

2

-.11

Respondent

3

-.16

.79

Respondent

4

-.19

.54

.44

Respondent

5

-.11

.89

.98

Respondent

6

.002

.92

.87

Com

pany

29

Respondent

1

Respondent

2

Respondent

3

Respondent

2

.36

Respondent

3

-.13

.31

Respondent

4

.25

-.07

.42

19

3

Com

pany

30

Respondent

1

Respondent

2

Respondent

3

Respondent

2

.86

Respondent

3

.20

.17

Respondent

4

.66

.73

.21

Com

pany

31

Respondent

1

Respondent

2

.69

19

4

Appendix 6

Correlation Coefficients of Individual Perceptions of Structure, Conduct and Performance within an Industry

No

te:

C =

Com

pa

ny

Agricu

ltu

re,

Fo

restr

y a

nd

Fis

hin

g I

nd

ustr

y

C

1

C 2

.9

1

19

5

Ma

nufa

ctu

rin

g I

ndu

str

y

C

1

C2

C

3

C 4

C

5

C 6

C

7

C 8

C

9

C 1

0

C 1

1

C 1

2

C 1

3

C 1

4

C 1

5

C2

.4

7

C 3

.3

4

.66

C 4

.5

4

.84

.55

C 5

.3

5

.96

.50

.83

C 6

-.

13

-.1

0

.31

.03

-.0

2

C 7

.7

2

-.0

1

-.0

7

-.0

5

-.0

6

-.0

7

C8

.3

2

.71

.75

.66

.57

-.1

2

-.1

0

C 9

.2

6

.92

.42

.68

.97

-.1

0

-.0

5

.44

C 1

0

.31

.79

.78

.80

.72

.15

-.1

4

.93

.57

C1

1

.57

.45

.60

.60

.27

-.0

4

-.0

9

.49

.13

.45

C 1

2

.52

.33

.57

.33

.10

-.1

0

-.0

2

.38

.01

.28

.92

C 1

3

.48

.91

.46

.91

.94

-.0

3

-.0

6

.48

.88

.64

.47

.26

C 1

4

.48

.42

.60

.66

.25

-.0

4

-.0

9

.76

.04

.69

.84

.69

.37

C 1

5

.12

.31

.45

.45

.23

-.0

7

-.0

7

.85

.06

.77

.22

.05

.13

.69

C1

6

.72

.57

.17

.39

.58

-.1

1

.75

.15

.61

.23

-.0

2

-.0

4

.57

-.0

9

-.0

7

19

6

Co

nstr

uction

Ind

ustr

y

C

1

C 2

C

3

C 4

C

5

C 6

C

7

C 8

C

9

C 2

1

.00

C 3

.1

6

.15

C 4

-.

05

-.0

5

.12

C 5

.8

9

.86

-.0

2

-.0

9

C 6

.2

6

.30

.75

.05

-.0

4

C 7

.8

5

.88

-.0

9

.07

.67

.33

C 8

.2

3

.22

.84

.11

-.0

1

.65

.08

C 9

.2

7

.25

.68

.04

-.0

4

.51

.13

.93

C 1

0

.26

.25

.68

.04

-.0

4

.52

.13

.94

1.0

0

19

7

Re

tail

Tra

de

In

du

str

y

C

1

C 2

C

3

C 4

C

5

C 6

C

7

C 8

C

9

C

10

C

11

C

12

C

13

C

14

C

15

C

16

C

17

C

18

C

19

C

20

C

21

C

22

C

23

C2

.0

1

C 3

.3

9

.36

C 4

.2

9

-.17

.37

C 5

.2

9

-.09

.53

.77

C 6

.0

7

.86

.66

-.21

-.10

C 7

.3

0

.14

.73

-.15

-.07

.62

C 8

.1

9

.47

.75

-.24

-.13

.85

.94

C 9

.3

5

.19

.73

-.15

-.09

.65

.99

.94

C 1

0

.48

.04

.71

.48

.21

.35

.65

.55

.66

C1

1

-.14

.99

.28

-.19

-.10

.82

.05

.40

.09

-.05

C 1

2

.28

.03

.40

.85

.46

.02

-.01

-.04

.01

.73

.00

C 1

3

.36

.61

.91

.18

.47

.79

.61

.74

.62

.42

.54

.17

19

8

Re

tail

Tra

de

In

du

str

y (c

on

tinu

ed

)

C

1

C 2

C

3

C 4

C

5

C 6

C

7

C 8

C

9

C

10

C

11

C

12

C

13

C

14

C

15

C

16

C

17

C

18

C

19

C

20

C

21

C

22

C

23

C1

4

-.03

.96

.54

-.19

-.07

.96

.39

.69

.43

.17

.94

.00

.74

C 1

5

.28

.04

.68

-.19

-.10

.54

.99

.90

.98

.62

-.04

-.05

.54

.30

C 1

6

.41

-.03

.57

.88

.72

.00

.06

-.01

.06

.70

-.07

.89

.34

-.02

.02

C 1

7

.40

-.05

.67

.85

.93

.02

.12

.04

.11

.48

-.09

.66

.54

.00

.08

.81

C 1

8

.37

-.04

.67

.82

.94

.04

.12

.06

.11

.42

-.07

.60

.57

.02

.09

.77

1.0

0

C 1

9

.39

-.01

.68

.85

.93

.05

.12

.06

.12

.48

-.06

.67

.56

.04

.08

.81

1.0

0

1.0

0

C 2

0

.39

-.05

.59

.91

.93

-.04

.00

-.07

.00

.48

-.08

.74

.46

-.04

-.04

.88

.98

.97

.98

C 2

1

-.10

.98

.36

-.05

.02

.81

.05

.39

.09

.02

.99

.12

.60

.93

-.05

.05

.05

.06

.08

.06

C 2

2

.35

.02

.62

.92

.89

.02

.02

-.03

.01

.53

-.01

.80

.48

.03

-.03

.90

.97

.95

.97

.99

.13

C 2

3

.30

.04

.73

-.09

-.02

.54

.99

.89

.98

.68

-.05

.04

.57

.30

1.0

0

.11

.17

.18

.17

.05

-.05

.07

C 2

4

.34

-.03

.50

.84

.48

.05

.15

.07

.15

.81

-.07

.98

.24

-.01

.11

.89

.70

.65

.70

.76

.05

.81

.20

19

9

Acco

mm

od

atio

n,

Cafe

s &

Re

sta

ura

nts

Ind

ustr

y

C

1

C 2

C

3

C 4

C

5

C 6

C2

.9

9

C 3

1

.00

.99

C 4

.9

4

.92

.94

C 5

.9

2

.90

.92

.99

C 6

.9

9

.99

.98

.95

.94

C 7

.3

5

.37

.29

.13

.13

.38

20

0

Tra

nsp

ort

an

d S

tora

ge

In

du

str

y

C

1

C2

C

3

C 4

C

5

C 6

C

7

C 8

C

9

C 1

0

C 1

1

C 1

2

C 1

3

C 1

4

C2

.7

8

C 3

.8

1

.98

C 4

.3

3

.15

.17

C 5

.9

5

.62

.65

.45

C 6

.6

5

.69

.66

.79

.63

C 7

.7

9

.66

.74

.36

.74

.60

C8

.6

1

.68

.77

.35

.56

.54

.83

C 9

-.

03

-.0

3

-.0

2

.16

-.1

1

.07

-.0

5

-.0

4

C 1

0

.85

.44

.52

.46

.89

.51

.83

.59

.16

C1

1

.42

.14

.25

.76

.57

.51

.56

.65

-.1

8

.60

C 1

2

.02

-.0

1

.01

.19

-.0

5

.09

.01

.01

1.0

0

.23

-.1

3

C 1

3

.61

.29

.44

.45

.68

.36

.83

.79

-.1

2

.82

.84

-.0

5

C1

4

.31

.35

.33

.20

.20

.32

.07

.10

.88

.29

-.1

5

.88

-.1

1

C 1

5

-.0

4

-.0

5

-.0

3

.16

-.1

2

.05

-.0

7

-.0

5

1.0

0

.16

-.1

8

1.0

0

-.1

2

.88

20

1

Co

mm

un

icatio

n S

erv

ice

s I

nd

ustr

y

C

1

C 2

C

3

C 4

C

5

C 6

C

7

C 8

C

9

C 1

0

C 1

1

C 1

2

C 1

3

C 1

4

C 1

5

C2

.6

2

C 3

.4

4

.71

C 4

.6

3

.92

.73

C 5

.6

8

.82

.64

.85

C 6

.4

0

.74

.59

.90

.77

C 7

.1

0

.12

.46

.47

.37

.66

C8

.5

3

.90

.73

.86

.62

.60

.19

C 9

.5

5

-.0

6

.03

.22

.07

.22

.49

.03

C 1

0

.40

.88

.63

.88

.55

.76

.25

.90

.05

C1

1

1.0

0

.64

.47

.65

.66

.40

.11

.58

.56

.44

C 1

2

-.0

1

-.0

5

.20

.32

.19

.50

.93

.09

.54

.13

.01

C 1

3

.57

.31

.35

.59

.29

.58

.66

.41

.88

.49

.60

.64

C1

4

.01

-.0

6

.41

-.0

4

-.0

4

-.0

3

.26

-.0

1

.06

-.0

6

.01

.04

.12

C 1

5

.58

.94

.73

.95

.70

.82

.31

.91

.15

.97

.61

.14

.55

.03

20

2

C

1

C 2

C

3

C 4

C

5

C 6

C

7

C 8

C

9

C 1

0

C 1

1

C 1

2

C 1

3

C 1

4

C 1

5

C 1

6

.03

.09

.56

.09

.05

.07

.29

.16

-.0

2

.10

.04

.05

.12

.98

.17

C 1

7

.63

.94

.86

.92

.82

.72

.32

.93

.06

.83

.66

.15

.41

.03

.91

C 1

8

.65

.46

.32

.47

.12

.19

-.0

1

.63

.57

.59

.70

-.0

1

.68

.01

.60

C 1

9

.63

.26

.39

.55

.38

.58

.72

.30

.90

.35

.64

.64

.96

.24

.47

C 2

0

.55

-.0

7

.04

.20

.06

.20

.50

.02

1.0

0

.04

.55

.54

.87

.09

.14

C 2

1

.55

-.0

7

.03

.21

.06

.21

.49

.02

1.0

0

.04

.56

.54

.87

.08

.14

C 2

2

.59

.98

.76

.96

.83

.83

.29

.91

.02

.91

.61

.11

.41

-.0

3

.97

C 2

3

.65

.29

.39

.56

.34

.50

.66

.42

.90

.41

.67

.65

.97

.13

.50

C 2

4

.42

.72

.89

.66

.53

.44

.21

.80

-.0

6

.66

.45

.05

.24

.05

.70

C 2

5

.48

.51

.76

.45

.58

.27

.16

.50

-.0

4

.27

.48

.00

.06

-.0

2

.40

C 2

6

.25

.23

.02

-.1

2

-.1

1

-.3

9

-.8

4

.17

-.3

1

.09

.25

-.9

3

-.3

5

.01

.10

C 2

7

-.0

1

.04

.56

.00

-.0

3

-.0

5

.10

.10

-.0

9

.03

.00

-.0

7

-.0

5

.19

.07

C 2

8

.44

.86

.88

.84

.59

.69

.32

.91

.02

.90

.48

.11

.44

.28

.93

C 2

9

.79

.32

.14

.37

.35

.33

.05

.13

.65

.23

.76

-.0

9

.59

.08

.39

C 3

0

.23

.78

.52

.77

.48

.76

.20

.71

-.0

4

.94

.26

.04

.40

.01

.89

C 3

1

.58

.96

.74

.95

.87

.86

.30

.82

-.0

2

.85

.58

.09

.37

.05

.93

20

3

Co

mm

un

icatio

n S

erv

ice

s I

nd

ustr

y (c

on

tin

ue

d)

C

16

C 1

7

C 1

8

C 1

9

C 2

0

C 2

1

C 2

2

C 2

3

C 2

4

C 2

5

C 2

6

C 2

7

C 2

8

C 2

9

C 3

0

C 1

7

.19

C 1

8

.05

.48

C 1

9

.22

.39

.55

C 2

0

.01

.05

.57

.90

C 2

1

-.0

1

.05

.57

.89

1.0

0

C 2

2

.13

.96

.45

.37

.00

.01

C 2

3

.13

.45

.68

.96

.90

.90

.39

C 2

4

.23

.86

.43

.21

-.0

5

-.0

6

.75

.30

C 2

5

.11

.69

.17

.15

-.0

2

-.0

3

.53

.19

.86

C 2

6

.02

.08

.37

-.3

8

-.3

1

-.3

1

.08

-.3

5

.20

.17

C 2

7

.26

.23

.05

.01

-.0

5

-.0

7

.09

.01

.66

.73

.21

C 2

8

.44

.90

.54

.38

.02

.02

.90

.41

.82

.50

.14

.28

C 2

9

.00

.23

.56

.66

.65

.65

.31

.56

.04

.09

.27

-.0

6

.20

C 3

0

.13

.66

.44

.27

-.0

5

-.0

5

.81

.25

.49

.10

.12

.01

.82

.27

C 3

1

.18

.91

.34

.36

-.0

3

-.0

3

.98

.33

.65

.47

.06

.02

.86

.35

.80

20

4

Fin

an

ce

and

In

sura

nce

In

du

str

y

C

1

C 2

C

3

C 4

C

5

C 6

C

7

C 8

C

9

C 1

0

C 1

1

C 1

2

C 1

3

C 1

4

C 1

5

C 1

6

C 1

7

C 1

8

C 1

9

C 2

0

C2

-.

10

C 3

.9

7

-.2

2

C 4

.1

5

-.1

2

.35

C 5

-.

14

-.1

3

-.1

0

.08

C 6

.1

0

-.1

6

.21

.80

.13

C 7

.9

6

-.1

5

.97

.21

-.0

7

-.0

1

C8

-.

11

.21

.05

.43

.04

-.1

5

.15

C 9

-.

11

.08

.06

.44

.04

-.1

5

.15

.97

C 1

0

-.0

3

.82

-.1

2

-.0

4

-.0

1

-.1

0

-.0

5

.23

.05

C1

1

.99

-.2

1

.97

.12

-.1

0

.02

.98

-.0

5

-.0

4

-.1

2

C 1

2

.79

-.2

9

.91

.70

-.0

9

.49

.82

.23

.26

-.1

6

.79

C 1

3

.50

-.3

1

.53

.15

.68

.23

.52

-.1

2

-.1

0

-.1

9

.54

.44

20

5

Fin

an

ce

and

In

sura

nce

In

du

str

y(co

ntin

ue

d)

C

1

C 2

C

3

C 4

C

5

C 6

C

7

C 8

C

9

C

10

C 1

1

C

12

C 1

3

C

14

C

15

C

16

C

17

C

18

C

19

C

20

C1

4

.90

-.2

3

.97

.54

-.1

1

.39

.91

.13

.14

-.1

1

.89

.98

.48

C 1

5

.95

-.1

1

.95

.36

-.1

9

.36

.89

-.1

2

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