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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
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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
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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;
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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:
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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
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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
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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
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& 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.
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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
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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
48
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.
50
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).
52
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
54
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,
58
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).
59
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.
60
• 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
61
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
63
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
79
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
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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
100
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
102
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
In
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
In
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
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