The Association Between
Industry-level Discretion and
Strategic Variety:
Long-term Strategic Positions and
Current Behaviours
by
Jack Keegan
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
School of Management
Queensland University of Technology
© Copyright by Jack Keegan 2006
1
ABSTRACT
Executive discretion, the latitude for executives’ strategic decisions, is a
powerful moderator of strategic decision making. In spite of its potential
contribution to strategic management studies, Hambrick and Finkelstein’s (1987)
socio-political model of executive discretion has received little empirical research
effort. Some of the basic propositions of the model, which incorporates industry,
firm and individual characteristics as determinants of discretion have not been
empirically tested. The restricted research effort is partly attributable to the lack of
quantitative measures for industry-level discretion.
This thesis initially uses the correlation between industry-level attentional
homogeneity, the similarity in foci of attention of executives in an industry, and
industry-level discretion to produce 116 new values for industry-level discretion for
23 U.S. 4-digit SIC coded industries for the years 1990 to1997. Predictive validity
for the new values is demonstrated using long-term debt data and annual accounts
adjustment data.
Theil’s (1992b) industry variety measure based on information theory is
modified to produce strategic variety measures that permit pan-industry
comparisons. Strong support is demonstrated for a positive association between
variety in long-term strategic positions and industry-level discretion. Some weak
evidence suggesting large firms in low discretion industries may compete using
behaviours that impact on current accounts is also identified.
Key Words:
Industry-level Discretion, Strategic Variety, Information Theory,
Debt Discipline Theory, Discretionary Accounts Adjustments
i
TABLE of CONTENTS
CHAPTER ONE ........................................................................................................1
INTRODUCTION TO EXECUTIVE DISCRETION .................................................................. 1
The Existence of and Need for Executive Discretion ................................................................. 1
Moderating Effects of Executive Discretion ............................................................................... 2
Legal Limits to Executive Discretion .......................................................................................... 4
What is Executive Discretion?..................................................................................................... 4
Executive Discretion as a Socio-political Phenomenon ............................................................. 5
Executive Discretion, Power and Leadership ............................................................................. 6
The Discretion Model .................................................................................................................. 7
Factors in the External Environment that Influence Executive Discretion.............................. 9
Organisational and Industry Task Environments .................................................................... 10
Industry-level Discretion ........................................................................................................... 12
Limited Empirical Research Using the Discretion Model........................................................ 13
RESEARCH QUESTION ............................................................................................................. 13
STRUCTURE OF THESIS........................................................................................................... 14
CONCLUSION TO CHAPTER ONE.......................................................................................... 15
CHAPTER TWO .....................................................................................................17
STUDY ONE ............................................................................................................17
INTRODUCTION ......................................................................................................................... 17
Measurement of Industry-level Discretion................................................................................ 18
Available Measures of Industry-level Discretion...................................................................... 18
Industry-level Discretion and Attentional Homogeneity .......................................................... 21
Critical Evaluation of Reviewed Research................................................................................ 24
CONTEMPORANEOUS MEASUREMENT ............................................................................. 29
Overview..................................................................................................................................... 29
Creating the Research Database ............................................................................................... 30
Measuring Attentional Homogeneity ........................................................................................ 32
Lexical Commonality. ............................................................................................................................32
Lexical Density ......................................................................................................................................34
Pre-treatment of text data ......................................................................................................................36
Sampling Criteria ..................................................................................................................................36
Extra Usage ...........................................................................................................................................37
Measuring Industry-level Discretion ........................................................................................ 37
Confidence Intervals of Industry-level Discretion.................................................................... 42
VALIDITY CHECKS ................................................................................................................... 45
Introduction to Validity Checks................................................................................................. 45
Comparison with Published Values .......................................................................................... 46
Examination of Values Over the Sample Years........................................................................ 46
Predictive Validity: Debt Avoidance.......................................................................................... 48
Predictive Validity: Discretionary Accounts Adjustments........................................................ 51
CONCLUSION TO CHAPTER TWO......................................................................................... 61
CHAPTER THREE .................................................................................................63
STUDY TWO ...........................................................................................................63
INTRODUCTION ......................................................................................................................... 63
EXISTING MEASURES OF STRATEGIC VARIETY ............................................................. 67
Distances and Patterns in Strategic Group Maps..................................................................... 67
Summing Coefficients of Variance ........................................................................................... 70
Summing the Coefficient of Variation of Natural Logs ........................................................... 73
THEORY AND HYPOTHESIS.................................................................................................... 74
METHODS .................................................................................................................................... 75
ANALYSIS..................................................................................................................................... 78
DISCUSSION ................................................................................................................................ 81
More on Adding and Averaging Coefficients of Variation ...................................................... 82
Looking for an Alternative Measurement Method ................................................................... 83
ii
CONCLUSION TO CHAPTER THREE .................................................................................. 84
CHAPTER FOUR....................................................................................................85
STUDY THREE .......................................................................................................85
INTRODUCTION ......................................................................................................................... 85
A SHORT PRIMER ON SIMPLE ENTROPY ........................................................................... 85
Basic Information Theory ......................................................................................................... 87
Illustrative Calculation of Industry Variety.............................................................................. 93
Implications for Variety Measurement ..................................................................................... 95
Data Adjustments Required....................................................................................................... 95
Need for Modifications to Theil’s Basic Method ...................................................................... 96
The influence of outliers.........................................................................................................................96
The influence of sample size. .................................................................................................................97
Modification of Theil’s Basic Method ...................................................................................... 97
Confidence Intervals for Variety Measurement ....................................................................... 99
Some Limits to Entropy-based Variety Tests .......................................................................... 102
THEORY AND PRACTICE OF STRATEGY MEASUREMENT ......................................... 103
Multiple Strategic Profiles in Industries................................................................................. 103
Removing Some Limits on Strategic Dimension Operationalisation..................................... 105
Strategic Variety and Small Firms .......................................................................................... 107
HYPOTHESES DEVELOPMENT ............................................................................................ 108
METHODS .................................................................................................................................. 112
Samples .................................................................................................................................... 112
Accounting Fields Used to Measure Strategic Variety........................................................... 114
RESULTS..................................................................................................................................... 116
Introduction ............................................................................................................................. 116
Comparison of Method I Variety Values and Method II Variety Values .............................. 116
Method I Variety Results ......................................................................................................... 117
Hypothesis tests. ..................................................................................................................................118
Analysis of Method I Variety Hypothesis Tests ....................................................................................122
Method II Variety Results........................................................................................................ 123
Hypothesis Tests ..................................................................................................................................125
Examination of Scatterplots with Confidence Intervals......................................................... 126
Binomial Tests ......................................................................................................................... 128
DISCUSSION .............................................................................................................................. 135
Introduction ............................................................................................................................. 135
Positive Associations Between Strategic Variety and Industry-level Discretion.................... 135
Strategic Convergence and Divergence .................................................................................. 137
Strategic Current Behaviour ................................................................................................... 139
Income and Expenses .............................................................................................................. 141
CONCLUSION TO CHAPTER FOUR ..................................................................................... 143
CHAPTER FIVE....................................................................................................145
CONCLUSIONS AND INTERPRETATIONS ......................................................................... 145
Contributions ........................................................................................................................... 148
Limitations ............................................................................................................................... 151
Future Research ...................................................................................................................... 151
CONCLUSION............................................................................................................................ 154
REFERENCES.......................................................................................................155
APPENDIX .............................................................................................................172
CASE DELETION RATIONALE AND ILLUSTRATION OF ANALYSIS TECHNIQUE
USED ............................................................................................................................................ 172
Sample Data and Confidence Intervals .................................................................................. 172
Confidence Intervals and Unstable Estimates ........................................................................ 173
Case Deletion for Correlation Tests Using Point Estimates................................................... 174
Scatterplots............................................................................................................................... 175
Additional Tests ....................................................................................................................... 175
Looking for Patterns................................................................................................................ 179
iii
LIST of TABLES
Table
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
2.12
2.13
2.14
3.1
3.2
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
Title
Industry-level Discretion: Means and 95% Confidence
Intervals
Simple Example of Calculation of Lexical Commonality
Simple Example of Calculation of Raw Lexical Density
Correlations Between All Lexical Measures
SIC Codes Deleted
Summary of Factor Analysis
Numbers of President’s Letters and Firms Used in Each Case
Standardised Industry-level Discretion Values with
Confidence Intervals Rounded to Two Decimal Places
Results of Piecewise Regressions Predictive Validity:
Discretionary Accounts Adjustments
New Positive Assets Sheet
New Positive Liabilities Table
New Stakeholder Equities and Held Equities Table
New Positive Income and Loss, and Expenses and Profit Table
Calculating Probability of Result of Accounts Adjustment Test
Final SIC4s Used to Test the Hypothesis, and Number of
Cases for Each
Cases and Values of Industry-level Discretion and Strategic
Variety
Illustrative Calculation of Variety in Three Accounting Data
Fields in a Four-firm Industry.
Industries with Cases in Study Three
Correlations Method I and Method II Variety Values
(All Firms Data, 20 or more Firms per Case)
Correlations Between Method I Variety and Industry-level
Discretion Main Accounting Subsets
Consolidated Results using Method I Variety Measures
Correlations Between Method II Variety and Industry-level
Discretion Main Accounting Subsets
Consolidated Results using Method II Variety Measures
Results of Binomial Tests on High and Low Industry-level
Discretion Cases in Groups with High and Low Variety
Cases in All Firms Data Set With Industry-level Discretion
Between -2 and Zero
Page
27
34
35
38
40
41
42
45
51
55
56
57
58
61
78
79
94
113
117
119
122
124
127
134
138
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LIST of FIGURES
Figure Title
2.1
Negative Association between Lexical Density (Attentional
Homogeneity) and Industry-level Discretion
2.2
Industry-level Discretion: SIC4 Codes, Means and 95%
Confidence Intervals
2.3
Negative Association Not Lost Using 95% Confidence Intervals
2.4
Raw Lexical Density ≈ f (1/Number of Documents)
2.5
95% Confidence Intervals of Estimates of Industry-level
Discretion, Ordered by Point Estimate
2.6
SIC4s with Eight Industry-level Discretion Values (1990-1997)
2.7
Cases with >19 Undifferentiated Firms (82 Cases)
Total Long-term Debt/Total Total Assets vs Industry-level
Discretion
2.8
Cases with >19 Undifferentiated Firms (82 Cases) Proportion of
Firms with Long-term Debt Vs Industry-level Discretion
2.9
Accounts Adjustment
(Cases where Difference from Zero had p–value < 0.05)
2.10
Number of Cases and Average Number of Firms per Case
3.1
Industry-level Discretion vs Strategic Variety
(Average of 4 CoVs)
3.2
Industry-level Discretion vs Strategic Variety, with 95%
Confidence Intervals
4.1
Noncurrent Liabilities: Scatterplots with Case Confidence
Intervals
4.2
Noncurrent Assets: Scatterplots with Case Confidence Intervals
4.3
Held Equities: Scatterplots with Case Confidence Intervals
4.4
Stakeholder Equities: Scatterplots with Case Confidence
Intervals
4.5
Long Term Strategic Ratio Inputs: Scatterplots with Case
Confidence Intervals
4.6
Outliers in Low Discretion Cases with High Variety in Current
Assets Data in Large Firms By Total Assets Data Set
A1.1a All Available Cases Scatterplots Shows Many Confidence
Intervals are Very Large (Current Ratio Inputs Variety) – No
Significant Correlation
A1.1b Illustrative Scatterplot of Current Ratio Variety Cases After
Case Deletion Rules Suggests Correlation is Weak (At Best) –
Significant Correlation
A1.2
All Available Cases Ordered by the Point Estimate Shows
Extensive Overlap of Confidence Intervals
A1.3
Identifying the Best Cut-Off Point to Maximise Membership of
High and Low Groups Defined by Non-overlapping 2.5%ile and
97.5%ile Respectively
A1.4
All the Upper Confidence Intervals in the Low Variety Group
are Lower than All the Lower Confidence Intervals in the High
Variety Group
Page
25
26
29
38
44
47
49
50
59
60
80
80
129
130
131
132
133
141
176
176
177
177
178
v
STATEMENT OF ORIGINAL AUTHORSHIP
This is to certify that the work contained in this thesis has never previously been
submitted for a degree or diploma in any university and that, to the best of my
knowledge and belief, the thesis contains no material previously published or written
by another person except where due reference is made in the thesis itself.
Signed:
Date:
vi
ACKNOWLEDGEMENTS
Many people have helped and supported me while I worked on this thesis. I
particularly would like to record my thanks to the research students who let me use
their computers at night and on weekends. Simply put, I would not have been able
to perform the computations in the final study of this thesis without their generosity,
which rescued the research when it became apparent that a new approach was
needed to answer the research question.
The advice and assistance supplied by the academic and administrative staff of
the School of Management made the research possible. At each stage, from the
original proposal, through to the final writing, someone has always been there to
help when I needed advice. Kerry Donohue and Professor Mark Griffin helped me
through the proposal stage. Dr Steven Cox was always ready to help me with my
methodology.
Professor Boris Kabanoff, my supervisor, must get a special mention. The first
study in the thesis rests on his suggestion that text analysis could be used to measure
discretion. Boris kept a close eye as the research progressed and was always
available when I needed to discuss the status of the project. His advice shaped the
thesis on many levels. I count myself lucky to have had such an exceptional
supervisor.
On the non-academic front, I would be remiss if I were not to thank my family
for their encouragement and patience. I especially thank Ruby and Audrey who give
so much but will take so little.
Any errors or flaws in this thesis are mine.
vii
CHAPTER ONE
INTRODUCTION TO EXECUTIVE DISCRETION
This introductory chapter reviews prominent viewpoints highlighting the
usefulness of the construct ‘executive discretion’ when researching strategic
behaviour in business studies. Details of Hambrick and Finkelstein’s (1987) sociopolitical approach to modelling executive discretion are provided and the construct
‘industry-level discretion’ (Abrahamson & Hambrick, 1997) is described. Once that
theoretical detail is provided, the research question is identified as focusing on the
association between industry-level discretion and the amount of variety of firm-level
strategies in industries. A brief overview of the structure of the thesis is then
provided.
The Existence of and Need for Executive Discretion
Discretion is about having options. “Executive discretion,” also called “chief
executive discretion” and “managerial discretion” (Hambrick & Finkelstein, 1987),
is the degree of freedom accorded to senior management that allows them to set and
attempt to attain organisational goals. The term ‘executive discretion’ is used in this
thesis to avoid confusion with discretion of non-executive managers. Executive
discretion is a necessary precondition for the exercise of the critical entrepreneurial
function in business (Mason, 1959).
Concerns about possible negative consequences of executive discretion in
modern corporate society were articulated as early as 1932 (Berle & Means, 1968).
The exercise of executive discretion draws ongoing attention from the legal and
1
accounting professions and lies at the heart of agency theory in organisational
studies. These approaches focus on characteristics of governance, reporting and
incentive regimes intended to ensure executives exercise their discretion
appropriately. They seldom discuss sources and levels of executive discretion in
detail.
The sources and characteristics as well as the role and consequences of
executive discretion are of both theoretical and practical interest. At the industry
level, which is the level of the analysis used in this thesis, increasing understanding
of the determinants of executive discretion and the associated limitations on strategic
dimensions or domains available to different industries and executives informs both
institutional and firm-level policy makers.
Recent decades have witnessed increasing application of competition policies
and market deregulation in industries historically characterised by low levels of
executive discretion (Joskow, 2001; Rajagopalan & Finkelstein, 1992). Conversely,
industries characterised by high levels of executive discretion face calls for stronger
corporate governance standards in response to highly publicised inappropriate use of
executive discretion and associated corporate collapses. Increased understanding of
executive discretion would improve both these debates and the policy decisions that
attend them.
Moderating Effects of Executive Discretion
Executive discretion is a powerful moderator of a range of executive
behaviours ranging from self interested behaviours to corporate philanthropy
(Buchholtz, Amason & Rutherford, 1999; Kay, 1997, 2002). In a large sample panindustry study, Finkelstein and Boyd (1998) demonstrated that executive discretion
2
is positively associated with CEO compensation and that firm performance tends to
be better when CEO pay and executive discretion align, a finding consistent with
earlier research that demonstrated positive linkages between high discretion
environmental periods and performance based reward systems for chief executives in
U.S. electric utility companies (Rajagopalan & Finkelstein, 1992).
Noting research indicating that the increased complexity and information
processing demands placed on executives in high discretion industries tends to lead
to higher executive remuneration (Finkelstein & Boyd, 1998; Finkelstein &
Hambrick, 1988; Henderson & Fredrickson, 1996; Sanders & Carpenter, 1998),
Hambrick, Finkelstein, Cho, and Jackson (forthcoming) suggest that increased
executive discretion results in increased influence of executives, especially CEOs, on
organisation strategies and performance. The increased capacity to influence firm
performance as executive discretion increases is, they suggest, one of the
determinants of the dramatic increases in CEO compensation in recent decades.
Hambrick, Finkelstein, Cho, and Jackson (forthcoming) also highlight a
number of macro-social trends that appear to be reducing isomorphic pressures on
organisations over the last two decades. They argue that strategic variety and
executive discretion should increase as isomorphic pressures decline and provide
some empirical support that this has occurred in the U.S. steel industry in particular,
and in a range of other U.S. industries. Porter (1996) offers a counter view when he
asserts that many firms are using the same management tools and technologies as
their competitors and, due to a lack of emphasis on developing strategic differences,
are converging with their competitors. These viewpoints draw attention to important
trends in business behaviour that impact on industry and national economies and
3
shape their futures. The influence of executive discretion on strategic behaviour is
central to understanding these major macro trends and debates.
Legal Limits to Executive Discretion
The laws of the jurisdiction(s) where a legitimate, for-profit business
incorporates and operates prescribe the outer limits of executive discretion for that
business. These laws typically provide broad prescriptions that require executive
managers to balance profit maximisation against societal, community, and specific
stakeholder interests. Executives have a legal duty of care to gather and use
sufficient information when arriving at decisions and a duty of loyalty to act in the
interests of the corporation when making business decisions that affect the
corporation (Mark, 2003). Additionally, the business judgment rule requires that
courts do not “interfere in unconflicted corporate decisions that satisfy the
procedures necessary to arrive at those decisions” (Mark, 2003: 5).
Legal systems and interpretations grow and modify as they adapt to new
circumstances but, even so, the legal perspective offers a coarse-grained view when
studying executive discretion. A purely legal approach to discretion is capable of
identifying outlying examples such as fraud, but offers little insight into the actual
limits encountered by executives as they make decisions in increasingly complex,
ambiguous and dynamic environments that morph and generate unprecedented and
unpredictable opportunities, threats and temptations. Economic, sociological,
behavioural, psychological and political perspectives permit a finer grained analysis
of discretion, especially executive discretion.
What is Executive Discretion?
4
Executive discretion is managerial discretion of the chief executive or, in
circumstances where an organisation’s upper echelons and the chief executive have
sufficient “behavioral integration” (Hambrick, 1998: 127), managerial discretion of
the top management team. Finkelstein and Hambrick (1990: 490) operationalised
the top management team as “All corporate officers who were also board members”.
Conceptualisation of executive discretion as a group phenomenon accommodates
Cyert and March’s (1992) behavioural view of organisational decision making by
dominant coalitions (Finkelstein, 1988).
Executive discretion can be viewed as executives’ latitude for strategic action
(Hambrick & Finkelstein, 1987). Action is distinct from choice: an executive can
choose but, in some circumstances, no necessary actions follow that cause the
organisation to move in the direction chosen. In any particular instance, the potential
to act may or may not be exercised. Evidence of affirmative action consistent with
the chosen strategic dimension demonstrates the exercise of executive discretion.
For the purposes of this thesis, a decision is defined as a choice followed by
appropriate actions or non actions intended to enact that choice and includes a choice
not to act where no action is required to achieve that choice. To simplify the
discussion, the word ‘action’ is regarded as including appropriate non actions. Thus,
executive discretion can equally be conceptualized as latitude for binding executive
decisions. This conceptualisation of executive discretion implies that organisations’
executives have the ability to influence strategic outcomes but that ability is
constrained, not absolute.
Executive Discretion as a Socio-political Phenomenon
5
Executive discretion theory focuses on executives’ strategic choices and
strategic actions (Finkelstein & Hambrick, 1996; Hambrick & Finkelstein, 1987).
Thomas and Pruett (1993) observed the content (strategy formulation) and process
(strategy implementation) sides of strategic management are intertwined and cannot
be meaningfully separated. They noted that a wide range of disciplines have been
used to gain insight into the complex phenomena studied in strategic management
research and highlighted the unpredictable influence of psychological and sociopolitical forces on strategic behaviour when economic rationalism does not dictate an
obvious action (i.e. where uncertainty exists). Treating discretion as a sociopolitical rather than a techno-economic phenomenon means executive discretion
should be viewed as the range of strategic choices that executives can choose from
and be reasonably sure will result in action because the choices lie within the zone of
acceptance (Barnard, 1938; Simon, 1997) of powerful stakeholders (Hambrick &
Finkelstein, 1987).
Executive Discretion, Power and Leadership
In the sense that power is the scope of significant choices open to an actor in a
social setting (Kaysen, 1959), at the level of the individual, executive discretion is a
form of power (Finkelstein, 1988) granted or ceded by stakeholders who directly or
indirectly interact with the executive (or top management team) in an organisational
context. While internal and external environments influence the general level of
discretion of a top management team, the distribution of power among members of
that team influences the individual executive’s discretion (Finkelstein, 1988).
Etzioni (1965) observed that leadership can be viewed as distinct from power
as leadership includes the capacity to broadly influence followers’ preferences. This
suggests that, to the extent that individual-level discretion reflects executives’
6
leadership qualities, the exercise of discretion may cause stakeholders to modify
their preferences and redefine their zones of acceptance. The actual level of
discretion of an individual executive is seldom explicitly defined and, while
executives and stakeholders often tacitly understand an existing enacted level of
executive discretion, the actual level is subject to flux and discovery, and is
influenced by the context and the strategic issues at hand (Finkelstein & Hambrick,
1996; Hambrick & Finkelstein, 1987).
All managers have some latitude for binding decisions in at least some matters
but the significance of their decisions may range from trivial to strategically
important. Managers with high levels of executive discretion have the ability to
make binding decisions that affect more strategic dimensions than managers with
low levels of executive discretion (Hambrick & Finkelstein, 1987). Strategic
decisions have long term effects and typically involve irreversible commitments of
substantial organisational resources (Hickson, 1986). Managers with high executive
discretion have greater potential to influence their organisation than managers with
low executive discretion (Finkelstein & Hambrick, 1990). Understanding executive
discretion helps understanding of strategic management.
The Discretion Model
A multi-level model that accommodates industry, firm and individual
personality effects on executive discretion has been developed to bridge competing
views of organisations as either inertial or adaptive (Abrahamson & Hambrick, 1997;
Hambrick & Finkelstein, 1987).1 The model’s potential contributions to legal,
1
Hambrick and Finkelstein favour the term ‘managerial discretion.’ My focus, and the
focus of the original discretion model, is on discretion of ‘upper echelons’ (Hambrick &
Mason, 1984).
7
accounting, agency theory and organisational research in general have not been
reflected in efforts to test and develop its components or theoretical consequences.
Hambrick and Finkelstein (1987) proposed their concept of ‘managerial
discretion’ as a useful means of bridging competing perspectives that viewed
organisations as either inertial or adaptive. The inertial or environmental
deterministic view posits that current circumstances and exogenous, uncontrollable
environmental factors that affect all firms in an industry determine organisational
destinies. That view acknowledges the necessity of executives but asserts that in
most circumstances their influence is smothered by organisational inertia and
rigidities and demands of the external environment (Aldrich, 1979; Hannan &
Freeman, 1977; Lieberson & O'Connor, 1972). In contrast, the adaptive view
suggests that upper echelons’ strategic choices influence organisations’ current and
future success or failure (Andrews, 1971; Child, 1972; Hambrick & Mason, 1984).
Hambrick and Finkelstein (1987) reconcile these two views by arguing that
environmental, organisational and personal characteristics of executives influence
the level of discretion available to executives.
Hambrick and Finkelstein (1987) proposed three broad sets of influences on
executive discretion: the task environment, consisting of forces exogenous to the
firm and the top executive(s); internal organisational influences consisting of forces
endogenous to the firm, but separate from the personal attributes of members of the
top management team; and managerial characteristics, which comprise the personal
qualities of the members of the top management team. Hambrick and Finkelstein
(1987) suggested that firms operating in similar external environments experience an
environmentally determined tendency to have similar levels of executive discretion.
8
In statistical terms, that tendency for similarity may be viewed as a relatively
narrow band that approximates a point mean, with some deviation derived from
random effects. Continuing the statistical analogy, organisational influences
determine the resistance to variation of executive discretion away from that narrow
band around the mean. Interaction between the task environment and organisational
factors determine the strength of inertial forces and provide the context within which
executives determine and enact their actual discretion. By exercising discretion,
executives influence the internal organisational and task environments. Thus, the
three-level model interactively combines both inertial and adaptive mechanisms.
Despite the importance of the construct and the appeal of the model developed
by Hambrick and Finkelstein (1987), their model of executive discretion has
received little attention to date. In particular, difficulties encountered in reported
attempts to operationalise and measure executive discretion have limited empirical
research on the discretion model.
Factors in the External Environment that Influence Executive Discretion
The external environment is perhaps the most fundamental determinant of
executive discretion (Hambrick, Geletkanycz & Fredrickson, 1993) as uncertainty
arising from interconnectedness of parts of the external environment is more difficult
to manage than uncertainty arising from internal processes (Emery & Trist, 1965).
Hambrick and Finkelstein (1987) originally identified six characteristics of the task
environment that theory and prior research suggested influence executive discretion.
They reasoned that product differentiability, market growth, and demand instability
all increase uncertainty about ends-means linkages and should be positively
associated with executive discretion while industry structure (especially competition
restricting oligopolistic characteristics), quasi-legal constraints, and powerful outside
9
forces should be negatively associated with executive discretion. The latter three
characteristics affect executive discretion by the agency of stakeholder power: the
greater the power held by stakeholders other than the executive(s) whose discretion
is the object of interest, the greater the stakeholders’ capacity to restrain executive
decisions that are outside the stakeholders’ zones of acceptance and the less
discretion available to the executive(s).
Organisational and Industry Task Environments
Conventionally, an organisation’s external environment is conceptualised as
everything that is not included within the boundaries of that organisation. This
conceptualisation assumes an organisation has clear boundaries that differentiate it
from its surroundings, an assumption which has not gone unchallenged (e.g.
Starbuck, 1976). Leaving aside questions about permeability and fuzziness that arise
from close analysis of organisational boundaries, the assumption that organisations,
particularly corporations, are identifiable as entities discrete from their surroundings
has practical, legal and theoretical applications. When that assumption is accepted, it
is possible to treat the external environment as an object of study and to describe the
environment in a variety of ways that has proved useful to theoretical understanding
of many organisational phenomena (Aldrich, 1979; Thompson, 1967).
Dill (1958) was amongst the first to distinguish between the general
environment and the task environment. He defined the task environment as external
environmental inputs relevant to organisational goal setting and attainment. Dill
studied autonomy within top management groups of two Norwegian firms. He noted
that, for individual members of a top management group, inputs included goals
specified by organisational management, and the condition of the organisation,
which somewhat confuses this early discussion of task environment. Nonetheless,
10
Dill discusses the “firm’s total task environment” (1958: 426), which he divides into
four sectors (customers, suppliers, competitors, and regulatory groups). Lawrence
and Lorsch (1969) suggested that the task environment had three important sectors
(market, science, and technical-economic), each of which created its own type of
uncertainty when making managerial decisions.
The notion that sectors of the task environment are important to the degree that
they introduce uncertainty, or ambiguity, into decision processes is a common, if not
ubiquitous, observation in the literature on task environments. The number of
segments suggested as significant in the task environment has varied as subsequent
researchers have found parsimonious typologies to describe their observations and
conclusions. For example, Miles and Snow (1978) suggest six sectors (suppliers,
customers, financial markets, competitors, labour unions, and government/regulatory
agencies). Daft’s (2001) popular textbook on organisational theory and design
identifies ten sectors of the task environment. Priem, Love, and Shaffer (2002)
suggested that characteristics of the general environment influence what are
considered relevant sectors of the task environment when they observed that unions
were not considered major sources of task environment uncertainties in a study of
Hong Kong executives’ perceptions of their task environments.
Dess and Beard (1984) made a distinction between an organisation’s task
environment and an industry’s task environment and suggested that the (more
specific) organisational task environment included the (more general) industry task
environment. The latter was defined as:
the set of all organizations with which members of a given industry
(including the focal organization) had transactions in the input and output
of resources, i.e., (Ritz, 1979) producer-to-producer transactions in input11
output analysis. It did not, however, include organizations outside the
industry of the focal organization that might otherwise have competed
with it for input resources (1984: 54).
The distinction between organisational task environment and industry task
environment was not emphasized in Hambrick and Finkelstein (1987), perhaps
because the former includes the latter. Their introductory discussion on
environmental influences on organisations and managers notes that, over time, firms
can change their task environment by overcoming mobility barriers and that, while
diversified firms may operate in highly complex task environments (because of the
summation and interaction of different industry environments), it is generally
practical to identify a principal task environment for most organisations.
Industry-level Discretion
Abrahamson and Hambrick (1997) further clarify Hambrick and Finkelstein’s
(1987) original model of executive discretion by a slight change of emphasis and
treatment of the task environment. Abrahamson and Hambrick (1997: 515) interpret
Hambrick and Finkelstein’s original model as an argument that “industry-,
organizational- and individual-level factors affect managerial discretion”. Their
research focuses on the construct “industry-level discretion” (the average level of
executive discretion in an industry), which is principally determined by
characteristics of the industry task environment. Industry-level discretion is the
average range of exercisable strategic options available to top management teams of
individual firms in an industry. This modification to the original discretion model
groups organisational task environment characteristics specific to individual
organisations with internal organisation factors when considering organisationallevel factors affecting discretion. This subtle change to the original discretion model
12
facilitates research at the commonly used industry, organisation, and individual
levels of analysis.
Limited Empirical Research Using the Discretion Model
The relatively few reported empirical studies that explore the discretion model
tend to focus on organisational-level discretion (e.g. Finkelstein & Hambrick, 1990;
Hambrick, Geletkanycz & Fredrickson, 1993). Kay (1997; 2002) measured
perceived individual-level discretion, while most of the small body of research using
industry-level discretion measures has been restricted to qualitative assessment of
discretion in industries where the determinants of industry-level discretion align and
thus support identification of industries with high, medium and low levels of
discretion. The rare attempts to quantify industry-level discretion are discussed in
the second chapter of this thesis. At this point, it is sufficient to note that the limited
set of available qualitative and quantitative values for industry-level discretion have
restricted the types of research questions that could be addressed in studies using
industry-level discretion. Consequently, some basic propositions of the discretion
model remain untested.
RESEARCH QUESTION
This thesis examines one of the basic propositions of the discretion model that
has not been formally tested. It tests for the expected positive association between
industry-level discretion and variety of strategies within industries. More formally,
the research question of this thesis is “What is the association between industry-level
discretion and strategic variety in industries?” Strategic variety is the variety of firm
level strategies in an industry. Lack of quantitative measures for industry-level
13
discretion is not the sole reason why this seemingly simple question has remained
untested: there is little advice and no consensus on how to measure strategic variety
within industries. This thesis examines and provides potential solutions to both these
obstacles. Quantitative values for industry-level discretion and strategic variety are
developed and the association between the two constructs is examined, firstly
broadly and then in some detail.
STRUCTURE OF THESIS
This thesis consists of five chapters. This, the first chapter, introduces the
construct ‘industry-level discretion.’ It also states the research question and provides
details on the structure of the thesis, which comprises three empirical studies.
Chapter Two describes Study One where an innovative approach to measurement of
industry-level discretion using archival data is developed, applied and validated.
The outcome of Study One is a list of 116 standardised values for industry-level
discretion for a range of U.S. four digit Standard Industry Classification coded
(SIC4) industries covering the years 1990 to 1997.
Chapter Three describes Study Two, which analyses the few methods in the
public domain that attempt to measure strategic variety. The available methods use
selected accounting ratios to operationalise generic strategic dimensions and use a
variety of techniques to consolidate the selected ratio data into a single value that
measures strategic variety. As far as practicable, Study Two replicates the
measurement approach that is best suited to pan-industry research and tests for the
expected positive association between industry-level discretion and the strategic
variety measures produced by the replicated measurement method. The discussion
of the results in Study Two highlights serious limitations to current approaches to
measuring strategic variety. These limitations cast doubt on the results of the test of
14
the association between industry-level discretion and strategic variety. The main
outcome of Study Two is the identification of the need for a new approach to
measuring strategic variety across multiple industries.
Chapter Four describes Study Three, which introduces an entropy-based
information theory approach to the study of strategic variety in industries. This
approach allows more detailed analysis of variety in industries and permits the
testing of a number of hypotheses that test for associations between variety in
different strategic behaviours and industry-level discretion. The results of tests in
Study Three lead to the conclusion that firms in high discretion industries compete
by adopting different long term positions, while firms in high and low discretion
industries have similar levels of variety in current or short term behaviours.
The fifth and final chapter of the thesis reviews the three studies and revisits
the main contributions and the findings of the thesis. The major theoretical and
methodological contributions as well as the limitations of the research are identified
and a number of avenues for future research are suggested. An appendix provides a
technical note on the case retention rationale used in critical statistical tests
throughout the thesis and includes an illustrative example of the battery of tests used
to test hypotheses. It is best read before reading Chapters 3 and 4.
CONCLUSION TO CHAPTER ONE
This chapter identifies executive discretion as an important and under- researched
construct in organisational research. Difficulties encountered when operationalising
and measuring discretion are identified as contributing to the relative lack of
discretion research, especially industry-level discretion research. Even some of the
most basic propositions of Hambrick and Finkelstein’s (1987) discretion model have
15
not been tested. The central research question for this thesis is “What is the
association between industry-level discretion and strategic variety in industries?”
Answering the research question requires measurement of two rather grand
constructs that have both proved difficult to measure in past research. The following
three studies systematically examine and develop measurement techniques and
association tests which seek to answer the research question.
16
CHAPTER TWO
STUDY ONE
INTRODUCTION
This study addresses the first issue of measurement identified in Chapter One:
the lack of quantitative measures for industry level discretion. The majority of
industry discretion studies have been limited to qualitative classification of industries
as having high, moderate, and low industry level discretion and, consequently have
been restricted to the small set of industries where the environmental determinants
align. In turn, the small number of industries and the lack of fine graduation in the
quantitative classification categories have restricted the research questions that can
be addressed. However, a small number of quantitative values for industry level
discretion have been produced (Hambrick & Abrahamson, 1995).
In this first study, rigorous re-examination of published quantitative point
estimates for industry-level discretion (Hambrick & Abrahamson, 1995) reveals they
have sizable confidence intervals that limit their use in statistical tests. However, the
published correlation between industry-level discretion and attentional homogeneity
(Abrahamson & Hambrick, 1997) holds up to scrutiny. That correlation permits
generation of contemporaneous quantitative values for industry-level discretion for
industries and periods outside Hambrick and Abrahamson’s (1995) original research
sample.
This large sample study identifies a necessary modification to the calculation of
lexical density of document sets and describes a procedure used to produce 116
values for industry-level discretion for a range of SIC4 codes between 1990 and
17
1997. Qualitative examination of the general behaviour of the values for industrylevel discretion over time, and empirical demonstrations of the ability of the values
to predict debt avoidance and to identify industries where accounts manipulation is
unlikely support the validity of the measures of industry-level discretion.
Measurement of Industry-level Discretion
Hambrick and Finkelstein’s (1987) model suggested that six environmental
factors largely determine industry-level discretion: product differentiability, market
growth, industry structure, demand instability, quasi-legal constraints, and powerful
outside forces. Their elaboration of these factors includes the observation that the
factors do not necessarily covary (Hambrick & Finkelstein, 1987). How they affect
discretion when they combine and conflict is unknown. The complexity of the
model’s determinants of industry-level discretion presents a formidable obstacle to
discretion research.
Early research relied on qualitative categorisation of industry-level discretion
as high, medium, or low (Finkelstein & Hambrick, 1990; Hambrick, Geletkanycz &
Fredrickson, 1993). This approach limited research to industries where the
determinants of industry-level discretion were unambiguously aligned (Hambrick &
Abrahamson, 1995). Thus, for example, Thomas and Peyrefitte’s (1996)
examination of associations between executive discretion and multinational firm
performance uses the same high and low discretion industries as used in Finkelstein
and Hambrick (1990).
Available Measures of Industry-level Discretion
Haleblian and Finkelstein (1993) produced contemporaneous measures of
industry-level discretion by computing the average of five standardised indicators
18
that operationalise variables identified in Hambrick and Abrahamson’s (1987)
discretion model as influencing industry-level discretion: average advertising
intensity, average R&D intensity, average annual sales growth, standard deviation of
annual sales, and the degree of regulation. This method assumes that the indicators
carry equal weight when assessing discretion. In the absence of additional
information, such an assumption is understandable in a practical sense. However it
is a simplification of the original model. As noted above, the multiple determinants
of managerial discretion do not covary (Hambrick & Finkelstein, 1987). Nor do they
necessarily combine in a linear fashion, which, in the absence of additional theory or
empirically-based information, makes weighting of input indicators of determinant
variables a subjective process (Hambrick & Abrahamson, 1995).
In 1992, to overcome these issues, Hambrick and Abrahamson (1995) used an
expert panel of fourteen academics who had referenced Hambrick and Finkelstein’s
(1987) paper to obtain ratings of industry-level discretion for seventeen U.S.
industries for the period 1985-1989. Standard Industrial Classification (SIC) codes
defined industries. Referencing Shrout and Fleiss (1979) as their analytic guide, the
authors report the academic panel demonstrated an overall intraclass correlation
coefficient of the industry means of 0.95. The results were validated against the
ratings of a second expert panel of seventeen professional security analysts with
specific expertise in one of the industries that had already been rated. The experts
also rated three control industries that Finkelstein and Hambrick (1987) had
qualitatively rated as High, Medium and Low discretion industries (Computers,
Chemicals, and Natural Gas, respectively). The security analysts’ ratings of the
three control industries were consistent with the existing qualitative ratings and a ttest indicated pairs were different at p-value < 0.01. The intraclass coefficient of the
19
security analysts’ ratings of the control industries was 0.89. The security analysts’
ratings of the industry in which they had expertise agreed with the academics’
ratings (Pearson r = 0.83, p-value = 0.00; Spearman r = 0.77, p-value = 0.00),
demonstrating the strength of convergent validity.
The point estimates (means) derived from the academic panel’s responses were
subsequently used in a regression analysis with objective industry characteristics
extracted from the Compustat database to estimate coefficients which were then
applied to industry characteristics of an additional 54 industries (Personal
communication, Don Hambrick, April, 2003). This produced a total of 71 ratings of
industry-level discretion for the sample period 1985-1989. The full 71 ratings were
published (Finkelstein & Hambrick, 1996), but no details of the input variables or
the regression specification or fit were supplied.
Murphy (1999) used the top, middle and bottom ten ratings in the full 71
ratings list to identify industries with high, medium and low industry-level discretion
for a study on industry-level discretion, CEO compensation and firm performance.
However, the compensation and performance data used were for 1996-1999, a
decade after the period for the industry-level discretion ratings. In their original
advice to the academic panel Abrahamson and Hambrick (1997: 518) noted: “Since
industry conditions vary over time, it may be useful to know that our period of
interest is 1985-1989.” Industry-level discretion varies across time. In the absence
of contemporaneous ratings, Murphy’s use of high, medium and low groupings of
dated ratings is understandable. However, it requires an assumption that weakens
the original theoretical model.
20
Industry-level Discretion and Attentional Homogeneity
Focusing on the attentional stage of the tripartite information processing
sequence (Daft & Weick, 1984; Dutton & Jackson, 1987; Hambrick & Mason,
1984), Abrahamson and Hambrick (1997: 514) defined attentional homogeneity as
“the degree of similarity in the foci of attention of top managers across
organizations”. They adopted a novel approach to the measurement of attentional
homogeneity that did not rely on expert opinion and used readily available archival
information. The attentional focus of top managers was inferred from the things they
focused on in their annual President’s Letter, that is the words they used, and the
similarity in word usage between President’s Letters was taken as indicating the
degree of attentional homogeneity.
Lexical density, a measure of frequency of shared use of words, and lexical
commonality, a measure of frequency of use of shared words, of President’s Letters
from undifferentiated firms on the Compact Disclosure database (1985-1989)
provided two separate indicators of attentional homogeneity of the selected
industries. Their study used thirteen industry discretion ratings from their 1995
paper and an additional industry (SIC Code 6331: Fire, Marine and Casualty
Insurance). While this extra industry rating is not in the list of 54 derived ratings, it
is reasonable to assume it was derived in a similar fashion
In their paper, Abrahamson and Hambrick (1997) addressed possible concerns
about the use of President’s Letters as a data source by citing published studies that
point to the conclusion that President’s Letters can be used to measure managerial
cognitions, while evaluative statements in President’s Letters are likely to be
influenced by impression management. Simply put, President’s Letters generally
identify issues the top management team thinks important, but the evaluation of
21
those issues may or may not reflect the top management team’s assessment of the
issues.
Abrahamson and Hambrick addressed possible concerns about operationalising
attentional homogeneity using derivatives of word counts of President’s Letters by
illustrating that words in President’s Letters are used in ways consistent with the
Whorf-Sapir hypothesis. That hypothesis suggests that the cognitive categories used
to attend to aspects of the universe are defined, constrained and embedded in the
linguistic system used when thinking about and discussing them (Sapir, 1944;
Whorf, 1956). The Whorf-Sapir hypothesis suggests that the characteristics of word
usage in documents (e.g. frequency, variety, commonality) can be used as indicators
of the constructs used by the persons generating the documents (Abrahamson &
Hambrick, 1997).
Specifically, Abrahamson and Hambrick (1997) analysed all President’s
Letters from all 688 SIC4 coded industries on the 1988 Compact Disclosure
database both collectively and at an industry level, a total of 5192 Letters.
Calculating the percentage of letters that used each word produced the “general use”
and “industry use” of that word. For example, the word ‘a’ had effectively 100%
general use (100gu) and 100% industry use (100iu) in all industries. The word
‘prices’ had 22gu and varying values for industry use (e.g. 93iu for the Oil and Gas
Industry). Each word’s ‘extra use’ in each industry was determined by subtracting
its general use from its industry use. For ‘prices’ and ‘a’ in the Oil and Gas Industry,
93iu - 22gu = 71eu; 100iu - 100gu = 0eu respectively. Thus, in 1988, the Oil and Gas
industry was more concerned about prices than the general industry average.
By ranking words according to their extra use it is possible to identify issues
that concern industries more or less than the industry average. Use of suitable cut22
off values (in the illustrative examples in their paper, +20%, -15%) produces
wordlists for each industry that vary in content and length. The content identifies the
specific issues that are relatively important and unimportant to the industry. The
length of the lists is an indication of the existence of an evolved and shared industryspecific vocabulary. A larger shared, industry-specific vocabulary indicates greater
attentional homogeneity for the industry. Abrahamson and Hambrick (1997)
illustrate this result using the Oil and Gas industry and the Software and
Programming industry.
After addressing these preliminary issues, Abrahamson and Hambrick (1997)
found the correlations between industry-level discretion ratings and lexical
commonality and between industry-level discretion ratings and lexical density were
negative and significant, with the largest (Pearson r = -0.85) and most significant (pvalue < 0.00) correlation occurring between lexical density and industry-level
discretion. Figure 2.1 graphically illustrates the absence of outliers.
These results provide support for Abrahamson and Hambrick’s (1997)
theoretical conceptualisation of attentional homogeneity as an indicator of industry
level discretion. More broadly, the validity of an information processing, cognitive
approach to understanding the nature of, and influences on, executive discretion is
supported. The results also support the methodological approach to measuring
attentional homogeneity by using an objective, unobtrusive, archivally-based lexical
similarity approach and open up the possibility of using this approach across other
industries, time periods, and for studying related issues. However, there were also
reasons to be cautious in interpreting these results.
23
Critical Evaluation of Reviewed Research
Liberal use of point estimates as the best estimate of values characterises the
statistical analyses used throughout empirical work just described. However, the low
number of academic raters who formed the original rating panel raises issues of
measurement error and confidence intervals that require attention. Not all academic
panel members supplied ratings for all fourteen industries. The number of academic
raters for each industry is unpublished but can be determined by examination of the
mean and standard deviation data supplied. For example, SIC4 7372 had a mean of
6.38 and standard deviation of 1.04. The only way these results can be obtained with
fourteen or less scores, all of which are integers between 1 and 7 inclusive, is by
using thirteen scores. A little forensic reconstruction based mainly on means
supplied in Hambrick and Abrahamson (1995) or the slightly different means
supplied in Abrahamson and Hambrick (1997) reveals the number of raters for each
industry. In most cases, when the number of raters is determined, a combination of
the possible scores can be found that produces the associated standard deviation in
Hambrick and Abrahamson (1995). Where the nearest possible standard deviation
using the derived number of raters is not a perfect match, the difference is typically
0.01-0.02, with one at 0.04.
Assuming the population of possible raters is large, values for the standard
error of the mean and the 95% confidence intervals around each of the point values
for industry-level discretion can be calculated using the derived numbers of ratings
for the industry and the supplied mean and standard deviation. Figure 2.2 and Table
2.1 display and list those 95% confidence intervals. No industry-level discretion
rating has a 95% confidence interval range greater than its point estimate, indicating
the ratings are stable. The ranges of the four lowest industry-level discretion ratings
24
FIGURE 2.1
Negative Association between Lexical Density (Attentional Homogeneity) and
Industry-level Discretion
7.0
6.5
7372
7312
6.0
3570
3841
Industry-level Discretion
5.5
2834
3663
5.0
3674
4.5
3825
6211
4.0
3.5
4512
3.0
4213
1040
1311
2.5
2.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
Lexical Density (Standatdized)
After Figure 1 in Abrahamson and Hambrick (1997: 527)
Notes
Numbers on graph are SIC codes.
Lexical density values approximated from original figure.
Original X and Y axes swapped for easy comparison with Figure 2.3.
overlap, indicating there is insufficient evidence to assert that their values are
different. SIC4 coded industries 4512 and 3674 have large ranges. SIC4 4512 may
be a low or a middle discretion industry. Grouping SIC4s 7312 and 7372 as high
discretion industries necessitates going down to SIC4 2731 to avoid overlap between
the upper confidence interval of the middle discretion group and the lower
confidence level of the high discretion group.
25
FIGURE 2.2
Industry-level Discretion: SIC4 Codes, Means and 95% Confidence Intervals
7
7372
7312
6
3841
2834 3944
3826
3570
Industry-level Discretion
3663
2731
5
3674
6211 3825
4
4512
3
4213
1311 1040
3312
2
1
In sum, conservative or rigorous statistical treatment suggests that the
measurement error in the ratings is such that only four low discretion industries, four
middle discretion industries and two high discretion industries are distinctly
identifiable, and there is insufficient evidence to rank the industries in each of these
categories. Ignoring the different number of raters for each industry and using
fourteen raters throughout the confidence interval calculation produces the same
overall result. However the varying number of raters influences the intraclass
correlation coefficient calculation (McGraw & Wong, 1996; Shrout & Fleiss, 1979).
26
TABLE 2.1
Industry-level Discretion: Means and 95% Confidence Intervals
Industry
SIC4
Mean
No. Of
Ratings
95%
Lower CI
95%
Upper CI
Range Of
95%CI
Blast Furnaces and
Steel Mills
3312
2.08
13
1.37
2.79
1.42
Petroleum/Natural
Gas Production
1311
2.33
12
1.39
3.27
1.88
Gold and Silver
Mines
1040
2.42
12
1.5
3.34
1.84
Trucking (except
local)
4213
2.73*
11
2.26
3.2
0.94
Certified Air
Transport
4512
3.23
13
2.2
4.26
2.06
Security Brokers
6211
4.27
11
3.68
4.86
1.18
Instruments to
Measure Electricity
3825
4.33
12
3.84
4.82
0.98
Semiconductors
3674
4.62*
13
3.64
5.6
1.96
Book Publishing
2731
4.92
13
4.25
5.59
1.34
Radio/TV
Communication
Equipment
3663
5.17
12
4.59
5.75
1.16
Surgical/Medical
Instruments
3841
5.41
12
4.85
5.97
1.12
Pharmaceuticals
2834
5.54
13
4.97
6.11
1.14
Games and Toys
3944
5.55
11
4.79
6.31
1.52
Engineering/Scientific
Equipment
3826
5.63
8
4.99
6.27
1.28
Computer Equipment
3570
5.77
13
5.22
6.32
1.1
Motion Picture
Production
7312
6.08
13
5.67
6.49
0.82
Computer
Programming
7372
6.38
13
5.81
6.95
1.14
Overall
4.50
Notes
SICs in bold were used in Abrahamson and Hambrick (1997).
*0.01 difference between 1995 and 1997 papers, 1997 value used because it fits
constraints of scoring and possible number of raters.
27
An additional issue is the non-random distribution of the ranges of the
confidence intervals. They are negatively and significantly correlated with the
means (Pearson r = -0.51, p-value = 0.04), raising issues of bias and consistency
when using the mean as the best available indicator in a statistical analysis (Gujarati,
1995). Certainly, without additional information, the 54 published point estimates of
industry-level discretion obtained by regression analysis are, at best, approximate
indicators. Their confidence intervals will be wider than the confidence intervals of
the academic panel results, but how much wider is unknown. Equally, the rating for
SIC4 6331 in the study of the industry-level discretion-attentional homogeneity
association has unknown confidence intervals and is best deleted when examining
that association using a conservative approach. Happily, as Figure 2.3 shows, the
use of the known confidence intervals supports the original conclusion that
attentional homogeneity is indeed negatively associated with industry-level
discretion. This reassuring conclusion underpins the development of
contemporaneous values of industry-level executive discretion described next.
28
FIGURE 2.3
Negative Association Not Lost Using 95% Confidence Intervals
7
Industry-level Discretion
6
5
4
3
2
1
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
Lexical Density (Standardized)
CONTEMPORANEOUS MEASUREMENT
Overview
While the need for contemporaneous quantitative measures of industry-level
discretion is obvious if the discretion model is to be extensively tested, duplication
of Hambrick and Abrahamson’s (1995) method of obtaining ratings is difficult, even
for recent sample periods. Identifying academics who have cited Hambrick and
Finkelstein (1987) or subsequent papers that explore the model is not difficult.
However, their familiarity with published industry-level discretion ratings has the
potential to influence their responses in any new rating survey. Furthermore, there is
no guarantee that sufficient raters will be available for less studied industries, or that
ratings for industries would be reliable if more than a few years have elapsed since
the rating period and the timing of the survey. Some form of objective use of
29
archival data is required if reliable ratings are to be made. This observation guided
the method for creating contemporaneous measures of industry-level discretion
described below.
The method used to produce contemporaneous quantitative values for industrylevel discretion involves a number of steps. A research database based on a
thoroughly vetted list of firms with annual report accounting data on the Compact
Disclosure compact discs published between 1988 and 1999 was created. The subset
of undifferentiated firms (i.e. firms that reported only one SIC4 code for the sample
year) where the President’s Letter for the annual report was available was identified.
Where SIC4s had sufficient President’s Letters, values for lexical density, lexical
commonality, high extra usage, and low extra usage were calculated. Examination
of correlations and exploratory factor analysis produced 116 values for industry-level
discretion for a range of SIC4s for the years 1990-1997. The validity of the values is
demonstrated by showing that 1) their general behaviour over time is consistent with
expectations; 2) they have high predictive validity, for long-term debt usage in
industries in a way predicted by discretion theory; and 3) they have high predictive
validity when used to identify industries where discretionary accounts adjustments
are insignificant.
Creating the Research Database
The Compact Disclosure database lists companies that: 1) provide direct goods
or services, and 2) file with the U.S. Securities and Exchange Commission (SEC).
SEC filing guidelines apply to companies listed on a U.S. securities exchange or that
trade securities over the counter. The company must have at least 500 shareholders
of one class of stock and have at least $5 million in assets (Disclosure, 1994). The
latter condition is slightly relaxed in the Compact Disclosure database, but was re30
imposed it when creating the research database. Only companies that, at the time of
the publication of the annual report, had 50 or more employees, and a valid stock
exchange ticker symbol were included in the final research database. The
requirement that a firm had 50 or more employees reduced the number of holding
companies in the database. The absence of very small firms in the database removed
complications arising from combining very small firms with very large firms. Small
firms often have specialist roles and strategies that vary considerably from their
industry norms (Chen & Hambrick, 1995; Lang & Calantone, 1997; Matthews &
Scott, 1995; Penrose, 1966). The absence of small firms is a necessary limitation in
the present research. The requirement that a firm had a valid ticker removed
duplicated annual reports attributable to mergers and acquisitions.
The sample years, 1988-1999, were determined by availability of Compact
Disclosure discs at the start of the research. Tracking company name changes across
the sample years ensured no firm had two annual reports in a sample year. The start
of each sample year was set at 14 January, as the period 14-20 January had no annual
reports published across the sampled years. This step reduced problems of two
annual reports from the one company in one sample year due to late publication of
annual reports. Remaining second annual reports were addressed by manual deletion
of the report that least fitted the pattern of other reports for the company, or by
deleting the earliest report if there were insufficient other reports for the company to
guide selection.
All available formulas of aggregate accounting values (e.g. Total Assets) were
used to ensure the component values in the accounting data were within the limits of
acceptable rounding errors. Annual reports with inconsistent accounting data were
deleted. All accounting ratios supplied in the Compact Disclosure database were
31
recalculated. All ‘different’ firms where accounting data were identical or similar
were paired and manually checked to identify and delete duplicates arising from
holding company reporting similar accounts to held companies.
Compact Disclosure frequently provides details of the same annual report on
more than one of its published compact discs. Slight variations of the same annual
report occur for legitimate reasons. The earliest and most recent version of close
duplicates of annual reports were identified and compared. In such cases President’s
Letters and annual accounting data from the most recent report were used as the
more recent accounting data typically included minor changes that reflected
corrections to the original entries and inclusion of President’s Letters often occurred
after the initial entry of accounting data on the Compact Disclosure database. SIC
and employee number data reflected the status of the company at the time of
publication of the Compact Disclosure disc, not the situation at the time of the
publication of the annual report. Consequently, these data were taken from the
earliest version of an annual report. After a thorough vetting, 69356 annual reports
remained, 21991 of which were from undifferentiated firms (i.e. reported only one
SIC4 code), 33825 had a President’s Letter, and 9549 undifferentiated firms had
President’s Letters.
Measuring Attentional Homogeneity
Lexical Commonality.
Industry-level discretion was operationalised as the converse of attentional
homogeneity. When measuring attentional homogeneity, Abrahamson and
Hambrick (1997) used an existing measure of homogeneity of word use, lexical
commonality, and to compensate for limitations in that measure, they developed an
32
additional measure, lexical density. Lexical commonality measures the average
frequency of use of words in documents. Table 2.2 reproduces Abrahamson and
Hambrick’s (1997) simple illustrative example of a lexical commonality calculation
where three organisations have a lexicon of only four words. The number of letters
that use a word determine the word’s commonality. For example ‘sales’ is used in
all three letters so it has 100% word commonality, while assets is only used in one
letter and has 33% word commonality. The number of times each word is used in
each letter is multiplied by the word’s commonality. The sum of all such
calculations for each word in each letter produces the letter’s commonality. For
example, in letter 1, which has three mentions of ‘sales’ , one mention of ‘assets’ and
five mentions of ‘costs’, each of which have word commonalities of 100%, 33% and
100% respectively, the letter’s commonality is 3*100+1*33+5*100 = 78. The other
letters have letter commonalities of 93 and 89. The lexical commonality of the set of
letters is the average of the letters’ commonalities: (78+93+89)/3 = 86. This
measure accommodates texts of different lengths and uses word usage and frequency
of usage information. However, it overweighs words that appear in only one
document or in a small proportion of the documents being analysed (Abrahamson &
Hambrick, 1997).
33
TABLE 2.2
Simple Example of Calculation of Lexical Commonality
WORD
sales
assets
costs
margins
Calculation of
letters'
commonalities
Each letter's
commonality
Letter 1
Letter 2
3
1
5
Letter 3
Word's commonality
10
1
(3x100
+1x33
+5x100)
/(3+1+5)
2
3
(10x100
+2x100
+3x 66)
/(10+2+3)
1
1
(1x100
+1x100
+1x66)
/(1+1+1)
78
93
89
Average of letter's commonality
100%
33%
100%
66%
(78+93+89)/3 = 86
After Table 1, Abrahamson and Hambrick (1997: 521)
Lexical Density
Lexical density, a measure developed by Abrahamson and Hambrick, measures
the density of word sharing in a set of documents. It addresses the identified
weakness of lexical commonality. Calculating lexical density involves two steps.
Firstly, raw lexical density, the actual number of words shared between possible
combinations of two documents in the document set divided by the theoretical
maximum number of times the words could be shared by documents in the set, is
calculated. Table 2.3 calculates the raw lexical density for the letters in Table 2.2.
The number of times each word is shared by two letters is determined by counting
the number of letters with the word at least once and calculating the number of
combinations of two letters that have the word. Thus, for ‘sales’ the number of
binary combinations of letters when all three letters (N = 3) have the word is (3*2)/2.
The total number of word sharings is then determined by summing the number of
sharings for each word. In the example, ‘sales’ has three sharings, ‘assets’ has no
sharings, ‘costs’ has three sharings, and ‘margins’ has one sharing. Thus the total
number of sharings is 3+0+3+1 = 7. The total number of possible sharings is
calculated by multiplying the number of words (W) by the number of binary
34
combinations of letters W*N*(N-1)/2 = (4*3*2)/2 = 12. The raw lexical density for
the letters is the total number of word sharings divided by the possible number of
word sharings: 7/12 = 0.58. This raw lexical density value is sensitive to the size of
the document set because the calculation includes division by N*(N-1), where N is
the number of documents in the set being analysed. Abrahamson and Hambrick’s
second step involved regressing the raw lexical density value on the number of
documents in the set and using the residual as the measure of lexical density. The
intent is to remove the sensitivity of the final measure (‘lexical density’) to the
number of documents analysed.
TABLE 2.3
Simple Example of Calculation of Raw Lexical Density
WORD
Letter 1 Letter 2
sales
Yes
Yes
assets
Yes
costs
Yes
Yes
margins
Yes
Total Number of Word Sharings
Total Possible Word Sharings
Raw Lexical Density
Letter 3
Yes
Yes
Yes
Calculation of
number of word
sharings
(3*2)/2
0/2
(3*2)/2
2/2
Number of
word sharings
4*(3*2)/2
7/12
3
0
3
1
7
12
0.58
Raw lexical density calculation requires a document set. The regression
requires a set of document sets. The final lexical density value of a document set
depends to some extent on the characteristics of the other document sets used in the
regression analysis. It is a conditional value. Comparison of lexical density values
derived from different sets of sets of documents is problematical if the raw lexical
density values are regressed separately for different sets of document sets. However,
if all raw lexical density values across all sets of sets are regressed together,
comparison of lexical density values from different document sets is possible.
35
Pre-treatment of text data
President’s Letters in the Compact Disclosure database often contain some
standard expressions (e.g. “The following text was taken directly from an EDGAR
filing,” “From annual report to shareholders,” “Photo omitted”). All such added
expressions were identified and deleted. Additionally, the company name was
replaced with the expression ‘Companyname.’ Alphanumeric expressions were
counted as unique words as they generally had specific meanings. Numbers were
treated in two different ways: the non-numbers treatment ignored all numbers; the
aggregated numbers treatment counted all numbers as though they were the same
‘word’. The different number treatments produced dual values for lexical
commonality and for lexical density.
Sampling Criteria
Abrahamson and Hambrick (1997) limited their sample of industries to those
that had at least twenty non-diversified firms in their single five-year sample period
(1985-1989). Their calculation of lexical commonality and lexical density used all
available Presidents’ Letters for all non-diversified firms in the selected industries,
so continuous membership to the set of non-diversified firms in the industry
throughout the sample period was not necessary. The current research used similar
selection criteria. All included industries had twenty or more distinct non-diversified
firms with President’s Letters in each five-year sample period. The sampling years
were 1988-1999, which contained eight overlapping five-year sample periods
(labeled P1-P8).
36
Extra Usage
General usage values used all available Presidents’ Letters for annual reports in
the vetted database in each five-year sample period. Using only President’s Letters
in the research database avoided contamination from duplicate annual reports and
annual reports outside the sample period – matters not controlled in Abrahamson and
Hambrick’s (1997) original demonstration of extra usage. Industry usage values
used all Presidents’ Letters for non-diversified firms in the industry in the same fiveyear sample period. Cut-off points of +25% and -20% produced positive and
negative extra use lists with at least one word in all qualifying industries in all
sample periods.
This treatment of President’s Letters produced 198 industry-sample period
combinations (cases) with values for lexical commonality, raw lexical density, length
of extra usage +25% list, length of extra usage -25% list, and number of President’s
Letters in each.
Measuring Industry-level Discretion
Table 2.4 shows the correlations of the lexically derived attentional
homogeneity variables. The two raw lexical density measures are highly correlated,
as are the two lexical commonality measures, as would be expected. Figure 2.4
demonstrates the association between the raw lexical density and the number of
documents in each case is an inverse relationship and that regression of raw lexical
density on the number of documents would create a systematic relationship between
the residuals and the number of documents. Therefore raw lexical density was
regressed on the inverse of the number of documents. This is consistent with the
37
observation that the influence of the number of documents on raw lexical density is
through its role in the denominator in the calculation.
TABLE 2.4
Correlations Between All Lexical Measuresa
1
1 Raw Lexical Density Non Numbers
2 Raw Lexical Density Aggregated
Numbers
3 Number of President’s Letters (N)
4 Lexical Density Non Numbers
(Residuals after Regressing on 1/N)
5 Lexical Density Aggregated
Numbers
(Residuals after Regressing on 1/N)
6 Lexical Commonality Non Numbers
7 Lexical Commonality Aggregated
Numbers
8 Extra Usage +25%
9 Extra Usage -20%
2
3
4
5
6
7
1.00**
-0.75**
0.50**
-0.75**
0.50**
-0.16*
0.50**
0.50**
-0.16*
1.00**
0.59**
0.59**
0.59**
0.59**
-0.19**
-0.20**
0.84**
0.81**
0.84**
0.81**
0.99**
0.39**
0.14*
0.39**
0.14*
-0.11
-0.16*
0.60**
-0.17*
0.60**
-0.17*
0.67**
-0.01
0.62**
0.04
0.18*
a Number of Cases = 196
* p-value <0.05
** p-value < 0.01
FIGURE 2.4
Raw Lexical Density ≈ f (1/Number of Documents)
400
350
Number of President's Letters
300
250
200
150
100
50
0
0.005
0.010
8
0.015
0.020
0.025
0.030
0.035
0.040
0.045
Raw Lexical Density (Non numbers treatment)
38
Table 2.4 shows that the lexical commonality values, which control for
artifactural influence of the number of documents, are still significantly correlated
with number of documents (Pearson r = -0.19 or -0.20). This is consistent with an
observable feature of the research database wherein industries intuitively assessed as
high discretion industries have more firms than industries intuitively assessed as low
discretion industries (e.g. for P8: N(7372 ) = 346(Computer Programming), N(4911)
= 52(Electrical Services)). Consequently, the significant correlation between the
residuals of the raw lexical density values regressed in the inverse of the number of
documents (Pearson r = -0.16 for both) supports the use of that regressor.
Analysis of the correlations of the extra usage variables suggests that extra
usage -20% is not significantly measuring the same latent variable as the lexical
density and lexical commonality measures. Again, this makes intuitive sense: the
list of words with extra low use should not be as industry specific as the list of words
with extra high use. Further examination of the correlations shows that the nonnumbers treatment produced slightly higher correlations than the aggregatednumbers treatment of lexical input.
Initial exploratory factor analysis (SPSS V12.0.1 ‘principal components
method’) using lexical density (non-numbers), lexical commonality (non-numbers)
and extra usage +25% did not capture sufficient variance of the 186 cases. Notably,
only 62% of extra usage +25% was captured. At this point, all cases with SIC4s
ending in ‘9’ were deleted as those codes represent miscellaneous firms not
classified in the main industry codes: they are collections of diverse firms rather than
firms in recognisable industries. Analysis of the scatterplots of the remaining cases
revealed that outliers represented specific health technology industries, banks and
savings institutions, and bank holding companies.
39
Holding companies are not ‘firms’ in the Penrosean sense of the word
(Penrose, 1966). Retaining banks and similar savings institutions in pan-industry
research is notoriously difficult and they are usually excluded as a matter of course.
Rapid health care technology change encourages executive discretion but the
industry’s market is highly regulated. It may be the case that the lexicon of some
health care technology industries may be overly influenced by regulatory regimes.
Hotels and Motels (SIC4 7011) and Commercial Physical Research (SIC4 8731) also
had outliers – perhaps because the activities undertaken by firms in these categories
are too heterogeneous to be treated as industries (US Department of Labour, 2001:
98). Not all cases in all these SICs were outliers, but, for consistency, all cases with
these SIC codes were deleted. Table 2.5 lists the SIC codes for the deleted cases.
TABLE 2.5
SIC Codes Deleted
All SIC4s ending in 9
2834 Pharmaceutical Preparations*
2835 Diagnostic Substances
3826 Analytical Instruments
3841 Surgical and Medical Instruments*
3842 Surgical Applications and Supplies
7011 Hotels and Motels
8731 Commercial Physical Research
6711 Holding Companies
6712 Bank Holding Companies
All SIC2 = 60 or 61 Banks and similar savings institutions
* Panel ratings are available for these industries for 1985-1989
The remaining 116 cases averaged 79 President’s Letters per case, with a
standard deviation of 56.2 President’s Letters. The minimum number of President’s
40
Letters per case was 24. The raw lexical density values were again regressed on the
inverse of the number of President’s Letters to get lexical density values for this
reduced dataset
Exploratory factor analysis (SPSS ‘principal components method’) of the
remaining cases produced one factor that captured 89.1% of the variance in the three
variables. Table 2.6 supplies details of that factor analysis. Factor loadings are all
very high. The absolute values of the off diagonal values of the residual correlation
matrix are small. The only possible issue is the low R2 values, particularly for
lexical density (71.1%). Overall, the factor analysis appears acceptable. Note that
the factor score coefficients are negative, which results in the factor score, the
measure for industry-level discretion, being the opposite sign to the three input
variables. Table 2.7 supplies descriptive statistics of the final sample.
TABLE 2.6
Summary of Factor Analysisa
Communalities
Factor
Loadings
Factor Score
Coefficients
From
Factor
Multiple
2
R
Residual Correlation Matrix
Extra
Lexical
Lexical
Usage
Density Commonality
+25
0.14
-0.06
-0.07
Lexical Density
-0.93
-0.35
0.86
0.71
Lexical Commonality
-0.95
-0.36
0.91
0.81
-0.06
0.09
-0.03
Extra Usage +25%
-0.95
-0.35
0.90
0.79
-0.07
-0.03
0.10
Eigenvalue
2.67
Proportion of total variance
0.89
a 116 cases
41
TABLE 2.7
Numbers of President’s Letters and Firms Used in Each Case
Case
1311P1
1311P2
1311P3
1311P4
1311P5
1311P6
1311P7
1311P8
3571P1
3571P2
3571P3
3573P1
3577P1
3577P2
3577P3
3577P4
3577P5
3577P6
3577P7
3577P8
3661P1
3661P2
3661P3
3661P4
3661P5
3661P6
3661P7
3661P8
3662P1
3663P4
3663P5
3663P6
3663P7
3663P8
3674P1
3674P2
3674P3
3674P4
3674P5
No. of
President’s
Letters
34
33
40
65
90
109
115
111
37
40
34
71
58
68
69
65
68
72
66
58
50
56
62
73
85
87
84
75
26
43
52
63
72
72
78
92
113
139
157
No. of
Firms
Case
23
22
28
41
51
57
58
58
27
29
28
67
35
39
39
43
45
42
39
33
29
30
37
43
44
42
38
34
23
24
30
33
33
31
41
44
49
66
70
3674P6
3674P7
3674P8
3825P6
3845P1
3845P2
3845P3
3845P4
3845P5
3845P6
3845P7
3845P8
4213P1
4213P3
4213P4
4213P5
4213P6
4213P7
4213P8
4812P4
4812P5
4812P6
4812P7
4812P8
4813P7
4813P8
4911P1
4911P2
4911P3
4911P4
4911P5
4911P6
4911P7
4911P8
4923P1
4923P2
4923P3
4923P4
4923P5
No. of
President’s
Letters
163
157
148
38
48
65
81
93
99
94
85
76
27
39
52
60
63
62
53
27
37
44
46
45
29
29
143
121
124
117
108
79
70
52
43
44
47
46
38
No. of
Firms
Case
69
72
69
20
35
40
41
47
52
53
52
42
20
20
26
28
29
28
24
21
25
25
23
23
24
23
69
58
53
44
40
32
29
26
25
25
26
23
26
4931P1
5812P1
5812P2
5812P3
5812P4
5812P5
5812P6
5812P7
5812P8
6211P1
6324P3
6324P4
6324P5
6324P6
6324P7
7363P4
7363P5
7363P6
7363P7
7363P8
7372P1
7372P2
7372P3
7372P4
7372P5
7372P6
7372P7
7372P8
7373P1
7373P2
7373P3
7373P4
7373P5
7373P6
7373P7
7373P8
8071P4
8071P5
No. of
President’s
Letters
45
113
122
127
121
119
104
92
77
40
30
42
44
41
38
29
37
43
47
45
126
131
155
201
258
313
332
346
28
33
42
46
52
59
66
56
24
30
No. of
Firms
27
55
57
62
63
66
57
53
42
26
20
27
25
22
20
20
22
23
23
20
90
82
89
125
166
195
205
212
20
22
27
29
31
37
42
39
20
21
Confidence Intervals of Industry-level Discretion
Stevens (2002) suggests as a rule of thumb that loadings of 0.512 are required
for sample sizes of 100 to produce loadings that are statistically significant from
zero. Using this as a guide and observing that the magnitude of lowest loading in the
principal components analysis is 0.93, it is reasonable to expect the point estimates
produced will have small ranges for their confidence intervals.2
2
I am unaware of any method that calculated the 95% confidence intervals of a principal
components analysis.
42
Reykov (2002) describes a procedure for estimating standard errors and
confidence intervals when using confirmatory factor analysis. However, calculation
of confidence intervals in exploratory factor analysis is a relatively rare activity,
which has led to some criticism when the method is applied to small to medium
sized samples (e.g. Aitkin & Aitkin, 2003). The shareware program Comprehensive
Exploratory Factor Analysis (CEFA, Version 2), released in 2004, has been
specifically designed to calculate confidence intervals for exploratory factor analysis.
The program does not currently have a principal components option so the data were
resubmitted to Maximum Wishart Likelihood factor analysis and standard errors for
the factor loadings were calculated using the Bordered Information Matrix and the
Analytic Derivatives settings, the default settings for CEFA V2 (Browne, Cudeck,
Tateneni & Mels, 2004).
The point estimates for the factor loadings for lexical density, lexical
commonality and extra usage +25% were 0.870, 0.946, and 0.926 respectively. The
respective standard errors were 0.026, 0.018 and 0.020. The standardised point
estimates calculated using these new factor loadings were almost perfectly correlated
with the results originally obtained using principal components analysis (Pearson r =
0.9998). The differences in scores were essentially rounding errors. The standard
errors were used to calculate the 95% confidence intervals for the point estimates.
Figure 2.5 plots the confidence intervals. Table 2.8 lists the point estimates and high
and low estimates of the 95% confidence intervals for the 116 cases. In the light of
rounding errors, the minimum confidence interval has been set at ± 0.01.
It is readily apparent that the confidence intervals are very small when
compared to the range of the scale and the value of the point estimate to which they
are attached. The largest confidence intervals occur in a few low industry-level
43
discretion cases. While overlaps of confidence intervals occur, they rarely extend
across many cases. These results suggest that, when using these results in further
analysis, it is reasonable to assume the point estimates are the best available
estimates of the case’s industry-level discretion.
FIGURE 2.5
95% Confidence Intervals of Estimates of Industry-level Discretion,
Ordered by Point Estimate
2.5
2
Industry-level Discretion
1.5
1
0.5
0
-0.5
-1
-1.5
-2
-2.5
-3
44
TABLE 2.8
Standardised Industry-level Discretion Values with Confidence Intervals
Rounded to Two Decimal Places
95%
Confidence
intervals
SIC-Year
1311_1990
1311_1991
1311_1992
1311_1993
1311_1994
1311_1995
1311_1996
1311_1997
3571_1990
3571_1991
3571_1992
3573_1990
3577_1990
3577_1991
3577_1992
3577_1993
3577_1994
3577_1995
3577_1996
3577_1997
3661_1990
3661_1991
3661_1992
3661_1993
3661_1994
3661_1995
3661_1996
3661_1997
3662_1990
3663_1993
3663_1994
3663_1995
3663_1996
3663_1997
3674_1990
3674_1991
3674_1992
3674_1993
3674_1994
Value
-0.98
-1.75
-2.01
-2.37
-2.43
-2.10
-2.13
-2.07
-0.32
-0.11
-0.02
0.57
0.45
0.40
0.33
0.47
0.67
0.75
0.80
0.86
0.26
0.07
-0.10
0.13
0.19
0.27
0.30
0.22
1.82
0.98
0.91
1.18
0.76
0.92
0.16
0.29
0.54
0.72
0.76
High
-1.02
-1.82
-2.10
-2.47
-2.54
-2.19
-2.22
-2.16
-0.34
-0.12
-0.03
0.59
0.47
0.41
0.35
0.49
0.70
0.78
0.84
0.90
0.27
0.08
-0.11
0.14
0.20
0.28
0.31
0.23
1.91
1.02
0.95
1.23
0.80
0.96
0.17
0.30
0.57
0.75
0.79
Low
-0.94
-1.67
-1.92
-2.26
-2.32
-2.01
-2.03
-1.98
-0.31
-0.10
-0.01
0.54
0.43
0.38
0.32
0.45
0.64
0.72
0.77
0.83
0.25
0.06
-0.09
0.12
0.18
0.26
0.29
0.21
1.73
0.93
0.87
1.12
0.73
0.88
0.15
0.28
0.52
0.68
0.73
95%
Confidence
intervals
SIC-Year
3674_1995
3674_1996
3674_1997
3825_1995
3845_1990
3845_1991
3845_1992
3845_1993
3845_1994
3845_1995
3845_1996
3845_1997
4213_1990
4213_1992
4213_1993
4213_1994
4213_1995
4213_1996
4213_1997
4812_1993
4812_1994
4812_1995
4812_1996
4812_1997
4813_1996
4813_1997
4911_1990
4911_1991
4911_1992
4911_1993
4911_1994
4911_1995
4911_1996
4911_1997
4923_1990
4923_1991
4923_1992
4923_1993
4923_1994
Value
0.77
0.71
0.57
1.19
0.96
0.99
0.95
0.99
1.04
0.95
0.84
0.78
1.41
0.36
-0.04
0.22
0.30
0.25
0.27
-0.63
-1.02
-0.82
-0.91
-1.00
-0.30
-0.05
-0.02
-0.23
-0.43
-0.60
-0.43
-1.24
-1.16
-1.23
-2.52
-2.30
-2.11
-2.13
-1.53
High
0.81
0.73
0.60
1.24
1.00
1.04
0.99
1.03
1.09
0.99
0.87
0.81
1.48
0.38
-0.05
0.23
0.32
0.26
0.28
-0.66
-1.07
-0.85
-0.96
-1.05
-0.32
-0.06
-0.03
-0.24
-0.45
-0.63
-0.45
-1.30
-1.21
-1.29
-2.64
-2.40
-2.21
-2.23
-1.60
Low
0.74
0.68
0.55
1.13
0.92
0.95
0.91
0.95
1.00
0.91
0.80
0.75
1.34
0.34
-0.03
0.21
0.29
0.24
0.25
-0.61
-0.97
-0.78
-0.87
-0.96
-0.29
-0.04
-0.01
-0.22
-0.41
-0.57
-0.41
-1.18
-1.10
-1.18
-2.41
-2.19
-2.02
-2.03
-1.46
95%
Confidence
intervals
SIC-Year
4931_1990
5812_1990
5812_1991
5812_1992
5812_1993
5812_1994
5812_1995
5812_1996
5812_1997
6211_1990
6324_1992
6324_1993
6324_1994
6324_1995
6324_1996
7363_1993
7363_1994
7363_1995
7363_1996
7363_1997
7372_1990
7372_1991
7372_1992
7372_1993
7372_1994
7372_1995
7372_1996
7372_1997
7373_1990
7373_1991
7373_1992
7373_1993
7373_1994
7373_1995
7373_1996
7373_1997
8071_1993
8071_1994
Value
-1.25
0.42
0.59
0.52
0.41
0.50
0.37
0.39
0.55
1.11
-1.55
-1.36
-1.33
-1.38
-1.33
-0.35
-0.55
-0.01
-0.04
0.11
0.87
0.79
0.67
0.74
0.88
1.08
1.03
1.15
0.61
0.44
0.47
0.77
0.74
0.82
0.66
0.74
0.51
0.03
High
-1.31
0.44
0.62
0.54
0.43
0.53
0.39
0.41
0.58
1.16
-1.61
-1.43
-1.40
-1.45
-1.40
-0.36
-0.57
-0.02
-0.05
0.12
0.90
0.83
0.69
0.77
0.92
1.13
1.08
1.20
0.64
0.46
0.49
0.80
0.77
0.86
0.69
0.77
0.54
0.04
Low
-1.20
0.40
0.57
0.50
0.39
0.48
0.36
0.37
0.53
1.06
-1.48
-1.30
-1.27
-1.31
-1.27
-0.33
-0.52
0.00
-0.03
0.10
0.83
0.76
0.64
0.71
0.84
1.03
0.98
1.10
0.57
0.41
0.45
0.74
0.71
0.79
0.63
0.70
0.48
0.02
VALIDITY CHECKS
Introduction to Validity Checks
The validity of the measure of industry-level discretion was assessed by
comparison with published values, examination to see if their behaviour was
consistent with general expectations, and two predictive validity checks: the first
using debt avoidance, the second using discretionary accounting practices. The
validity checks are detailed in the following sections.
45
Comparison with Published Values
Sample Period 1 (P1 = 1988-1992), the closest period to the academic panel’s
rating period, had only five SICs that also had panel ratings in Hambrick and
Abrahamson (1995). While the five newly calculated industry-level discretion
scores and their published scores for 1985-1989 had a Pearson correlation of 0.34,
the correlation was not significant (p-value = 0.57). The ten matches of the newly
calculated industry-level discretion scores and the 72 published scores had a Pearson
correlation of 0.40. Again, it was not significant (p-value = 0.25). In the light of
prior discussion on the desirability of contemporaneous tests, confidence intervals of
the panel ratings and the limited number of groupings in the panel ratings, these
correlations are neither confirmatory nor disconfirmatory, especially with only five
and ten pairs available for comparison.
Examination of Values Over the Sample Years
The values for industry-level discretion for SIC4 7372 (Computer
Programming and Software) were consistently high, and the values for SIC4 1311
(Oil and Natural Gas Production) were consistently low, which is consistent with
Finkelstein and Hambrick’s (1990) original qualitative ratings of these two
industries. Again, this is neither confirmatory nor disconfirmatory.
Nine SIC4s had sufficient President’s Letters to generate industry-level
discretion values in all eight five-year sample periods. Figure 2.6 plots those values
and illustrates two notable features: 1) the existence of a relatively stable group of
high discretion industries, and 2) the decrease in discretion in two industries: SIC4
1311 (Oil and Natural Gas Production) and SIC4 4911 (Electric Services). Electric
Services migrated from the bottom of the high discretion range to the low discretion
range. Rajagopalan and Finkelstein’s (1992) research into electrical utilities in the
46
1970s and 1980s shows that deregulation raised industry-level discretion in that
industry during the period of their study. However, in his review of U.S. energy
policy in the 1990s, Joskow (2001) noted the oil price shock of 1990-1991 and
observed that both these energy industries experienced similar complex regulatory
pressures that included relaxation of price protective regulation and the introduction
of market-based competition, while environmental regulations imposed increasing
restrictions. Joskow (2001) also noted the plethora of State and Federal regulation
intended to foster competition subjected these industries to complex and extensive
compliance requirements. The decline in industry-level discretion of these two
industries is consistent with Joskow’s observations and suggests that regulators
“over- egged the pudding.”
FIGURE 2.6
SIC4s with Eight Industry-level Discretion Values (1990-1997)
1.50
1.00
0.50
1311
0.00
4911
3674
-0.50
3577
7372
-1.00
3661
7373
-1.50
3845
5812
-2.00
-2.50
-3.00
Examination of the industry-level discretion values for SIC4s with one or more
values missing from the possible set of eight values reveals three other SIC4s with
obviously low discretion: SIC4 4812 (Certified Air Transportation), SIC4 4923 (Gas
Transportation and Distribution), and SIC4 6211 (Security Brokers and Dealers) –
all highly regulated industries. Interestingly, SIC4 6211 had the highest value in
1990, and the greatest decline in discretion. The reason for the decline is open to
47
speculation but I suspect the introduction and growth of internet trading services was
a major contributing factor. Overall, the behaviour of the industry-level discretion
values over time is consistent with intuitive logic based on industry-task
environments, which supports claims of face validity.
Predictive Validity: Debt Avoidance
Examination of long-term debt use in the research database provided support
for the predictive validity of the industry-level discretion measure. Hambrick and
Finkelstein’s (1987) model suggests that increasing a firm’s long-term debt increases
external stakeholders’ interest and power, which reduces executive discretion. This
‘debt discipline’ hypothesis is a perennial topic in financial management studies of
agency theory and capital structure and control, and has produced both an extensive
and still growing body of empirical research and a substantial theoretical
sophistication (e.g. Bathala, Moon & Rao, 1994; Berkovitch & Israel, 1996). At the
industry level, this literature leads to the hypothesis:
Hypothesis 2.1
High discretion industries will use long-term debt less
than low discretion industries.
Long-term debt had a zero value in a large proportion of annual reports in the
research database. One annual report for a bank even had a negative value for longterm debt in its noncurrent liabilities (most people would call it a loan and treat it as
a noncurrent asset), but that annual report had been deleted along with the other
banks’ annual reports during the exploratory factor analysis process. For the middle
year in each five-year sample period (target year), the ratio of total long-term debt to
total assets and the proportion of firms with positive long-term debt for the 82 cases
that had twenty or more undifferentiated firms in the target year’s data were
calculated.
48
When matched to the industry-level discretion value, scatterplots (see Figures
2.7 and 2.8) revealed an outlier (SIC4 6324 Hospital and Medical Services Plans),
which is essentially a specialised insurance industry, an industry consisting of
institutional investors, not borrowers. Ignoring that outlier, the scatterplots
suggested all other low discretion industries have high levels of long-term debt, but
the industry-wide use of long-term debt rapidly declines when the industry-level
discretion is above -0.33.
FIGURE 2.7
Cases with >19 Undifferentiated Firms (82 Cases)
Total Long-term Debt/Total Total Assets vs Industry-level Discretion
Total Long-term Debt/Total Total Assets
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Industry-level Discretion
49
FIGURE 2.8
Cases with >19 Undifferentiated Firms (82 Cases)
Proportion of Firms with Long-term Debt Vs Industry-level Discretion
Proportion of Firms with Long-term Debt
1.2
1
0.8
0.6
0.4
0.2
0
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Industry-level Discretion
This proposition was tested using piecewise linear regression with a knot (K*)
of -0.33. The regression equation used was:
Yi = α1 + β1Xi + β2(Xi – K*)Di + ui ,
where
Yi = either:
Total long-term debt/Total total assets; or
Proportion of firms with long-term debt > 0;
Xi = Industry-level discretion; and
Di = 1 if Xi > K*
= 0 if Xi < K*,
β1 estimates the slope for the low discretion industries, β1 + β2 estimates the slope
for remainder of the cases (Gujarati, 1995: 520).
50
Table 2.9 supplies the results of both regressions. The regression explained
56% of the variance in Total long-term debt/Total total assets, and 59% of the
variance in proportion of firms with long-term debt. Neither variable had a
significant slope for the low discretion industries. The slope differential (β2) was
significant for both dependent variables (p-value = 0.00 for both regressions),
indicating there is a break in slope at (or about) the knot. This result is very strong
support for the hypothesis that high discretion industries use long-term debt less than
low discretion industries and indicates the predictive validity of the industry-level
discretion variable: it strongly suggests the measure is tapping into properties
associated with the construct.
TABLE 2.9
Results of Piecewise Regressions
Rsq
Total Long Term Debt/
Total Total Assets
0.57
Adjusted
Rsq
0.56
df
Regression
Residual
Proportion of Firms
with Long Term Debt
0.60
0.59
β1
β2
Regression
Residual
β1
β2
F
Sig
2
51.38
0.00
78
Unstandardised
Coefficient
-0.03
-0.13
2
Standardised
Coefficient
-0.26
-0.51
58.10
Sig
78
Unstandardised
Coefficient
-0.03
-0.32
Standardised
Coefficient
-0.12
-0.66
0.09
0.02
0.00
Sig
0.41
0.02
Predictive Validity: Discretionary Accounts Adjustments
Accrual accounting methods aspire to record ‘complete’ information about
economic events. For example, a sale of an item is recorded as a transaction that
results in a reduction of firm’s inventory and an increase in accounts receivable even
if the purchaser has not take the item from the store or paid for the item. There may
also be an additional entry in provision for bad debt. Similarly, the economic life of
51
newly-purchased property plant and equipment (PPE) is often unknown and any
depreciation schedule involves some discretionary choices.
The complete characteristics of an economic event are often unknown at the
time of the event and management has the capacity to exercise some discretion as to
how the unknown characteristics of events are reported (Caslan, 1992). The
discretionary use of accruals to increase or reduce apparent profitability has received
particular attention in recent accounting studies (e.g. Dechow, Richardson & Tuna,
2003). More generally, accounting standards include choices about how economic
events are recorded and management uses its available discretion when selecting
from the range of available accounting options.
As an example, recording a negative liability rather than a positive asset and
recording a negative asset rather than a positive liability reduces the reported Total
Assets (and Total Liabilities and Net Worth) of the firm. In some circumstances, this
may seem a desirable choice for the decision maker: perhaps it increases the
apparent return on assets. The reasons why management may choose to manipulate
how events are recorded in accounts range from reputable to nefarious and are not
the focus of the current thesis or the present test. However, in general, managers
with high executive discretion should have more capacity to exercise the range of
available discretionary accounting choices than managers with low executive
discretion. At an industry level, this leads to the hypothesis:
Hypothesis 2.2
High discretion industries will adjust their accounts
more than low discretion industries.
This hypothesis was tested by adjusting accounts in 81 cases where there were
twenty or more undifferentiated firms with annual accounts in the target year. (The
single SIC1 = 6 case was not included in this dataset.)
52
Detail of Accounts Adjustments
Annual account data in the Compact Disclosure database include assets,
liabilities, equity information, income statements, and cash flows.3 Many of the
available fields had both positive and negative values. For example, Other Longterm Liabilities and even Shareholder Equity had some negative values.
Negative values were removed by splitting any accounting field with
negative values into two columns and sorting the data into positive and negative
values. Each negative value was multiplied by minus one and called it a positive
value for the negative of the original accounting field. For example, if a firm’s
annual accounts had a negative value for ‘Other Long-term Liabilities’, that value
became a positive value in the new column ‘Negative Other Long-term Liabilities’
and the firm had a zero value in the ‘Positive Other Long-term Liabilities’ column.
Similarly, a negative profit became a positive loss, and so on.
As my observation on the bank with the negative long term debt illustrated,
in double entry bookkeeping, a negative liability is a positive asset and a negative
asset is a positive liability. Similarly, a negative expense is a positive income. By
moving the absolute values of the columns of negative values to the opposite side of
the balance sheet, a new set of balanced accounts was created where all values were
non-negative. Thus, negative liabilities columns became positive assets columns,
negative income columns became positive expanse columns, and so forth. In some
cases, the newly located column was a match or near match with an already existing
column. For example, ‘Negative Deferred Charges’ (originally an asset) and
‘Deferred Charges’ (a liability) were essentially capturing the same accounting
3
Accounting standards for recording cash flows changed during the sampling period and
those data were not used in this thesis.
53
construct. In such cases, the columns were summed to produce a new field (e.g.
New Deferred Charges (a liability)).
This treatment of the accounting data produced three sets of balanced accounts:
assets-liabilities; stakeholder equities-held equities; and income and loss-expenses
and profit. This treatment produced new values for Total Current Assets, Total
Noncurrent Assets, Total Assets, Total Current Liabilities, Total Noncurrent
Liabilities, and Total Liabilities and Net Worth. Four additional totals summed the
other two sets of balanced accounts: Stakeholder Equities, Held Equities, Total
Income and Loss, and Total Expenses and Profit. Tables 2.10 to 2.13 supply details
of the three new balance sheets of only positive values.
I calculated the percentage difference between positively corrected and
published values for Current Assets, Noncurrent Assets, Total Assets, Current
Liabilities, Noncurrent Liabilities, and Total Liabilities and Net Worth for each
annual report. Using one sample t tests, cases where the percentage difference was
significantly different from zero were identified. For each case with a significant
difference (p-value <0.05) from zero, the effect size (d) was calculated by dividing
the mean difference of the case sample by the sample’s standard deviation (Green,
Salkind & Akey, 2000).
The six panels of Figure 2.9 plot the Effect Size and Industry-level Discretion
values for all cases where account adjustments were significant (p-value < 0.05).
Three features are notable: 1) no significant accounts adjustments occurred in cases
where the standardised industry-level discretion value was less than minus one (-1);
2) the effect size was typically medium (less than 0.8 and greater than 0.2) for
current accounts and non current accounts, with a few small effect sizes in Total
Assets and Total Liabilities and Net Worth; and 3) current accounts appear to be
54
TABLE 2.10
New Positive Assets Sheet
Code
CH
MS
RE
IV
NR
+OC
-IC
NewOC
NewCA
PF
IA
+DK
IW
+DJ
-LL
NC
NewONC
NewNCA
-ML
-SE
NewTA
Cash
Marketable securities
Receivables
Inventory
Notes Receivable
Positive Other Current Assets and
Prepaid Expenses
Negative Income Taxes
New Other Current Assets
New Total Current Assets
Net Property Plant and Equipment
Investments and Advances to Subsidiaries
Positive Deferred Charges
Intangibles
Positive Deposits and Other Assets
Negative Other Long-term
Liabilities
Other Noncurrent Assets
New Other Noncurrent Assets
New Total Noncurrent
Assets
Negative Minority Interest
Negative Shareholder Equity
New Total Assets
55
TABLE 2.11
New Positive Liabilities Table
Code
NP
AP
CD
XL
AE
+IC
OL
-OC
Notes Payable
Accounts Payable
Current Long-term Debt
Current Portion of Capital Leases
Accrued Expenses
Positive Income Taxes
Other Current Liabilities
Negative Other Current
Assets & Prepaid Expenses
NewOL
NewLI
MG
DF
-DK
NewDF
CV
LD
NL
+LL
-DJ
NewLL
NewTL
+ML
+SE
NewLN
New Other Current Liabilities
New Total Current Liabilities
Mortgages
Deferred Charges
Negative Deferred Charges
New Deferred Charges
Convertible Debt
Long-term Debt
Noncurrent Capital Leases
Positive Other Long-term
Liabilities
Negative Deposits & Other
Assets
New Other Long-term Liabilities
New Total Noncurrent Liabilities
Positive Minority Interest
Positive Shareholder Equity
New Total Liabilities & Net Worth
56
TABLE 2.12
New Stakeholder Equities and Held Equities Table
Code
+SE
-SR
-RT
-RL
+TK
-SN
TEL
-SE
+SR
+RT
+RL
-TK
PS
+SN
TEA
Positive Shareholder Equity
Negative Capital Surplus
Negative Retained Earnings
Negative Other Liabilities
Positive Treasury Stock
Negative Common Stock Net
Total Stakeholder Equities
Negative Shareholder Equity
Positive Capital Surplus
Positive Retained Earnings
Positive Other Liabilities
Negative Treasury Stock
Preferred Stock
Positive Common Stock Net
Total Held Equities
more commonly adjusted that non current accounts.
This result raises the question whether the absence of significant accounts
adjustments for cases where the industry-level discretion is less than minus one is
significant. Figure 2.10 graphically illustrates the distribution of the number of cases
grouped by 0.1 increments of industry-level discretion, and the average number of
undifferentiated firms in the target year of each case in each increment. Two
questions must be addressed before the absence of significant accounts adjustments
in low discretion industries can be regarded as significant: 1) is the result an artefact
of the lower number of firms in low industry-level discretion cases? and 2) is the
result an artefact of the low number of cases with low industry-level discretion?
The first question is easiest addressed by looking at the six cases with only 20
firms in their target year. Four of these cases had at least one significant difference
between positively corrected and published accounts. This is a clear indication that
the sample size is not a critical factor in this analysis. The second question is
addressed by calculating the probability that, given the proportions of cases above
57
TABLE 2.13
New Positive Income and Loss, and Expenses and Profit Table
Code
SA
-DA
Net Sales
Negative Depreciation and
Amortization
Positive Non-operating Income
Negative Interest Expense
Negative Provision for Income
Taxes
Negative Minority Interest
Positive Investment Gains
Positive Other Income
Positive Extraordinary Items
Negative Net Income (i.e.
Positive Loss)
+NO
-IF
-PT
-MI
+IL
+OI
+XI
-NI
TI&L
CG
RD
SY
OC
+DA
-NO
+IF
+PT
+MI
-IL
-OI
-XI
+NI
TE&P
Total Income and
Loss
Cost of Goods
Research and
Development
Selling, General and
Administrative Expenses
Operating Costs
Positive Depreciation and
Amortization
Negative Non-operating Income
Positive Interest Expense
Positive Provision for Income
Taxes
Positive Minority Interest
Negative Investment Gains
Negative Other Income
Negative Extraordinary Items
Positive Net Income
Total Expenses and
Profit
and below the minus one value for standardised industry-level discretion, a random
selection of the total number of cases would all belong to the ‘above minus one’
group. Table 2.13 details that calculation and demonstrates that the distribution is
significant for five of the aggregate accounts fields, with only noncurrent liabilities
having a p-value > 0.05. It is reasonable to conclude that the hypothesis that high
discretion industries adjust their accounts more than low discretion industries is
58
FIGURE 2.9
Accounts Adjustment (Cases where Difference from Zero had p–value < 0.05)
supported. This result supplies additional support for the construct and validity of
the measure for industry-level discretion that has been developed in this chapter.
59
FIGURE 2.10
Number of Cases and Average Number of Firms per Case
60
TABLE 2.14
Calculating Probability of Result of Accounts Adjustment Test
All
Number of
Cases
where
Industrylevel
Discretion <
-1
Number of
Cases
where
Industrylevel
Discretion >
-1
Totals
13
Number of Cases where Percentage Difference between Positive and
Published Accounts was Significantly Different From Zero
(p-value < 0.05)
Current Noncurrent Total
Current
Noncurrent Total
Assets
Assets
Assets Liabilities Liabilities
Liabilities
0
0
0
0
0
0
68
33
29
20
38
12
18
81
33
29
20
38
12
18
Expected
Proportions
Industry0.16
level
Discretion <
-1
Industry0.84
level
Discretion >
-1
Probability of result
Current
Assets
5.3
Noncurrent
Assets
4.65
Expected Values
Total
Current
Assets Liabilities
3.21
6.1
Noncurrent
Liabilities
1.93
Total
Liabilities
2.89
27.7
24.35
16.79
31.9
10.07
15.11
0.00
0.01
0.03
0.00
0.12
0.04
CONCLUSION TO CHAPTER TWO
Industry-level discretion is an under-utilised construct with substantial
theoretical possibilities. Lack of contemporaneous quantitative measures of
industry-level discretion typically limits use of the construct to mainly qualitative
research contexts (Hambrick & Abrahamson, 1995). After testing the robustness of
existing quantitative measures and their association with attentional homogeneity,
the three measures of attentional homogeneity (lexical density, lexical commonality,
and high extra usage) were subjected to exploratory factor analysis. Large sample
analysis identified a slight change to the way lexical density should be calculated.
61
Validity checks demonstrate that the industry-level discretion measure behaves as
expected in a general sense and has predictive validity consistent with expectations
based on discretion theory. In particular, the values 1) have substantial explanatory
power for long-term debt avoidance; and 2) can be used to identify industries where
discretionary accounts adjustment is unlikely.
I calculated 116 quantitative industry-level discretion ratings for a range of
industries for years 1990 to 1997. Further application of the method used would
supply contemporaneous industry-level discretion values for most industries and
periods if appropriate numbers of President’s Letters were collected. In Study Two I
begin to use the values for industry-level discretion to test the hypothised association
between industry-level discretion and strategic variety.
62
CHAPTER THREE
STUDY TWO
INTRODUCTION
“Strategic variety,” also called “industry variety,” refers to the mix of
competitive strategies in an industry (Miles, Snow & Sharfman, 1993). Strategic
variety plays a central role in organisational cybernetics theory, economic theories of
the firm, organisational ecology theory, and strategic management theory (Miles,
Snow & Sharfman, 1993). Strategic variety is theoretically linked to the industry life
cycle processes and to the balance of interfirm benefits and costs arising from
competitive behaviour (Miles, Snow & Sharfman, 1993). Use of the alternative
terms “strategic homogeneity” and “strategic heterogeneity” (Abrahamson &
Hambrick, 1994; Dooley, Fowler & Miller, 1996), identify the same construct but
anchor it at different ends of an implied strategic variety spectrum. The term
‘strategic variety’ avoids possible debate about Blau’s (1977) distinction between
heterogeneity and inequality, and is the preferred expression in this thesis.
Organisational cybernetics theory applies Ashby’s (1956) original law of
requisite variety (the larger the variety of actions available to a control system, the
larger the variety of perturbations it is able to compensate) in its strongest sense to
mean that the variety in an open system must be equal to or larger than the variety of
the stimuli it encounters. When applied to organisations or industries, this is taken to
imply that an organisation or an industry must have at least as much complexity or
variety as its external environment. While this stronger interpretation can be
criticised as an overstatement of Ashby’s original formulation, the gist of the
63
argument – that organisations and industries in complex environments should be
more complex and have more variety than organisations and industries in simple
environments – is widely accepted as having sound practical implications. At the
industry level, strategic variety is seen as a healthy characteristic than gives an
industry the capacity to respond to environmental disturbances that might otherwise
cause the industry to decline (Miles, Snow & Sharfman, 1993). Such a view is
compatible with Schumpeter’s (1976) observation that a system that, at any point in
time uses all its capacity to best advantage may, in the long run, be inferior to a
system that runs suboptimally at a point in time but gains a long term advantage by
so doing. Strategic variety may produce localised inefficiencies that have long term
advantages.
Basic economic theories of the firm suggest that perfect competition will erode
competitive advantage and reduce any existing variety; however, even when limited
rationality and communication are included as factors, game theory suggests that
repeated iterations should see the addition of new competitive dimensions as firms
seek to differentiate and gain competitive advantage (Brandenburger & Nalebuff,
1995; Camerer, 1991; Parkhe, 1993; Radner, 1997; Saloner, 1991; Taylor, 1976; Wu
& Axelrod, 1995). Thus, industries evolve and deliberate innovation that seeks to
undermine the existing bases and develop new bases for competitive advantage
results in ongoing creative destruction that generates strategic variety in industries in
capitalist economies (Nielson & Winter, 1982; Schumpeter, 1976). Comprehensive
economic theories of the firm that combine erosion of competitive advantage by
competitive imitation and the creation of competitive advantage by competitive
innovation appear to have application in contemporary hyper-competitive economies
(D'Aveni & Gunther, 1994; Fiegenbaum, Thomas & Ming-Je, 2001).
64
The organisational ecology approach borrows from biological ecology theory
and suggests that organisations occupy niches in the economy. Some firms, like
some organisms, are highly specialised while others are generalists. A range of
successful strategies can coexist in any industry and typical industries will consist of
a population of firms which occupy a variety of niches and have appropriate
strategies (Boeker, 1991; Hannan & Freeman, 1977). The mechanisms of change
include positive and negative feedback arising from interaction with the environment
that induce a tendency towards complexity and dynamic equilibrium in adaptive
systems (Stacey, 1995).
Strategic management theory suggests that firms will seek to differentiate
themselves to gain competitive advantage. However, some strategies are better than
others and firms in an industry will seek to occupy the most desirable strategic space
and to limit the number of firms that share its strategic space. This leads to processes
of differentiation and imitation that produce a level of strategic variety in any
industry. In some industries, the ‘dominant logic’ or ‘industry recipe’ (Prahalad &
Bettis, 1986; Spender, 1989) will suggest a limited range of strategic options and
firms can be grouped by similarities in their strategies (Fiegenbaum & Thomas,
1995; Nair & Suresh, 2001; Porter, 1979; Reger & Huff, 1993; Thomas &
Venkatraman, 1988). Differing strategic choices by firms’ managers will, to some
degree, reflect managers’ individual preferences (Child, 1972) and strike differing
balances between the need for social legitimacy obtained by conforming to industry
norms and the need to differentiate the firm to gain competitive advantage
(Deephouse, 1999). These path dependent processes invariably produce strategic
variety in industries, but the level of that variety in different industries is only lightly
65
discussed in strategic management theory perhaps because of difficulties in
measurement of strategic variety.
Miles, Snow and Sharfman’s (1993: 165-166) thoughtful and concise review of
the role of strategic variety in organisational theory, which provided the frame of the
above analysis, concludes that “From a theory-building perspective, variety appears
to be a concept of central importance…(yet), with the possible exception of
ecological analysis, variety has not been used in a systematic way to explain firm
behaviour and performance.” They go on to suggest that variety generates interfirm
benefits that contribute to industry performance and the level of variety in an
industry should be a consideration when formulating industry policy and firm level
strategy.
This second study tests for an association between industry-level discretion and
strategic variety. As far as is practicable, this study is a large scale replication of an
unpublished pilot study (Abrahamson & Hambrick, 1994), which appears to offer the
best approach currently available for measuring strategic variety in a pan-industry
study. I examine all three currently available methods for measuring strategic variety
developed in attempts to compare strategic variety between different industries and
identify their various strengths and weaknesses. That examination, and the
discussion of the results, includes in-depth analysis of the use of coefficients of
variance (CoVs) to measure variety. Some existing advice on using CoVs is refined
and additional advice on use of CoVs of samples is developed.
Consistent with the conservative statistical tests applied to the panel data in the
first study, the hypothesis test uses point estimates and 95% confidence intervals for
strategic variety. The results suggest that the range of strategic variety is greater in
66
high discretion industries than in low discretion industries. However, the
compromises necessary to measure strategic variety in this study leave room for
doubt about the result and indicate the need for an alternative method to measure
strategic variety. The development and use of an alternative method is reported in
the third study of this thesis.
EXISTING MEASURES OF STRATEGIC VARIETY
In spite of the importance of the construct, there is no generally accepted
method of measurement of industry-level strategic variety to enable inter-industry
comparisons (Miles, Snow & Sharfman, 1993). Two relatively recent papers attempt
to address this issue and describe alternative ways of measuring strategic variety
(Dooley, Fowler & Miller, 1996; Miles, Snow & Sharfman, 1993). Miles, Snow, and
Sharfman used characteristics of strategic group maps to measure strategic variety.
Dooley, Fowler, and Miller summed CoVs of selected strategic indicator variables to
measure strategic variety. Abrahamson and Hambrick (1994) summed CoVs of
natural logs of a different set of strategic indicator variables. The following
examination of the three approaches reveals issues that influenced the method
adopted in this second study, which is an attempt to replicate Abrahamson and
Hambrick’s approach.
Distances and Patterns in Strategic Group Maps
Miles, Snow and Sharfman (1993) sampled twelve manufacturing industries
where the principal income of the majority of firms in the industry was from that
industry. Their sampling period was 1983-1987. Their industry samples included
the firms that accounted for the top 70% of sales in the industry. Their firm-level
data were five-year averages of three accounting ratios (net Property Plant and
67
Equipment(PPE)/Number of Employees, Advertising Expense/Sales, Research and
Development (R&D) Expense/Sales) that measured firms’ production, marketing,
and research and development effort, three key competitive factors (Khandwalla,
1981). Khandwalla (1981) provided a more comprehensive list of potential
indicators of competitive behaviour. A pre-test using a separate sample of firms
indicated the three selected ratios were good indicators of the key factor and “clearly
discriminated between strategic groups” (Miles, Snow & Sharfman, 1993: 169).
Hierarchical cluster analysis (Ward’s minimum variance method) of the
industry-grouped firm-level five-year averaged ratios produced clusters of the firms
in each industry. Miles, Snow, and Sharfman (1993) determined cluster numbers by
examining tree diagrams and ensuring that the final clusters within each industry
were significantly different on at least one of the three input variables. The small
number of industries precluded assumptions of normal distribution when using the
characteristics of resulting industry clusters to measure industry-level variety.
Consequently, Miles, Snow, and Sharfman (1993) used the averages of three
different ranks of spatial characteristics to rank the industries in their sample in
descending order of strategic variety. Their method used 1) numbers of clusters and
distances between clusters; 2) the sum of the differences between clusters’
standardised means of each of the three variables; and 3) adding one (1) to the mean
scores of each cluster for each variable, taking the natural log of that value, and
ranking each industry by the sum of each variable’s resulting natural log, and then
averaging those single variable rankings to rank the industries using each variable
separately.
This method of measuring strategic variety is complicated, which is not
necessarily a criticism. Indeed, the use of multiple approaches has some appeal as
68
the final measure captures a range of industry characteristics that collectively have
bearing on the construct. However, the method is not intuitive and it is open to
criticism that the resulting ranking is an artefact of the method. Moreover, it is
limited to industries where the major source of income for the large majority of firms
in the industry is the industry under investigation. Miles, Snow, and Sharfman
(1993) do not explain how such industries can be rigorously identified. Further,
reliance on the selected competition variables automatically excludes industries,
especially non-manufacturing industries, where PPE, Advertising, or R&D are not
standard accounting terms. These constraints prevent widespread application of the
measurement method.4
In their comments on Miles, Snow, and Sharfman (1993), Dooley, Fowler, and
Miller (1996) somewhat inappropriately use Lehmann’s (1989) rule of thumb that
30-50 cases are needed for reliable clusters in marketing survey data and argue that
clustering with fewer cases is intrinsically flawed in general. While the low number
of firms in four of the twelve industries sampled by Miles, Snow, and Sharfman
(1993) (Major Home Appliances: eight firms; Tires and Tubes: seven firms; Farm
Machinery and Equipment: seven firms; and Cigarette Manufacturers: six firms) is a
potential criticism of the paper, this does not necessarily mean that the method is
only applicable when the number of firms in the industry sample is 30 to 50 times the
number of clusters found. The distributions and covariance properties of the input
variables also influence the required sample size in cluster analysis techniques.
Lehmann’s rule of thumb, offered as advice for marketing research, reflects that
4
Wu, Wu, and Xu (2004) added to Miles, Snow, and Sharfman’s (1993) list of variables and
adopted this general approach of cluster map feature analysis. Unfortunately, their article is
only available in Chinese pictogram format and a full appreciation of their work must await
translation.
69
discipline’s practice of gathering data on moderate to large numbers of variables. It
is not automatically applicable to cluster analysis using small numbers of variables.
Summing Coefficients of Variance
Dooley, Fowler, and Miller (1996) also sampled manufacturing industries.
They used the same three competition variables, the same sampling years (19831987) and the same averaging of the firms’ ratios over five years as Miles, Snow, and
Sharfman (1993). They sampled 613 firms from 61 SIC4 industries. All industries
sampled had at least four firms listed on the New York or American stock exchanges.
Dooley, Fowler, and Miller (1996) summed the CoVs of each of the three averaged
competition variables for each industry, producing a measure of strategic variety for
each industry.
As evidence that CoVs can be used when measuring heterogeneity, Dooley,
Fowler, and Miller (1996) cited Bantel and Jackson (1989) and Murray (1989), two
papers that used CoVs to measure heterogeneity in top management teams. In both
the cited papers, the CoV measures described variation of characteristics of
populations of members of top management teams. Dooley, Fowler, and Miller
(1996) used CoVs of small samples of potentially medium sized populations. The
difference is important.
Examination of the properties of CoVs suggests use of small samples requires
extreme caution. Two properties of CoVs in general have been identified as
important considerations when measuring and comparing inequalities: CoVs are
scale invariant (i.e. CoV(X) = CoV (kX), k ≠ 0), but CoVs vary with changes in
points of origins of scales (i.e. CoV(X) ≠ CoV(X+k), k ≠ 0). The latter property is
70
addressed by requiring the variable be a natural ratio scale, that is, have a naturally
fixed zero point.
An additional rule developed in Allison’s (1978) seminal paper on measures of
inequality is that the ratio scale should have only positive values. This addresses
problems that arise in mixed sign samples when the mean approaches or, even worse,
equals zero. Allison developed this rule in a discussion of inequality variables that,
by their nature, always had some positive values. The more general rule is that all
values in the ratio scale are the same sign. Multiplication by minus one of all values
in a same sign data set will not affect the absolute values of the mean, the standard
deviation, or the CoV. The requirement of same-sign, natural-zero ratio scales
applies to CoVs from both censuses and sample data.
Properties specific to CoVs from samples arise from sampling variance. The
traditional approach to estimation of the standard error of an estimated parameter
relies on a parametric model of the distribution. If a reasonable and mathematically
tractable parametric model can be assumed, an empirical analog of a specific formula
that theoretically measures or approximates the accuracy measure is used to estimate
sampling related uncertainty (Shao & Tu, 1995). Such an assumption was used in
Study One when estimating the confidence intervals of the panel estimates of
industry-level discretion. The standard error of the mean, a relatively simple
function, is typically derived from the standard deviation of the sample and the sizes
of the sample and the population (Shao & Tu, 1995; Shao, 1976; Smithson, 2003).
Calculating the standard error of a CoV using traditional statistical approaches
requires the standard error of the standard deviation as well as the standard error of
the mean. The distributional properties of the population, the sample size and the
population size influence the standard error of the standard deviation. The lower and
71
upper confidence intervals for a CoV of a sample are SDL/MeanU and SDU/MeanL,
where ‘L’ and ‘U’ denote the lower and upper bounds of the confidence levels, which
are, in turn, determined by the required level of confidence. Traditional statistical
approaches for estimating the standard error of the standard deviation require sample
sizes of at least 40 cases if a normal distribution is assumed, and, depending on the
kurtosis, samples of between 100 and 600 cases are required if a gamma distribution
is assumed (Klein, 1990; Schulz, 1976).
CoVs have a lower bound of zero. When using traditional parametric
estimation methods, if the lower bound of the mean is less than or equal to zero, the
CoV cannot be used (assuming all input data values are, or have been made,
positive). Equally, when the lower bound of the standard deviation is negative, the
CoV cannot be used. Similarly, if the standard deviation is orders of magnitude
greater than the mean, the CoV will extrapolate towards infinity and should not be
used because it is unstable and misleading. Fortunately, non-parametric bootstrap
techniques that always estimate positive CoVs and test for unacceptable bias in the
data are available (Efron & Tibshirani, 1993). Even then, however, the assumption
of the bootstrap, that the sample is representative of the population, implies a
reasonable sample size is required.
Generally, for a given population, the probability that CoV should not be used
increases as the sample size decreases. Dooley, Fowler, and Miller’s (1996)
samples, which are as small as four firms per industry, appear too small for the
calculation of usable CoVs. This is partly attributable to the averaging of five annual
values before calculating industry CoVs for each variable. Miles, Snow, and
Sharfman’s (1993) averaged their annual accounting data to reduce noise arising
from the snapshot effect associated with cut-off dates of annual accounts. Dooley,
72
Fowler, and Miller’s (1996) averaging reflects their original intent to duplicate Miles,
Snow, and Sharfman’s (1993) method for measuring strategic variety.
The existence and identification of strategic groups in an industry is not a
prerequisite for the existence and measurement of strategic variety. While the
persistence of similarities within groups of firms in an industry suggests meaningful
ways to measure strategic variety, the construct is measurable without first reducing
variation by clustering of firms or averaging values of input variables. Had Dooley,
Fowler, and Miller (1996) not averaged each firm’s annual ratios, they would have
had minimum sample sizes of twenty. This would have increased the chances that
some of the industries sampled produced useful CoVs on all three variables.
Additionally, Dooley, Fowler, and Miller’s (1996) sample included industries that do
not use either or both advertising expense and R&D expense in their accounting
procedures. They gave these variables CoVs of zero when this occurred in their
sample. A better treatment would have been deleting all industries where the
required accounting fields were not used.
Summing the Coefficient of Variation of Natural Logs
Abrahamson and Hambrick’s (1994) unpublished research was not limited to
manufacturing industries. They used five of the six variables identified in
Finkelstein and Hambrick (1990) as potentially controllable by managers and
strategically important:
•
PPE Newness (Net PPE/Gross PPE);
•
Capital Intensity (Net PPE/Number of Employees);
•
Receivables Turnover (Sales/Accounts Receivables);
•
Current Ratio (Current Assets/Current Liabilities); and
73
•
Debt to Equity (Long-term Debt/Shareholders Equity).
The absence of Advertising- and R&D-based variables suggests this five
variable list is more suitable to a pan-industry study. They used the inverse of the
average of annual averages of CoVs of the natural logs of all five indicators to create
a single measure of strategic homogeneity for the sample period 1985-1989.
Using CoVs of natural logs of ratios raises methodological issues. Many of the
values of accounting ratios used were less than e, indeed they were often less than
one. Logs of numbers whose value is less than the log base are negative, which can
result in breaking Allison’s same-sign rule for CoVs. Pre-multiplication by a
constant before getting the log (e.g. expressing the ratio as a percentage) may result
in only positive natural log values, but this is the equivalent of adding a constant to
all values before calculating the CoV. In other words, the natural zero of the data is
abandoned, which is not acceptable. Use of natural logs of ratios (or ratios expressed
as a percentage) suggests a possible cause of Abrahamson and Hambrick’s (1994)
counter-intuitive findings described in the next section.
THEORY AND HYPOTHESIS
Strategic decisions generally involve complex unstructured problems where
uncertainty abounds (Gordon, Miller, Mintzberg, (U.S.) & Canada, 1975; Gorry &
Scott Morton, 1971; Simon, 1960). In such circumstances, techno-economic
rationality is inadequate and behavioural theory (Cyert & March, 1992; March &
Olsen, 1976; March & Simon, 1958) suggests decision makers’ choices will be
strongly influenced by their biases, values and subjective foci, perceptions and
interpretations of equivocal environmental and organisational stimuli (Hambrick &
Mason, 1984; Weick, 1979, 1995). In such circumstances, the socio-political nature
74
of executive discretion suggests that increasing executive discretion means
executives in firms will not only have a greater range of possible strategic actions
they could pursue, it also means they will tend to identify different sets of possible
actions and make different choices when making binding strategic decisions. At an
industry level, this leads to the hypothesis:
Hypothesis 3.1
The greater the industry-level discretion, the greater
the strategic variety.
As part of the research effort that produced their 1997 paper, Abrahamson and
Hambrick (1994) examined a similar hypothesis: “The greater discretion in an
industry, the less homogenous the strategic profiles of organizations in an industry.”
Their industry sample was the same as that used in their attentional homogeneity
study and their finding did not support the hypothesis. The results indicated
industry-level discretion was negatively associated with strategic heterogeneity. In
other words, increasing industry-level discretion was associated with reduced
strategic variety – the counter intuitive result that triggered the present research. The
present research uses a larger and more recent sample of industries.
METHODS
The research database described in Study One was used. The Compact
Disclosure database has data that allow the calculation of all five ratios used by
Abrahamson and Hambrick (1994). The strong association between industry-level
discretion and long-term debt avoidance demonstrated in the validity tests in Study
One precluded using the debt to equity ratio when measuring strategic variety. For
the other four variables used by Abrahamson and Hambrick (1994), the necessary
data were collected for all undifferentiated firms in the research database belonging
75
to cases with values for industry-level discretion. All annual reports (927) where the
denominator of a ratio was zero or the value of the ratio was negative were deleted.
This reduced the number of cases with sufficient firms to calculate stable CoVs. In
many cases, the number of annual reports in a sample year was too small to calculate
reliable CoVs from annual data. For example SIC4 5691 had 40 annual reports in
Period 1, but only seven annual reports in 1988. To ensure enough cases were
available to test the hypothesis, it was necessary to calculate CoVs for each ratio at
the five-year sample period, rather than calculating five annual CoVs and averaging
the five resulting values to get an average CoV for individual variables.
CoVs and their 95% confidence intervals for each of the remaining four
strategic variety indicator variables in each case were calculated with 5000 resamples
using the bias corrected and accelerated (BCa) and the jackknife-after-bootstrap
features of S-PLUS 6.2 for Windows. Bootstraping methods produce estimates by
repeated random, independent resampling with replacement from the sample data.
Bootstrap methods for estimating confidence intervals include the bootstrap-t,
percentile, and bias-corrected and accelerated (BCa) methods (Efron & Tibshirani,
1993).
In effect, the bootstrap-t method builds a table similar in function to the
common Student’s t table but uses the sample data rather than the distribution
assumed to develop Student’s t table. Confidence intervals are then determined
using z scores derived from the data-specific table. This method works best for
location statistics, that is, statistics like the mean where increasing the values of input
data by a constant produces a result also increased by the constant (Efron &
Tibshirani, 1993). Based on the analysis of the characteristics of CoVs, the
bootstrap-t method was inappropriate for the present study.
76
The bootstrap percentile method directly estimates confidence intervals from
the distribution of estimates of a parameter produced by the resamples from the
sample data. The values of the 95% confidence interval, for example, are the values
of the 2.5 percentile and the 97.5 percentile of the estimates of the parameter
produced by repeated resampling. This method is “range-preserving” in the sense
that if, by its nature, a parameter has a bound (such as the lower bound of a CoV), all
values of the parameter from all resamples will be within the bound. Consequently,
the values of all percentiles are within the bounds of the parameter (Efron &
Tibshirani, 1993).
The bootstrap percentile method is sensitive to the characteristics of the tails of
the distribution. Ideally, the distribution of the resample parameter estimates has a
normal distribution with a central value identical to the original parameter estimate
of the sample. If a few cases dominate the input data, the resulting biased or nonnormal distribution of the resample estimates can shorten or lengthen the tails and
produce inefficient estimates of the confidence intervals. The BCa method adjusts
the percentile used to estimate the confidence intervals in a way that reduces the
errors associated with bias and non-normal distributions of resample parameter
estimates. The jackknife-after-bootstrap is a technique that permits the calculation of
the means and standard errors of the confidence interval bounds to check their
reliability (Efron & Tibshirani, 1993).
I used 5000 resamples from each case to identify all cases with usable CoVs in
all four variables (i.e. the five-year data distribution was not too biased to produce
reliable CoVs from the available sample). The original firm-level data for those
cases were subjected to BCa and jackknife-after-bootstrap using 20000 resamples to
calculate the mean and 95% confidence intervals for the average of the four CoVs.
77
Cases were retained where the input data produced a reliable average of the four
CoVs and the 95% confidence interval of that average. The case deletion rules
discussed in Appendix of this thesis were then applied. Even when using five-year
sample periods, only 43 cases (i.e. industry-five year sample period combinations)
with values for industry-level discretion had sufficient data to produce usable point
estimates and confidence intervals of this measure of strategic variety. They were
limited to SIC1s 3 (Manufacturing Durables), 4 (Transportation and Infrastructure),
and 7 (Services). Table 3.1 lists the SIC4s retained and their industry name. Table
3.2 provides industry-level discretion and strategic variety values of the final sample
used to test the correlation implied by the hypothesis.
ANALYSIS
The point values of the average of the four CoVs were significantly correlated
with industry-level discretion (Pearson r = 0.37, p-value = 0.02). However, the
correlation captured only 13.5% of the variance. As the scatterplot in Figure 3.1
TABLE 3.1
Final SIC4s Used to Test the Hypothesis, and Number of Cases for Each
SIC4
3577
3661
3663
3674
3845
4213
4813
4923
4931
7363
7372
7373
Total
Industry
Computer Peripheral Equipment, nec*
Telephone and Telegraph Apparatus
Radio and TV Communications Equipment
Semiconductors and Related Devices
Electro-medical Equipment
Trucking, except Local
Telephone Communications, except Radio
Gas Transmission and Distribution
Electric and Other Services Combined
Help Supply Services
Prepackaged Software
Computer Integrated Systems Design
Number of Cases
7
2
4
7
6
1
1
3
1
1
8
2
43
*nec means ‘not elsewhere classified’
78
TABLE 3.2
Cases and Values of Industry-level Discretion and Strategic Variety
Case
3577P1
3577P2
3577P4
3577P5
3577P6
3577P7
3577P8
3661P1
3661P8
3663P5
3663P6
3663P7
3663P8
3674P1
3674P2
3674P3
3674P4
3674P5
3674P7
3674P8
3845P3
3845P4
3845P5
3845P6
3845P7
3845P8
4213P3
4813P8
4923P1
4923P4
4923P5
4931P1
7363P5
7372P1
7372P2
7372P3
7372P4
7372P5
7372P6
7372P7
7372P8
7373P6
7373P7
Industrylevel
Discretion
0.45
0.40
0.47
0.67
0.75
0.80
0.86
0.26
0.22
0.91
1.18
0.76
0.92
0.16
0.29
0.54
0.72
0.76
0.71
0.57
0.95
0.99
1.04
0.95
0.84
0.78
0.36
-0.05
-2.52
-2.13
-1.53
-1.25
-0.55
0.87
0.79
0.67
0.74
0.88
1.08
1.03
1.15
0.82
0.66
No. of Annual
Reports used
to calculate
Strategic
Variety
58
68
65
68
68
66
58
50
75
52
52
72
72
78
92
113
139
157
157
148
81
93
99
99
85
76
39
29
43
46
38
45
37
126
131
155
201
258
258
332
346
52
66
Strategic
Variety
(Mean of 4
CoVs)
2.17
2.12
2.17
2.04
2.01
2.03
2.14
3.07
1.98
3.03
2.90
2.97
2.84
2.06
2.20
2.28
2.31
2.44
2.36
3.51
3.23
3.21
3.28
3.17
3.16
2.87
1.52
2.77
1.92
2.10
2.09
2.57
3.31
4.11
3.95
3.93
3.85
3.72
2.95
2.92
3.01
2.36
2.29
Strategic
Variety Lower
95%
Confidence
Boundary
2.01
1.99
1.95
1.84
1.81
1.83
1.94
2.29
1.86
2.55
2.45
2.49
2.41
1.77
1.90
2.03
2.07
2.22
2.18
2.32
2.78
2.72
2.83
2.63
2.59
2.37
1.29
2.61
1.77
1.95
1.95
1.40
2.80
2.64
2.62
2.86
2.83
2.79
2.72
2.71
2.80
2.16
2.10
Strategic
Variety Upper
95%
Confidence
Boundary
2.39
2.33
2.66
2.50
2.44
2.48
2.56
4.15
2.27
3.85
3.69
3.77
3.65
2.65
2.63
2.63
2.68
2.85
2.72
5.11
3.80
3.77
3.90
3.73
3.75
3.72
2.02
3.09
2.19
2.37
2.35
3.68
4.28
5.63
5.46
5.37
5.34
5.19
3.37
3.35
3.40
2.72
2.63
Range of
Confidence
Interval/Mean
(%)
17%
16%
33%
32%
31%
32%
29%
61%
21%
43%
43%
43%
44%
43%
33%
26%
26%
26%
23%
79%
32%
33%
33%
35%
37%
47%
48%
17%
22%
20%
19%
89%
45%
73%
72%
64%
65%
64%
22%
22%
20%
24%
23%
illustrates, some cases had relatively high industry-level discretion and very low
averages of the four CoVs. Further, as Figure 3.2 illustrates, plotting the confidence
intervals for the average of the four CoVs creates a less clear picture and suggests the
correlation captures even less of the possible variance.
Overall, the result suggests that the range of strategic variety increases with
industry-level discretion. In other words, high discretion industries can display high
79
and low strategic variety, while low discretion industries tend to display relatively
low strategic variety. The result does not indicate a general monotonic trend. The
FIGURE 3.1
Industry-level Discretion vs Strategic Variety (Average of 4 CoVs)
4.4
4.2
4.0
3.8
3.6
Strategic Variety
3.4
3.2
3.0
2.8
2.6
2.4
2.2
2.0
1.8
1.6
1.4
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Industry-level Discretion
FIGURE 3.2
Industry-level Discretion vs Strategic Variety, with 95% Confidence Intervals
Strategic Variety
5
4
3
2
1
-2.6
-2.1
-1.6
-1.1
-0.6
-0.1
0.4
0.9
1.4
Industry-level Discretion
80
hypothesis “The greater the industry-level discretion, the greater the strategic
variety” is not supported.
DISCUSSION
The result is unexpected. Either mismeasurement or inadequate theory or both
are implied. If the measurement is adequate, a model with at least one additional
variable is needed to explain the variation in strategic variety as industry-level
discretion increases. In their discussion of their counter intuitive results,
Abrahamson and Hambrick (1994) offered the tantalising speculation that
uncertainty may moderate the association between industry-level discretion and
strategic variety.
They speculated that, while ends-means ambiguity contributes to industry level
discretion, it also reduces the application of rationality when making strategic
choices. They noted that DiMaggio and Powell’s (1983) isomorphism proposals
imply that mimetic isomorphism has greater influence than discretion in industries
characterised by high ends-means ambiguity. They suggested that this phenomenon
could inform strategic group theory: systematic influences on strategic choices as
industry level discretion and uncertainty vary should influence the proliferation and
divergence of strategic profiles in industries.
However, testing for the phenomenon requires a more robust measurement of
strategic variety. The method used in the current research relies too heavily on
selecting individual ratios to operationalise generic or decontextualised strategic
dimensions. Readers are entitled to question whether the ratios selected are strategic
in all industries, and whether the results reflect idiosyncratic choices of researchers.
81
Relying on the ratio choices of published researchers is not necessarily convincing or
wise: the practice may simply perpetuate a poor or context-specific selection of
variables made by the original user.
More on Adding and Averaging Coefficients of Variation
The averaging of CoVs of the ratios produces a measure with poor axiomatic
properties. Apart from Dooley, Fowler, and Miller (1996), no published works were
found that sum or average CoVs, and the method is not discussed in any published
works on methods researched. Nonetheless, some basic insights into the
consequences of summing or averaging values of separate variables to produce a
single measure are applicable. At least four issues appear to be important when
adding values of variables with common metrics: the number of variables, their
relative magnitudes, their covariances, and their measurement errors.
Magnitude refers to the typical values of the variable and is best represented by
the mean. Temporarily setting aside measurement error, if all variables are
uncorrelated, and have equal means, adding the values of increasing numbers of
variables will cause the aggregated value for each case to converge. Adding
negatively correlated variables of equal magnitude cancels out or reduces the effect
of both variables. Variables with large magnitudes have greater influence than
variables with smaller magnitudes. Finally, variables with large absolute
measurement error may smother the contributions of small magnitude variables to
the aggregate value. This suggests that adding CoVs when there is no reason to
assume that one variable may be more important than another is acceptable for small
numbers of CoVs providing the means are similar, they are not highly and negatively
correlated, and the measurement error of one or more of the CoVs does not smother
the contribution of any of the other CoVs.
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The obvious alternative to adding three or more CoVs is subjecting them to
factor analysis. However, factor analysis introduces weighting of the contributions
of the individual CoVs to the final score, which may result in loss of information
about the variety of the least weighted CoVs. Factor analysis could be the preferred
method of data reduction when the number of variables is high, and is useful during
the data familiarisation process, but it is not necessarily superior to adding CoVs
when the number of variables is small and there is no theoretical reason to believe
one variable may be more important than another.
While standardising CoVs and checking confidence intervals against the range
of other variables before adding the CoVs might address identified potential
problems of adding CoVs, the method raises questions of veracity that reduce
credibility. Demonstration of convergent validity would be useful. Additionally,
low firm numbers in annual data necessitated the calculation of CoVs using five-year
data. This introduces the potential for a firm with five annual reports in the sample
to have greater influence on the CoV than a firm with fewer annual reports in the
sample. Even using five-year sample data, the loss of cases arising from zero values
in the ratio denominator, negative values for the ratio, and from confidence interval
calculations eventually limits the utility of strategic variety measurement using
CoVs.
Looking for an Alternative Measurement Method
Study Two’s literature search on use of CoVs led to Allison’s (1967) seminal
paper on measures of inequality. That paper notes the many useful characteristics of
Theil’s (1967) axiomatically defined measure of inequality based on Shannon’s
entropy concept (1948). Theil’s inequality measure is additive: it permits summation
83
of decompositions of differences and generates none of the suspect qualities
identified in our discussion about averaging CoVs.
Furthermore, my literature search on index creation spurred by the findings of
Study Two revealed that entropy-based measures are generally superior to other
measures of inequality (Maasoumi, 1986, 1997). Study Three uses Theil’s entropy
approach to measure strategic variety and retests the association between industrylevel discretion and strategic variety.
CONCLUSION TO CHAPTER THREE
The two constructs measured so far in this research are both abstract latent
variables on a somewhat grand scale. Study One developed an innovative method
for obtaining measures for industry-level discretion using publicly available archival
text data. Study Two was an attempt at a large scale duplication of prior exploratory
research and, as far as practical, the methodological path followed one already
surveyed. That experience and the findings of a weak association between industrylevel discretion and the range of strategic variety suggest that the accuracy of the
measurement instruments presently developed limits research to simple propositions
and tests. Improved measurement methods appear to be the key to further progress
in this area of research. Study Three reports my attempt at improving the
measurement of strategic variety.
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CHAPTER FOUR
STUDY THREE
INTRODUCTION
To address the need for a new and comprehensive way to measure strategic
variety identified in the discussion section of the last chapter, this third and final
study introduces entropy-based measurement of strategic variety. The chapter begins
with a brief primer on the mathematics, intuitive and non-intuitive logic, and
axiomatically defined properties of the entropy-based approach to measuring variety.
The versatility of this measurement approach allows development and testing of new,
more detailed hypotheses about the associations between industry-level discretion
and strategic variety. The methods section includes details of the considerations that
shaped the sampling regimes used to generate point values and confidence intervals
for the entropy based measures of strategic variety. The results support a bifurcation
of strategy into long-term strategic positions and current period strategic behaviour.
The association between variety in long-term strategic positions and industry-level
discretion is strong, positive and significant. The evidence of an association between
variety in current period strategic behaviour and industry-level discretion is mixed
and raises the question whether low discretion industries focus on these behaviours
more than high discretion industries. This question is especially applicable to large
firms.
A SHORT PRIMER ON SIMPLE ENTROPY
Shannon (1948) is widely acknowledged as the person who drew a number of
mathematical strands of thermodynamics with bearing on the theory of
communication engineering and introducing the term ‘entropy’ to describe the
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amount of a message that does not convey information. Shannon admitted that he
adopted the term ‘entropy’ following advice that he should use it because it would
attract attention to his work (Trubus, 1979). However, the term is appropriate as it
refers to the amount of information required to change a current incomplete state of
knowledge about a message, or more generally, a system, to full knowledge about
the message or system. Shannon’s work introduced a new approach to general
partitioning theory that quickly evolved into a new approach to information theory
and statistics that has been adapted to a wide range of scientific disciplines (Hooper
& Theil, 1965; Maasoumi, 1986; Morales, Pardo & Vajda, 1996; Straathof, 2003a,
2003b; Theil, 1967; 1992a, 1992b). To date, the main use of Shannon’s entropy
construct in strategic management research has been to measure diversification in
multi-business or multi-product corporations (e.g. Pelepu, 1985; Robins &
Wiersema, 2003).
Entropy in information theory is analogous to entropy in physics, but a slightly
non-intuitive insight is required to see the connection. In physics, maximum entropy
in a system occurs when energy or matter is as dispersed as possible. Under
conditions of maximum entropy, any sample will have the same amount of energy or
matter as any other equally sized sample. While entropy is commonly thought of as
the amount of disorder in a system, in a sense maximum entropy is absolute equality,
a form of perfect order. Absolute disorder means absolute equality. Increasing the
similarity of parts of a system means increasing the entropy of the system. This is
the slightly non-intuitive insight that underpins the use of entropy in information
theory.
In information theory, entropy is the amount of uncertainty, or lack of
knowledge about a message or a system. When a system has a number of possible
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states and we know nothing about the actual state of the system, the most we can do
is assign equal probability to each possible state of the system. Equal probabilities
equate to minimal knowledge or understanding, which, in information theory, is
maximum entropy. From this perspective, Haleblian and Finkelstein’s (1993) equal
weighting of their five standardised indicators when measuring industry-level
discretion can be seen as appropriate, given that there is no information available to
indicate that any particular variable is more or less important than any other.
During his extremely productive and distinguished academic career, Henri
Theil [1924-2000], the prominent post-WWII economist and econometrician,
championed the use of Shannon’s entropy in economics. He authored and coauthored a range of papers and a book demonstrating and advocating the benefits of
the information theory approach to a wide range of economic theory, including the
measurement of inequality and differences between economic entities. The
following description of entropy based measures draws heavily on Theil’s book
“Economics and information theory” (1967) and his paper “On the use of
information theory concepts in the analysis of financial statements” (1992b). My
footnotes in the description include some relevant aspects of information theory not
directly addressed by Theil.
Basic Information Theory
Assume, at a time t, that we know the probability an event will happen is pt. If
pt = 1, we know with absolute certainty that the event will happen. If pt = 0, we
know with absolute certainty that the event will not happen. If 1 < pt < 0, we know
the event may or may not happen. If we subsequently receive information in time
t+1 that the probability of the event happening is now pt+1 and pt+1 ≠ pt, the new
information changes what we know about the event. If pt = 1 and pt+1 = 0, or if pt = 0
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and pt+1 = 1, we would be infinitely surprised.5
More generally, for all values of pt
and pt+1, the amount of new information received is a function the difference
between pt and pt+1.
Shannon’s entropy-based information theory uses the log of the inverse of p
(log 1/p (= –log p)) to measure the amount of entropy (i.e. uncertainty = h(p))
contained in the probability p. This is the amount of uncertainty that would be
removed if we received a message that the event had occurred. This treatment has
some desirable properties:
•
as p → 1, h(p) → 0;
•
as p → 0, h(p) → ∞;
•
h(p) is a continuous function that increases monotonically as p
decreases; and
•
for independent events, entropy is additive, that is the entropy h(px
and py) = h(px) + h(py).
For our simple example where an event can either occur or not occur, the
probabilities of each outcome are p and 1-p. Before we receive the message, the
amount of expected information (H) of a message giving the outcome is:
H = p*log(1/p) +(1-p)*log(1/(1-p)) , where 0*log(1/0) = 0.
In words: the expected information of the message that an event has occurred or has
not occurred is the probability of the event occurring times the information content of
the message that the event has occurred plus the probability of the event not
occurring times the information content of the message that the event has not
occurred.
5
Indeed, while Theil does not make the point, any change from absolute certainty (i.e. from
pt = 0 or pt = 1 to pt+1 ≠ pt) causes infinite surprise, as it involves creation of a new possibility
out of nothing.
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When we consider an event with a number of possible outcomes (E1, E2,…En),
each with its own possibility, such that (p1 + p2 + …+ pn) = 1, the amount of
expected information that a message that one of the outcomes has occurred is:
n
H = Σ pilog(1/pi), where, if pi = 0, pilog(1/pi) = 0.
i=1
‘H’ is called the entropy of the distribution of the probabilities. It is the amount of
uncertainty expected to be removed by complete information. It has a minimum
value of zero and a maximum value of log(n), which occurs when all outcomes have
an equal probability of occurring (i.e. p1 = p2 =… = pn = 1/n). In other words, the
entropy of a distribution of probabilities is highest when we know so little about the
system that we assign each probability an equal chance of occurring. This means
that the greater the similarity in the probabilities, the less is known and the greater
the entropy in the distribution. Alternatively, the further away the set of probabilities
are from being identical, the further away the entropy value is from its maximum
possible value.
This leads to Theil’s common measure6 of inequality:
n
Inequality = log n – H(y) = Σyilog nyi ,
i =1
where: n is the number of entities whose inequality is being studied;
yi is the iths entity’s share of the total of the variable whose
inequality is being studied; and 0*log0 = 0.
6
Theil (1967) actually developed two measures of inequality, but gave most attention to this,
his first measure, which has also gained the most use. Theil also co-authored a paper that
combined both measures (Hooper & Theil, 1965) but that combined measure is seldom
mentioned, except in footnotes.
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Theil’s inequality measure has some attractive properties:
•
when all inputs change proportionately, the inequality measure does
not change;
•
as the number of entities increases, the potential maximum inequality
increases;
•
moves towards equality in shares decrease inequality; and
•
total inequality in decomposed data can be calculated as the sum of
between group inequality and the weighted sum of the within group
inequalities (‘additive decomposability’ or ‘aggregation consistence’
(Maasoumi, 1997).
The unit of measure of entropy or inequality is dependant on the base of the log
used. Depending on the application, bases of two or e are generally used. The units
of measure are called ‘bits’ when log2 is used and ‘nits’ when loge is used. Loge2 =
0.693, which means 1 bit = 0.693 nits, or 1 nit = 1.443 bits. To ease some of the
mathematical discussion, this primer uses natural logs (ln or loge). The units of
measure used in this study are detailed in the Methods section.
Now let us return to consider a message that only changes the possibilities of
the possible outcomes, rather than news that a particular outcome has occurred.
Using Theil’s symbols and expressions, if the prior possibilities are (p1, p2,…pn) (∑pi
= 1) and the posterior possibilities are (q1, q2,…qn) (∑qi = 1), we can measure the
amount of information contained in the message that changes the prior possibilities
to the posterior possibilities I(q:p). For each possible outcome (Ei), the change in
information change is:
ln(1/pi) – ln(1/qi) = ln(qi/pi),
and the chance that Ei will occur is qi.
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Therefore, for the full probability distribution:
n
I(q:p) = Σ qiln(qi/pi) .
i=1
If no pi equals zero unless qi also equals zero7, I(q:p) is always a positive value
unless pi equals qi for all i (i = 1, 2,…n), in which case I(q:p) equals zero. If any qi
equals one, the equation reduces to I(q:p) = ln(1/pi), which is consistent with the
initial discussion of the simple case.
Subject to the condition that qi must equal zero if pi equals zero, this treatment
of probabilities is applicable to any full rectangular matrix of non-negative real
numbers. Dividing each element in the matrix by the sum of its row produces the
proportion that the element contributes to the row. The sum of the proportions in a
row is one. Where each row is a set (or subset) of annual account data from a
separate company, the proportion is the chance that a randomly selected dollar will
be in a particular accounting field. More generally, proportions play the same role as
probabilities. By treating one row of proportions as a prior possibility and a second
row as a posterior possibility, it is possible to measure the information required to
transform one row of proportions into the other. Conceptually, this is the “distance”
between the two rows of proportions (Maasoumi, 1993: 138). However, the distance
varies depending on which row is treated as the prior proportions and which row is
treated as the posterior proportions: piln(pi/qi) ≠ qiln(qi/pi), if qi ≠ pi. If there is no
reason to set one proportion as the prior probability, one solution is to calculate the
distances in both directions and sum or average them.
7
Theil does not discuss the case where pi = 0 ≠ qi. However, it is discussed elsewhere (see
Lev, 1969: 19). This eventually limits the application of this approach to situations where all
non-zero posterior probabilities must have had non-zero prior probabilities.
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However, when seeking a single simple measure for inequality, the binary
approach using all combinations of existing rows rapidly becomes inconvenient as
the number of rows increases. Even if the inconvenience is accepted, the constraint
that, if pi equals zero, qi must also equal zero, limits this simple use of information
distance to a special subset of candidate matrices. To overcome this limitation, Theil
(1992b) suggests a way to create a matrix-specific, common row of prior proportions
when measuring distances between rows of proportions. The common row is the
proportion that the separate sums of each column make to the grand total of all the
elements of the whole matrix. This approach automatically ensures that, if pi equals
zero then values for qi for all rows equal zero. Thus, information content in matrices
with non-negative elements with or without zero value elements can be measured in
a consistent way.
If each row of the matrix has accounting data for each separate firm in an
industry and we use those industry-as-a-whole proportions as the prior proportions
and the proportions of each firm’s accounting information as the posterior
proportions, we can measure the distance each firm is from the industry-as-a-whole.
Using the additive property of Shannon’s entropy measure, we can sum the distances
firms in an industry are away from the industry-as-a-whole8 and get a value that
measures the differences between firms in the industry. This is a measure of variety
in the industry.
8
The proportions of the industry-as-a-whole should not be confused with the notion of ‘the
centre of the industry’. The proportions in the industry-as-a-whole tend to smooth out
differences between proportions in firms. This means the proportions in the industry-as-awhole tend to be more similar than proportions of individual firms.
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Illustrative Calculation of Industry Variety
Table 4.1 provides an illustrative calculation using natural logs and three
current assets fields from a hypothetical industry consisting of four firms. The totals
for each column in the 3x4 matrix (45, 20 and 40) are divided by the sum of all
elements in the matrix (105) to produce the industry-as-a-whole proportions (the
prior proportions from which the distance for each row will be calculated). The firm
proportions are calculated for each firm by dividing each element in the firm’s row
by the sum of the elements in the row. Thus, for example, for firm A, the
proportions for Cash, Marketable Securities, and Receivables respectively are 15/29
= 0.52, 1/29 = 0.03, and 13/29 = 0.45.
These proportions are the posterior proportions used to calculate the distance
each firm is from the industry-as-a-whole proportions. For each firm, that distance is
the sum of the distances each element in the firm’s posterior proportions is from its
corresponding element in the industry-as-a-whole’s prior proportions. To use Firm
A again, for Cash, p = 0.43 and q = 0.52. Plugging these values in the formula H =
p*ln(p/q) produces the result 0.10. Similarly, for Firm A, the results for Marketable
Securities and Receivables are -0.06 and 0.07 respectively. Summing the three
distances produces the distance Firm A is from the industry-as-a-whole (0.11).
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TABLE 4.1
Illustrative Calculation of Variety in Three Accounting Data Fields in a Four-firm Industry.
Firm
A
B
C
D
Cash
15
17
12
1
15+17+12+1 = 45
Marketable Securities
1
3
5
11
1+3+5+11 = 20
Receivables
13
14
9
4
13+14+9+4 = 40
Row Total
15+1+13 = 29
17+3+14 = 34
12+5+9 = 26
1+11+4 = 16
29+34+26+16 = 105
45/105 = 0.43
20/105 = 0.19
40/105 = 0.38
1
15/29 = 0.52
17/34 = 0.50
12/26 = 0.46
1/16 = 0.06
1/29 = 0.03
3/34 = 0.09
5/26 = 0.19
11/16 = 0.69
13/29 = 0.45
14/34 = 0.41
9/26 = 0.35
4/16 = 0.25
1
1
1
1
A
0.52*ln(0.52/0.43) = 0.10
0.03*ln(0.03/0.19) = -0.06
0.45*ln(0.45/0.38) = 0.07
B
0.50*ln(0.50/0.43) = 0.11
0.09*ln(0.09/0.19) = -0.07
0.41*ln(0.41/0.38) = 0.03
C
0.46*ln(0.46/0.43) = 0.03
0.19*ln(0.19/0.19) = 0.00
0.35*ln(0.35/0.38) = -0.03
D
0.06*ln(0.06/0.43) = -0.12
0.69*ln(0.69/0.19) = 0.88
0.25*ln(0.25/0.38) = -0.11
Measure of total differences of firms in the industry (Industry Variety)(in Nits)
0.10-0.06+0.07 = 0.11
0.11-0.07+0.03 = 0.07
0.03+0.00-0.03 = 0.00
-0.12+0.88-0.11 = 0.65
0.11+0.04+0.00+0.66 = 0.83
Input data
Column Totals
Prior Proportions
(Industry Proportions)(ps)
Posterior Proportions
(Firm Proportions)(qs)
A
B
C
D
Calculation of inequality
'distance' for each firm
(Difference between each firm
and the industry average)
94
Summing the distances each firm is from the industry-a-as-whole results in a single
value which measures the variety in the original 3x4 matrix. In the example, the
final value for variety in the matrix is 0.83 nits.
Implications for Variety Measurement
While the entropy-based technique can be used to produce values for variety
using only two accounting fields, it also can be used to produce a single measure of
variety for multiple accounting fields. The measurement technique frees the
researcher from some of the methodological limitations that essentially limit
researchers to use accounting ratios derived from the selected pairing of accounting
fields (e.g. PPE Newness = Net PPE/ Gross PPE). Additionally, the calculation does
not necessitate the data deletion required in traditional ratio analysis when the
denominator is zero. This ensures greater case numbers and a more extensive use of
available data.
Data Adjustments Required
Because logs of negative numbers do not exist in rational mathematics, the
method only works when the accounts contain no negative values.9 This issue can
be addressed without loss of information by rearranging the balance sheets to remove
negative values, as described in the second predictive validity test in Study One.
Combining number of employees data with financial data presents some issues
arising from scale choice: the variety measure varies depending on the units used to
record the accounts. For example, combining number of employees data with
accounts data expressed in units of $1000 produces different values for variety than
when units of $10000 are used. There is no naturally superior solution to this
problem, which also occurs when calculating CoVs and variance. The $1000 unit is
9
More correctly, the elements in the matrix must be the same sign.
95
commonly used in research using accounting ratios (e.g. Hambrick et al.,
forthcoming). For consistency, when combining employee number and accounts
data, accounts data were measured in $1000 units, the unit of measurement used in
Compact Disclosure and the CoV calculations in Study Two.
Need for Modifications to Theil’s Basic Method
Theil’s basic method for measuring variety in an industry has two
characteristics that need to be clearly understood and addressed, or at least
accommodated. Firstly, small numbers of outliers dramatically increase the measure
of variety in a matrix. Secondly, in heterogenous data sets the variety measure
increases in value as sample size increases. That is, Theil’s basic calculation of
variety is influenced by sample size.
The influence of outliers.
Calculation of variety using Thiel’s basic method gives extra weight to cases
that vary greatly from the ‘typical’ firm in the industry. Consider a simple example:
a sample of thirty-one firms and a three-column matrix where only the thirty-first
firm uses only one of the columns and the remaining thirty firms only use the other
two columns. Inclusion of the thirty-first firm causes the information distance from
the industry-as-a-whole proportions for each of the thirty ‘typical’ firms to increase
by some constant amount. The information distance for the outlier firm would also
be large as it includes the distance contribution for the two commonly used columns.
The total variety dramatically increases because of the single atypical firm.
The extra weight given to outliers is a less extreme manifestation of the same
effect that generates infinite surprise when a new possibility occurs in the posterior
proportions: an outlier should generate more surprise than another example of a
‘near-typical’ firm. The larger the group of near-typical firms, the greater should be
96
the surprise on encountering an outlier. While, in some ways, the extra weight given
to less typical sets of proportions is appropriate, it also has the potential to distort a
variety measure if a small number of extreme outliers is part of a dataset of firms that
otherwise would be considered relatively similar.
The influence of sample size.
Theil’s basic approach to measuring variety in an industry simply sums the
distances each firm’s accounting proportions are from the industry-as-a-whole
proportions. This means that, unless all firms have identical proportions, the larger
the number of firms in the industry (or sample), the greater the variety measure.
While it would be very interesting to examine relationships between population size
and industry-level discretion, the plain fact is that, even though sample size in the
research database was positively correlated with industry-level discretion, data on
population size of industries were not available. Thus, any suspected correlation
between sample size and population size could not be tested. This means variety has
to be measured in a way that controls for sample size: comparison of gross variety in
the available samples cannot be used to test hypothesised relationships between
industry-level discretion and strategic variety.
Modification of Theil’s Basic Method
One approach that addresses the problem of sample size is to use either the
average or the median of the distances firms in the industry sample are from the
industry-sample-as-a-whole proportions. The median is preferable to the average as
the distributions of distances firms are from the industry-sample-as-a-whole are
generally very positively skewed. This approach, which I call Method I variety, has
the advantage of great simplicity. However, a considerable amount of the
97
information in the data is lost and the method’s potential is limited to exploratory
analysis.
An alternative, more computationally intensive approach is to set the sample
size used in the calculation of variety. There is no naturally self evident ‘ideal’
sample size. Intuitively, the smaller the sample size, the less information is captured
and the greater the instability of the resulting estimates. However, the larger the
sample size, the fewer the number of cases available for analysis, which reduces the
likelihood of a significant test result. Conceptually, for any available set of data,
there should be a ‘sweet spot’ where a set sample size maximises the accuracy of the
variety values and minimises the confidence intervals around the estimate of the
correlation with industry-level discretion. While a series of well designed Monte
Carlo experiments might provide guidance on identifying such sweet spots, such
experiments were beyond the resources and scope of the present research. To be
consistent with the methods adopted in Studies One and Two, the standard set
sample size in this study was set at twenty firms. However, in an attempt to reduce
the range of confidence intervals around the point estimates of variety, some
additional analysis was preformed using a set sample size of forty firms.
When the available sample is larger than the set sample size, this approach
requires taking multiple random same size subsamples using selection without
replacement when taking each independent subsample. Taking the average of the
multiple estimates of the same size subsamples’ variety values produces an estimate
of the average variety of a random sample of fixed size for the industry.
This second approach dilutes the influence of outliers: the majority of
subsamples will not have outliers included and the more numerous ‘typical’ firms
will have greater influence. This measure, which I call Method II variety, is the
98
same as Theil’s gross variety measure if the available sample size equals the
subsample size. In such a case, subsampling is not possible. When subsampling was
possible, the number of subsamples was initially set at 5000. As described in the
next section, the number of subsamples was increased when bias tests indicated more
subsamples should be used.
Confidence Intervals for Variety Measurement
To be consistent with the standard of rigour established in Study One and
Study Two, it is necessary to address the fact that the estimated variety values are
derived from sample data and, consequently, will have sampling error. Confidence
interval estimation is not a common practice in entropy studies, which is regrettable
since even large sample entropy studies can have large standard errors (Maasoumi,
1997). The occasional papers reporting confidence intervals of entropy-based
measures are scattered across disciplines and provide little advice and no consistent
method for confidence interval estimation.
The distributions of the distances firms are from the industry-sample-as-awhole are positively skewed. There tends to be a major clustering near the industrysample-as-a-whole and a rapidly declining but often extended tail of firms with
larger distances from the industry-sample-as-a-whole. Outliers can extend this tail
dramatically. While this supports the use of the median instead of the average as the
best simple summary statistic, the meaning of confidence intervals around a median
is obscure. Consequently, the first method of variety measurement only supports
tests using point estimates. This limits the use of Method I variety measures to
indicative tests that can be used to identify potentially interesting phenomena that
can then be rigorously tested using the more computationally intensive Method II
variety measurement method.
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Using the standard deviation of the subsample point estimates is not
appropriate when calculating confidence intervals for Method II variety values. As
noted, no subsampling is possible in cases where the available sample size equals the
subsample size. There is only one point estimate for such cases. The absence of a
range of point estimates in such cases means there is no value for the standard
deviation of the point estimate. In other words, the standard deviation is zero. If the
standard deviations of the point estimates were used to determine confidence
intervals, for cases where the available sample equalled the sample size, the range of
the confidence intervals would be zero even though the point estimates were derived
from samples, which is a nonsense.
Considering the difficulties in finding a tractable model for conventional
estimation techniques, bootstrapping is the logical choice for estimating confidence
intervals of Method II variety measures. Research reporting estimation of
confidence intervals of entropy and inequality measures using bootstrap approaches
is in its infancy with very few papers available. Mills and Zandvakili (1997) used
bootstrap techniques to test inferences of differences between state post tax income
and youth inequality. Fritsch and Hsu (1999) used the bootstrap-t method to test
hypotheses relating to entropy-based measures of dinosaur extinctions. The merit of
Fritsch and Hsu’s approach, at least for the purpose to which it was put, was
subsequently supported by a simulation study (Salicru, Vives & Ocana, 2005).
More propitiously, Biewen (2002: 339) reported results of Monte Carlo
experiments that indicated that for simple Theil inequality estimation “confidence
intervals based on the simplest possible bootstrap procedure achieve the same
convergence accuracy as intervals based on conventional normal approximation.”
While the sample sizes in the current research are often less than Biewen’s smallest
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sample size of one hundred, his conclusion gives some support for the use of a
simple bootstrap approach to estimating confidence intervals in the current research.
In line with Biewen’s conclusion, the simple bootstrap percentile method was
used. Each fixed-size subsample without replacement used to generate a point
estimate was subjected to 3000 bootstrap resamples with replacement to generate a
distribution of variety estimates. The 2.5 percentile and 97.5 percentile of that
distribution were saved along with the point estimate of variety of the subsample.
For each case, the averages of the 2.5 percentiles and the 97.5 percentiles were
calculated along with the average of the point estimates.
Potential bias was addressed by comparing the average point estimate of the
subsamples that were subjected to bootstrap resampling with the average of a far
larger set of un-bootstrapped subsamples. In each case, initially the point estimate of
5000 bootstrapped subsamples was compared with the point estimate of 100000 nonbootstrapped subsamples. If the difference between the two estimates was greater
than 1%, an additional 5000 subsamples were subjected to bootstrapping. This was
necessary for 2.1% of cases when the subsample size was set at twenty firms and for
only 0.71% of cases where the subsample size was set at forty firms.
If the point estimate of the 10000 bootstrapped subsamples was more that 1%
different from the point estimate of the 100000 non-bootstrapped subsamples, an
additional 10000 subsamples were submitted to bootstrapping and an additional
100000 non-bootstrapped subsamples were drawn. This was necessary for only
0.48% of cases when the subsample size was set at twenty firms and for 0.36% of
cases when the subsample size was set at forty firms.10
10
Additional experiments with this approach using ratio input data (two column matrices)
revealed that bias was more likely in two column matrices and in matrices with one or more
101
For the few cases where the sample size equalled the subsample size,11 no
subsampling without replacement was possible. In such cases, 3000 bootstrap
estimations of the variety measure determined the 95% confidence intervals of the
single point estimate of the sample. The standard procedure for all remaining cases
was excessive for cases with relatively low numbers of firms and worked well for the
cases with large numbers of firms. There is no reason why the extra calculation for
the cases with low numbers of firms would distort the result for those cases.
The calculations were performed on multiple PCs running a relatively simple
PERL program. This somewhat cledgy processing reflects the innovative and
exploratory nature of the analysis. Development of a faster and more efficient
software program was beyond the resources and scope of the present research.
Following Theil’s original example of the variety calculation (Theil, 1992b), the
PERL program used log base 2, so the variety values were measured in bits.
Some Limits to Entropy-based Variety Tests
There are some limitations to using entropy-based variety measures to identify
important strategic dimensions. Because entropy-based variety measures are
sensitive to the number of fields used, direct comparison of different decompositions
can only occur when the fields in the decompositions match each other. For
example, no corresponding line in stakeholder equity matches the preferred stock
line in held equities (see Table 2.12). Consequently, speculations about the relative
columns where the elements had predominately zero values. In some of the experiments,
80000 bootstrapped subsamples and 800000 non-bootstrapped subsamples were needed to
reduce bias to less than 1%.
11
Six cases had only twenty firms when using all available firm data. One case had only
forty firms when using all available firm data. Four cases had only twenty firms when using
only large firm data (defining large firms by the median of total assets).
102
contributions of held equities and stakeholder equities to strategic variety cannot be
tested by directly comparing variety in held equities to variety in stakeholder equity.
Similarly, it is tempting to speculate as to whether assets or liabilities
contribute more to total variety. However, the fields in fully decomposed assets and
liabilities do not match and such direct comparisons are specious.12 Fortunately,
these limitations do not impede tests of correlations between entropy-based variety
values for selected subsets of accounting data and industry-level discretion. The
limitation is only on direct comparisons of variety values for different subsets of
accounting fields.
THEORY AND PRACTICE OF STRATEGY MEASUREMENT
Multiple Strategic Profiles in Industries
Before formal development of hypotheses, it is appropriate to examine the
implications of the flexibility and limitations of entropy-based measurement of
variety. As noted earlier, use of entropy-based variety measures frees the researcher
from the need to select specific ratios as indicators of aspects of competitive
strategies, a circumscribing practice in strategic variety research that reflects
conventional mathematical procedural constraints as much as it reflects the desire to
operationalise constructs about competitive or strategic conduct. This freedom
allows a fuller treatment of strategic variety that incorporates competition for factors
and means of production as well as for patronage, or sales of products and services.
12
Dividing a variety value by the maximum possible variety value in a matrix with the same
number of rows and columns would produce ‘relative variety,’ which offers some potential
for comparison of variety in different matrices with different column headings and different
sized matrices. This would be similar to “relative entropy,” a concept used in information
theory (Schwartz, 1963: 19-25). However this must wait for future research.
103
Any activity in the factor (inputs) markets (including the market for finance),
transformation process (value adding), and sales (outputs) market can be a source of
competitive advantage (Khandwalla, 1981; Penrose, 1966). Further, in any industry,
different firms may have different sources of competitive advantage. Indeed, if
competing firms only competed on the same dimensions, the less competitive firms
would be driven from the industry. The co-existence of a number of viable strategies
in a single industry is fundamental to organisational ecology approaches where
‘specialist’ and ‘generalist’ firms occupy different niches and prosper (Brittain &
Freeman, 1980; Carroll, 1984; Hannan & Freeman, 1977). Additionally, strategy
research shows that firms following different generic strategies coexist in the same
industry (Miles & Snow, 1978; Porter, 1980).
More generally, theory suggests and empirical studies have demonstrated that
firms in an industry seldom disperse evenly along strategic dimensions (Tang &
Thomas, 1992). Firms can generally be classified into strategic groups that share
similar positions along single or multiple strategic dimensions. Information on the
presence (or absence) and membership characteristics of group of firms within
industries adds additional detail to descriptions of industry structures and is
fundamental to strategic group research. This is especially the case in industrial
economics approaches to strategic group research, which use firm-level economic
variables that operationalise selected aspects of strategy to chart or group firms by
their competitive strategies. Product line (Hunt cited in Oster, 1982; Thomas &
Pollock, 1999), vertical integration (Newman, 1978), technology (Nair & Suresh,
2001), geographic scope (Houthoofd & Heene, 1997), resource commitments (Colla,
2003), marketing strategy (Panayides, 2002) and firm size (Porter, 1979) have been
104
the main dimensions used to cluster firms in the industrial economics approach to
strategic groups.
A wide range of strategic dimensions is available to create strategic profiles. In
any instance, the particular strategic dimensions used to cluster firms to identify
strategic groups reflect the methodological and theoretical perspective selected and
the intended use of the insights drawn from the resulting clusters. The strategic
dimensions and the data reduction techniques used in strategic group studies
determine the cluster result and the presence or absence of group membershipperformance relationships. Reviews of group membership-performance research
identify a mixed result with some researchers reporting significant linkages, while
others find no significant linkages (Barney & Hoskisson, 1990; Dranove, Peteraf &
Shanley, 1998; Hatten & Hatten, 1987; Thomas & Venkatraman, 1988). This has
caused some commentators to suggest that the groups identified in research are
methodological artefacts, rather than theoretically relevant phenomena (Barney &
Hoskisson, 1990; Hatten & Hatten, 1987).
Removing Some Limits on Strategic Dimension Operationalisation
Any strategic management research that preselects a small subset of variables
from a wide range of available variables can be criticised at some level because it
artificially simplifies complexity by ignoring other potentially important variables.
This criticism is inescapable as selecting what to observe is a part of the scientific
method. All researchers select and define research questions and gather data in ways
that are contingent on their internal world views or conceptual frameworks (Lewins,
1992).13 Typically, variable selection reflects theory or prior research findings. In
13
The logical implication of this position is a ‘behavioural’ approach to scientific research
reminiscent of Cyert and March’s (1992) theory of the firm. The periods between paradigm
105
other words, there is reason to believe the selections are appropriate. However, this
does not exclude the probability that important variables are not considered.
The data recorded in annual accounts reflects worldviews about what is
important to measure and report, and has be criticised as conditioning users to focus
on a subset of relevant information (Schaltegger, Muller & Hindrichsen, 1996).
Nevertheless, other potential accounting data that may be relevant are not
systematically recorded or available. Research into firm economic behaviour must
accept the constraints imposed by data availability. Further information loss occurs
when researchers impose additional constraints by selecting a limited number of
subsets of available accounting data to generate a small set of strategic indicators.
The entropy-based method for measuring variety can measure variety in all
available accounting fields at once.14 It can eliminate the information loss associated
with an unnecessarily constrained variable selection processes. However, if
industry-level variety in all available accounting fields is measured, the measure
captures variety in both strategies and strategic outcomes (firms’ performances).
Even at the firm level, linkages between strategy and strategic outcomes are
highly moderated and mediated by factors not captured in accounting data.
Examination of associations between strategy and strategic outcomes at the industry
level is beyond the scope of the current research, which is limited to testing for
shifts in Kuhn’s (1970) theory of science can be viewed as periods where behaviouralism
applies to some extent.
14
However, experimentation with a wide range of combinations of accounting fields reveals
that combining accounting fields where there is no strong theoretical connection smothers
variety patterns observable in theoretically meaningful subsets of accounting fields.
Measuring variety in all available accounting fields in one matrix is the equivalent of
measuring variety in a matrix of randomly selected columns and the equivalent of a white
noise result is produced.
106
associations between industry-level discretion and strategic variety in industries.
Consequently, the hypotheses, methods and analysis reported in this thesis will be
limited to subsets of accounting fields that are indicators of a firm’s policies rather
than the outcome of interactions between a firm’s policies and other variables.
Strategic Variety and Small Firms
Resource partitioning theory (Carroll, 1985; Carroll & Hannan, 2000) suggests
that small firms in an industry tend to fill specialist niche roles and have
idiosyncratic elements to their strategies as a consequence. The slightly older
product and industry life cycle literature suggests that strategic proliferation is
highest during the formative and growth stages of an industry life cycle, when no
dominant model or industry leaders have emerged (Anderson & Zeithhaml, 1984;
Day, 1981; Levitt, 1991; 1966; Rink & Swan, 1979; Thorelli & Burnett, 1981).
Additionally, Penrose (1966) argued that, in growing economies, the limits to the
rate of growth of large firms means that opportunities will exist for small firms to
grow in the unexploited gaps (“interstices” (Penrose, 1966: 222)) that the large firms
do not occupy. These theories point to the conclusions that small firms contribute
more to strategic variety in an industry than do large firms and fast growing
industries will have more small firms than slow growing, stable or shrinking
industries.
Discretion theory suggests highly competitive (i.e. atomised, not oligopolistic)
industry structures, highly differentiable products, and high growth rates increase
discretion (Hambrick & Finkelstein, 1987). Strategic variety arising from increasing
numbers of small firms is, no doubt, important and consistent with the general thrust
of discretion theory. However, discretion theory also suggests that executives of
large firms in high discretion industries will, on average, have higher levels of
107
executive discretion. Thus, strategic variety in large firms in high discretion
industries should also contribute to the expected correlations between strategic
variety and industry-level discretion. In sum, theory suggests industry task
environments impact on strategic variety via a direct influence and indirectly via
small firms.
Measuring strategic variety using all firms in the available industry samples
captures variety attributable to both the direct influence and the indirect effect
attributable to increased numbers of small firms. I labelled this measure of variety
‘overall strategic variety.’ I labelled the strategic variety attributable to
environmental effects independent of firm size ‘direct strategic variety.’ Direct
strategic variety should be apparent in samples of large firms and samples of small
firms. Sample sizes precluded isolating and testing associations between direct
strategic variety in small firms and industry-level discretion. All of the hypotheses
and the proposition developed in this section apply to both overall strategic variety
and direct strategic variety in large firms.
HYPOTHESES DEVELOPMENT
The original hypothesis in Study Two will be tested:
Hypothesis 3.1
The greater the industry-level discretion, the greater
the strategic variety.
With the freedom to look at new ways of measuring variety comes the need to
revisit the research question “What are the associations between industry-level
discretion and strategic variety in industries?” There are, of course, different
concepts of what is ‘strategy.’ Strategy has been the object of extensive and
prolonged research in organisation studies and has become a pervasive, almost
obligatory, word in the lexicon of business.
108
‘Strategy’ has been analysed and dissected and embellished and extended to
such an extent that the meaning of the term has become lost in multiple
interpretations and uses. Some commentators have suggested that strategy has come
to mean whatever the user wants it to mean (Hambrick & Fredrickson, 2001). Thus,
for example, Mintzberg’s (Mintzberg, Lampel, Quinn & Ghoshal, 2003) five Ps
typology suggests that strategy can be defined as a Plan, Ploy, Pattern, Position or as
a Perspective. Mintzberg and Lampel (1999) also identify ten schools or points of
view that focus on different major aspects of strategy formation processes and
suggest ‘strategy’ is a construct that can only be partly understood by any particular
viewpoint. The essential point here is that how strategy is viewed or defined will
determine the meaning of the research question and how strategy should be
described and measured.
By the nature of the arguments already used, strategy in this thesis is treated as
a series of decisions (Hambrick, 1983; Miles & Snow, 1978) and associated actions
that an organisation undertakes as it endeavours to achieve its goals, which, in
ongoing for-profit organisations at least, include creating competitive advantage to
ensure organisational success. Those actions impact on accounting data, which serve
as surrogate, slightly distal indicators of the enacted strategies. The emphasis on
activities aligns with Porter’s (1985; 1996) observation that strategically consistent
orchestrated sets of actions produce competitive advantage. However, there is no
need to commit to Porter’s positioning thesis, or to any other strategy formation
school or model other than to assume that executives’ decisions can have a
significant role in determining strategy in some situations.
Binding decisions that involve making major long term asset and liability
commitments are strategic in intent, even if, ultimately, the outcomes of the actions
109
that follow do not produce the competitive advantage envisioned when making the
decisions. However, as evidenced by the inputs into common operationalisations of
generic strategic dimensions, strategy can be implemented by consistently adopting
patterns of current behaviours that create incrementally accumulating effects that
have substantial long term impacts. Strategy enactment is an emergent process that
consists of both occasional major actions and numerous small incremental actions
(Burgelman, 1988; Chandler, 1962; Mintzberg, 1973; Mintzberg, 1978; Mintzberg et
al., 2003).
In other words, a full treatment of strategy must also include current
behaviours. Accounting data explicitly differentiate between long term (noncurrent)
data and short-term (current) data. This distinction permits operationalisation of
variety in long term commitments to major strategic positions and variety in current,
incremental strategic behaviours. It is reasonable to propose that executive
discretion will impact differently on these two aspects of strategy.
A by-product of the second predictive validity test in Study One was the
observation that current accounts appear to be more frequently adjusted than
noncurrent accounts. This implies that managers find current accounts easier to
influence than noncurrent accounts. Even managers with low executive discretion
should be able to influence current accounts, while higher levels of executive
discretion would be required to influence noncurrent accounts. Additionally,
noncurrent accounts are better buffered from random variation arising from the
snapshot effect associated with selecting a day to close the annual accounts. In other
words, noncurrent accounts should be more stable and reflective of the firm’s longterm strategy than current accounts. This leads to hypothesis 4.1, which applies to
both assets and liabilities:
110
Hypothesis 4.1
Variety in noncurrent accounts will be more strongly
associated with industry-level discretion than variety in current accounts.
Equity positions represent a combination of difficult-to-change legacy profiles
and some (often peripheral) components that are subject to contemporaneous
managerial influence. This suggests that equity positions should capture some
aspects of a company’s long-term strategy. Examination of Table 2.12 shows that
held equities includes items that increase executive control while stakeholder
equities has items that increase stakeholder control. This leads to hypothesis 4.2:
Hypothesis 4.2
Variety in held equities will be more strongly
associated with industry-level discretion than variety in shareholder
equities.
In contrast to equities data, most expenses and income data tend to capture
more current activities. However, extraordinary items in incomes and expenses
could capture significant strategic moves that involve major investments and/or
divestments. Similarly, depreciation and amortization and provisions for income tax
offer some scope for managerial adjustment of accounts data that would require
executive discretion. However, generally, income is a mediated and moderated
outcome of strategy rather than an indicator of a strategy. The following hypotheses
are advanced to assist analysis:
Hypothesis 4.3
Variety in income and expenses will be weakly and
positively associated with industry-level discretion.
Hypothesis 4.4
Variety in expenses will be more strongly associated
with industry-level discretion than variety in income.
111
Finally, it is reasonable to argue that the widely used strategic indicator
variables do, in fact, capture information about important strategic characteristics.
This leads to the proposition:
Proposition 4.1
Variety in inputs of widely used strategic indicator
variables will be positively associated with industry-level discretion.
METHODS
Samples
Variety was measured using positively adjusted firm accounting data taken
from the target year of the industry-level discretion ratings developed in Study One.
This ensured no firm had more than one annual account in the raw data used to
generate any of the variety measures. Where there were sufficient firm and case
numbers, data sets were created to allow comparison of large firms’ data and all
firms’ data. Firm size was measured using total assets and sales. This produced two
lists of large firms. Twenty or more firms per case was used as the standard
criterion for cases size. However a sample based on the criterion of forty or more
firms per case was also created. In all, four sets of cases were created. SIC4s and
names of the industries in each data set are supplied in Table 4.2.
112
TABLE 4.2
Industries with Cases in Study Three
SIC4
Industry Name
1311
Crude Petroleum and Natural
Gas
Electronic Computers
Computer Peripheral
Equipment, nec
Telephone and Telegraph
Apparatus
Radio and TV Communications
Equipment
Semiconductors and Related
Devices
Electro-medical Equipment
Trucking, Except Local
Radiotelephone
Communication
Telephone Communications,
except Radio
Electric Services
Gas Transmission and
Distribution
Eating Places
Hospital and Medical Service
Plans
Pre-packaged Software
Computer Integrated Systems
Design
Medical Laboratories
3571
3577
3661
3663
3674
3845
4213
4812
4813
4911
4923
5812
6324
7372
7373
8071
In All
Firms Set
X
In Both Large
Firms Sets
X
In All
Firms_40 Set
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
The research database had eighty-two (82) cases (SIC4-target year
combinations) with twenty or more nondiversified firms (labelled the ‘all firms set’).
The 3503 annual reports from 1412 companies in seventeen (17) industries in this
data set made it the largest collection of firms used in this study.
Twenty-nine (29) cases had forty or more nondiversified firms (all firms_40
set). There were 2050 annual reports from 801 firms in five (5) industries in this
database.
The median of total assets for all undiversified firms in the database used to
calculate industry-level discretion values was $71,974,000. When the median was
used to divide the firms into large firms (above the median) and small firms (below
the median), thirty one (31) cases spread across the range of industry-level discretion
values had more than twenty large, nondiversified firms (large firms by total assets
113
set). This data set had 1230 annual reports from 470 firms in eight (8) industries.
There were insufficient cases with forty or more large undiversified firms to conduct
statistical tests and the cases with twenty or more small, nondiversifed firms were
limited to the high discretion industries.
The median of sales for all undiversified firms in the database used to calculate
industry-level discretion values was $64,823,000. When that median was used to
divide the firms into large firms (above the median) and small firms (below the
median), thirty-six (36) cases, spread across the range of industry-level discretion
values, had more than twenty large nondiversified firms (large firms by sales set).
This data set had 1259 annual reports from 475 firms in eight (8) industries. The
eight industries were the same as those in the large firms by total assets set. Again,
there were insufficient cases with forty or more large undiversified firms to conduct
statistical tests and the cases with twenty or more small nondiversifed firms were
limited to the high discretion industries.
Accounting Fields Used to Measure Strategic Variety
For each of the four data sets, variety was measured in the following subsets of
accounting data:
•
Current Assets,
•
Current Liabilities,
•
Noncurrent Assets,
•
Noncurrent Liabilities,
•
Held Equities,
•
Stakeholder Equities,
•
Income Only,
•
Expenses Only,
114
•
A matrix of the data fields used in past strategic variety studies, and
•
A matrix of long-term data fields used in past strategic variety
studies.
The fields used for the first strategic variety matrix were:
•
Number of Employees,
•
Receivables,
•
Total Current Assets,
•
Total Current Liabilities,
•
Gross Property Plant and Equipment,
•
Depreciation,
•
Long Term Debt,
•
Positive Shareholder Equity,
•
Negative Shareholder Equity,
•
Sales, and
•
Research and Development.
While this matrix uses available inputs from the ratios used in the studies of strategic
variety cited in Study Two, it combines current accounting fields and noncurrent
accounting fields and includes long term debt, a field avoided in the variety
calculations in Study Two because of the identified association between long term
debt use and industry-level discretion. Consequently, a reduced matrix that used
only long-term ratio inputs was also tested. That matrix used Number of Employees,
Gross Property Plant and Equipment, and Depreciation.
SPSS V12.0.1 was used to estimate correlations and associated p-values
between point estimates of variety in the subsets of accounting data and industrylevel discretion. Confidence intervals produced Fisher’s z prime (z’) transformations
115
were used to estimate 95% confidence intervals for the correlations (Cohen &
Cohen, 1983). The confidence intervals were obtained using correlation point
estimates rounded to two significant places and asymptotic assumptions. The SPSS
correlation p-values are also based on asymptotic assumptions, but used the input
data directly, while the confidence intervals derived from Fishers’ z’ transformation
used the correlation point estimate, which was derived from the input data. Due to
the increased distance from the original data, the confidence interval estimates were
more conservative (i.e. larger) than the SPSS p-values.
The case 6324_1992 (Hospital and Medical Services Plans in 1992), the only
SIC1 = 6 case in the all firms data, was an outlier in noncurrent assets data.
Consequently, that data set was analysed both with and without the outlier when
using Method I variety values. Method I variety results were used to guide the
selection of data sets where Method II variety data was used.
RESULTS
Introduction
In this section, the correlations between Method I variety values and Method
II variety values are examined. The results of hypothesis tests using Method I
variety results are then reported, along with some observations that provide
additional insight into the phenomena being investigated. Based on the conclusions
drawn from Method I variety values, the large firms by sales data set was not used
for Method II hypothesis tests. The results of the Method II variety tests are reported
second. The combined results are then consolidated and interpreted.
Comparison of Method I Variety Values and Method II Variety Values
Table 4.3 lists details of correlations between point estimates of Method I and
Method II variety in the all firms data sets where there was twenty or more firms per
116
case. Not surprisingly, all corresponding measures are significantly correlated (all
have p-values < 0.00). The moderate correlations between Method I and Method II
values for noncurrent liabilities, held equities, and stakeholder equities are a product
to a number of factors, notably they have the largest differences between the average
mean case distance from the industry-sample-as-a-whole and the average median
case distance from the industry-sample-as-a-whole. They also have the highest
standard deviations of mean case distance from the industry-sample-as-a-whole. The
moderate correlations suggest that, for these three subsets of accounting data,
Method I variety results should have less weight than Method II variety results when
looking for patterns.
TABLE 4.3
Correlations Method I and Method II Variety Values
(All Firms Data, 20 or more Firms per Case)
Current Assets
Noncurrent Assets
Current Liabilities
Noncurrent Liabilities
Held Equities
Stakeholder Equities
Income
Expenses
Strategic Ratio Inputs
Long Term Strategic Ratio Inputs
Pearson r
0.90
0.93
0.82
0.67
0.69
0.79
0.92
0.96
0.95
0.87
Sig. (2-tailed)
0.00**
0.00**
0.00**
0.00**
0.00**
0.00**
0.00**
0.00**
0.00**
0.00**
N†
80
77
82
67
74
42
48
69
74
73
† Number of cases after case deletion rules had been applied. (See Chapter 1.)
* p-value < 0.01.
Method I Variety Results
Details of the correlations between the Method I variety values and industrylevel discretion for each subset of accounting fields in the three main data sets are
reported in Table 4.4. One- and two-tailed significance p-values are reported
because, unexpectedly, some significant negative correlations are present. However,
one-tailed p-values are used when testing directional hypotheses. Lines with a
confidence interval value in bold identify results where the p-value indicates a
117
significant result while the confidence interval indicates a non-significant result. In
such cases, the p-value is used because it is derived directly from the input data,
while the confidence interval involved a two-step calculation that introduces greater
chance of overestimation. The following paragraphs address each hypothesis and
the proposition in turn, in the order in which they are listed in the hypothesis
development section. Table 4.5 lists each hypothesis and the proposition in order
and consolidates the results of the Method I variety tests.
Hypothesis tests.
Hypothesis 3.1, which states that the greater the industry-level discretion, the
greater the strategic variety, is supported in all tests where the one-tailed p-value is
significant and the correlation is positive. The support is strongest for noncurrent
liabilities and noncurrent assets data, where all data sets have p-values lower than
0.00. Large firm noncurrent accounts data show the strongest correlations, but the
overlapping confidence intervals means the correlations are not significantly
different from the correlations for the all firms data sets. Variety in large firms by
total assets data had a significant negative correlation with industry-level discretion
when current liabilities, current assets, income and expense data are analysed.
Variety in all firms data also has significant negative correlations with industry-level
discretion when expenses and income data are analysed. Variety in all firms data is
significantly and positively correlated when stakeholder equities and held equities
data are analysed. Noncurrent accounts and equities data represent long-term
118
TABLE 4.4
Correlations Between Method I Variety and Industry-level Discretion
Main Accounting Subsets
Data Set
N
Pearson
r
Twotailed
p-value
Noncurrent Liabilities
Large† Firms By Total Assets
32
0.74
0.00**
Large‡ Firms By Sales
36
0.68
0.00**
All Firms
82
0.48
0.00**
All Firms, No SIC6
81
0.48
0.00**
Noncurrent Assets
Large Firms By Total Assets
32
0.57
0.00**
Large Firms By Sales
36
0.55
0.00**
All Firms
82
0.49
0.00**
All Firms, No SIC6
81
0.55
0.00**
Current Liabilities
Large Firms By Total Assets
32
-0.35
0.05*
Large Firms By Sales
36
-0.08
0.63
All Firms
82
0.11
0.32
All Firms, No SIC6
81
0.09
0.41
Current Assets
Large Firms By Total Assets
32
-0.33
0.06
Large Firms By Sales
36
-0.02
0.91
All Firms
82
0.20
0.07
All Firms, No SIC6
81
0.18
0.10
Stakeholder Equities
Large Firms By Total Assets
32
-0.04
0.85
Large Firms By Sales
36
0.37
0.03*
All Firms
82
0.32
0.00**
All Firms, No SIC6
81
0.32
0.00**
Held Equities
Large Firms By Total Assets
32
0.26
0.16
Large Firms By Sales
36
0.30
0.08
All Firms
82
0.27
0.01*
All Firms, No SIC6
81
0.31
0.01*
Expenses
Large Firms By Total Assets
32
-0.27
0.13
Large Firms By Sales
36
-0.04
0.82
All Firms
82
-0.20
0.07
All Firms, No SIC6
81
-0.23
0.04*
Income
Large Firms By Total Assets
32
-0.39
0.03*
Large Firms By Sales
36
-0.05
0.79
All Firms
82
-0.25
0.02*
All Firms, No SIC6
81
-0.26
0.02*
Strategic Ratio Inputs
Large Firms By Total Assets
32
-0.01
0.94
Large Firms By Sales
36
0.35
0.04*
All Firms
82
-0.01
0.96
All Firms, No SIC6
81
-0.02
0.89
Long-term Strategic Ratio
Inputs
Large Firms By Total Assets
32
0.74
0.00**
Large Firms By Sales
36
0.72
0.00**
All Firms
82
0.58
0.00**
All Firms, No SIC6
81
0.60
0.00**
† Firms with Total Assets above $71,974,000
‡ Firms with Sales above $64,823,000
Onetailed
p-value
95% Confidence
Limits
lower
upper
0.00**
0.00**
0.00**
0.00**
0.53
0.45
0.29
0.29
0.87
0.82
0.63
0.63
0.00**
0.00**
0.00**
0.00**
0.28
0.27
0.31
0.38
0.77
0.74
0.64
0.69
0.02*
0.32
0.16
0.20
-0.62
-0.40
-0.11
-0.13
0.00
0.26
0.32
0.30
0.03*
0.46
0.03*
0.05*
-0.61
-0.35
-0.02
-0.04
0.02
0.31
0.40
0.38
0.42
0.01*
0.00**
0.00**
-0.38
0.05
0.11
0.11
0.31
0.62
0.50
0.50
0.08
0.04*
0.01*
0.00**
-0.10
-0.03
0.06
0.10
0.56
0.57
0.46
0.49
0.07
0.41
0.03*
0.02*
-0.57
-0.36
-0.40
-0.43
0.09
0.29
0.02
-0.01
0.01*
0.39
0.01*
0.01*
-0.65
-0.37
-0.44
-0.45
-0.05
0.28
-0.03
-0.04
0.47
0.02*
0.48
0.45
-0.36
0.02
-0.23
-0.24
0.34
0.61
0.21
0.20
0.00**
0.00**
0.00**
0.00**
0.53
0.51
0.42
0.44
0.87
0.85
0.71
0.72
* Correlation is significant at the 0.05 level
** Correlation is significant at the 0.01 level
119
positions that generally require considerable strategic repositioning to change, while
current accounts and annual expenses and income data are more volatile and subject
to short-term influences. These results suggest that hypothesis 3.1 is only supported
for long term strategic positions.
Hypothesis 4.1, which suggests that variety in noncurrent accounts will be
more strongly associated with industry-level discretion than variety in current
accounts, is supported when the confidence intervals around the point estimates for
the correlation between noncurrent accounts and current accounts do not overlap.
For example, the lower bound of the 95% confidence interval for the correlation
between variety in noncurrent liabilities in large firms defined by total assets and
industry-level discretion is r = 0.53. The upper bound of the 95% confidence
interval for the correlation between variety in current liabilities in large firms defined
by total assets and industry-level discretion is r = 0.00, which is less than 0.53. This
lack of overlap supports the hypothesis that there is a significant difference between
the two correlations and that hypothesis 4.1 is supported in that data set. Table 4.5
notes all tests that show support for hypothesis 4.1. All data sets have significant
differences between the correlation for noncurrent liabilities and current liabilities.
Only the large firms by total assets data have a significant difference between the
correlation for noncurrent assets and current assets. However, for the other data sets,
the overlap in confidence intervals is very slight, suggesting that larger case numbers
would produce significant differences in those data sets. The bold numbers in Table
4.3 show that the evidence that the Fishers z’ based estimates of confidence intervals
are overestimates is strongest in the assets results, which adds further support to the
conclusion that the differences in correlations for current assets and noncurrent
assets are sufficient to conclude that H4.1 applies to assets as well as liabilities.
120
Hypothesis 4.2, which states that variety in held equities will be more strongly
associated with industry-level discretion than variety in shareholder equities, is not
supported in any of the data sets. In each set, the lower bound of the confidence
interval for the correlation between variety in held equities and industry-level
discretion is lower than the higher bound of the confidence interval for the
correlation between variety in stakeholder equities and industry-level discretion.
Hypothesis 4.3, which states that variety in income and expenses will be
weakly and positively associated with industry-level discretion, is not supported in
any of the data sets. Indeed the correlations between industry-level discretion and
variety in income or expenses is negative and significant in the all data set with
twenty or more firms per case. The correlation between variety in income in the
large firms by total assets data set and industry-level discretion is also negative and
significant.
Hypothesis 4.4, which states that variety in expenses will be more strongly
associated with industry-level discretion than variety in income, is not supported in
any of the data sets. There are substantial overlaps of the upper and lower
confidence intervals of the correlations in every data set.
Proposition 4.1, which states that variety in inputs of widely used strategic
indicator variables will be positively associated with industry-level discretion is only
supported in the large firms by sales data sets when the larger matrix of strategic
indicator inputs is used. The use of sales to define large firms combined with the use
of sales as a strategic ratio input matrix may be contributing in a confounding way to
the significant correlation. However, Proposition 4.1 is supported when the smaller
matrix consisting only of long-term strategic indicator inputs is used. In the latter
series of tests, all data sets have significant positive correlations between variety and
121
TABLE 4.5
Consolidated Results using Method I Variety Measures
Hypothesis or
Proposition
H3.1
All +ive
H4.1
Noncurrent Accounts>
Current Accounts
H4.2
Held Equities >
Stakeholder Equities
H4.3
Income, Expenses Small,
+ive
H4.4
Income >Expenses
P4.1
+ive
Tested
Data
Current
Assets
Noncurrent
Assets
Current
Liabilities
Noncurrent
Liabilities
Held
Equities
Stakeholder
Equities
Income
Expenses
Assets
Liabilities
Income
Expenses
Strategic
Ratio Inputs
Long Term
Strategic
Ratio Inputs
All Firms
All
Firms,
No SIC6
Large by
TA
Yes
Yes
-ive
Yes
Yes
Yes
Large by
Sales
Yes
-ive
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-ive
-ive
Yes
-ive
-ive
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
-ive
-ive
-ive
No
-ive
-ive
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
-ive
industry-level discretion. This result is consistent with the interpretation already
placed on the result for hypothesis 3.1.
Analysis of Method I Variety Hypothesis Tests
Consolidated results of the Method I variety tests are displayed in Table 4.5.
Overall the concentration of ‘Yes’ results of the hypothesis tests using Method I
variety values suggest long term (noncurrent) strategic positions should be treated
separately from current strategic behaviours. The negative correlations between
variety in current accounts and industry-level discretion suggest that in low
122
discretion industries, some large firms compete by differentiating their current
strategic behaviours rather than their long-term strategic positions.
Additionally, it should be noted that the strengths of three main positive
correlations (long term strategic ratio inputs, noncurrent assets, and noncurrent
liabilities) increase when only large firms are used. This is evidence for the
existence of direct strategic variety.
Unfortunately, due to the small numbers of small firms in low discretion
industries, it was not possible to isolate variety arising from small firms. It is quite
possible that strategic variety for small firms is not significantly and positively
associated with industry-level discretion. This could occur if variety in small firms’
strategies was high in all industries. Finally, the generally stronger positive and
negative correlations obtained when total assets is used to define large firms and the
problematic interaction between sales and the strategic ratio inputs matrix suggest
the size of the asset base rather than the magnitude of sales is the best variable to use
when identifying large firms for variety studies.
Method II Variety Results
Details of the correlations between the Method II variety point estimates and
industry-level discretion for each subset of accounting fields in the three main data
sets are reported in Table 4.6. As with the Method I results, one- and two-tailed
significance p-values are reported because some significant negative correlations are
present. Again, one-tailed p-values are used when testing directional hypotheses.
Each hypothesis and the proposition is addressed in the order in which they are listed
in the hypothesis development section. The only difference in approach is the use of
confidence intervals to delete cases where the point estimate of variety is unstable.
The point estimate examination is followed by examination of scatterplots and
123
binomial tests of associations where significant correlations are identified using the
Method II point estimates of variety. These additional tests suggest that the
conclusions drawn from the point estimates may sometimes overstate the evidence.
Table 4.7 lists each hypothesis and the proposition in order and consolidates the
results of the Method I variety tests.
TABLE 4.6
Correlations Between Method II Variety and Industry-level Discretion
Main Accounting Subsets
Data Set
N
Pearson
r
Twotailed
p-value
Noncurrent Liabilities
All Firms 20 Firms Subsample
67
0.71
0.00**
All Firms 40 Firms Subsample
29
0.77
0.00**
Large† Firms By Total Assets
29
0.49
0.01**
Noncurrent Assets
All Firms 20 Firms Subsample
77
0.57
0.00**
All Firms 40 Firms Subsample
28
0.73
0.00**
Large Firms By Total Assets
26
0.47
0.01**
Current Liabilities
All Firms 20 Firms Subsample
82
0.09
0.42
All Firms 40 Firms Subsample
29
-0.16
0.41
Large Firms By Total Assets
32
-0.15
0.42
Current Assets
All Firms 20 Firms Subsample
80
0.17
0.13
All Firms 40 Firms Subsample
29
0.22
0.26
Large Firms By Total Assets
31
-0.07
0.71
Stakeholder Equities
All Firms 20 Firms Subsample
42
0.40
0.01**
All Firms 40 Firms Subsample
26
0.42
0.03*
Large Firms By Total Assets
14
-0.34
0.23
Held Equities
All Firms 20 Firms Subsample
74
0.66
0.00**
All Firms 40 Firms Subsample
29
0.69
0.00**
Large Firms By Total Assets
27
0.52
0.01**
Expenses
All Firms 20 Firms Subsample
69
-0.17
0.18
All Firms 40 Firms Subsample
28
-0.23
0.24
Large Firms By Total Assets
32
-0.18
0.33
Income
All Firms 20 Firms Subsample
48
-0.19
0.19
All Firms 40 Firms Subsample
16
-0.37
0.16
Large Firms By Total Assets
22
-0.17
0.46
Strategic Ratio Inputs
All Firms 20 Firms Subsample
74
-0.01
0.97
All Firms 40 Firms Subsample
29
-0.15
0.44
Large Firms By Total Assets
32
-0.10
0.59
Long-term Strategic Ratio
Inputs
All Firms 20 Firms Subsample
73
0.71
0.00**
All Firms 40 Firms Subsample
28
0.68
0.00**
Large Firms By Total Assets
27
0.53
0.00**
† Firms with Total Assets above $71,974,000
Onetailed
p-value
95% Confidence
Limits
lower
upper
0.00**
0.00**
0.00**
0.57
0.56
0.15
0.81
0.89
0.73
0.00**
0.00**
0.01**
0.40
0.49
0.10
0.70
0.87
0.73
0.21
0.21
0.21
-0.13
-0.50
-0.47
0.30
0.22
0.21
0.07
0.13
0.36
-0.05
-0.16
-0.41
0.38
0.54
0.29
0.00**
0.02*
0.12
0.11
0.04
-0.74
0.63
0.69
0.23
0.00**
0.00**
0.00**
0.51
0.43
0.17
0.77
0.84
0.75
0.09
0.12
0.16
-0.39
-0.56
-0.50
0.07
0.16
0.18
0.10
0.08
0.23
-0.45
-0.73
-0.55
0.10
0.15
0.27
0.48
0.22
0.30
-0.24
-0.49
-0.43
0.22
0.23
0.26
0.00**
0.00**
0.00**
0.57
0.41
0.19
0.81
0.84
0.76
* Correlation is significant at the 0.05 level
** Correlation is significant at the 0.01 level
124
Hypothesis Tests
Hypothesis 3.1, which states that the greater the industry-level discretion the
greater the strategic variety, is supported in all three data sets for noncurrent
liabilities, non current assets, held equities and long term strategic ratio inputs. The
hypothesis is also supported for both all firms data sets when using stakeholder
equities data. There are no significant negative correlations in any of the tests where
the one-tailed p-value is significant. The correlations have the highest r-values in
noncurrent accounts and, interestingly, in the noncurrent accounts tests, the large
firm data set has a lower r-value than the all firms data set. However, the higher
confidence limit for the large firms correlations is greater than the lower confidence
limits for the all firms data sets correlations, so the difference is not significant.
Hypothesis 4.1, which suggests that variety in noncurrent accounts will be
more strongly associated with industry-level discretion than variety in current
accounts, is supported for liabilities and assets by non-overlapping 95% confidence
intervals in the all firms data where each case has 20 or more firms. In the all firms
data set where each case has 40 or more firms, the hypothesis is supported by nonoverlapping confidence intervals in liabilities data while the overlap is slight for
assets data – suggesting more cases would produce a significant difference. In the
large firms data, both correlations for liabilities and assets have overlapping
confidence intervals, however the overlap in liabilities is slight, which again suggests
that more cases would produce a significant difference between the correlations. In
both the all firms data set where cases have 40 or more firms and the large firms data
set, the number of cases are less than 30, which explains the relatively large and
overlapping confidence intervals.
125
Hypothesis 4.2, which states that variety in held equities will be more strongly
associated with industry-level discretion than variety in shareholder equities, is not
supported in any of the data sets. However the overlap in the confidence intervals is
small (0.06) for the large firms data set, which suggests the non-significant result
reflects the small number of cases and additional cases might produce a significant
result.
Hypothesis 4.3, which states that variety in income and expenses will be
weakly and positively associated with industry-level discretion, is not supported in
any of the data sets. As with the Method I variety results, the correlations are all
negative, although none of the Method II variety correlations are significant.
Hypothesis 4.4, which states that variety in expenses will be more strongly
associated with industry-level discretion than variety in income, is not supported in
any of the data sets. The confidence intervals around the correlations all data sets
have substantial overlaps.
Proposition 4.1, which states that variety in inputs of widely used strategic
indicator variables will be positively associated with industry-level discretion is not
supported in any of the data sets. Instead of being positive, the correlation is
negative, although not significantly different from zero. However, Proposition 4.1 is
supported when the smaller matrix consisting only of long-term strategic indicator
inputs is used: all data sets have significant positive correlations between variety and
industry-level discretion.
Examination of Scatterplots with Confidence Intervals
So far the confidence intervals for Method II variety have only been used to
determine which cases should be retained for point estimate analysis. A number of
significant correlations between the point estimates of variety and industry-level
126
TABLE 4.7
Consolidated Results using Method II Variety Measures
Hypothesis or
Proposition
H3.1
All +ive
H4.1
Noncurrent Accounts>
Current Accounts
H4.2
Held Equities >
Stakeholder Equities
H4.3
Income, Expenses
Small, +ive
H4.4
Income >Expenses
P4.1
+ive
Tested
Data
All Firms
20 firms
subsample
All Firms
40 firms
subsample
Large
Firms by
Total
Assets
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Maybe
No
Yes
Yes
Maybe
No
No
Maybe
-ive
-ive
-ive
-ive
-ive
-ive
No
No
No
No
No
No
Yes
Yes
Yes
Current
Assets
Noncurrent
Assets
Current
Liabilities
Noncurrent
Liabilities
Held
Equities
Stakeholder
Equities
Income
Expenses
Assets
Liabilities
Income
Expenses
Strategic
Ratio Inputs
Long Term
Strategic
Ratio Inputs
discretion have been identified. Figures 4.11 to 4.15 graph the scatterplots for the
five subsets of data that had at least one significant correlation in the Method II point
estimates data: noncurrent liabilities, noncurrent assets, held equities, stakeholder
equities, and long term strategic ratio inputs.
As a general observation, the cases with standardised industry-level discretion
in the range -1.5 to zero tend to have low variety in most graphs, while cases with
even lower values for industry-level discretion often tend to be slightly higher,
especially in the all firms data where each case had 40 or more firms and the large
firm data. Most graphs could support a linear relationship, but in many cases a split
127
or a curvilinear relationship would also be supported and would capture more of the
variation.
Curvilinear relationships between variety and industry-level discretion are
suggested in the bottom two graphs in Figures 4.1 to 4. 5. That is, noncurrent
liabilities, noncurrent assets, stakeholder equities and long-term strategic ratio inputs
for the all firms data set where each base has 40 or more firms per case and the large
firms data set. The top graph in Figure 4.5 suggests variety for stakeholder equities
in the all firms data set where each case has 20 or more firms per case also appears to
have a curvilinear relationship with industry-level discretion. However, the overlaps
of the confidence intervals for variety in the stakeholder equity graphs (see Figure
4.5) suggest it is possible that there is no significant correlation between variety and
industry-level discretion.
Binomial Tests
Table 4.8 supplies details of binomial tests for the five subsets of data that had
at least one significant correlation in the Method II point estimates data. As detailed
in Appendix, the upper and lower confidence intervals of the case variety values
were used to isolate two groups of cases for each test (a ‘high’ variety group and a
‘low’ variety group). The number of cases in each group with standardised industrylevel discretion values above and below zero were then counted. This second count
was used to determine the probability that the group membership was attributable to
chance. If the group membership was not reasonably attributable to chance, the
hypnotised positive association between variety and industry-level discretion was
supported.
As the table shows, the data for stakeholder equities does not support tests that
show a positive association between variety and industry-level discretion.
128
FIGURE 4.1
Noncurrent Liabilities: Scatterplots with Case Confidence Intervals
a
All Firms, 20 of More Firms per Case
80
Variety
60
40
20
0
-3
-2
-1
0
1
Industry-level Discretion
b
All Firms, 40 or More Firms per Case
100
Variety
80
60
40
20
0
-3
-2
-1
0
1
Industry-level Discretion
c
Large Firms, 20 or More Firms per Case
Variety
60
40
20
0
-3
-2
-1
0
1
Industry-level Discretion
129
FIGURE 4.2
Noncurrent Assets: Scatterplots with Case Confidence Intervals
a
All Firms, 20 of More Firms per Case
40
Variety
30
20
10
0
-3
-2
-1
0
1
Industry-level Discretion
b
All Firms, 40 of More Firms per Case
50
Variety
40
30
20
10
0
-3
-2
-1
0
1
Industry-level Discretion
c
Large Firms, 20 or More Firms per Case
30
25
Variety
20
15
10
5
0
-3
-2
-1
0
1
Industry-level Discretion
130
FIGURE 4.3
Held Equities: Scatterplots with Case Confidence Intervals
a
All Firms, 20 of More Firms per Case
50
Variety
40
30
20
10
0
-3
-2
-1
0
1
Industry-level Discretion
b
All Firms, 40 of More Firms per Case
100
Variety
80
60
40
20
0
-3
-2
-1
0
1
Industry-level Discretion
c
Large Firms, 20 or More Firms per Case
Variety
30
20
10
0
-2
-1
0
1
Industry-level Discretion
131
FIGURE 4.4
Stakeholder Equities: Scatterplots with Case Confidence Intervals
a
All Firms, 20 of More Firms per Case
25
20
Variety
15
10
5
0
-3
-2
-1
0
1
Industry-level Discretion
b
All Firms, 40 of More Firms per Case
Variety
60
40
20
0
-3
-2
-1
0
1
Industry-level Discretion
c
Large Firms, 20 or More Firms per Case
Variety
15
10
5
0
-3
-2
-1
0
1
Industry-level Discretion
132
FIGURE 4.5
Long Term Strategic Ratio Inputs: Scatterplots with Case Confidence Intervals
a
All Firms, 20 of More Firms per Case
12
Variety
8
4
0
-3
-2
-1
0
1
Industry-level Discretion
b
All Firms, 40 of More Firms per Case
20
Variety
15
10
5
0
-3
-2
-1
0
1
Industry-level Discretion
c
Large Firms, 20 or More Firms per Case
8
Variety
6
4
2
0
-2
-1
0
1
Industry-level Discretion
133
TABLE 4.8
Results of Binomial Tests on High and Low Industry-level Discretion Cases in Groups with High and Low Variety
All Firms, >19 Firms/Case
Noncurrent Liabilities
Noncurrent Assets
Held Equities
Stakeholder Equities
Long-term Strategic
Ratio Inputs
Low Industry-level Discretion
High Industry-level Discretion
Observed/Expected Proportions
Exact Sig. (1-tailed)
Low Industry-level Discretion
High Industry-level Discretion
Observed/Expected Proportions
Exact Sig. (1-tailed)
Low Industry-level Discretion
High Industry-level Discretion
Observed/Expected Proportions
Exact Sig. (1-tailed)
Low Industry-level Discretion
High Industry-level Discretion
Observed/Expected Proportions
Exact Sig. (1-tailed)
Low Industry-level Discretion
High Industry-level Discretion
Observed/Expected Proportions
Exact Sig. (1-tailed)
All Firms, >39 Firms/Case
Low Variety
Group
15
3
0.83
0.00**
18
8
0.69
0.00**
12
0
1.00
0.00**
5
0
1.00
0.01**
High Variety
Group
2
34
0.06
0.00**
2
22
0.08
0.00**
3
12
0.20
0.01**
4
15
0.21
0.10
Totals
16
2
18
9
0
9
11
1
12
1
0.94
0.00**
19
0.10
0.00**
20
0.47
2
0.82
0.01**
11
0.00
0.00**
13
0.41
0
1.00
0.00**
10
0.09
0.00**
10
0.55
17
37
0.31
20
30
0.40
15
12
0.56
Low Variety
Group
10
0
1.00
0.00**
9
0
1.00
0.00**
7
0
1.00
0.00**
9
15
0.38
High Variety
Group
0
13
0.00
0.00**
0
12
0.00
0.00**
0
9
0.00
0.00**
Large Firms, >19 Firms/Case
Totals
10
13
0.43
9
12
0.43
7
9
0.44
Insufficient cases
in high and low groups
to conduct tests
Low Variety
Group
11
0
1.00
0.00**
10
1
0.91
0.01**
5
0
1.00
0.13
High Variety
Group
0
12
0.00
0.00**
9
1
0.90
0.01**
1
3
0.25
0.18
Totals
11
12
0.48
19
2
0.90
6
3
0.67
Correlation of Point Estimates
was not significant
** Significant at the 0.01 level
134
Furthermore, the test using large firm data on held equities does not support the
hypothised association. This almost certainly reflects the very small number of cases
that could be differentiated into the high and low variety groups. However, all other
tests show clear support for the hypothesis that variety is positively associated with
industry level discretion.
DISCUSSION
Introduction
This discussion analyses the results of Study Three and offers some possible
theoretical interpretations of the results. The analysis uses both an expansive and a
restricted view of strategy. The expansive view includes all current or incremental
activities and all noncurrent or long term positions. The restricted view limits
strategy to major, irrevocable decisions that involve large resource commitments
(Hickson, 1986). The results show that strategic variety is positively associated with
industry-level discretion only if strategy is considered in the restricted sense. Even
then, there is some marginal evidence that some very low discretion industries have
slightly higher strategic variety than industries with slightly higher, but still low,
industry-level discretion. This may be a consequence of unaligned environmental
signals fostering conservative change which results in mimetic isomorphic behaviour
(DiMaggio & Powell, 1983) or strategic convergence. While Porter’s (1996)
strategic convergence thesis is supported in the low industry-level discretion cases,
Hambrick et als’(forthcoming) strategic divergence thesis could still apply to high
industry-level discretion cases.
Positive Associations Between Strategic Variety and Industry-level Discretion
In all the data sets analysed, the results from all tests using both variety
measures consistently show a strong, positive and significant association between
135
variety in noncurrent assets, noncurrent liabilities, held equities, and long term
strategic ratio inputs and industry-level discretion. Those correlations in the large
firms data sets are clear evidence of direct associations between industry-level
discretion and strategic variety.
The long term strategic ratio inputs is a slightly artificial collection of
accounting fields and was only introduced to illustrate the effect that occurs when
current data are separated from long term data. The consistent finding that variety in
held equities is positively and significantly associated with industry level discretion
is not surprising. The components of held equities (see Table 2.11) represent the
accounting fields where, in a sense, the company ownership of part of itself is
recorded. Increasing use of held equities options increases executive influence over
ownership, a major source of stakeholder power. The correlation between variety in
held equities and industry-level discretion is almost tautological in the sense that
increasing use of held equities options is a logical indicator of increasing discretion.
While the held equities result is of interest in the sense that it captures
information about the strategy of corporate control, the correlations between variety
in noncurrent accounts and industry-level discretion are of more relevance when
considering a narrower view of strategy as making major decisions involving large
irrevocable commitments of an organisation’s resources (Hickson, 1986). The
original hypothesis, ‘The greater the industry-level, the greater the strategic variety,’
is supported only if strategic variety is regarded as relating solely to long-term
positions. Even then, when only long-term accounting data are used to
operationalise strategic variety, the analysis of scatterplots of Method II variety
confidence intervals suggests that some very low discretion industries may have
slightly more strategic variety than industries with low to moderate industry-level
136
discretion. This evidence of curvilinearity in the association between variety in
noncurrent accounts and industry-level discretion is marginal, but there is consistent
evidence of curvilineraity in the association between stakeholder equities and
industry-level discretion.
All the very low discretion cases in the data sets analysed come from two
industries: crude oil and natural gas production (SIC4 = 1311) and gas transmission
and distribution (SIC4 = 4923). Compared to high discretion industries, the two very
low industries show low variety in their long term positions when compared to high
discretion industries because, essentially, their major capital assets are large, fixed
and determined by technologies available to all firms in their industries. Financing
the acquisition of those assets requires stable long term arrangements, which limits
the long term liabilities and ownership options available. Nonetheless, it appears
that slightly more options are available for noncurrent liabilities than exists in the
noncurrent assets. It may be that characteristics unique to the oil and gas industry
such as the volatility of oil and natural gas supplies and prices contribute to the
possible slight increase in long term variety in these industries when compared to
other industries with low industry-level discretion.
Strategic Convergence and Divergence
The low values for variety in most cases where the standardised industry-level
discretion value is between minus two and zero is perhaps the strangest feature of the
results. The discussion of the results in Study Two noted Abrahamson and
Hambrick’s (1994) speculation that uncertainty may moderate the association
between industry-level discretion and strategic variety. Elsewhere in this thesis it
has been noted that, due to measurement difficulties, industry-level discretion has
primarily been mainly restricted to a small set of industries where the determinants
137
of discretion were aligned and permitted categorisation of high, medium and low
discretion industries. The cases in Study Three with standardised industry-level
discretion between minus two and zero are listed in Table 4.9. The cases are
predominantly from the Electric Services and the Telephone Communication
Industries, with a few cases from other industries. My speculation is that, during the
sampled period, these industries may have had unaligned determinants of discretion
resulting in conflicting pressures on executives. Some determinants would work to
increase discretion while others would work to decrease discretion. In such an
environment, where critics could always rely on some clear environmental signals
that suggest strategic conservatism, novel strategic developments carry a very high
risk. However there would be pressures for strategic change. This would appear to
be a circumstance where strategic convergence via mimetic isomorphism (DiMaggio
& Powell, 1983) would be most likely to occur.
TABLE 4.9
Cases in All Firms Data Set With Industry-level Discretion
Between -2 and Zero
Case
1311_1991
6324_1992
4923_1994
4911_1995
4911_1996
4812_1996
4812_1995
4911_1993
4911_1992
4911_1994
3571_1990
4813_1996
4911_1991
3661_1992
4813_1997
4213_1993
4911_1990
Standardised Industry-level
Discretion
-1.75
-1.55
-1.53
-1.24
-1.16
-0.91
-0.82
-0.60
-0.43
-0.43
-0.32
-0.30
-0.23
-0.10
-0.05
-0.04
-0.02
Industry
Crude Petroleum and Natural Gas
Hospital and Medical Service Plans
Gas Transmission and Distribution
Electric Services
Electric Services
Radiotelephone Communications
Radiotelephone Communications
Electric Services
Electric Services
Electric Services
Electronic Computers
Telephone Communications Except Radio
Electric Services
Telephone and Telegraph Apparatus
Telephone Communications Except Radio
Trucking, Exc. Local
Electric Services
In many subsets of accounting data analysed the unexpected low and similar
levels of variety for cases where the standardised industry-level discretion was below
138
zero suggest Porter’s (1996) firm convergence thesis is the best explanation for
industries with below average industry-level discretion. This still leaves room for
Hambrick et al’s (forthcoming) increasing variety thesis, which asserts that executive
discretion and strategic variety increased over the sample period in most industries.
The small number of industries sampled in this current research does not allow
testing of the increasing variety thesis. Only two industries, SIC4 = 3674
(Semiconductors) and SIC4 = 5812 (Eating Places) were analysed in Hambrick et al.
(forthcoming) and had sufficient sequential cases in the research data base to allow
trend graphing. Both were above average discretion industries. Only variety in
noncurrent accounts (assets and liabilities) for Semiconductors showed an upward
trend. The trend was in all Method I and Method II variety data sets analysed.
The available results suggest that high discretion industries should provide the
most evidence for the increased variety thesis. There are more industries with high
industry level discretion than with low industry-level discretion. This is true for the
samples used throughout this thesis and it is reasonable to assume that it applies to
the population of U.S. industries. Assuming that is the case, the increasing variety
thesis may well be supported by larger samples and case numbers.
Strategic Current Behaviour
The findings of Study Three show point estimates of variety in current
behaviour indicators are often negatively correlated with industry-level discretion
when large firm data are analysed, although the evidence is mixed and the
correlations are only significant for Method I variety, the simplest but crudest variety
measure. Additionally, the confidence intervals around the Method II variety point
estimates mean the slight negative correlations may be even less significant than the
simple point estimate analysis suggest. This non-significant result may be a
139
reflection of the low case numbers in the available data sets.15 Larger case numbers
might identify a slightly greater reliance on incremental, current strategic behaviours
in low discretion industries. However, it is reasonable to assume that most top
management teams in most industries have substantial discretion over behaviours
that impact on current accounts data and that most industries have similar amounts of
variety in their current accounts.
The significant negative correlation for Method I values in current accounts in
the large firms by total assets data set is attributable to high levels of variety in a few
low discretion cases. These cases are SIC4-Target years 1311_1991, 1311_1994,
1311_1997 and 4932_1991. Method I measurement is based on the median. This
suggests that in some low discretion industry cases, there are reasonable numbers of
outliers that skew the distributions of current accounts distance distributions. This
suggestion is supported examining the histograms of distances of firms from the
industry-sample-as-a-whole. Figure 4.6 demonstrates the presence of isolated
outliers and skewed distributions for current assets data.
15
The possibility that aggregations of current account data used in the variety measures
reported somehow combined information used in published research to make strategic
behaviour indicators in a way that smothered the information captured by only combining
two theoretically linked ratio inputs was tested by additional examination of correlations
between variety in individual ratio inputs and industry-level discretion. However, no
significant correlations were identified. The illustrative data used in the case retention
discussion in Appendix is an example of one such test.
140
10
25
8
20
Frequency
Frequency
FIGURE 4.6
Outliers in Low Discretion Cases with High Variety in Current Assets Data in
Large Firms By Total Assets Data Set
6
15
4
10
2
5
0
0
0.00
0.25
0.50
0.75
1.00
1.25
1.50
0.00
0.20
Current Assets 1311_1991
0.40
0.60
0.80
1.00
1.20
1.40
Current Assets 1311_1994
25
6
5
20
Frequency
Frequency
4
15
3
10
2
5
1
0
0
0.00
0.50
1.00
1.50
Current Assets 1311_1997
2.00
0.20
0.40
0.60
0.80
1.00
Current Assets 4923_1991
Income and Expenses
Variety in income and expenses data was always negatively associated with
industry level discretion, with the associations being both negative and significant in
the all firms data set when Method I variety values were used. It is important to
remember that the research question is about strategy, not strategic outcomes.
Variety in income is not measuring variety in profitability; it is measuring variety in
income streams. As such it reflects variety in revenue generating activities and is of
141
legitimate interest when using an expansive definition of strategy. Similarly, variety
in expenses measures the diversification of expenditures and captures an aspect of
expansively defined strategy. The negative correlations suggest firms in low
discretion industries differentiate themselves and seek competitive advantage by
diversifying their activities within their industry.
Low discretion industries in the research data base are predominantly utilities
that have been subject to competition policies and deregulation. As deregulation
removes protective barriers and these utility firms are obliged to compete, it seems
plausible they will try to gain competitive advantage in the areas where they have
leeway to create variety, that is, where they can make changes that differentiate them
from competitors. While some differentiation in major long-term positions may be
achievable over time, the firms should find it easiest to change smaller, more current
behaviours in their efforts to differentiate themselves from competitors.
In such a circumstance, strategy should mainly focus on how the firm does
business in the here and now, rather than on long term capital infrastructure positions
that are essentially determined by legacies or external environmental, especially
technological, imperatives. Strategic innovation in low discretion industries obliged
to compete should tend to focus on redefining, repackaging and remarketing the
basic product or service, which typically has commodity-like characteristics in a
traditional protected market. Thus, for example, spot markets and futures markets
and options and similar contractual embellishments are easier to introduce than
fundamental changes to the actual production and supply infrastructure.
Additionally, the firm may seek new ways to market its in-house expertise in
managing the technology. Thus, for example, an electric utility might enter into
contracts to assist a utility corporation in a developing economy set up new supply or
142
management systems. The resulting diversification of income streams would be
consistent with the negative correlations found between variety in income and
industry-level discretion.
Redefinition of the product sold would almost certainly be accompanied by
attempts to change suppliers’, customers’ and organisational members’ views about
the firm and its role. Successful executives in low discretion industries subject to
new competition policy need to be able to change the way the firm’s current reality
is perceived. Again, this would involve diversification of expenditure as image
management, training, learning and personal change are encouraged using a range of
marketing and organisational development devices. The resulting diversification of
expense items would be consistent with the consistent negative correlations found
between variety in expenses and industry-level discretion.
Conversely, the results suggest successful executives in high discretion
industries redefine the firm’s expected future and have greater licence to introduce
strategies that reflect their visionary ideas about the future of their industry. This
does not mean executives in high discretion industries can ignore current reality
perception management. Few business models can ignore current accounts for
sustained periods. However, the licence to attempt to shape the future tends to
reduce the onus on managing the present, and this is reflected in the marginal
tendency for very low discretion industries to have more variety in current accounts
than high discretion industries.
CONCLUSION TO CHAPTER FOUR
The aim of this study was to develop and apply a new method for measuring
strategic variety so that the research question could be addressed without raising
concerns similar to those which cast doubts on the results of the hypothesis test in
143
Study Two. Study Three breaks new ground in variety measurement by applying
modifications to Theil’s basic entropy-based industry variety measurement method.
These modified methods were used to measure variety in a number of standard
subsets of positively adjusted accounting fields. The flexibility of the resulting
variety measurement approach enabled variety to be measured using more on the
available accounting information than can be easily used in the traditional strategy
operationalisation approaches based on dimensions and accounting ratios. The data
sets analysed included sets where all available firms were included and sets where
only large firms were included.
There were insufficient small firms in low discretion industries to test for
associations between variety in small firms and industry-level discretion, but there
were sufficient cases to demonstrate direct associations between variety in large
firms and industry-level discretion. If strategy is viewed in the restricted sense as
making long term decisions that require large irrevocable commitments, there is
general support for positive associations between industry-level discretion and
strategic variety. However there is also some support for the view that large firms in
low discretion industries compete by focusing on current strategic behaviours rather
than adopting different long term positions.
144
CHAPTER FIVE
CONCLUSIONS AND INTERPRETATIONS
This final chapter summarises both the methodological and theoretical
contributions of the thesis. It also identifies limitations to the reported research and
suggests a number of possible areas for future research.
Executive discretion in corporatised capitalist societies has long been
recognised as a necessity and as a source of risk (Berle & Means, 1968; Mason,
1959). External environmental characteristics exert strong influences on the level of
executive discretion typically ceded to individual top management teams
(Finkelstein & Hambrick, 1996; Hambrick & Finkelstein, 1987). In particular, the
characteristics of the industry task environment should be expected to exert
considerable influence on the typical level of executive discretion experienced by top
management teams in any particular industry (Abrahamson & Hambrick, 1997;
Hambrick & Abrahamson, 1995). Reason suggests this environmental influence on
executive discretion should be evident in strategic variety, a concept of central
importance to organisational theory building, but seldom included in empirical
studies.
However, industry-level discretion and strategic variety are abstract constructs
that, in the past, have proved difficult to measure. Executive discretion is a sociopolitical phenomenon with multiple determinants that interact in unknown ways.
We do not know enough about the interactions to measure industry-level discretion
by measuring and combining its determinants. At present, industry-level discretion
must be measured holistically and, if the measures are historical, indirectly.
145
Existent methods developed to measure strategic variety have limited use when
used in pan-industry studies. All three approaches examined use a conventional
strategy operationalisation approach that requires the researcher to select a limited
set of indicator variables and then manipulate the variables’ values to produce a
single value for variety. By default, the conventional approach involves discarding
potentially important and readily available data. Problems associated with the
selection of indicator variables increase with the ‘distance’ between industries and
limit the conventional approach to strategic variety measurement to similar
industries, a real encumbrance to pan-industry research. Even when using the ‘best
practice’ extracted from the existing measurement methods, the compromises
necessary to apply the method cast doubt on the resulting measures and any tests
where they were used. In response to these concerns, a new approach using entropy
based information theory was developed to measure industry variety using the full
sets of standard accounting fields supplied with annual reports.
In this thesis, values for industry-level discretion are obtained from archival
text data whose lexical properties have been shown to be correlated with industrylevel discretion. The methodology used is predicated on the Whorf-Sapir hypothesis
(Sapir, 1944; Whorf, 1956), which argues that language defines the way we see the
world. This means that the language we use to communicate ideas reveals more
about what we think than just the ideas being communicated. Properties and patterns
in the language reveal information about our world view. The text analysis used in
this thesis is based on simple analysis of shared word usage. It illustrates that even
simple computer aided text analysis can be used to extract meaningful information
from public documents, one of the widest available and least utilised data sources in
organisational studies (Kabanoff, 1997).
146
Before this thesis, our limited ability to measure industry-level discretion
meant that the extent of environmental influence on executive discretion was
undescribed. In turn, this restricted efforts to unpack the interactions between
environmental determinants and strategic choice when studying strategic actions.
While most scholars or practitioners would agree that both environmental influences
and executive preferences shape strategies, their roles relative to each other have
remained in a black box.
The results of Study Three in this thesis shed some light on the matter. The
results suggest that most top management teams have the ability to influence current
strategic behaviours. This suggests that meta-social characteristics of the general
environment, which are shared by all industry task environments, appear to have
similar effects on current strategic variety in all industries. Strategic choice is least
fettered by environmental influences when executive decisions are made concerning
current strategic behaviours. However, environmental influences have a greater
influence when executives make long-term strategic commitments. Some industryspecific task environments can severely restrict long-term strategic choices, while
others grant executives wide latitude.
This conclusion forces a rethink about what is strategy and how it should be
measured. For ongoing concerns at least, strategy can be seen as binding decisions
intended to achieve some desirable future state. Actions taken as a consequence of
those decisions can be incremental and accumulative or large, once-off
commitments. While, hypothetically, a firm could have only one of the two types of
strategic actions, it is difficult to imagine an ongoing concern where strategy did not
involve a mix of both types of actions. The results of Study Three strongly suggest
that the two types of actions should be treated separately. Combining measures
147
based on current term and long term actions obscures an important distinction and is
likely to produce specious results. The confusion that results from mixing the two
groups of strategic indicators may be contributing to the mixed results that are
reported in much strategic management research, notably in strategic group research
(Barney & Hoskisson, 1990; Dranove, Peteraf & Shanley, 1998; Hatten & Hatten,
1987; Thomas & Venkatraman, 1988).
Contributions
This thesis develops and uses a number of methodological innovations as it
addresses the challenge of measuring and testing for associations between two
difficult-to-measure constructs: industry-level discretion and strategic variety in
industries. The former is the average level of executive discretion of top
management teams in industries, with executive discretion being the latitude for
making binding strategic decisions. The latter is the mix of competitive strategies in
an industry. The thesis also makes a theoretical contribution by demonstrating that
high industry-level discretion is positively associated with major, long term strategic
differentiation of firms within an industry and raising the possibility that some large
firms in low discretion industries may compete by focusing on current strategic
behaviours.
Prior to this thesis, quantitative industry-level discretion measures were only
available for fourteen U.S. industries for the year 1987 (Hambrick & Abrahamson,
1995). This restricted set of contemporaneous quantitative values for industry-level
discretion reflects the abstract nature of the construct and the unknown interactions
of the theorised determinants of industry-level discretion. The first study of the
thesis uses a novel approach based on text analysis of archival data to produce 116
contemporaneous values for industry-level discretion across a range of U.S.
148
industries for the years 1990-1997. Those values have been placed in the public
domain (Keegan & Kabanoff, 2005) and represent a valuable addition to the set of
industry-level discretion ratings available to researchers.
The method used to produce these values is itself a contribution as further
application would produce additional values for industry-level discretion in other
industries and other time periods. Another contribution of the first study is a
refinement of the regression step used when calculating lexical density. Each of the
three significant contributions of Study One increases the stock of tools available to
researchers studying executive discretion or lexical phenomena in general.
Additionally, although simple and obvious when it has been thought of, the
adjustment of annual accounts to remove negative values is a technique that will be
of interest of researchers using accounting data who have an interest in identifying
and examining accounting adjustments in annual accounts.
Study Two includes detailed analysis of the statistical assumptions
underpinning prior methods for measuring strategic variety using selected
accounting ratios to operationalise selected generic or decontextualised strategic
dimensions. The study makes contributions to the understanding of the limitations
on use of coefficients of variance of sample data that supplement advice in Allison’s
(1978) seminal paper on inequality measures. The hypothesis test produced
unexpected results that, when viewed in the light of the results of Study Three, may
be attributed to combining current and noncurrent accounting data into a single
index. The main conclusion of Study Two is that a new method needs to be
developed to measure strategic variety.
Study Three uses entropy-based information theory to measure strategic
variety. The method relies heavily of Theil’s (1992b) suggestions on measuring
149
inequality in industries. The thesis extends Theil’s approach to develop variety
measures that permit the comparison of variety in different industries. This new
approach to measuring variety allows the researcher to combine more than two
columns of accounting data (as used in ratio analysis) and still produce a single
defensible measure for strategic variety. In turn, this allows the measurement of
variety in subsets of accounting data of theoretical interest to determine measures of
variety that reflect essentially different strategic approaches differentiated by
standard partitioning of annual accounts, most notably, the current and the
noncurrent aspects of strategic actions.
Introducing entropy-bases measurement to strategic variety measurement
provides new freedoms to the researcher, but it also comes at a cost: the method is
computationally intensive, especially when calculating confidence intervals around
point estimates. While, as evidenced by the studies of dinosaur bones briefly
discussed in Study Three, the use of bootstrap techniques to determine confidence
intervals of point estimates of entropy-based measures is by no means unique, the
combined use of set sample size, the bootstrap percentile method to calculate
confidence intervals for entropy-based values, specific case retention rules, and
secondary binomial tests to test apparently significant associations between two
variables makes the analysis used in this thesis both unique and ground breaking for
organisational studies and, almost certainly, for other disciplines. The additional
insights provided by the use of medians of firm distances from the industry-sampleas-a-whole as an alternative measure of variety illustrate the benefit of multiple
measures of variety when using the entropy-based approach.
150
Limitations
This thesis only uses U.S. data from firms traded on U.S. stock exchanges.
Very small firms, differentiated firms and privately owned firms are not included in
the database. The prospect that very small firms will display far greater strategic
variety than large firms has been noted when discussing the creation of the research
database. It may also be speculated that private firms have greater executive
discretion when compared to otherwise comparable publicly traded firms.
The impact of differentiation on executive discretion is unknown, but it seems
reasonable to speculate that the type of differentiation would be an influencing factor
on that impact. The unknown influence on executive discretion of multiple industry
task environments on firms with high levels of unrelated differentiation limits
generalisation of the findings to undifferentiated firms and firms with highly related
differentiation. Additionally, the Banking and Finance Sector (SIC1 = 6) and the
health care technology industries should be treated a special cases where additional
research is needed.
The sampled period is limited to the 1990s. Rapid change on many
macrosocial dimensions that impact on business behaviour in the 21st century may
erode the unexplored mechanisms that support the association between industrylevel discretion and strategic variety identified in this thesis. However, it is more
likely that those mechanisms will persist and the levels of executive discretion will
change in response to macrosocial change (Hambrick et al., forthcoming).
Future Research
From a theoretical perspective, one of the most interesting research questions
that remains unaddressed is the association between strategic variety in small firms
and industry-level discretion. Does the association between strategic variety and
151
industry level discretion weaken and disappear as firm size decreases? Answering
this question would give some insight into the relative strengths of industry task
environment effects and firm level effects. Examination of the question would
require large numbers of small firms in low discretion industries – the very industries
where they are least likely to exist. Due to its size, the U.S. economy is the most
likely setting where sufficient small firms in low discretion industries would be
found.
Research into executive discretion in health care technology industries in
particular is likely to produce insight into the interaction between conflicting
determinants of executive discretion. The stresses arising from the coupling of rapid
technological innovation and stringent regulation suggests that these industries will
experience ambiguous environmental signals. If my speculation is correct, these
industries should display high levels of mimetic isomorphism (DiMaggio & Powell,
1983).
Concerns about generalising to other economies highlight the need for
comparative studies. Some preliminary discussions are already underway to
examine the practicability of developing an Australian database that would permit
testing the thesis’s main findings in a socio-economic setting with many
characteristics similar to the U.S., albeit, in a smaller economy with few large firms.
Assuming lexical and linguistic issues could be addressed, a parallel study in an
economy that has few Western socio-economic characteristics but had large numbers
of small firms would be even more intriguing.
On the methodological front, using the entropy-based measurement methods
described and used in this thesis allows identification of individual outlying firms
that are making significant contributions to differences in Method I and Method II
152
variety values. These outliers appear to be firms adopting novel strategies, which
raises questions of risk taking and innovation. Additional research linking these
firms to performance variables should produce interesting results and insights.
The possibility mentioned in a footnote that relative variety, that is the actual
variety value divided by the maximum possible variety value, might permit
comparison of variety in different subsets of accounting fields or in different samples
opens up a new set of possible research questions relating to emphasis of competitive
strategy. Again, additional work in this area promises substantial return for research
effort. Direct comparison of the relative variety in assets and liabilities in particular
would assist in identifying their relative importance to competitive strategy in
industries.
The entropy based variety measurement approaches used in this thesis use
proportions and, consequently an important strategic variable, firm size, is lost.16
This could be avoided by weighting each firm’s contribution to the industry variety
measure according to the firm size. I have not seen any work along these lines in my
readings on information theory. Experiments with size and derivatives of size (e.g.
logs, squares) would almost certainly reveal a fresh way of conceptualising and
measuring strategic variety that would be a valuable addition to strategy research,
and, indeed, have application in other disciplines.(Allison, 1978)
This thesis limits the use of entropy-based variety measurement to accounting
data. The technique appears readily adaptable to text data, especially word usage
counts. It would be interesting to know if firms that are outliers in their lexical
usages are also outliers in their strategic behaviour. The approach offers some
prospect of a new way to analyse risk-performance linkages at the firm level.
16
Firm size is lost when using single ratios as well.
153
While this thesis has measured the two main constructs with sufficient
accuracy to permit the research question to be addressed, the measurement methods
used are by no means the final word in measurement approaches. Industry-level
discretion and strategic variety research would benefit from additional measurement
approaches, both to establish convergent validity and to increase the ability to
measure the constructs in industries that display characteristics that prevent the
application of available measurement techniques.
CONCLUSION
The underlying purpose that drove this research was to demonstrate that
Hambrick and Finkelstein’s (1987) discretion model has considerable untapped
potential to contribute to understanding a wide range strategic behaviours. It is clear
that research involving multiple industries that does not take industry-level
discretion into account runs the risk of model misspecification and production of
misleading conclusions. This is especially the case in longitudinal studies involving
industries that experience significant changes in industry-level discretion. Executive
discretion has long been recognised as both necessary and open to abuse (Berle &
Means, 1968). It has a pervasive influence on strategic decisions but explicit
discretion research is rarely undertaken. If this thesis assists and encourages more
research into executive discretion, it will have served its underlying purpose.
154
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APPENDIX
CASE DELETION RATIONALE AND ILLUSTRATION OF ANALYSIS
TECHNIQUE USED
Sample Data and Confidence Intervals
This research uses samples of undifferentiated firms in U.S. industries. The
samples are as large as can be extracted from the Compact Disclosure discs that were
available at the time the research was undertaken. Not all firms in the populations
from which the samples are drawn are in the Compact Disclosure database and the
industry population sizes are unknown. While it is necessary and reasonable to
assume that the samples used are representative of their parent populations, no way
was available to determine if the sample was a large proportion of the industry
population or a small proportion. Thus, even though the samples used may actually
contain large proportions of the parent populations and even though the parent
population were not infinitely large, when calculating confidence intervals around
point estimates it was necessary to fall back to a conservative assumption that the
population is relatively large when compared to the available sample. In other
words, the estimated confidence intervals used are larger than they would be if
population numbers were known.
In this thesis, as far as is practicable, cases with unreliable point estimates of
population values are deleted before correlation tests are performed. Unreliable
estimates of population values can occur in normally distributed populations when
the sample size is too small. It can also occur when the population has outliers that
are included in the sample and result in large ranges for confidence intervals. In
172
either circumstance, the variance in the sample generates uncertainty about the true
value of the variable in the population. That uncertainty can only be reduced by
increasing the sample size. In populations with true outliers, addressing that
uncertainty can sometimes require census data, which, for this research, was not
available.
The research uses all available information in the Compact Disclosure database
to gather the largest samples possible. It may have been useful to access the
Compustat database, which appears to have more extensive coverage of U.S. firms
but that database was unavailable to this Australian-based researcher. Even if
additional databases had been available, eventually the sample sizes would have
reached a limit and the problem of large confidence intervals and unreliability of
point estimates would have had to have been addressed.
Confidence Intervals and Unstable Estimates
The problem boils down to answering the question “How large can the range of
a confidence interval be before a researcher must conclude that the point estimate
from a sample is too unstable to use?” In answering that question, it is useful to
consider the simplest case first, that is, the normal distribution. When using normal
distribution assumptions, if 1.96 times the standard deviation is greater than the
value of the point estimate of a variable that cannot have negative values, the lower
confidence interval is less than zero, which means the sample size is too small
(assuming the conventional 95% confidence standard is applied).
However, consideration of the possibility that the lower confidence interval
may be just above zero demonstrates that using 1.96 times the standard deviation as
a rule to identify cases where the point estimates are unstable is still extremely
generous and, if applied, it would result in accepting point estimates based on data
173
that supplied almost no information about the population value of the variable other
than the fact that there was a one in twenty chance it was more than zero. Clearly, a
more restrictive rule is required for general use, especially when it is acknowledged
that population distributions are often skewed, as is the situation for most of the
variables used in the research reported in this thesis.
Case Deletion for Correlation Tests Using Point Estimates
In this thesis, several more restrictive case deletion techniques are used. In
correlation tests, when using point estimates for which there are confidence intervals,
if the confidence intervals are estimated using the normal distribution assumptions,
all cases where the range of the estimated 95% confidence intervals exceeds the
value of the point estimate are deleted. The rationale is as follows: if the population
has a normal distribution, this rule ensures the standard deviation of the sample does
not exceed one quarter of the value of the point estimate. This gives a 50% buffer
between the lower confidence interval and zero to absorb the effect of biases in
distributions in populations.
If the confidence intervals are estimated without using the normal distribution
assumptions, that is, if the confidence intervals are estimated using the bootstrap, all
cases are deleted where the range of the estimated 95% confidence intervals exceeds
both the value of the point estimate and the average of the point estimates available.
The second condition is added to prevent the deletion of cases with very low point
estimates where the range of the confidence intervals, although small, is still greater
than the value of the point estimate. The second condition can be introduced
because the bootstrap estimations used are range respecting. In other words, they
will not have a negative value for the lower estimated confidence interval for a
variable that can have only positive values.
174
Scatterplots
Irrespective of the technique used to estimate confidence intervals, if a
correlation test using retained cases’ point estimates produced a significant result,
scatterplots showing the point estimates and their 95% confidence intervals were
always examined. As an illustration, Figures 1.1a and 1.1b show scatterplots with
confidence intervals for variety in current ratio inputs that have been determined
using the bootstrap percentile method. The point estimates of variety in Figure
A1.1a, where no case deletion has occurred, are not significantly correlated with
standardised values of industry-level discretion (Pearson r = 0.15, one-tailed p-value
= 0.09, N = 82). The point estimates in Figure A1.1b, which only shows cases
retained after both case deletion rules have been applied, are significantly correlated
with the standardised values of industry-level discretion (Pearson r = 0.30, one-tailed
p-value = 0.02, N = 52). Figure A1.1b reveals the confidence intervals of most
retained cases overlap and that the strength of the apparent correlation may be less
than suggested by a simple point estimate analysis.
Additional Tests
In this thesis, to reduce the potential for specious results, when a test using point
estimates for cases retained after the application of the case deletion rules produced a
significant result, an additional test was used. As Figure A1.2 demonstrates with the
illustrative data, when the cases are ordered by their point estimates, the confidence
intervals demonstrate that the distinction between cases is not as strong as suggested
by simple consideration of the point estimates. It is, however, possible to identify
some cases where the confidence intervals do not overlap and assert, with at least
95% confidence, that the cases have different values for the measured variable.
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FIGURE A1.1a
All Available Cases Scatterplots Shows Many Confidence Intervals are Very
Large (Current Ratio Inputs Variety) – No Significant Correlation
12
Variety
8
4
0
-3
-2
-1
0
1
Industry-level Discretion
FIGURE A1.1b
Illustrative Scatterplot of Current Ratio Variety Cases After Case Deletion
Rules Suggests Correlation is Weak (At Best) – Significant Correlation
Variety
12
8
4
0
-2
-1
0
1
Industry-level Discretion
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For each association test, the best split of the cases was identified so that
groups of ‘high’ and ‘low’ cases, defined by the 2.5 percentile and the 97.5
percentile respectively, did not overlap. The split point was determined manually
and represents the point where the numbers in the high and low groups are
maximised in each group and in sum. Figure A1.3 illustrates the selection of the best
cut off point in the illustrative data. This process results in loss of cases and the
confidence intervals of retained cases within a group typically overlap, which means
there is not enough information to order the cases inside a group. Figure A1.4 shows
the retained cases in the illustrative data. The ‘low’ group have been ordered by
their 97.5 percentile confidence interval and the ‘high’ group have been ordered by
their 2.5 percentile. The highest 97.5 percentile in the low group is less than the
lowest 2.5 percentile in the high group.
Once the variety variable had been used to identify cases where the estimates of the
upper and lower confidence intervals did not overlap, that is, cases that could be
confidently allocated to a low and a high group, the number of cases in each group
with high and low values for the second variable of interest (industry-level
discretion) was counted. Statistical tests determined the likelihood that the groups’
proportions of cases with high and low values in the second variable of interest were
attributable to random selection.
In the illustrative data there were twenty-seven cases retained after identifying
the two groups. Nine of the fifteen cases in the low group had standardised industrylevel discretion values below zero, while two of the twelve cases in the high group
had standardised industry-level discretion values below zero. The sample sizes
available after using this case selection process were so small that a t-test based on
177
FIGURE A1.2
All Available Cases Ordered by the Point Estimate Shows Extensive Overlap of
Confidence Intervals
Current Ratio, All Firms Data, 82 Cases
14
12
Variety
10
8
Mean
6
97.5%ile
2.5%ile
4
2
0
1
6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81
Cases Ordered by Point Estimate of Variety
FIGURE A1.3
Identifying the Best Cut-Off Point to Maximise Membership of High and Low
Groups Defined by Non-overlapping 2.5%ile and 97.5%ile Respectively
70
60
Number of Cases in High Group
50
40
Number of Cases in Low Group
30
Total Number of Cases in Both
Groups
20
10
0
1
13 25 37 49 61 73 85 97 109 121 133 145 157 169 181
Incremental Increases in Possible Spliting Point
Best Point is Marked by Large Triangle, Square and Diamond
FIGURE A1.4
All the Upper Confidence Intervals in the Low Variety Group are Lower than
All the Lower Confidence Intervals in the High Variety Group
Variety
Two Groups Identified
14
12
10
8
6
4
Point Estimate
97.5%ile
2.5%ile
2
0
1
3
5 7
9 11 13 15 17 19 21 23 25 27
Low Group ordered by 97.5%iles,
High Group Ordered By 2.5%ile
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the average and standard deviation of the point values of the second variable was
inappropriate. Instead, two binomial tests were used. The probability of getting nine
low industry-level discretion cases out of fifteen randomly selected cases when the
combined group’s probability of a low case is 11/27 (= 0.407) is 0.08, while the
probability of getting two low industry-level discretion cases out of the twelve cases
in the high group is 0.11. This result could be attributable to random selection using
conventional levels of significance.
This second test is a very crude and conservative treatment of data and only
identifies the chances that the separation of cases with low and high values in the
second variable could be attributable to chance. If the separation is not likely to be
attributable to chance, it is reasonable to conclude the there is an association between
the two variables. In the illustrative example, the combined results Pearson
correlation test, the scatterplot, and the binomial tests suggest there is insufficient
support to conclude that there is a significant association between industry-level
variety in the current ratio inputs and industry-level discretion.
Looking for Patterns
This treatment of data, which some may regard as unnecessarily harsh, seems
preferable to a more liberal approach that would have a higher probability of
producing specious results. Eventually statistical analysis has the purpose of
informing the user who must discern the story from the data and analyses available.
While 95% confidence intervals are reported throughout this thesis, in some batteries
of statistical tests slightly conflicting results may reflect the different characteristics
of the tests and the assumptions used when determining which data is used in the
test. The results of multiple tests are analysed to find patterns. Occasionally
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marginal leniency is required to reconcile apparently conflicting results from
different tests. This may require relaxation of the 95% confidence standard.
The case deletion rationale and an illustrative analysis have been provided in
this appendix to avoid interrupting the continuity of the remainder of the thesis.
Results and analysis in the thesis are presented without repeating unnecessary details
of the methods described above or their rationale.
180