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Organisational Slack and Industry Level Executive Discretion
By Anthony (Tony) Niven
Bachelor of Business Administration
Queensland University of Technology Faculty of Business, School of Management
A Thesis Submitted for Masters of Business (Research)
2008
ABSTRACT
This thesis examines the associations between organisational slack, that pool of actual or
potential cushion of resources of an organisation, and executive discretion - the
executives’ latitude for strategic action.
Bourgeois and Singh (1983), George (2005), Sharfman et al. (1988) and Sharma (2000)
have referred to slack as having a discretionary dimension because its ‘ease of recovery’
varies depending on where it is gained from. For the obverse of this association, slack
contributes to resource availability in the task environment and therefore executive
discretion (Hambrick & Finkelstein, 1987). However until now, this bi-direction
association has been largely unexplored empirically. This thesis contributes to both
fields by bringing them together to examine and measure aspects of these interactions.
These constructs are applied to the annual reports of U.S. firms by measuring industry
level discretion using content analysis of presidents’ letters to shareholders and industry
average slack using financial ratios. Correlations show that industries with higher levels
of slack enjoy greater industry level discretion. However the associations between slack
types and industry level discretion are not uniform suggesting that the discretionary
dimension of slack is influenced by the task environment and industry context. The
present study replicated Keegan and Kabanoff’s (2007) method to examine slack within
industries but could not extend their results to available and recoverable slack, which
suggest a curvilinear relationship between potential slack and executive discretion.
The limited sub-industry results offer opportunity for further research as does the idea of
applying the same research question to the organisational and individual level studies of
different cohorts of firms and industries. Future efforts should also improve the
measurement of the slack construct.
Keywords: Organisational Slack, Executive Discretion, Industry Level Discretion.
CONTENTS
Chapter One – Introduction to Organisational Slack and Managerial Discretion .... 1 Introducing Organisational Slack ............................................................................................................. 1 Introducing Executive Discretion ............................................................................................................. 2 Mutuality between Slack and Executive Discretion ................................................................................. 3 The Empirical Gap between Slack and Executive Discretion .................................................................. 6 Research Question .................................................................................................................................... 7 Thesis Structure ........................................................................................................................................ 8 Chapter Summary ..................................................................................................................................... 8
Chapter Two – Interacting Models: Organisational Slack and Executive Discretion ............................................................................................................................................ 9
Organisational Slack ................................................................................................................................. 9 Forms of Organisational Slack .......................................................................................................... 10
Excess Inputs, Unnecessary Payments and Unexploited Opportunities ....................................... 10 Financial and Non-Financial Forms of Slack .............................................................................. 11
Sources of Organisational Slack (Slack as DV) ................................................................................ 12 Slack from Internal and External Sources .................................................................................... 12 ‘Ease of Recovery’ Based Models of Organisational Slack .......................................................... 13
Functions of Organisational Slack - (Slack as IV) ............................................................................ 15 Slack is an Organisational Resource ................................................................................................. 17 Organisational Slack versus Budgetary Slack ................................................................................... 18 Empirical Research Using Organisational Slack .............................................................................. 19
Executive Discretion .............................................................................................................................. 21 What is Executive Discretion and Who Has It? ................................................................................. 21 Why is Executive Discretion an Important Concept? ........................................................................ 22 Three levels of Determinants of Executive Discretion ....................................................................... 23
Characteristics of the Manager .................................................................................................... 24 Characteristics of the Organisation .............................................................................................. 24 Characteristics of the Task Environment ...................................................................................... 25
Executive Discretion in Industries - Industry Level Discretion ......................................................... 27 Social Influences on Industry Level Discretion ................................................................................. 29
Empirical Research Using Executive Discretion .................................................................................... 30 Slack and Executive Discretion - Some Interactions .............................................................................. 32
The Discretionary Dimension of Slack .............................................................................................. 32 Slack, Industry Level Discretion and Industry Forces ....................................................................... 33
Chapter Summary ................................................................................................................................... 36 Chapter Three – Introduction to Content Analysis of Annual Reports ................... 37
Content Analysis .................................................................................................................................... 37 Defining Content Analysis is defining ‘Content’ ............................................................................... 37 What is ‘Content’? ............................................................................................................................. 38 Reliability and Validity Considerations for Content Analysis ........................................................... 39
Reliability ...................................................................................................................................... 39 Face and Social Validity ............................................................................................................... 39 Sampling Validity .......................................................................................................................... 40 Semantic Validity .......................................................................................................................... 41 Construct Validity and Pragmatic Functional Validity (Replication) .......................................... 41 Convergent and Discriminate Validity.......................................................................................... 41
Predictive Validity ........................................................................................................................ 42 Validity considerations for Computer Assisted Text Analysis of Presidents’ Letters ........................ 42
Financial Ratio Analysis of Slack .......................................................................................................... 43 Reliability ...................................................................................................................................... 43 Concurrent Validity ...................................................................................................................... 43 Construct Validity ......................................................................................................................... 44
Relative versus Absolute Measurement ............................................................................................. 44 Level of Measurement and Analysis .................................................................................................. 45
Chapter Summary ................................................................................................................................... 45 Chapter Four – Method ................................................................................................. 46
An Example of the Method .................................................................................................................... 46 Design Overview .................................................................................................................................... 47 Measures of Organisational Slack .......................................................................................................... 48
Composite Measurement of Slack ................................................................................................. 48 Multiple Measures of Slack ........................................................................................................... 48
Available Slack .................................................................................................................................. 50 Retained Earnings ........................................................................................................................ 50 Negative Dividend Payout ............................................................................................................ 50 Cash – Working Capital ............................................................................................................... 52
Recoverable Slack ............................................................................................................................. 52 Accounts Receivable ..................................................................................................................... 52 Inventory ....................................................................................................................................... 52 Sales, General & Administration Expense .................................................................................... 53
Potential Slack ................................................................................................................................... 53 Negative Long Term Debt ............................................................................................................. 53
Total Slack ......................................................................................................................................... 54 Measures of Executive Discretion .......................................................................................................... 55
Attentional Homogeneity as a Measure of Executive Discretion ...................................................... 55 Lexical Commonality .................................................................................................................... 56 Lexical Density ............................................................................................................................. 57 Extra Usage .................................................................................................................................. 57 Positive Extra Usage .................................................................................................................... 57
Sampling and Data Preparation .............................................................................................................. 58 Research Database ............................................................................................................................ 58 Sample Frame .................................................................................................................................... 59 Industry Level Data Preparation ....................................................................................................... 60
Negative Retained Earnings ......................................................................................................... 60 Missing and Zero Values .............................................................................................................. 60
Industry Level Sample ........................................................................................................................... 62 Describing the Industry Level Variables ................................................................................................ 63
Industry Level Outliers and Normality .............................................................................................. 65 Describing the Univariate Outliers .............................................................................................. 65 Visual Test for Bivariate Outliers ................................................................................................. 67 Describing the Bivariate Outliers ................................................................................................. 67
Normality ........................................................................................................................................... 69 Linearity ............................................................................................................................................ 69 Correlations among Slack Measures ................................................................................................. 72 Industry Level Data Analysis ............................................................................................................. 74
Sub-Industry Sampling and Data Preparation ........................................................................................ 74 The Sub-Industry Level Sample ............................................................................................................. 75 Describing the Sub-Industry Thirdtile Groups ....................................................................................... 77
Sub-Industry Data Analysis Method .................................................................................................. 79 Chapter Summary ................................................................................................................................... 79
Chapter Five – Results of Industry and Sub-industry Analyses ............................... 80 Study A – Associations Between Slack and Discretion across Industries .............................................. 81
Available Slack and Industry Level Discretion .................................................................................. 83 Industry Average Retained Earnings / Sales vs. Industry Level Discretion .................................. 83 Industry Average Negative Dividends / Net Worth vs. Industry Level Discretion ........................ 84 Cash and Securities less Current Liabilities / Sales vs. Industry Level Discretion ....................... 84
Summary of Hypothesis 1(a) Tests ........................................................................................................ 85 Recoverable Slack and Industry Level Discretion ............................................................................. 86
Industry Average Accounts Receivables / Sales vs. Industry Level Discretion ............................. 86 Industry Average Inventory / Sales vs. Industry Level Discretion ................................................ 87 Industry Average S,G&A / Sales vs. Industry Level Discretion .................................................... 88
Summary of Hypothesis 1(b) Tests ........................................................................................................ 89 Potential Slack and Industry Level Discretion .................................................................................. 90
Industry Average Negative Long Term Debt/Net Worth vs. Industry Level Discretion ................ 90 Total Slack and Industry Level Discretion......................................................................................... 91
Summed Slack vs. Industry Level Discretion ................................................................................ 91 Summary of Study A .............................................................................................................................. 91 Study B - Slack and Discretion within Industries ................................................................................... 92
Available Slack/Recoverable Slack and Executive Discretion within Industries ............................... 93 Summary of Hypothesis 2(a) and 2(b) Tests .......................................................................................... 96
Association between Potential Slack and Executive Discretion within Industries ............................ 97 Study B Summary .................................................................................................................................. 98 Chapter Summary ................................................................................................................................... 98
Chapter Six – Conclusions and Implications ............................................................. 100 A Short Recap of (the purpose and process of) this Study ................................................................... 100 Discussion ............................................................................................................................................ 101
Associations between Slack and Discretion ..................................................................................... 101 Discretionary Dimension of Slack ................................................................................................... 104 Slack and Executive Discretion within Industries............................................................................ 105
Contributions ........................................................................................................................................ 106 Limitations ........................................................................................................................................... 108 Opportunities for Further Research ...................................................................................................... 108 Thesis Summary ................................................................................................................................... 110
References ..................................................................................................................... 111
Appendix A ................................................................................................................... 123 Illustration of Lexical Commonality Measure of Attentional Homogeneity ........................................ 123 Illustration of Lexical Density Measure of Attentional Homogeneity ................................................. 124 Illustration of Extra Use Measure of Attentional Homogeneity ........................................................... 126
Appendix B ................................................................................................................... 127 Rationale of the Thirdtiles Approach ................................................................................................... 127
Appendix C ................................................................................................................... 128 SEC Narrative of 17 Sampled Industries by 4 Digit SIC Code ............................................................ 128
LIST OF TABLES
Table 1 - Models of Slack using Recovery Dimensions .................................................. 14
Table 2 - Functions of Slack in Organisations ................................................................. 16
Table 3 - Studies using Financial Measures of Slack ....................................................... 49
Table 4 - Slack Measures after Bourgeois and Singh (1983) ........................................... 50
Table 5 - Sampled Industries ............................................................................................ 59
Table 6 - Cases Containing Annual Reports Reporting Zero Sales ................................. 61
Table 7 - Industry Level Sample ...................................................................................... 62
Table 8 - Industry Level Univariate Descriptive Statistics .............................................. 64
Table 9 - Organisation Level Outlier Analysis Summary ................................................ 66
Table 10 - Average Slack and Industry Level Discretion by Industry Target Year ......... 71
Table 11 - Pearson Correlations among Slack Measures ................................................. 73
Table 12 - Thirdtile level Sample of President's Letters by Industry-Target Year .......... 76
Table 13 - Thirdtile level Sub-measures of Attentional Homogeneity ............................ 77
Table 14 - Summary of Thirdtile level Discretion Sub-measure Factor Analysis ........... 78
Table 15 - Pearson Correlations between Slack and Industry Level Discretion .............. 82
Table 16 - The Contributions of This Study .................................................................. 107
Table 1A - Lexical Commonality Calculation ............................................................... 124
Table 1 B - Raw Lexical Density Calculation ................................................................ 125
LIST OF FIGURES
Figure 1 - Industry Average Slack and Industry level Discretion .................................... 68
Figure 2 - Slack and Industry Level Discretion Frequency Histograms .......................... 70
Figure 3 - Hypothesised Executive Discretion versus Slack within Industries ............... 74
Figure 4 - Available Slack Thirdtile Group Executive Discretion ordered by Industry
Level Discretion ............................................................................................................... 94
Figure 5 - Recoverable Slack Thirdtile Group Executive Discretion ordered by Industry
level Discretion ................................................................................................................ 95
STATEMENT OF ORIGINAL AUTHORSHIP The work contained in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. 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. Anthony (Tony) Niven Date:
ACKNOWLEDGEMENTS
I acknowledge and pay thanks for the academic and non-academic support I received
that made this thesis possible.
I am especially grateful to Dr. Jack Keegan for his scholarly guidance and the generous
use his research database, both of which were vital to this research. Jack understood my
learning needs well and enthusiastically gave me sound direction at all stages of my
research. Jack’s help and advice made my project interesting, as well as academically
and professionally rewarding. I am also grateful for the support of Professor Boris
Kabanoff whose supervisory approach and understanding worked well for me. In
addition, I would like to acknowledge the support from my academic colleagues Mark,
Tony and Francis, who worked with Jack to encourage me to persist with my research
when all seemed lost.
To Annalise - thank you. I expect my life’s work will be to repay you for the patient and
faithful support you have shown me every day of this journey. I hope you spare some
pride for your contribution to this worthy project. Let this thesis stand, for as long as a
library holds it, as an inspiration to our children, Alex and Samuel, and theirs, so that
they might pursue education and achieve their dreams. I am also hopeful that my next
learning journey will be more shared with you and be of joy, not sacrifice.
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Chapter One – Introduction to Organisational Slack and Managerial Discretion
This chapter introduces organisational slack and managerial discretion - the two
constructs at the centre of this thesis. Organisational slack is introduced using Bourgeois
and Singh's (1983) construct which highlights why it is intuitively appealing to scholars
and practitioners. The discretion model of Hambrick and Finkelstein (1987) is then used
to introduce executive discretion as an important contextual construct that promises to
help explain organisational processes and outcomes. Introducing these two separate
constructs lays a base on which to draw them together with a discussion of what is
known about how slack affects and is effected by executive discretion. Sitting between
the two constructs, a research gap is described by arguing that, although the term
‘discretion’ is used to describe some types of slack, little is known about how slack and
executive discretion interact. A research question is set to frame this gap and set in place
a path for this thesis to proceed.
Introducing Organisational Slack Organisational slack is what practitioners have traditionally called ‘the fat’ of an
organisation. Conventional logic suggests slack should be minimised for the sake of
efficiency. Alternatively, it is seen as an essential buffer used to cope with unforeseeable
shocks and a facilitator of strategic opportunity. Paradoxically then, organisational slack is
simultaneously a poison and a panacea: it frustrates and facilitates organisational success
(Nohria & Gulati, 1996). Defining slack as a pool of resources in excess of that required to
maintain a given output encourages the perception of slack as wasteful. The alternative
definition, which is adopted in this thesis, defines slack as an actual or potential cushion of
resources which allows an organisation to adapt to pressures for internal changes and to
initiate strategic changes to respond to the external environment (Bourgeois, 1981). The
latter definition creates the space to explore slack as a critical resource for organisational
survival and raises interesting questions about the roles of slack and the possibility of
optimal slack. Irrespective of the discomfort managers may feel when slack is discussed,
strategic theorists find the construct of slack useful and appealing.
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Slack has been theoretically linked to such organisational outcomes as performance,
innovation, political behaviour, risk taking and knowledge transfer (e.g. Daniel, Lohrke,
Fornaciari, & Turner, 2004; Nohria & Gulati, 1996; Singh, 1986) but, as we shall see
later in this thesis, empirical research into the role of slack has produced ambiguous
results. Thus, for example, the relationship between slack and performance has been
conceptualised as positive (Cyert & March, 1963), negative (Jensen, 1986) and
curvilinear with an ideal level of slack for an organisation (Bourgeois, 1981) and some
support for each view has been found in empirical studies (e.g. George, 2005; Mishina,
Pollock, & Porac, 2004; Singh, 1986; Tan & Peng, 2003). Overall, existent research into
slack points to the conclusion that slack is a nebulous subject on a conceptual level and
our limited understanding of how to describe slack inhibits its measurement on a
practical level. Scholars seeking to understand slack are faced with the reality that it is
often covert, intangible and dynamic with respect to time and relative to environment,
industry, organisation size and indeed organisational processes (Daniel et al., 2004). In
short, the role and importance of slack is almost certainly dependent on context, an
insight which serves as one plank in building this thesis’ research question.
Introducing Executive Discretion Johns' (2006) useful discussion on the importance of context in organisational research
included the observation that managerial discretion is one of the often overlooked
contextual variables that could potentially explain empirical anomalies uncovered in
organisational research. Importantly, managerial discretion has been attributed to
influence, and be influenced by, organisational process that identify, gain and use slack
(George, 2005; Sharfman, Wolf, Chase, & Tansik, 1988; Yasai-Ardekani, 1986).
Managerial discretion, also called ‘executive discretion’, is the top executive’s latitude
for managerial actions. In this thesis, the term ‘executive discretion’ is the adopted to
emphasise that the construct focuses on discretion of upper echelons, as distinct from
middle level managers (Hambrick & Finkelstein, 1987). Executive discretion manifests
in the actions of executives, based on their choices taken in a discretionary domain.
Merely having choices does not demonstrate discretion; it is the actions of executives
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that show they have discretion (Hambrick & Finkelstein, 1987). The level of discretion
an executive holds is determined by their own attributes but also by the organisation and
its environment.
As with slack, executive discretion is an important concept with the potential to explain
and predict organisational phenomena that lead to success. Although, as Hambrick &
Finkelstein (1987) conceded, there are many essential managerial tasks that do not
require discretion. Hambrick, Finkelstein, Cho, and Jackson, (2005) argue that an
increase in executive discretion due to reduced isomorphic pressures (to conform to
industry norms) leads to an increase in the managerial effects on organisational
outcomes. In industries where discretion is high, the manager matters more than where
discretion is low. Understanding executive discretion helps us understand the strategic
behaviours of executives and how these behaviours influence organisational responses to
the environment (Child, 1972).
Mutuality between Slack and Executive Discretion A long held assumption of strategic theory is that managers need to adapt their
organisations’ resources to often harsh and competitive environments (Andrews, 1971;
Barnard, 1938; Pfeffer & Salancik, 1978). Even before organisations look for ways to
grow and develop, their basic maintenance can be challenging (Nelson & Winter, 1982).
Thus, while strategic adaptation is necessary for organisations to compete in
contemporary dynamic environments, internal maintenance which sometimes conflicts
with adaptability, is also important for organisational survival. Although appropriate
strategic adaptation and internal maintenance contribute to the level of performance,
failure and sustainability of organisations (Sharfman et al., 1988; Yasai-Ardekani,
1986), few would disagree that both processes are to a largely predicated on the
organisation’s resource base (Penrose, 1966).
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An organisation’s tangible and intangible resources allow it to adapt to its environment
and to maintain its processes. They facilitate and enable risk taking, experimentation and
strategic choice which in turn and amongst other things provide opportunities to increase
an organisation’s resource base. For example, the creation of new products and services
is facilitated by the organisation’s financial resources such as raised capital and cash.
Knowledge resources do this by creating the ideas necessary to introduce new products
and services. Human resources do this when they work to produce the product or
provide the service. Finally, reputational resources do this by providing opportunity for
the market acceptance of new product and services. Success of new products or services
provides the resources required to maintain and grow each of the resource pools
(Penrose, 1966). Given the crucial role of resources, it is not surprising that the
identification and management of resources receives considerable scholarly and practical
attention (George, 2005).
While effective management of organisational resources aims to maximise the benefits
extracted for profit and growth, uncertainties generated by dynamic internal and external
environments ensure the task of understanding, measuring, evaluating and deploying
resources is ongoing. Cyert and March (1963) introduced the term ‘organisational slack’
to describe the buffer of excess capacity intentionally or unintentionally held by
organisations to manage this uncertainty. The construct was central to their
behaviouralist, problem-solving view of the organisation. They described slack as “the
disparity between the resources available to the organization and the payments required
to maintain the coalition” (p. 36). Slack, they argued, is an uncommitted resource used
for maintenance (internal maintenance) and problem solving (including adaptation).
Slack acts as a facilitator of internal maintenance by protecting or buffering the
organisation’s core and so reducing the need for structural changes to adapt to external
contingencies. Slack also endows organisations with alternatives when deciding on
actions to adapt to their environment. In effect, this reduces the constraints on the
organisation because managers can broaden their search for solutions to problems
generated by the (task) environment (Thompson, 1967).
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Slack resources are managed using executive discretion and discretion is used by
executives to gain, maintain and then deploy slack for internal maintenance and
adaptation processes. The two processes are mutually supportive. Irrespective of
causality, executives with high discretion enjoy more access to resources than those with
low discretion. Consequently, executives in conditions of low slack search more
intensely for solutions but in conditions of high slack they search less thoroughly but
more broadly and have greater discretion to influence organisational adaptation and
internal maintenance (Cheng & Kesner, 1997; Yasai-Ardekani, 1986).
The amount of discretion held by an executive is seldom, if ever, clearly delineated: it is
open to discovery by enactment, sensemaking and negotiation (Hambrick & Finkelstein,
1987; Keegan, 2006; Weick, 1995). The amount of executive discretion is influenced by
dynamic interactions between the managers own attributes, their organisation and the
task environment. Hambrick and Finkelstein (1987) suggested resource availability
positively affected executive discretion and at the same time powerful outside forces,
negatively affected executive discretion. Slack, commonly seen as part of that internal
organisational resource, bridges the components of Hambrick and Finkelstein's (1987)
discretion model because it can be a resource potentially gained from the task
environment and because slack is available from within the organisation (Bourgeois &
Singh, 1983).
As a simplistic generalisation, in the absence of other factors, executives should have
more discretion if their organisation has abundant slack resources and executives with
high discretion should have increased opportunities to create slack in their organisations.
However these mutually reinforcing propositions do not create an open ended process as
external environmental dynamics create restraints and feedback in open systems (Emery
& Trist, 1965). Nonetheless, theory and intuition suggest slack resources and executive
discretion should have strong associations.
6
The Empirical Gap between Slack and Executive
Discretion The foregoing discussion supports the conclusion that organisational slack and executive
discretion intersect where slack resources are managed using executive discretion.
Whether it is discretion’s influence on slack or the obverse, the space where they meet is
largely undefined theoretically and its dimensions and characteristics have not been
measured empirically.
‘Discretionary slack’, was a term used by authors (George, 2005; Sharfman et al., 1988;
Sharma, 2000) to describe an apparent attribute of slack, which suggests discretion is
more a characteristic of the resource than of a manager. Whereas Hambrick and
Finkelstein (1987) consider that managers have discretion in an environmental context,
including the presence or absence of slack. The discretionary slack assumption was
made clearest by George (2005), in a study of privately held firms, who maintained that
cash is more discretionary than debt. He argued that slack accessibility is a continuum of
discretion and proceeded to measure high and low discretion slack as theorised by
Sharfman et al. (1988). Sharfman et al. (1988) interpreted excess cash resources as high
discretion slack and extra machine capacity as low discretion slack.
This assumed connection between slack and executive discretion largely stems from
Bourgeois and Singh's (1983) classification of slack depending on its accessibility which
implicitly accepts that the more accessible a type of slack, the more discretionary it is.
This assumption has escaped scholarly scrutiny – almost certainly because Bourgeois
and Singh's (1983) construct is seen as intuitively correct since it relies on two well
published constructs, resource flexibility and financial liquidity. Resource availability
and flexibility are central to the role of the manager and therefore to assume resources
can be high or low discretion is intuitively not a large leap in thinking when
conceptualised in terms of financial liquidity, particularly in measurement (Bourgeois &
Singh, 1983). Although it may be that slack’s discretionary attributes align perfectly
with liquidity or availability, it is yet to be determined empirically.
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The broad concept of slack increasing discretion was mentioned in Hambrick and
Finkelstein's (1987) discretion model but the interaction between slack and discretion
has not been considered in detail and the interaction between different types of slack and
discretion has never been empirically examined. At the theoretical level, Sharfman et
al.'s (1988) discretionary slack assumption suggests that different types of slack give
managers more or less discretion at the individual level rather than higher executive
discretion yielding higher slack at the organisational level. This aligns with Hambrick
and Finkelstein's (1987) more general assertion that organisations with abundant slack
will have a greater executive discretion. Sharfman et al.'s (1988) perspective, which
assigns discretion to managers in environments of various types and levels of slack, is in
contrast to the discretionary slack perspective, which assigns discretion as an attribute of
slack. Although it is yet to be discussed in the literature of either field, one might
suggest a bridge between these perspectives. Managers with high discretion may be able
to gain low discretion slack whereas low discretion managers may be limited to only
gaining high discretion slack (Yasai-Ardekani, 1986).
Research Question This thesis examines the interactions between organisational slack using Bourgeois’
(1981) model built on by Bourgeois and Singh (1983) and Hambrick and Finkelstein’s
(1987) executive discretion model. Reframing this poses the research question: What are
the associations between organisational slack resources and executive discretion?
The purpose here is to better understand how these two constructs interact to explain
what appears to be a bidirectional relationship. This thesis will examine organisational
slack and executive discretion as they interact in the industry task environment.
8
Thesis Structure This introductory chapter is the first of six. By developing a general overview of what is
meant by the terms organisational slack and executive discretion a base is now laid for
an understanding of the two constructs to be built by the second chapter.
The second chapter reviews in detail the literature on organisational slack and executive
discretion. The result of this is that hypothesises are posed, which are designed with the
intention of answering the research question at the industry and sub-industry levels. The
third chapter establishes a methodological foundation by introducing and reviewing the
measurement method - content analysis of annual reports. Chapter four details the
methods used in this two-part empirical study which includes details of the measures of
slack and industry level discretion along with a description of the samples for each
study. Chapter five reports the findings of each hypothesis test of the industry level
study and sub-industry level study. The sixth and final chapter discusses the findings and
the insights they provide for the topic. This chapter also explores the implications for
theory and method and opportunities for the future of this research.
Chapter Summary This chapter introduced the two central constructs in this thesis. Organisational slack
was introduced by highlighting how it is seen as both a poison and a panacea for
organisations: an interesting but problematic construct to scholars and practitioners.
Executive discretion was introduced as an important contextual construct that promises
to help explain organisational processes and outcomes. By introducing these two
separate constructs, a base was laid down to draw them together with a discussion of
what we known about how slack affects and is affected by executive discretion. Sitting
between the two constructs, the research gap was described by arguing that, although the
term ‘discretion’ is used to describe some types of slack, little is known about how they
interact with executive discretion. With this base laid, the next chapter progresses this
thesis by critically examining the two fields of literature which focus on the central
constructs.
9
Chapter Two – Interacting Models: Organisational Slack and Executive
Discretion
The previous chapter posed the research question: What are the associations between
organisational slack resources and executive discretion? To explore this question, the
chapter to follow critically examines the constructs of organisational slack and executive
discretion in the published literature.
By dissecting Bourgeois’ (1981) organisational slack definition and intertwined
construct shows slack to be recoverable from multiple sources, to have various forms,
and to have different functions. Exposing these facets of slack also clarifies where it is
treated as a dependant variable (slack as a consequence) or independent variable (slack
as a cause). Hambrick and Finkelstein’s (1987) approach to executive discretion is then
reviewed as a prelude to examining Abrahamson and Hambrick’s (1997) industry level
discretion (ILD) which highlights the forces of the industry task environment which
influence executive discretion. The chapter then synthesises the two literatures and
develops hypotheses to test the research question at the pan-industry and within-industry
levels of analysis.
Organisational Slack Organisational slack, or simply ‘slack’, is an important concept for organisational and
strategy theorists. Daniel et al. (2004) found it to be a popular variable for the study of
firm performance in their meta-analysis of 66 empirical articles. The slack-performance
relationship can be viewed in two ways; slack is either a good resource or a bad
inefficiency. ‘Slack as a resource’ proponents see it as a beneficial, perhaps essential,
resource to facilitate innovation and risk taking, and to enhance performance (Cyert &
March, 1963; Lawson, 2001). Conversely, ‘slack as an inefficiency’ proponents have
suggested that organisational slack should be minimised to discourage satisficing, self
10
serving managerial behaviours and in order to maximise profit (Jensen, 1993; Jensen &
Meckling, 1976). However ‘cutting out the fat’, by activities such as downsizing, can
also be problematic (Lawson, 2001; Love & Nohria, 2005). Allowing for both views,
Bourgeois (1981) proposed a curvilinear relationship for the slack-success relationship
by suggesting that up to a point slack has a positive impact on performance but
additional slack has a negative impact.
The concept of organisational slack can be seen through three aspects of its various
definitions from the literature. Reviewing those definitions, Bourgeois (1981) suggested
that:
Organizational slack is that cushion of actual or potential resources which allows
an organization to adapt to internal pressures for adjustment or to external
pressures for change in policy, as well as to initiate changes in strategy with
respect to the external environment. (p. 30)
Bourgeois (1981) made the point that “the definitions of slack are often intertwined with
the description of the functions that slack serves” (p. 31). Taking Bourgeois’ (1981)
point further, the definitions of slack also describe what the resource looks like and
where it comes from. The following sections show that organisational slack, (1) exists in
various forms, (2) originates from various sources and (3) serves a number of functions.
This approach to slack serves to introduce a discussion of where executive discretion
may influence, and be influenced by, the sources and functions of slack.
Forms of Organisational Slack
Excess Inputs, Unnecessary Payments and Unexploited Opportunities
Cyert and March (1963) introduced the notion of organisational slack, in a behavioural
economics sense, to describe the “… disparity between the resources available to the
organization and the payments required to maintain the coalition” (p. 36). Slack
resources take the form of payments to members in excess of what is required to
maintain the organisation. Excess dividends to shareholders, lower prices or excess
services to buyers, excess wages than required to maintain labour, personal luxuries
11
afforded to executives in excess of that required to retain them and permitted
inefficiencies in organisational structure without regard for revenue are examples of the
many forms organisational slack may take (Cyert & March, 1963). Similarly,
organisational slack may take the form of uncommitted resources. Dimick and Murray
(1978) described the concept of organisational slack as “those resources which an
organization has acquired which are not committed to a necessary expenditure. In
essence these are resources which can be used in a discretionary manner” (p. 616).
Congruent with this, organisational slack can be viewed as a pool of excess resources
necessary to allow the organisation to operate (Nohria & Gulati, 1996). Organisational
slack takes the form of excess inputs, unnecessary expenditures and also unexploited
opportunities.
Financial and Non-Financial Forms of Slack
Typically, financial measures are applied to operationalise organisational slack
resources. However slack has been conceptualised to have both a financial and non-
financial form (Daniel et al., 2004; Marino & Lange, 1983) and to be an actual or
potential resources (Bourgeois, 1981). It is perhaps because of the difficulty in bounding
and operationalising the construct that it has received only modest empirical treatment
and is defined so variably. Financial forms of organisational slack include cash and other
assets while non-financial slack exists in the more intangible resources such as human
resources, social capital, reputation and goodwill (George, 2005). Thus, although slack
commonly takes a financial form it can also take a non financial form such as reputation
and goodwill.
12
Sources of Organisational Slack (Slack as DV)
Slack from Internal and External Sources
Even though slack is commonly thought of as originating from within the organisation,
the source of slack can be internal or external to the organisation. From a measurement
perspective, Bourgeois (1981) pointed to two sources of slack: managerial actions and
the external environment. Linking the two sources is the idea that managers can
deliberately create slack by effecting firm success and gain slack by identifying and
exploiting positive shifts in the external environment (Cyert & March, 1963). Once
recovered, deliberately or not, slack may be either deployed, absorbed into the
organisation as costs, or remain unabsorbed liquid resources (Singh, 1986).
Slack recovery, from internal and external sources, reflects the process that links
managerial factors and environmental factors of slack. The ease of slack recovery has
been discussed in terms of the speed of resource recovery for potential deployment.
Bourgeois and Singh (1983) suggested that slack be measured along a continuum of ease
or ‘quickness’ of recovery. Nohria and Gulati (1996) focussed on short term slack as
resources that can be recovered over the temporal cycle of managers (i.e. one year).
Slack recovery, in terms of the effort or discretion required by the manager was
discussed by Sharfman et al., (1988) as being influenced by the environment, the
organisation and the values and beliefs of the dominant coalition. The recoverability
dimension of organisational slack is a conceptual attempt to capture how slack is
accessed differentially from various sources by managers and organisations.
13
‘Ease of Recovery’ Based Models of Organisational Slack
The three categories of Bourgeois and Singh’s (1983) model (available, recoverable and
potential slack) sit in a hierarchy which they labelled “ease-of-recovery” (p. 43).
Available slack can be recovered in the short term, with the most ease, from the
organisation’s resources that are not yet assimilated or absorbed into the organisation.
These are the most liquid uncommitted assets such as cash. Recoverable slack can be
recovered internally from the organisation with some effort in structural redesign or
reconfiguration. These are the resources built into the organisation such as inventory and
sales expenses. Potential slack can be recovered over the longer term and with
significant effort, from the external environment. This represents the ability of the
organisation, for example, through reputation or similar, to generate additional capital or
debt from the external environment.
Three alternative models rely on the ease of recovery of slack from its source (see Table
1). Derived from Bourgeois and Singh’s (1983) model, Singh (1986) focused on the
location or source of slack in the organisation with a two dimension model of absorbed
and unabsorbed slack. Using a measure resembling Bourgeois and Singh’s (1983)
recoverable slack to represent absorbed slack and a measure resembling Bourgeois and
Singh’s (1983) available slack to represent unabsorbed slack, Singh (1986)
operationalised slack as being within the organisation. Unabsorbed and absorbed
organisational slack can also be viewed with a recovery time dimension and a
discretionary dimension respectively. Unabsorbed and absorbed slack was recast as
short-term and long term slack by Nohria and Gulati (1996) and as high and low
discretion slack by Bowen (2002) and Sharfman et al. (1988).
Although this literature review examines these alternative models of slack, Bourgeois
and Singh’s (1983) model is used by this thesis as the framework for to explore the
associations between slack and executive discretion.
14
Table 1 - Models of Slack using Recovery Dimensions
Bourgeois and Singh
(1983) Singh (1986) Nohria and Gulati (1996) Sharfman et al. (1988)
Central Construct
Ease of Recovery Absorption Time Discretion
Dimensions
Available Slack Unabsorbed Slack Short – Term Slack High Discretion Slack
Recoverable Slack Absorbed Slack Long – Term Slack Low Discretion Slack
Potential Slack
15
Functions of Organisational Slack - (Slack as IV)
The foregoing discussion of organisational slack considered its form and source which
assumes slack is dependant on managerial actions. The discussion now shifts to the
function or role of organisational slack as an independent variable supporting managerial
actions, organisational processes and strategic outcomes.
The importance of understanding the role slack plays in organisations centres on the
organisation’s profit maximisation purpose. Identifying the form, source and
recoverability of organisational slack are important preceding steps for identifying its
effect on the organisation. However, since Cyert and March (1963) asked “What is the
function and consequence of organizational slack?” (p. 21) much of the work of
organisational slack scholars has focused on its organisational roles.
Bourgeois’ (1981) definition of organisational slack captured three primary roles of
organisational slack as an independent variable: (1) as a cushion or internal shock
absorber of spare resources to allow proactive adaptation to internal stresses, (2) as an
assistant to reactive strategic adjustments to shifts in the external environment, and (3)
as a strategic resource that allows proactive responses to changes in the external
environment. Bourgeois (1981) expanded on his definition of slack to propose four
primary functions of organisational slack as: (1) an inducement to maintain coalitions,
(2) a resource for conflict resolution, (3) a workflow buffer, and (4) a facilitator of the
internal strategic behaviours of innovation, satisfying and politics. Bourgeois’ (1981)
detailed review of the functions of slack identified the six functions listed in Table 2
(Bowen, 2002).
16
Table 2 - Functions of Slack in Organisations Six Functions of Slack
1. Inducement
Internal Maintenance 2. Conflict resolution
3. Workflow buffer
4. Innovation
Facilitator of Strategic Behaviour 5. Satisfying
6. Politics
Adapted from Bourgeois (1981) and Bowen (2002)
The first three functions of organisational slack all contribute to the internal maintenance
of the organisation (Bourgeois, 1981). Firstly, slack is viewed as an inducement for
members to remain with and contribute to the organisation. Relying on Barnard’s (1938)
inducement to contribution ratio, Cyert and March (1963) suggested slack payments are
excess payments of inducements greater than that required to maintain the coalition, in
essence an inducement to contribution ratio of greater than one. This excess inducement
function of slack is seen in the form of high wages and salaries, executive perks and
excess dividends. Secondly, slack is a resource for the resolution of conflict between an
organisation’s competing sub-units (Pondy, 1967). By facilitating decentralised decision
making and sequential attention to goals, slack allows options to be distributed among
competing sub-units. Without sufficient slack, sub-units are constrained and forced to
compete for resources to achieve their often conflicting operational goals. For example,
by allowing a non-standard investment in the production department, their conflict with
the sales department may be eased because of the lessening of conflict between
production (quality) and sales (volume) goals. Thirdly, slack as a workflow buffer serves
the shock absorbing function by buffering deficits in inputs to organisations and
variability in organisational systems (Bourgeois, 1981). Slack acts as a substitute for
technical resources and complete information about workflows. Whether from a physical
technical resource or information processing perspective, slack resources smooths
organisations’ workflows. Slack in inventories, for example, buffers discontinuities in
external supplies as well as variations in production (Galbraith, 1973; Thompson, 1967).
In summary, slack functions within organisational systems to smooth internal processes
and insulate it from the effects of environmental shifts.
17
The second set of three functions place organisational slack as a facilitator for executive
strategic behaviours (Bourgeois, 1981). Creative behaviours such as experimenting with
new strategies or introducing innovation are possible functions of slack resources.
Studying the positive slack-innovation relationship, Nohria and Gulati (1996)
highlighted that slack was important for innovation because of its ability to protect the
organisation from the uncertainty of success. Conversely, slack may be seen as an
organisational inefficiency because it is potentially consumed by members and not be
utilised in profit making innovations. Nohria and Gulati, (1996) argued that the slack-
innovation relationship is curvilinear, a view also expressed by Bourgeois’ (1981)
assertion that slack is positive for success up to a point, after which it becomes negative.
Capturing the negative aspect this relationship, the second and third functions of slack
described by Bourgeois (1981) suggested it is a facilitator of suboptimal executive
behaviour. Alternative to allowing innovative behaviours, the presence of slack allows
executives to satisfice earlier in the search for strategy options and solutions to
problems. Equally, slack functions as a promoter of political activity by encouraging
participants to compete for their share of slack, consuming slack in the process. In
contrast, as stated above, slack reduces conflict and therefore the need for suboptimal
political behaviour (Bourgeois, 1981). In summary, slack functions to positively and
negatively affect innovation, satisficing and political behaviours of organisations’
executives at the strategic level.
Slack is an Organisational Resource
Slack is an organisational level and not industry level resource. The Penrosian resource
based view of the firm dictates that resources are controlled at the organisational level
and therefore slack resources are created, consumed or redistributed within firms
(Penrose, 1966). Although slack resources held by an organisation may be appropriated
by another, they begin as slack resources for the transferring organisation, but do not
transfer as slack. For the receiving organisation, the resources may or may not be slack.
The receiving organisation determines whether these newly acquired resources are slack
or committed resources - an organisational level transfer of slack. For example,
according to Bourgeois & Singh’s (1983) model, an organisation may have recoverable
18
slack in the form of inventory. Comparing the aggregated inventories of industry cannot
suggest an industry level slack. An industry member can only recover their portion of
this aggregated recoverable slack. Equally, an organisation may have potential slack.
This resource represents the potential held by an organisation to gain additional
resources from its task environment. Aggregating this to an industry average cannot
suggest that industries have a potential slack: It is organisations not industries that can
trade on their past performance and reputation. For the present study slack was measured
at the organisational level and then an industry mean was calculated to represent the
average organisation in each industry.
Organisational Slack versus Budgetary Slack
In parallel to the conceptual development of organisational slack, budgetary slack was
developed as a theory of organisational control in the accounting and finance literature.
For example, Onsi (1973) discussed budgetary slack in terms of the managerial
behaviours that influence slack build up and utilisation. Curiously, the organisational
literature on slack ignores budgetary slack yet the budgetary slack literature, in part,
embraces the organisational slack literature (e.g. Merchant, 1985; Mohan 1996). Onsi
(1973) interviewed 32 managers from five national and international organisations to
develop a questionnaire measure of attitudinal and behavioural factors of budgetary
slack and organisational financial controls systems. Onsi's (1973) interviews found that
budgetary slack is not an unintentional budgetary or forecasting inaccuracy, whereas
organisational slack is portrayed as an unintentional recoverable resource. The scope of
this thesis does not extend to an examination of budgetary slack.
19
Empirical Research Using Organisational Slack
Empirical research exploring organisational slack can be can be traced back to
Bourgeois (1981) who suggested that “the [organisational slack] concept is rarely treated
in anything other than conceptual terms” (p. 29). One empirical study of slack preceding
Bourgeois (1981) was that of Dimick and Murray (1978) who measured slack as a
contextual variable for human resource policies using a financial measure. Returning to
Bourgeois (1981), he refined the definition of, and proposed a model for, slack that is
unobtrusively measurable with financial ratios derived from annual reports. Bourgeois
and Singh (1983) applied the slack model empirically to 14 U.S. organisations to show
significant correlations with the political behaviour and goal disagreement of the
dominant coalition (of executives) after controlling for coalition size (4 - 10 members).
An analysis of the relationship among slack types was not offered, nor was a measure of
slack recoverability applied. (Singh, 1986) applied three financial ratio measures to 63
medium to large U.S organisations and found that good performance was related to
absorbed and unabsorbed slack, and absorbed slack is related to increased risk taking but
unabsorbed slack is not.
Moses (1992) found support for the hypothesis that with increased slack comes
increased risk taking. He was of the first to examine the interrelations between slack
measures empirically and found that the three categories of Bourgeois and Singh's
(1983) model are not statistically distinct. Although interrelationships between financial
measures were not unexpected, Moses (1992) warned against drawing conclusions about
the separate roles of available, recoverable and potential slack. Using a sample of the
costs history of 53 weapon systems programmes (organisations), ten financial ratio
measures produced a four factor model of slack measures (investment, financing, funds
and expense). This calls into question the widespread yet generally untested reliance on
Bourgeois and Singh's (1983) ease of recovery model.
Daniel et al. (2004) found 66 empirical studies (not including Moses [1992]), that
measured available, recoverable or potential slack similarly to Bourgeois and Singh's
(1983) model and examined performance implications. They found that recoverable
20
slack has a weaker relationship to performance (r = 0.05) than available (r = 0.17) and
potential slack (r = 0.28). They also found that organisations performed better when their
level of slack was appropriate for their industry.
Cheng and Kesner (1997) found that organisations responded to environmental shifts by
using slack strategically and differentially in their study of the U.S. airline industry
going through deregulation from 1978. Sampling 30 airlines, available, recoverable and
potential slack was measured as per Bourgeois’ (1983) model for the three years prior to
deregulation. Their results suggested that available slack and potential slack play similar
roles in organisational adaptation processes.
Studying the impact of the manager in an organisation’s strategic choice in an oil and
gas industry context, Sharma (2000) measured ‘discretionary slack’ using a self report
questionnaire because an objective measure was not available. They reported that higher
discretionary slack, as an organisational contextual variable, lead to managers
interpreting their environments as opportunities rather than threats.
In a rare study of privately held firms, George (2005) also measured slack using a
discretionary dimension. He used cash to measure high discretion slack and debt to
equity ratio for low discretion slack, which, as noted in Table 1, aligns with available
and potential slack. This study of 900 technology and non-technology organisations over
four years found that performance increased and then decreased with increases in low
discretion slack and also that the performance to high discretion slack relationship is
positive and linear. George (2005) interpreted this to suggest that the level of discretion
that a particular form of slack gives managers moderates slack’s influence on
performance.
21
Executive Discretion
What is Executive Discretion and Who Has It? Discretion in the normal use of the word implies the latitude or freedom to decide (The
Oxford English Dictionary online, 2008). Hambrick and Finkelstein (1987) defined
managerial discretion as the top executives’ latitude for strategic action and argued it is
determined by the industry environment, the organisational environment and the
characteristics of individual managers. Managerial discretion manifests in the actions of
those who hold it. Hambrick and Finkelstein (1987) make the distinction between
managerial actions and choices in defining managerial discretion. An executive has
many options from which to choose and then decide in their domain of discretion. By
merely having a large rather than small pool of choices for decision making, does not
endow discretion. Keegan (2006) adds to this point by clarifying that executive
discretion includes the binding executive decision to take no action. Key to this
definition is that it identifies, by their level in decision making processes, the group of
managers who hold the freedom or latitude of strategic action.
Hambrick and Finkelstein’s (1987) reference to managerial discretion as a concept of
“chief executive discretion” might suggest an individual context by the implication that
usually there is only one chief executive (p. 369). ‘Top management’ and ‘upper
echelons’ are terms that could also be used to describe the strategic level managerial
discretion (Finkelstein and Hambrick, 1990) in a collective sense. Following Hambrick
and Mason’s (1984) upper echelons perspective, Hambrick and Finkelstein (1987) and
Finkelstein and Hambrick (1990) argue that management and managerial discretion is
shared by the top management team members of the organisation. This is the dominant
coalition, from Cyert and March’s (1963) perspective, that shares organisational
decision making. The managerial discretion used in that decision making is the central
interest here. Whilst Hambrick and Finkelstein’s (1987) definition of managerial
discretion is clear and useful, it is possible that it may be misinterpreted to mean
discretion at all managerial levels of the organisation. For the purpose of clarity, the
present study adopts Hambrick and Finkelstein’s (1987) alternative term ‘executive
discretion’ as did Keegan (2006).
22
Why is Executive Discretion an Important Concept?
The concept of executive discretion has evolved from the literature of organisational and
strategy theorists in theory building (Boyd & Gove, 2006). Two aspects of its evolution
are (1) as a link between two theoretical perspectives of strategy and (2) as a moderator
of organisational outcomes.
Prior to Hambrick and Finkelstein (1987), strategy theorists held two polar views of
strategic control. Strategic choice proponents argued that organisations and their
managers determine their own actions and outcomes (Andrews, 1971). In contrast,
population ecologists Hannan and Freeman (1977) argued that internal structural
arrangements and environmental constraints maintain dominating inertial pressure on
organisations.
Hambrick and Finkelstein (1987) identified the similarities of these two competing
views of the organisation and its environment and noted that strategy theorists, while
working to explain their separate viewpoint, were actually working in a common
direction. Hambrick and Finkelstein (1987) offered executive discretion as a bridge to
link these two views of strategy formulation. Executive discretion showed promise as a
linking concept because it could add to both views. Where executive discretion was
high, the strategic choice view provided important insights and where executive
discretion was low, the inertial or population ecology view would prove more influential
in explaining organisational outcomes. Supporting the latter view, Boyd and Gove
(2006) found overlaps between executive discretion and Dess and Beard’s (1984) task
environment construct.
Progressing the acceptance of executive discretion as an important concept in its own
right, organisational theorists have suggested it is an important moderator of
organisational outcomes such as, inter alia, executive compensation and firm
performance. Executive compensation in high discretion industries has been found to be
greater than in low discretion industries (Finkelstein & Boyd, 1998). Going further,
23
executive compensation in higher discretion industries has a greater proportion of
incentive pay (Cordeiro & Rajagopalan, 2003). The influence of executive discretion on
performance through strategic behaviour has been researched using Hambrick and
Mason’s (1984) upper echelons theory. Where executive discretion is high, executive
characteristics are reflected in strategy and performance and where executive discretion
is low, executive characteristics are not influential (Hambrick, Finkelstein, Cho, &
Jackson, 2005; Hambrick, 2007). The evolving knowledge of executive discretion serves
to develop our understanding of strategic behaviours and organisational outcomes.
Three levels of Determinants of Executive Discretion
Hambrick and Finkelstein (1987) proposed three sets of factors that determine executive
discretion. Executive discretion is a function of (a) the managerial characteristics that the
executive is personally able use to create and execute strategic actions, (b) the
organisation’s internal forces that empower the executive and (c) the task environmental
forces in which strategic actions are taken. Hambrick and Finkelstein (1987) highlight
that the linkages between various elements of the three levels have been regularly
studied, however pose their model as new a way of understanding the effects of the three
sets of factors on the central concept of executive discretion. The three sets of factors
bring together the various influential elements to link the inertial and adaptive views of
the organisation.
24
Characteristics of the Manager
The importance of the personal characteristics of the individual manager is embodied in
well cited works including Child (1972), Cyert and March (1963) and Hambrick and
Mason (1984). Acknowledging and extending this point, Hambrick and Finkelstein (1987)
made the assertion that there is a mediating role for executive discretion in decision
making and strategic processes. Hambrick and Finkelstein (1987) proposed six personal
managerial characteristics that positively and one that negatively influences executive
discretion. The individual characteristics of: aspirational level, tolerance for ambiguity,
cognitive complexity, internal locus of control, power base and political acumen all
increase executive discretion. Empirical exploration of the broad array of individual
characteristics that influence executive discretion holds considerable potential.
Characteristics of the Organisation
Hambrick and Finkelstein (1987) assert that, from the internal characteristics of the
organisation, resource availability positively influences executive discretion while inertial
forces such as culture, size and age along with powerful internal forces negatively
influence executive discretion. Of particular interest to the present study, Hambrick and
Finkelstein (1987) noted that resource availability has a significant impact on executive
discretion. An organisation’s uncommitted financial resources such as cash or unused debt
capacity and uncommitted human resources such as surplus managerial talent create an
expanded array of options for the executive. Managers of organisations holding such
organisational slack resources have increased discretion (Cyert & March, 1963).
A less obvious resource held by an organisation is its level of legitimacy in the eyes of its
stakeholders. Organisational legitimacy is defined and described with such phrases as “an
explanation for existence” and “justification to peers and subordinates this resource has its
origins in the evaluations of an organisation’s social audience” (Suchman, 1995, p. 573).
The level of an organisation’s legitimacy is a product of the powerful outside forces or
“powerful parties” and the organisation’s other attributes (Hambrick & Finkelstein, 1987,
p. 74) as is executive discretion. Executive discretion is endowed by the task environment
directly but also (mediated) indirectly through organisational legitimacy (Hambrick &
Finkelstein, 1987).
25
Characteristics of the Task Environment
The third set of factors affecting executive discretion originates in the organisation’s
environment (Hambrick & Finkelstein, 1987). Environments supply both opportunities
and challenges to their dependant organisations and their managers. As organisations
compete for scarce resources, uncertainty and risk in the environment challenges
executives and impacts their discretion. An organisation’s environment is perhaps the
single most influential factor to its structure, internal processes and managerial decision
making (Daft, Sormunen, & Parks, 1988). Extending this, an organisation’s environment
is perhaps the most fundamental source of executive discretion because of the associated
complexity and uncertainty faced by executives (Hambrick, Geletkanycz, &
Fredrickson, 1993).
Early work by Emery and Trist (1965) suggested four types of environments based on
organisations’ interdependence. Importantly, they recognised that interconnectedness
among organisations influenced how their environments could be defined. Taking the
open systems approach, an organisation’s environment includes those relevant physical
and social factors outside the boundaries of the organisation (Duncan, 1972). Although
the boundaries between an organisation and its external environment and between parts
of the environment can be seen as subjective constructs, the distinctions are useful
nonetheless (Starbuck, 1976). The environment can conceivably include every event that
an organisation might be affected by, however not all events are felt equally and so an
organisation’s environment can be viewed as having multiple layers (Bourgeois, 1980;
Dill, 1958; Pfeffer & Salancik, 1978).
A two layer model of the organisational environment has the task environment as a sub-
set of the general environment. Dill (1958) defined the task environment as that part of
the environment that is potentially relevant to organisational goal setting and attainment.
Dill (1958) also described this as the “stimuli that the organization might respond to” (p.
411). Making the distinction between the task and general environment empirically for
two Norwegian firms, Dill (1958) identified the elements of the task environment as
customers, suppliers, competitors and regulatory groups. Put another way, the closest
26
layer, the task environment, affects the day to day direct transactions of the organisation
and the outer layer or general environment has only an indirect affect (Daft et al., 1988;
Thompson, 1967).
Dill’s (1958) task environment elements approximate an ‘industry’ but Dess and Beard
(1984) made the subtle distinction between the unit of analysis - the organisational task
environment, and the unit of measurement - the industry task environment which is
commonly measured using Standard Industrial Classification (SIC) definitions of
industries. For example, the effects of regulatory groups are commonly measured at the
industry level of the task environment. Hambrick and Finkelstein’s (1987) original
model of industry level discretion implicitly looked at the firm task environment.
Hambrick and Abrahamson (1995) slightly changed the model by looking at the industry
task environment when they developed the construct of industry level discretion
(Keegan 2006). This thesis follows Hambrick and Abrahamson’s (1995) lead.
27
Executive Discretion in Industries - Industry Level Discretion
The industry task environment determines the discretion of executives in an industry.
Hambrick and Finkelstein’s (1987) original model asserted three characteristics of the
task environment that positively influenced executive discretion and three that
negatively influenced executive discretion. Executive discretion increases with increases
in product differentiability, market growth and demand instability and decreases with
increases in industry structure, quasi-legal constraints and powerful outside forces.
Abrahamson and Hambrick (1997) slightly changed Hambrick and Finkelstein’s (1987)
executive discretion model by recasting these organisational task environment factors as
industry level factors. The updated executive discretion model focused on the general or
average amount of executive discretion in an industry and called industry level
discretion (Abrahamson & Hambrick, 1997).
Industry level determinants mainly influence executive discretion through the agency of
powerful stakeholders. Quasi-legal constraints imposed by legislators, regulators and
courts limit executive discretion of industries differently. Hambrick and Finkelstein (1987)
highlight three manifestations of this constraint. A high level of regulation experienced by
an industry limits its discretionary set of strategic options. Similarly industries reliant on
public funds are constrained as they are dependant on government resources. Contractual
obligations, a mechanism of the legal system, also constrain discretionary options. Other
powerful outside forces emanating from powerful suppliers and buyers also limit industry
level discretion. An Industry structure where oligopolistic competition exists constrains
executive discretion within industries as powerful competitors are aware of and responsive
to possible strategic moves, limiting executives’ discretionary options. A fragmented and
highly competitive industry structure in contrast, allows a greater number of options to
executives and they have greater executive discretion (Abrahamson & Hambrick, 1987).
The complexity and interconnectedness of industry task environment influences on
industry level discretion is evidenced by the variety of task environment dimensions
offered by the literature (e.g. Dill, 1958; Miles & Snow, 1978; Priem, Love & Shaffer,
2002). Hambrick and Abrahamson (1995) acknowledged this by emphasising that
“industry level discretion has multiple origins that do not necessarily covary” (p. 1430).
28
Industry level factors also influence executive discretion through the ambiguity that
stakeholders see between means and ends. In product differentiable industries managers
are able to exploit such ambiguity and consider more options than in a commoditised
product/service industry where means-ends linkages are more transparent. Industries
experiencing market growth similarly provide opportunities for increasing executive
discretion by means of competitive variations and typically unplanned decision making.
Similarly, demand instability enhances executive discretion. Stable demand for products
and services allows little latitude for executives as the organisation’s response is well
understood and stable (Hambrick et al., 2005; Hambrick & Abrahamson, 1995;
Hambrick & Finkelstein, 1987).
Hambrick and Abrahamson (1995) further clarified the Hambrick and Finkelstein,
(1987) model by recognising that capital intensity is a factor of the industry and not an
internal force. They argue that organisational capital intensity increases the strategic
options to executives reduce under constraints associated with large sunk capital and
debt. At the industry level, executives in those industries needing relatively less capital
will have more discretion.
29
Social Influences on Industry Level Discretion
Industry level discretion is influenced by social forces in the industry task environment.
Institutional isomorphism was seen by DiMaggio and Powell (1983) as a force
constraining organisations facing the same environment to resemble each other.
Expanding on DiMaggio and Powell’s (1983) concept of institutional isomorphism,
Hambrick et al. (2005) argued that due to forces in the macro-social environment,
industries can become more heterogeneous as well as more homogenous at different
times. The lack of this constraining force and perhaps the presence of a liberating force
permits increased executive discretion and strategic variety in industries - “isomorphism
in reverse” (Hambrick et al., 2005). The obverse of discretion is constraint and it exists
when executives take actions outside the stakeholders’ “zone of acceptance” (Hambrick
& Finkelstein, 1987, p. 374). A typical example of this is targeting central or industry
average as a target for performance (Finkelstein & Hambrick, 1990). Balancing between
these two positions is Keegan and Kabanoff’s (2007) assertion that, by conforming
(under an isomorphic pressure) to industry norms in some domains, discretion is gained
by the expansion of the stakeholders’ zone of acceptance in other domains. In a similar
vein, Hambrick et al. (2005) suggested that future research could investigate and even
measure a threshold level of conformity to industry norms after which organisations are
expected differentiate strategically. An understanding of the social influences on
executive discretion at this industry level further contributes to the bridge between the
inertial and the strategic choice views of the organisation: the point that this chapter
began with. Changes in social pressures influence executive discretion and in turn
influence strategic choice.
30
Empirical Research Using Executive Discretion Discretion is a term used broadly in management literature but executive discretion, as a
more precisely defined sub-topic, has received modest empirical attention in scholarly
managerial studies. Boyd and Gove (2006), in a review of the empirical use of Hambrick
and Finkelstein’s (1987) theory found just 16 empirical articles measuring discretion at
the individual, organisational and industry levels, with an even smaller number of multi-
level analyses.
At the individual level, Carpenter and Golden (1997) administered a food company
simulation to 20 experienced practising managers and 78 second year MBA students to
examine perceived discretion. A 15 item questionnaire measured the discretion
perceived by players over organisational issues on a 7-point Likert scale. Generally, they
found empirical support for Hambrick and Finkelstein’s (1987) individual level
managerial characteristics.
At the organisational level, Finkelstein and Boyd (1998) produced a six factor model of
discretion (R2 = 0.61) to predict CEO compensation. Market growth, R&D intensity,
advertising intensity, capital intensity, industry concentration and regulation were
measures similar to those used by Hambrick and Abrahamson (1995) at the industry
level. Boyd and Gove (2006) raised concerns about such approaches to discretion
management as they confuse discretion measures with measures of the industry task
environment.
At the industry level, the empirical measurement of executive discretion has undergone
some development. Finkelstein and Hambrick (1990) estimated high, medium and low
discretion industries notionally using Hambrick and Finkelstein’s (1987) model to
examine the moderating role of industry level discretion on top management team tenure
and organisational outcomes. Hambrick and Abrahamson (1995) used 15 academics to
create discretion values for 17 U.S. industries. They corroborated the industry discretion
ratings with 17 security analysts from a major investment bank. They reported
significant correlations between their discretion ratings and four financially measured
31
industry characteristics (r = 0.83, p< 0.001). However this only explained roughly half
of the variance (R2 =0.492, p < 0.001). Cordeiro and Rajagopalan (2003) relied on the
same industry ratings in their empirical study of industry level discretion’s influence on
executive compensation. In a subsequent industry level study, Abrahamson and
Hambrick (1997) compared Hambrick and Abrahamson (1995) industry discretion
ratings to measures of attentional homogeneity of industries, which Keegan (2006)
developed as an unobtrusive measure of industry level discretion. Keegan and
Kabanoff’s (2007) empirical study further developed and tested the attentional
homogeneity measure of industry level discretion. Using this as an industry average
measure of discretion Keegan and Kabanoff (2007) found support for a curvilinear
relationship between discretion and leverage within industries. Their measure for
leverage, long term debt as a proportion of net worth paralleled the inverse of Bourgeois
and Singh’s (1983) operationalisation of potential slack. All of the empirical
development of this unobtrusive measure of industry level discretion has sampled from
the Compact Disclosure database of U.S. annual reports. The method of Keegan and
Kabanoff’s (2007) study is reviewed in chapter four.
Common to all empirical studies operationalising executive discretion involves choosing
the appropriate level of measurement and analysis. Boyd and Gove (2006) argued for the
use of multi-level measures by suggesting that that there is little difference, in terms of
Hambrick and Finkelstein’s (1987) discretion model, in measures at the organisational
and (aggregated) industry levels. Just two studies that have addressed discretion across
levels could be found (Boyd & Salamin, 2001; Magnan & ST-Onge, 1997). The levels
of discretion measurement and analysis are revisited in chapter three.
32
Slack and Executive Discretion - Some Interactions
The Discretionary Dimension of Slack
George (2005) noted that Bourgeois (1981), Bourgeois and Singh (1983) and Sharfman
et al. (1988) classified slack based on discretion, and suggested that “slack resources are
anchored along a continuum of managerial discretion…” which infers that resource
accessibility and recoverability are functions of discretion (p. 663). Although the
assumption of Sharfman et al. (1988) and others that slack has a ‘discretionary’
dimension is central for their studies, it is made intuitively and without direct
consideration of Hambrick and Finkelstein’s (1987) construct of discretion as a
managerial attribute mediating organisational processes.
Sharfman et al. (1988) used the dichotomous classification of high discretion slack and
low discretion slack to argue that “the addition of visibility and discretion helps make
the Bourgeois definition [of slack] complete” (p. 602). Drawing on Bourgeois and Singh
(1983), high discretion slack was argued to be available slack and low discretion slack
was seen as absorbed (recoverable and potential) slack. Put plainly, Sharfman et al.’s
(1988) high and low discretion slack approach is similar to Bourgeois’ (1981)
recoverability approach based on resource liquidity. Sharfman et al. (1988) argued that
for a resource to be slack it must be visible and available to the manager which, along
with the intuitive appeal of Bourgeois’ (1981) recoverability construct, aligns with
Hambrick and Finkelstein’s (1987) notions of ‘discretionary domain’ and ‘discretionary
set’. Organisational slack’s discretionary dimensions, more specifically the discretionary
nature of the source of slack, are yet to be measured using a measure of executive
discretion based on Hambrick and Finkelstein’s (1987) model.
33
The discretionary aspects of slack are linked to the level of executive discretion at the
source of that slack. Depending on recoverability, different types of slack allow
executives more or less options in their strategies for solving internal problems or
addressing external pressures. The more easily recovered available slack can endow
executives with more options and be applied in a wider variety of situations than the less
easily recovered potential slack. More slack endows executive discretion and different
sources of slack function differently when endowing discretion (Sharfman et al., 1988).
Slack, Industry Level Discretion and Industry Forces
Slack plays an important role for executives because it is used to initiate and deliver
strategies. Executive discretion is enhanced by slack and equally a lack of slack
constrains an executive’s range of options for strategic initiatives. In short, with greater
slack comes greater executive discretion (Finkelstein & Hambrick, 1990). At the
industry level, industry munificence eases constraints on slack recovery in the industry
task environment. When firms in industries have higher levels (i.e. industry norms) of
slack, industry-level discretion increases and in turn, industries with higher levels of
executive discretion have greater levels of slack. This bi-directional relationship is
different for different types of slack. Restating this are the following hypotheses which
differentiate between available, recoverable and potential slack:
Hypothesis 1(a): At the industry-level, available slack is positively associated with
executive discretion.
Hypothesis 1(b): At the industry-level, recoverable slack is positively associated with
executive discretion.
Hypothesis 1(c): At the industry-level, potential slack is positively associated with
executive discretion.
34
However, as with the slack to performance relationship, discretion may only increase
with the gaining of slack up to a point until additional slack is seen as less efficient,
triggering social constraints on the range of options available to executives. The
judgement that additional slack is inefficient is made by the organisation’s stakeholders,
an influential force from the task environment, which is a source of industry level
executive discretion. Stakeholders in the task environment apply this social constraint
using tests of plausibility on available information to determine when the level of slack
falls outside their zone of acceptance (Hambrick & Finkelstein, 1987). Bourgeois’
(1981) suggestion of a curvilinear relationship between slack and success, to address the
slack as a negative contention may be interpreted, in part, as industry level discretion
acting on slack. Also, by suggesting that different sources of slack (e.g. internal or
external) endow industry level discretion differently, the curvilinear relationship
between slack and industry discretion may also be different for each type of slack:
available, recoverable and potential.
The social forces on industry level discretion from the industry task environment
motivate executives to conform to industry norms for organisational slack. As stated
previously in this chapter, constraint exists when executives take actions outside the
stakeholders’ zone of acceptance and so executives feel constrained to maintain their
strategies within their stakeholders’ zone of acceptance (Hambrick and Finkelstein,
1987). Keegan and Kabanoff (2007) found that, within industries, executive discretion
decreased as organisations moved away from their industry’s norm for leverage. Keegan
and Kabanoff (2007) suggested that, by targeting an industry median for leverage (the
inverse of potential slack) and so conforming to industry norms in some domains,
discretion is gained by the expansion of the stakeholders’ zone of acceptance in other
domains. This raises questions about the associations between the other types of slack
and industry level discretion. The following hypotheses address this sub-industry
assumption of central tendency.
35
Hypothesis 2(a): Inside industries, executive discretion will decrease as firms move
away from their industry’s target range for levels of available slack.
Hypothesis 2(b): Inside industries, executive discretion will decrease as firms move
away from their industry’s target range for levels of recoverable slack.
Keegan and Kabanoff’s (2007) results demonstrated that potential slack has a curvilinear
relationship with discretion inside industries: managers of firms with more or less
potential slack than their industry norm had less discretion than those whose firm
complied with the industry norm. For consistency, Keegan and Kabanoff’s (2007)
results will be interpreted using the following statement although, strictly speaking, it is
not a hypothesis as it was not tested by the present study.
Hypothesis 2(c): Inside industries, executive discretion will decrease as firms move
away from their industry’s target range for levels of potential slack.
36
Chapter Summary This chapter approached the research question: what are the associations between
organisational slack resources and executive discretion? by critically examining
published literature on the two central constructs. An analysis of theoretical treatments
of slack and executive discretion results in the development of 6 hypotheses, five of
which will be tested leaving one to be discussed using Keegan and Kabanoff’s (2007)
results.
Using a systems approach, the exploration of Bourgeois’ (1981) organisational slack
definition and intertwined construct, showed slack to be recoverable from multiple
sources, to have various forms, and to have different functions. Discussing these facets
of slack clarified that the literature treats it as both a dependant and independent
variable. Hambrick and Finkelstein’s (1987) approach to executive discretion was
examined as a prelude to Abrahamson and Hambrick’s (1997) industry level discretion
which essentially reflects the forces of the industry task environment that act on
executive discretion.
This review laid a base from which to draw attention to some of the interactions between
executive discretion and organisational slack in the industry task environment because it
is yet to be explored empirically. Following this, the chapter adjusted its focus by
offering hypotheses to test the interactions between slack and discretion across and
within industries. In the next chapter, relevant methods taken from the two fields are
examined and then brought together to test the hypotheses and address the research
question.
37
Chapter Three – Introduction to Content Analysis of Annual Reports
Methodology is more than simply the methods used in research, rather it is the study and
philosophical discussion of research methods (Neuman, 2007) and so this section
provides a background discussion of the present study’s (1) use of computer aided test
analysis of presidents’ letters from annual reports to operationalise executive discretion
and (2) its use of financial ratios to operationalise different types of slack. First, the
concept of annual reports as ‘content’ is examined before reliability and validity of the
specific content analysis approach is considered. This is followed by a discussion of the
validity of financial ratios as indicators of slack.
Content Analysis Archival content analysis is increasingly used across disciplines such as business,
education, psychology, communications and politics and so it is not surprising to find
variation, particularly over the definitions used to describe its components and
assumptions. While sharing the common goals of sound research and epistemological
development, different disciplines use different definitions, assumptions and concepts of
validity.
Defining Content Analysis is defining ‘Content’
As a primer to the discussion of content analysis some well cited definitions can be
offered. Berelson (1952) for example, wrote: “Content analysis is a research technique
for the objective, systematic and quantitative description of the manifest content of
communication” (p. 18). Holsti (1969) similarly wrote: “Content analysis is any
technique for making inferences by objectively and systematically identifying specified
characteristics of messages” (p. 25). These definitions have similar elements, however
their differences point to a lack of consensus. Elements such as ‘quantitative
description’, ‘manifest content’ and ‘specified characteristics’ either require further
definition or in fact limit the definition to a sub-field of content analysis (Krippendorff,
2004).
38
What is ‘Content’?
Krippendorff (2004) observed that definitions of content analysis can be grouped
around three approaches to the conceptualisation ‘content’. Firstly, that content
inherently exists in the studied messages within text; secondly, that content is a property
of the text’s source; and thirdly, that content is neither latent nor manifest in the text, but
is an attribute that emerges as a result of the researcher’s analysis relative to a particular
context.
Kabanoff (1997) also made the point that text is just one type of content, while
Neuendorf, (2002) preferred a more general definition suggesting text includes “not just
written text but also any other message type considered in its entirety” (p. 15). Morris
(1994) inferred that text in the written form is one form of content analysis and other
material such as pictures, physical artefacts, songs, speeches and movies are non-written
forms of content. Defining written and non-written texts as ‘text’ reflects the
sociological tradition while treating text separate from other material is a linguistic
tradition (Ryan & Bernard, 2005; Tesch, 1990). Drawing from both approaches
Krippendorff (2004) accepted that, as a source of content to be analysed, ‘text’ is distinct
from ‘other meaningful matter’.
Krippendorff’s (2004) differentiation of three defined types of content represents a three
level approach to the interaction between researcher and data. At the least abstract and
most surface level, text is like a physical container which contains latent or manifest
content (Riffe, Lacy, & Fico, 1998). At the next level, content is seen as a property that
reveals insights to the source of the text. Finally the deepest level of content includes the
researcher as a participant in the creation of content from a text and also includes the
research situation (Krippendorff, 2004).
39
Reliability and Validity Considerations for Content Analysis
Reliability and validity are critical considerations in any scientific measurement process.
As a generalisation, the more abstract the content being measured and the more cryptic
the data, the greater the importance of demonstrating reliability and validity. Because
content analysis involves measuring information in symbolic data that is designed to
convey potentially complex ideas, establishing measurement reliability and validity is a
central theme in content analysis research. Krippendorff (2004) provides a useful
validity framework used in the discussion below.
Reliability
The reliability of computer assisted quantitative textual analysis can be described by
applying tests of stability, reproducibility and accuracy (Krippendorff, 2004). Computer
assistance not only allows large volumes of text to be processed it also affords a stable
environment for reliability. The present study relies on the measures detailed in Keegan
(2006). At each sampling and processing step, relevant sections of Keegan’s (2006)
results were replicated and verified by checking values across results. The reliability of
the present study is underpinned by the computer’s ability to create traceable records at
each step.
Face and Social Validity
Face validity and social validity are not central to the present study but were considered.
Face validity in content analysis plays the role of gatekeeper in the largely intuitive
analysis of human created content. Social validity for content analysis is a construction
around those who are familiar with the intended purpose of particular texts and the
purposes applied by researchers (Krippendorff, 2004). For the present study, social
validity is assumed by relying on peer reviewed methods of organisational scholars,
particularly Keegan and Kabanoff (2007).
40
Sampling Validity
The validity of the sample’s content relies on the purpose of the study (Krippendorff,
2004). President’s letters are purposely chosen here to access the ‘soft information’ of
executive discretion alongside the hard financial data for slack. Unlike the Management
Discussion and Analysis sections (MD&A), president’s letters are not mandatory in
financial reporting. Ober, Zhao, Davis, and Alexander’s (1999) study used MD&A
instead of president’s letters because the latter is not regulated by the Securities and
Exchanges Commission (SEC), suggesting that presidents’ letters are not be as valid as
the audited MD&A. However this overlooks the possibility of capturing valuable non-
audited disclosures particularly of future failure. Although once thought of as self
serving ‘advertising’, the discretionary disclosure of information in presidents’ letters in
contrast, provides important covert content to its readers. The annual reports are a valid
purposive sample of organisational information that provide information not available in
other sources (Smith & Taffler, 2000), (see also Abrahamson & Amir, 1996; Breton &
Taffler, 2001; Murray, Decker, & Dittmar, 1993).
Content analysis of presidents’ letters provides unobtrusive access to aspects of top level
management’s strategic sensemaking. Unobtrusiveness has two sampling validity
advantages in this context. Firstly, access to top managers is difficult to achieve and
their narratives are a direct window to their cognitions. Secondly, as a historical
document, the integrity of the data is not compromised by the researcher’s method of
enquiry and remains intact and available for subsequent research questions (Abrahamson
& Park, 1994; Kabanoff, 1997).
Only public organisations are required to file with the SEC and therefore publish their
annual reports. Therefore the sample frame for the present study does not include private
firms. Although privately owned firms and small firms are excluded from annual report
databases, the position of the present study is that they may not be open to the same
level of public scrutiny and therefore the construct of industry level executive discretion
would apply differently. Research questions investigating organisational relationships
within countries are well served by mandatory filing of textual public company
information as are studies within and between industries (Smith & Taffler, 2000).
41
Semantic Validity
At a deeper level than sampling validity, the semantic validity of using president’s
letters in content analysis is often criticised by the suggestion that impression
management disguises, and coder error misinterprets, the true meaning of texts (Courtis,
2004). The method used by the present study does not rely on discovering meaning in
presidents’ letters but instead looks at aspects of shared lexicons. Fiol (1995) suggested
that non-evaluative statements in presidents’ letters reflect managerial cognitions and
evaluative statements reflect impression management. Measuring attention similarity, as
done here, does not rely on the truth or otherwise of evaluative statements and therefore
is less likely to be influenced by impression management (Abrahamson & Hambrick,
1997).
Construct Validity and Pragmatic Functional Validity (Replication)
Construct validity challenges in content analysis can largely be attributed to the attempt
to use imperfect proxy measures for covert phenomena (Morris, 1994). Construct
validating evidence is found in the structural consistency of existing knowledge with the
theories that are being used in the present study (Krippendorff, 2004).
Krippendorff (2004) suggested the pragmatic functional validity of a construct is
evidence by its successful use or vindication by its history of successful use rather than
its structural soundness. Although Krippendorf’s (2004) subtle distinction within
construct validity is yet to be fully recognised by the content analysis literature, it is
useful to note that authors in the field rely on previous authors’ use of constructs to
argue their validity as is done here. Managerial attentional focus by Abrahamson and
Amir, (1996), impression management by Smith and Taffler (2000), agency theory by
Abrahamson and Park (1994) and readability studied by Courtis (2004) are examples of
textural constructs with evidence of pragmatic functional validity.
Convergent and Discriminate Validity
Validating evidence can be based on the inference that the resulting scores of the
construct variables being measured correlate (concurrently) in a discriminate or
convergent way with the scores of alternative variables (Krippendorff, 2004).
42
Venkatraman and Grant (1986) observed that few organisational strategy studies
employed correlative validity tests. Although authors provide evidence of construct
validity by triangulation, alternative methods of measuring constructs inferred in
president’s letters are limited by their very nature and by the fact that presidents’ letters
affords a unique access to the letter’s writer (Kabanoff, 1997). However convergent
validity of the measures of executive discretion used in the present study, using factor
analysis, were originated by Abrahamson and Hambrick (1997) and refined by Keegan
(2006), and Keegan and Kabanoff (2007).
Predictive Validity
Although the present study does not make predictions, content analysis of presidents’
letters can be used for this purpose. For example, Smith and Taffler’s (2000) showed
that content analysis of presidents’ letters can be used to forecast bankruptcy. The
content analysis measures of executive discretion used by the present study require large
numbers of presidents’ letters. It is expected that it would be difficult to achieve
sufficient cases for predictive studies.
Validity considerations for Computer Assisted Text Analysis of
Presidents’ Letters
Whilst validity criticisms of computer assisted text analysis of presidents’ letters were
anticipated by testing it against all of Krippendorff’s (2004) validating framework, the
key validity considerations for the present study were sampling validity and construct
validity. The unobtrusive access to the cognitions of the authors of presidents’ letters
validly represented the construct of executive discretion and the content of the sample
used for the present study reflected the purpose of the present study. The method used
here was reliable because the computer assistant replicates accurately and reliably the
quantitative textual processing while at the same time creates verifiable steps and check
values. The present study cites Keegan (2006) and Keegan and Kabanoff (2007) as
evidence of measurement validity and replicated their techniques for the validation of
text analysis of presidents’ letters.
43
Financial Ratio Analysis of Slack Even though financial ratios are widely accepted analytical tools, their application to
operationalising slack required scrutiny for the present study. Financial ratio analysis of
accounting data has two main uses. Traditionally, practitioners have compared financial
ratios to normative standards, in many cases the industry average or exemplar. Financial
ratio analysis is also increasingly being used by researchers to investigate relationships,
commonly predictive analyses forecasting organisational parameters such as future
performance or failure. Contemporary studies of slack employ financial ratios as proxy
measures to operationalise the latent construct suggesting that financial ratios also reveal
organisational attributes (Barnes, 1987).
Reliability
A threat to the reliability and external validity of the present study was identified in the
sensitivity of its analysis to the non-normality of financial ratio distribution (Barnes,
1987; Deakin, 1976; Frecka & Hopwood, 1983). Such non-normality in the form of an
outlier for example may be a threat to reliability if it is due to a mathematical error or a
threat to validity if the value is correct but not representative of the general population.
The design of the present research closely attended to examining and managing
problems of outliers and clusters of interest and to reporting skewness and kurtosis. The
explicit reporting of these measures allows the reader to evaluate the present study’s
results and findings in light of the normality assumption (Tabachnick & Fidell, 2007).
Concurrent Validity
The concurrent validity or correlative validity of the measures of slack in the present
study was supported by the extent to which the multiple measures of the same construct
converge, while that of different constructs diverged (Bryman & Bell, 2003). Although
multiple measures primarily serve to indicate a composite measure of slack, in the
present study the convergent and discriminant correlations between measures were
calculated and reported to support concurrent validity.
44
Construct Validity
Construct validity is a key challenge to the use of financial ratios to operationalise slack.
Promulgated by Bourgeois’ (1981) theoretical paper, researchers measuring slack have
largely relied on financial ratios as proxy measures with limited construct validation.
Marino and Lange (1983) and more recently Wefald, Katz, Downey, and Rust (2006)
are examples where the use of financial ratio for slack measurement have been
investigated.
Relative versus Absolute Measurement
The present study measures absolute levels of slack as they are reported in annual
reports. The relative versus absolute measurement of slack is subject of debate (Daniel et
al., 2004; Marino & Lange, 1983). Bourgeois (1981) argued for the measurement of
slack relative to time because behavioural reactions result from changes in slack rather
than merely having slack. Following Bourgeois (1981) some empirical studies measured
lagged slack suggesting that if performance and other organisational outcomes were to
be an effect of and affect slack, then timing of that response is not immediate, but lagged
(Marino & Lange, 1983). Marino and Lange’s (1983) study of 83 manufacturers tested
measures of slack and recommended judicious selection between relative or absolute
measures depending on the research question. In their meta-analysis of slack to
performance studies Daniel et al. (2004) found a stronger slack to performance
relationship for studies using current year measures than for those using lagged slack
although these studies were in the minority. They suggested that organisations may in
fact be able to ‘unlock’ and convert slack into performance in one fiscal year.
Chapter two suggested that executive discretion moderates the bidirectional slack to
performance relationship. Executive discretion, as it manifests in president’s letters, is a
response to and has an affect on slack concurrently. The cross-sectional capture of slack
from an annual report represents those resources available to the executives as they
author their letter to shareholders. It also represents the level of executive’s discretion
that facilitated the current year’s slack level being reported. Consequently the present
study did not consider lagged slack.
45
Level of Measurement and Analysis
Slack is measured exclusively at the organisational level as it is an organisational
resource (Daniel et al., 2004). For the present study, slack was measured at the
organisational level and then an industry mean was calculated to represent the average
organisation in each industry.
Executive discretion, in contrast, has been studied at three levels: individual,
organisational and industry (Boyd & Gove, 2006). Although the artefact of interest here
is an annual report generated at the organisational level, industry determinants of the
discretion enjoyed by that individual are measured as industry level discretion.
Chapter Summary This chapter brought the methods of the two fields of organisational slack and executive
discretion together as a background for the method described in the next chapter.
Content analysis was defined and clarified before its reliability and validity were
examined for the context used here. The operationalisation of organisational slack using
financial slack was also clarified from a methodological position before its reliability
and validity considerations were discussed. The next chapter reports the methods as they
were applied by the present study.
46
Chapter Four – Method Keegan and Kabanoff’s (2007) recent work is used to initially illustrate the method used
here. The operational measures of organisational slack and industry level discretion are
then described ahead of an explanation of industry level data sampling. That process
yielded the industry level sample from which the sub-industry sample was extracted.
The sub-industry data sampling process and data preparation methods are described as
they were applied and finally the resultant sub-industry sample is detailed.
An Example of the Method Keegan and Kabanoff’s (2007) operationalisation of industry and sub-industry level
discretion is an empirical example of content analysis using the textual and financial
data of annual reports. Abrahamson and Hambrick (1997) proposed that attentional
homogeneity could be measured using lexical similarity in the president’s letters of an
industry. Attentional homogeneity (the degree of similarity of executive attentional foci)
was operationalised by measuring the lexical commonality and lexical density of the
president’s letters. These measures were shown to be negatively correlated with an
academic panel’s ratings of industry level discretion (e.g. lexical commonality vs. ILD: r
= - 0.68, p < 0.01, lexical density vs. ILD: r = - 0.85, p < 0.001, n=14). Abrahamson and
Hambrick (1997) reduced the sensitivity of their lexical density measure to sample size
by regressing raw lexical density on the number of letters in their sample. Keegan and
Kabanoff (2007) found that the reciprocal of the number of letters is the more
appropriate regressor and suggested that their method was more valid, considering their
larger sample (n=196).
Keegan and Kabanoff (2007) refined positive extra usage as an additional sub-measure
of lexical similarity. The sub-measure was originally developed by Abrahamson and
Hambrick’s (1997) to demonstrate the validity of using lexical content of presidents’
letters to operationalise attentional homogeneity. Keegan and Kabanoff (2007) used
SPSS Principal Components Exploratory Factor Analysis to produce a one factor
solution which captured 89% of the three industry level sub-measures at the first
eigenvalue (2.67). Similar results were reported at the sub-industry level.
47
Keegan and Kabanoff (2007) used a number of qualitative and quantitative tests to
demonstrate that attentional heterogeneity (the negative of attentional homogeneity) is a
practical operationalisation of the average level of executive discretion in and industry
or subgroups of organisations in an industry. Their tests included the use of the leverage
ratio (long term debt/ total assets) which is seen as a constraint on executive discretion.
They divided industries into thirds by leverage, measured attentional homogeneity of
each thirdtile group and found that, within industries, as industry level discretion
increased, debt’s disciplining effect decreased. The methods they developed and
demonstrated are replicated in the present study.
Design Overview As did Keegan and Kabanoff (2007), the present study used the research database that
Keegan (2006) created from the Compact Disclosure database. This database is a
repository of U.S. annual reports arranged by industry. The four digit SIC codes classify
organisations into industries at the third and fourth digit level. The second digit classifies
industries into ‘major groups’ and the first digit designates industry ‘divisions’(Standard
Industrial Classification System Manual, 1987). The research database contained 21991
screened annual reports from 1988 to 1999 for undifferentiated (single industry)
organisations. Two nested samples were used, firstly at the industry and then at the
‘within’ industry level.
The industry level sample included only the industries for which greater than 19 usable
annual reports in each overlapping five year period were available. By using seven of
Bourgeois and Singh’s (1983) slack ratios for every organisation they were grouped into
low, middle or high by sample percentile by industry and five year period. The sub-
industry sample included only the sub-industry groups with greater than 19 presidents’
letters in each overlapping five year period. Keegan’s (2006) database contained
discretion values by industry so only the values of average executive discretion for each
sub-industry group (low, middle or high) needed to be calculated. These discretion
values were extracted using quantitative textual analysis software and factor analysis.
48
With slack and discretion values assigned to the industry and sub-industry sample, the
data sampling and preparation was complete.
Although data analysis is reported in the next chapter, it is helpful to note that at the
industry level, cases were analysed by examining the correlations between values of
discretion and organisational slack. At the sub-industry level, cases were analysed using
paired sample t-tests. To support this analysis, the normality and linearity of the samples
were also examined using scatterplots.
Measures of Organisational Slack Composite Measurement of Slack
Bourgeois and Singh’s (1983) composite measure of slack was theoretically designed to
operationalise a latent concept using financially derived measures. While a factor model
of slack appeals to a notion of simplicity, Bourgeois and Singh’s (1983) composite view
of slack represents an operationalisation of some of the facets or components of slack
using widely understood proxy measures. Indeed facets of slack may be operationalised
without using financial measures. Although some subsequent work utilised
questionnaire measures (e.g. Nohria & Gulati, 1996) and other non-financial measures
of slack (e.g. Forte, Hoffman, Lamont & Brockmann, 2000), most research employed
financial ratio measures attributed to Bourgeois and Singh (1983).
Multiple Measures of Slack
In his seminal paper on the measurement of slack, Bourgeois (1981) proposed eight
“unobtrusive” surrogate measures of slack based on financial data. His measures were:
retained earnings (RE), dividend payout (DP), general and administrative expense
(G&A), working capital as a percentage of sales (WC/S), debt as a percentage of equity
(D/E), credit rating (CR), short term loan interest to prime rate (I/P), and the price to
earnings ratio (P/E). Bourgeois (1981) suggested these measures tap the internal and
external sources of slack. Bourgeois and Singh (1983) refined the operationalisation of
slack through financial ratios by incorporating the ‘ease of recovery’ dimension.
49
The studies using empirical financial measures of slack are summarised in Table 3.
Daniel et al.’s (2004) meta-analysis of 66 empirical research articles in a ten year period
(1990 to 2000) is further evidence of the prevalent use of financial ratio measures for
slack.
Table 3 - Studies using Financial Measures of Slack
FINANCIAL RATIO MEASURE OF SLACK STUDY
Current/ Quick ratio Bromiley (1991) Cheng and Kesner (1997) Geiger and Cashen (2002) Singh (1986)
Leverage Bromiley (1991) Cheng and Kesner (1997) Davis and Stout (1992) Geiger and Cashen (2002)
Selling, general and administrative expense (S,G&A)
Bromiley (1991) Cheng and Kesner (1997) Geiger and Cashen (2002) Singh (1986)
Working capital Singh (1986) Wally and Fong (2000)
Interest coverage Bromiley (1991)
Cash flow Davis and Stout (1992)
Growth in operating margin Zajac, Golden, and Shortell (1991)
The design of the present study was limited to the use of only financial measures of
organisational slack. Seven measures adapted from Bourgeois and Singh’s (1983) eight
measures were used. The price to earnings ratio measure of potential slack was not
available in the database used. The three measures of available slack, the three measures
of recoverable slack and the single measure of potential slack are outlined in Table 4 and
discussed next.
50
Table 4 - Slack Measures after Bourgeois and Singh (1983)
Slack Category Financial Ratio Expected
Direction*
Slack Measure
Available Slack
Retained earnings Positive (Net Profit- Dividends) / Sales
Dividends Negative Dividends/Net Worth
Working capital Positive (Cash and Securities less Current Liab.)/Sales
Recoverable
Slack
Working capital Positive Accounts Receivable / Sales
Working capital Positive Inventory / Sales
S,G&A Positive (Selling,Gen. and Admin. Exp) /Sales
Potential Slack
Leverage negative Long Term Debt/ Net Worth
PE ratio Positive Share Price/ Earnings per Share
(not used for the present study)
*A positive expected direction means that an increase in the financial ratio should indicate an increase in slack. A negative expected direction means that a decrease in the financial ratio should indicate an increase in slack.
Available Slack
Retained Earnings
Bourgeois and Singh’s (1983) measurement model begins by measuring available slack
using retained earnings as a proportion of sales. Retained earnings are the portion of
after tax profit that is held in an organisation after dividends have been paid (Disclosure,
1994). This measure represents the power balance between shareholders and the
organisation mediated by the capital market. The ability of an organisation’s managers
to have available slack in the form of retained earnings is endowed broadly by
shareholder judgement of past performance and future expectations (Baker & Wurgler,
2004). Bourgeois (1981) argued that this internal source of slack can be created and
appropriated by the deliberate actions of managers of the organisation. He further
suggests that the successful actions of organisation management creates the pool of
profit from which retained earning are taken.
Negative Dividend Payout
The second measure of available slack is the negative value of dividend payout (i.e.
dividend payout multiplied by minus one) as a proportion of net worth. Dividend payout
is set by the organisation’s dividend policy and is defined as that proportion of after tax
51
profit returned to shareholders (Disclosure, 1994). This is slack appropriated (not paid
out) from profits. Although Bourgeois and Singh (1983) deliberately avoided financial
theory, neither they, nor subsequent researchers who have used this measure specify
why this is an appropriate proxy measure. Indeed Bourgeois and Singh (1983) do not
differentiate between retained earnings and dividend payout as measures of slack. While
it is neither within the bounds nor the intention of the present study to delve into
financial theory, the finance literature provides some guidance.
Miller and Modigliani (1961) showed that, in a perfect and efficient capital market, the
rate of dividends paid by an organisation was independent of shareholder wealth and
rational investors prefer dividends over capital gains: Higher dividend payouts lead to
lower retained earnings. However organisations deliberately design dividend policies for
various strategies (Lintner, 1956). La Porta, Lopez-de-Silanes, Shleifer, and Vishny
(2000) explored an agency view of dividend policies which permits a differentiation of
retained earnings from dividends as measures of available slack. Under this approach,
the sharemarket pressures managers to ‘disgorge’ cash (i.e. lower available slack) to
address two agency problems: firstly, to limit marginal investments and secondly to
reduce expropriation of retained earnings by insiders. It cannot be expected that
dividends will be minimised if retained earnings are maximised because managers as
agents introduce imperfections to the equation and may, for example, manipulate
apparent retained earning through accounting adjustments. Slack in the form of retained
earnings can be gained by managers from shareholders while slack in the form of
dividend payout can be gained from the wider sharemarket. The subtle difference is that
managers may seek to gain slack from both sources and as such these gains are best
captured by employing both measures simultaneously (La Porta et al., 2000).
Bourgeois and Singh (1983) highlighted the need to attend to the directionality of the
relationship between dividend payout and available slack. While the common purpose of
the dividend payout financial ratio is to evaluate dividends, here it is used as a proxy to
represent resources held by the organisation from income. In essence this is the opposite
of the dividend payout to shareholders as a proportion of total assets.
52
Cash – Working Capital
The third measure of available slack is the cash component of working capital as a
proportion of sales. Cash and marketable securities minus current liabilities as a
proportion of sales, measures the most accessible component of slack in working capital.
The cash component of working capital is available for use, requiring minimal
intervention by managers (Bourgeois & Singh, 1983).
Recoverable Slack
Accounts Receivable
The first measure of recoverable slack is the accounts receivable component of working
capital as a proportion of sales. Accounts receivable, the incoming payments to the
organisation for invoiced charges, are resources absorbed into the organisation that can
be recovered with effort (Bourgeois & Singh, 1983). By reducing accounts receivable,
the organisation potentially can access and redeploy slack.
Industries may differ in the proportion of accounts receivables held. For example, it is
generally recognised that the fast food restaurant industry experiences a small (if any)
proportion of sales on account while, in contrast, utilities companies would have a large
proportion of sales on account and negligible in cash.
Inventory
The second measure of recoverable slack is inventory as a proportion of sales. This
represents the slack resources absorbed into the operations of the organisation
recoverable by realising inventory. As with the receivables measure of recoverable
slack, inventory is expected for some industries and not for others. For example,
manufacturers could be expected to rely on inventory whereas for service companies it
may be negligible.
53
Sales, General & Administration Expense
The third measure of recoverable slack is S,G&A expenses as a proportion of sales.
While Bourgeois and Singh (1983) did not include sales expenses in their general and
administration expense measure, it could not be isolated for the present study. Daniel et
al., (2004) found that S,G&A has been used by a number of studies. Indeed the sales,
general and administration expense measure of slack captures the slack that is
recoverable from all three expenses with some effort and organisational redesign.
Potential Slack
Negative Long Term Debt
Potential slack was measured using negative long term debt (reverse coded) as a
proportion of total assets. This ratio reflects the organisation’s potential to borrow
resources from external sources. With debt come interest obligations, which reduce the
size of the pool of potential slack. While current interest liabilities influence available
slack, debt maintenance obligations have a financially limiting effect in the future and
restrict potential slack (Bourgeois & Singh, 1983).
The financing strategy of using long term debt is not universal across industries which
differ in the level of leverage that is acceptable to shareholders and creditors. For
example, the more mature and stable utilities industry might be expected to employ a
higher leveraging strategy in comparison to the higher risk (and higher credit cost) oil
and gas exploration industry (Jensen & Meckling, 1976).
Bourgeois and Singh (1983) also highlighted the need to attend to the directionality of
the relationship between long term debt and potential slack. Long term debt to total
assets financial ratio is typically used to evaluate leverage, here it is used as a surrogate
for slack potentially recoverable by the organisation from capital markets. If an
organisation experiences lower long term debt their potential to recover slack is high.
Equally, if they have large amounts of long term debt as a proportion of total assets, they
are said to be highly leveraged and their potential to gain slack is low.
54
Total Slack
Bourgeois (1981) suggested that changes in slack are a function of their eight financial
ratio measures (seven are replicated by the present study). Subsequently, Bourgeois and
Singh (1983) found some empirical support for the individual measures of slack but
could not show a significant relationship between overall (summed total) slack and their
other variables, political behaviour and strategic discord, until they controlled for the
influence of management teams size (4-10 members, n=24) using a measurement of
executive influence akin to perceived discretion.
It is for convenience that the seven surrogate financial measures, originally designed to
measure the different aspects of available recoverable and potential slack, are used by
the present study in simple summation to measure total slack. The conceptualisation of
total slack might be best represented by the simplicity of this composite model and
measures, rather than a complex factor model or latent concept (Law & Wong, 1999).
Just one published study examined this in detail - Moses' (1992) factor analysis of the
interrelationships among slack measures resulted in four factors however, available,
recoverable and potential slack were not found to be distinct dimensions. Without clear
guidance from past studies, the present study aggregated the seven slack measures into a
single measure of total slack.
55
Measures of Executive Discretion The present study relied on Keegan’s (2006) and Keegan and Kabanoff’s (2007) indirect
measurement of industry level discretion based on Abrahamson and Hambrick’s (1997)
attentional homogeneity measure of industry level discretion. To measure attentional
homogeneity, the lexical commonality, lexical density and the length of the positive
extra use list were extracted from the president’s letters using PERL (Practical
Extraction and Reporting Language) open source software, attributed to Wall (1987).
The three sub-measures of attentional homogeneity were reduced to one discretion
measure using two exploratory factor analysis programs, SPSS and Comprehensive
Exploratory Factor Analysis (CEFA) shareware (Browne, Cudeck, Tateneni, & Mels,
2004). The development of executive discretion values from the three attentional
homogeneity sub-measures is discussed next.
Attentional Homogeneity as a Measure of Executive Discretion
The three step information processing model of Daft and Weick (1984) suggests that
managers firstly focus their attention on information, then on interpretation and finally
on action. Abrahamson and Hambrick, (1997) argued that the attentional homogeneity
of executives in an industry increases as industry level discretion decreases. Attentional
homogeneity was defined by Abrahamson and Hambrick (1997) as “the degree of
similarity in the foci of attention of top managers across organizations” (p. 514). They
suggest low discretion industries have narrower options on which executives can focus
their attention than industries with greater discretion. This implies that the homogeneity
of managerial attention in an industry is negatively related to industry level discretion.
Abrahamson and Hambrick (1997) took a novel approach to develop a measure of
attentional homogeneity. They used the similarity of the words used in president’s letters
to represent the attentional homogeneity of industries. In support of their lexical
approach, Abrahamson and Hambrick (1997) referenced the Whorf-Sapir hypothesis.
Whorf and Carroll’s (1956) ‘linguistic relativity’ concept, built on work by Sapir (1944)
suggests that differences in language systems parallel differences in non linguistic
56
cognitions. This has been used as an assumption to create ‘maps of attention’ wherein
the use of a word indicates the direction of attention and its frequency of use indicates
the intensity of attention (Huff, 1990). Building on this, Abrahamson and Hambrick
(1997) suggested that the quantitative aspects of word usage of managers in industries
could be used to measure the cognitions of managers which, according to the
information processing model, reflect their attentional homogeneity.
Abrahamson and Hambrick (1997) used two separate lexical measures, lexical
commonality and lexical density to measure attentional homogeneity. Keegan and
Kabanoff (2007) added the positive extra use measure with factor analysis to produce a
single indicator of attentional homogeneity. Keegan (2006) developed not only a useful
research database including industry level discretion values, but also refined the lexical
density measure.
Lexical Commonality
Abrahamson and Hambrick (1997) use the term ‘lexical commonality’ for this measure
“because it measures how commonly words belonging to an industry’s lexicon are used
across organization in this industry” (p. 521). This measure of attentional homogeneity
captures word usage to represent attentional direction and frequency of usage to
represent attentional intensity.
A compromise in this measure is that single usage words are given more weight than
more commonly used words. Abrahamson and Hambrick (1997) argued that although
single use words will always be found in industries the problem is mitigated by greater
numbers of president’s letter. An important consideration for the present study, which is
reported later, was that potentially small sample sizes for sub-industry groups were
sufficient to measure attentional homogeneity. Appendix A illustrates Abrahamson and
Hambrick’s (1997) hypothetical example of how lexical commonality is calculated.
57
Lexical Density
Lexical density is a measure of the density of word sharing which Abrahamson and
Hambrick (1997) designed to address the problem of lexical commonality overweighting
low use words. This measure captures attentional homogeneity by counting how many
times every word is shared across an industry. This measure is calculated in two steps:
first raw lexical density is calculated, second raw lexical density is then regressed on the
reciprocal of the number of presidents’ letters in the sample to remove the sensitivity to
the number of letters in the sample (Keegan, 2006). The residual is taken as the measure
of lexical density. Appendix A illustrates the calculation of lexical density in more
detail.
Extra Usage
Abrahamson and Hambrick (1997) illustrated the validity of the word count method by
measuring the extra use of words in high and low discretion industries with respect to all
industries. Ranking words in industries by their extra use, relative to all industries,
identified words that contributed to attentional homogeneity as an industry’s core
vocabulary. The length of this list reflects the breadth of an industry’s vocabulary and
therefore its level of attentional homogeneity (Abrahamson & Hambrick, 1997). A
longer list of high extra use words equates to higher attentional homogeneity (lower
industry level discretion) because it shows a large shared vocabulary. Appendix A
illustrates the calculation of extra usage in more detail.
Positive Extra Usage
While Abrahamson and Hambrick (1997) used both positive and negative values of
extra use, Keegan’s (2006) results suggested that negative extra usage does not measure
the same latent variable as the lexical density and commonality measures. Keegan
(2006) reasoned that the extra low use words would be less industry specific than extra
high use words. Put plainly, the positive extra use measure of reveals discretion
attributes of industries and negative extra use does not. Negative extra use was not
included for the present study.
58
Sampling and Data Preparation
Research Database
The present study used the research database Keegan (2006) created from the Compact
Disclosure database. The Compact Disclosure database contained the annual reports
compulsorily filed by public companies with at least 500 shareholders and five million
dollars in assets. Companies with fewer than 50 employees were excluded from the
research database to exclude shelf companies. Keegan’s (2006) preparation of the
research database used here included screening 65356 annual reports over the period
from 1988 to 1999 from which 21991 reported having a single four digit industry
classification (i.e. undifferentiated firms). Of these, 9549 annual reports had presidents’
letters available.
A specialised component of Keegan’s (2006) research database was that contained the
industry level discretion values for 116 industry-year cases. Following Abrahamson and
Hambrick (1997), Keegan (2006) assigned annual reports to a five year period when
they filed with the SEC and only sampled industries with more than 19 annual reports
for the period. There were eight overlapping periods from 1988-92 to 1995-99.
Another component of Keegan’s (2006) research database important to the present study
is the financial data. Keegan (2006) examined the accounts, excluded those that were
found to contain accounting anomalies and duplication and made adjusting journal
entries to remove negative values. The long term debt account, used by Keegan (2006),
Keegan and Kabanoff (2007) and the present study, reflects such an account where
‘positively adjusted’ values were used.
59
Sample Frame
The convenience sample taken from Keegan’s (2006) research database varied from
slack measure to measure depending on data availability. It contained 40 industry-year
cases for dividend payout, 38 cases for the retained earnings measure and 82 cases for
the remaining five measures. Each case was made up of the group of annual reports
categorised by a 4-digit SIC code sampled from the eight overlapping five year sample
periods, each represented by the central target year (TY). This produced the SIC target
year case notation – SIC_TY. For example the notation ‘1311_1997’ refers to the case of
the industry categorised by the 1311 4-digit SIC code with a target year of 1997. These
cases were from 3503 annual reports from 1412 companies grouped into 17 industries
from a 12 year period (1988-1999). The 17 industries are listed in Table 5 and the
detailed narrative of each is reproduced in Appendix C.
Table 5 - Sampled Industries
SIC SEC Industry Description
1311 Crude Petroleum and Natural Gas3571 Electronic Computers3577 Computer Peripheral Equipment, Not Elsewhere Classified3661 Telephone and Telegraph Apparatus3663 Radio and Television Broadcasting and Communications Equipment 3674 Semiconductors and Related Devices3845 Electromedical and Electrotherapeutic Apparatus4213 Trucking, Except Local4812 Radiotelephone Communications4813 Telephone Communications, Except Radiotelephone4911 Electric Services 4923 Natural Gas Transmission and Distribution5812 Eating Places 6324 Hospital and Medical Service Plans7372 Pre-packaged Software7373 Computer Integrated Systems Design8071 Medical Laboratories
Source: http://www.osha.gov/pls/imis/sic_manual.html (U.S. Department of Labor Occupational Safety and Health Administration).
60
Industry Level Data Preparation
Negative Retained Earnings
Of the 3503 usable annual reports, 1410 showed negative retained earnings, an account
which was expected to have a positive balance. Similarly it might be expected that
available slack as a resource cannot have a negative value. However, paid in capital may
be reported as negative retained earnings in the statement of financial position. This may
be the case for a healthy organisation however a troubled organisation may also report
negative retained earnings (DeAngelo, DeAngelo, & Stulz, 2006). Reliance on archival
financial reports necessitates a dependence on the accounting standards reflected in the
data source. To maintain data integrity, adjusting the reported accounts of organisations
was not undertaken. In preference, observations of negative retained earnings were
removed from further analysis. Sixty-seven annual reports with zero retained earnings
were kept as they were plausible values of retained earnings and available slack.
Following the removal of these observations, 42 cases no longer contained 20 or more
annual reports. The useable sample for the retained earnings measure of available slack
totalled 1547 annual reports contributing to 40 cases. The useable sample for the
dividend measure of available slack was also taken from these 1547 annual reports
however the use of sales as a denominator made this measure susceptible to non-
calculable values where zero sales were reported.
Negative values of retained earnings do not inform the hypotheses of the present study
however their presence may inform the research question in a wider sense. The presence
of negative values of retained earnings could be interpreted as support for Keegan’s
(2006) conclusion that high discretion industries adjust their accounts more than low
discretion industries.
Missing and Zero Values
Missing values in industry averages at the industry level are not possible, however at the
organisational level, missing values and zero values for slack inputs were present. At the
data processing stage, such values were absorbed by the aggregating of values into one
industry average value.
61
A particular feature of five of the financial ratio measures used for the present study is
the use of sales as a denominator in an attempt to control for organisation size
(Bourgeois & Singh, 1983). Nineteen of the 3503 annual reports reported zero values for
sales which, in ratio calculations and subsequently in industry year averages, produced
an erroneous result. Electing not to transform the data, 95 ratio value observations of
24421 at the organisational level were recorded as having missing values. The 19 annual
reports that reported zero sales were spread over 17 industry year cases and 15
industries. After the removal of cases due to negative retained earnings two extra cases
fell below the minimum 20 annual report requirement.
Table 6 summarises the cases containing annual reports containing zero values in the
sales account and illustrates that no industry or year or indeed industry year case is
particularly dominated by organisations reporting zero sales.
Table 6 - Cases Containing Annual Reports Reporting Zero Sales
Case Number of Annual Reports Containing Zero Sales*
1311 1996 11311_1997 13577_1996 13661_1997 13663_1994 13674_1994 13845_1994 13845_1995 13845_1997 24812_1996 14813_1997 14911_1990 15812_1992 17372_1993 17372_1995 17373_1994 17373_1995 2
* Without a corresponding zero numerator
62
Industry Level Sample Table 7 summarises the industry level on three dimensions: the number of usable
organisations’ annual reports for the sample, the number of industry-year cases for each
ratio measure and the number of industries represented in the sample for each ratio
measure.
Table 7 - Industry Level Sample
Number of
Observations
Number of SIC_TY's
(Sample)
Number of
Industries Slack Measure
Available Slack
Retained Earnings 1530a 38 8
Negative Dividend Payout 1547b 40 8
Working Capital
Cash 3484c 82 17
Recoverable Slack
Working Capital Recievables 3484c 82 17
Working Capital
Inventory 3484c 82 17
Sales, General and Admin 3484c 82 17
Potential Slack
Negative Long Term Debt 3503 82 17
a: 3503 less observations in industry –year cases with less than 20 Ar's after negative RT values and zero sales denominator removed
b: 3503 less observations in industry –year cases with less than 20 Ar's after negative RT values removed
c: 3503 less 19 missing values (zero sales denominator)
63
Describing the Industry Level Variables The industry level sample was initially analysed by examining the descriptive statistics,
histograms and bivariate scatter plots. For this examination, two branches of analysis
were undertaken. Firstly, various data points were identified as outliers, clusters and
influential points. Secondly, the normality and linearity of each variable’s distributions
was examined to determine if the assumptions of the statistical tests used in hypothesis
testing were sustainable (Tabachnick & Fidell, 2007). The univariate descriptive
statistics for the sample are presented next (see Table 8).
64
Table 8 - Industry Level Univariate Descriptive Statistics
Measure n Range Minimum Maximum Mean SD Skewness SEsk Kurtosis SEk
Industry Level Discretion
ILD 82 3.45 -2.34 1.11 0.07 1.00 -1.23 0.27 0.32 0.53
Available Slack
Retained Earnings/Sales 38 0.45 0.11 0.56 0.25 0.01 1.14 0.38 2.17 0.75
Negative Dividends / Net Worth 40 0.18 0.06 0.24 0.13 0.00 0.14 0.37 -0.93 0.73
Cash & Securities less Current
Liabilities/ Sales 82 2.57 -0.79 1.78 0.07 0.15 1.70 0.27 4.65 0.53
Recoverable Slack
Accounts Receivables / Sales 82 0.25 0.01 0.26 0.16 0.00 -0.86 0.27 -0.31 0.53
Inventory/ Sales 82 0.55 0.00 0.55 0.11 0.01 1.55 0.27 2.72 0.53
S,G&A / Sales 82 0.80 0.11 0.90 0.35 0.03 1.20 0.27 1.04 0.53
Potential Slack
Negative Long Term Debt / Net Worth 81 0.32 -0.33 -0.01 -0.13 0.01 -0.62 0.27 -1.29 0.53
Total Slack
Summed Slack Measures 38 2.04 -0.24 1.8 0.62 0.52 0.52 0.38 -0.92 0.75
65
Industry Level Outliers and Normality
The measurement of organisations’ financial ratios characteristically results in a high
variance in values (Frecka & Hopwood, 1983). Wefald et al. (2006) suggested that
outliers in financial measures of slack may be due to changes in accounting procedures,
changes in business model, changes in international market conditions, litigation
(including bankruptcy) and mergers, acquisition and restructuring. Keegan (2006)
tracked mergers and acquisitions and name changes when preparing the research
database used by the present study.
Univariate outliers are cases that have extreme values on one variable while bivariate
outliers are those cases that have an unusual combination of scores on two variables
(Tabachnick & Fidell, 2007). The data was tested for both univariate and bivariate
outliers.
Describing the Univariate Outliers
Because univariate outliers are somewhat obscured by the smoothing effect of
aggregating individual organisation data into industry data, the present study identified
and removed extreme cases before aggregation. Univariate outliers were investigated by
comparing their values to the mean. Univariate outliers were identified as lying beyond
three standard deviations from the mean, sample wide (Wefald et al., 2006).
From 21489 organisation level observations of slack, 167 observations were more than
three standard deviations from the mean for each slack measure. No apparent
consistency on industry or year basis was identified to merit further investigation.
However, the magnitude of the 167 outliers’ distance from the mean extended from
three to over 50 standard deviations for the inventory measure of recoverable slack. The
distance from the mean of each outlier was verified to ensure there was not a failure of
the sampling or measurement processes (Stevens, 1996). Table 9 summarises the
outlying values removed. The outcome was averaged organisational data that formed
cases (at the industry level) which retained a representative sample of those industries.
66
Table 9 - Organisation Level Outlier Analysis Summary
Total
observations
Outliers
(observations)
Industries
with outliers
Cases with
outliers
from
sample
Sample
frame
mean
Outlier
mean
Sample
frame SD
Outlier
SD
Outlier
distance from mean
(SD’s)
Slack Measure Minimum Maximum
Available Slack
Retained Earnings/Sales 1530a 17 3 of 8 7 of 38 0.25 1.27 0.21 0.59 3.03 13.87
Negative Dividends / Net Worth 1547b 27 5 of 8 12 of 40 0.12 -0.74 0.14 0.13 3.01 4.58
Cash & Securities less Current
Liabilities/ Sales 3484c 10 3 of 17 8 of 82 0.49 137.49 10.13 134.83 3.66 48.81
Recoverable Slack
Accounts Receivables / Sales 3484c 21 9 of 17 15 of 82 0.17 1.24 0.17 1.35 3.06 39.71
Inventory/ Sales 3484c 3 2 of 17 3 of 82 0.15 63.81 2.23 49.70 28.31 51.00
S,G&A / Sales 3484c 16 6 of 17 11 of 82 0.50 26.59 2.64 32.61 3.09 48.26
Potential Slack
Negative Long Term Debt /
Net Worth 3503 73 14 of 17 38 of 82 0.14 0.78 0.19 0.05 3.02 4.09
a: 3503 less observations in industry –year cases with less than 20 Ar's after negative RT values and zero sales denominator removed b: 3503 less observations in industry –year cases with less than 20 Ar's after negative RT values removed c: 3503 less 19 missing values (zero sales denominator)
67
Visual Test for Bivariate Outliers
With 167 univariate outliers removed, scatter plots of scores for industry average level
slack to industry level executive discretion were generated (see Figure 1) and examined
for bivariate outliers (Tabachnick & Fidell, 2007). In addition to observing one point of
interest on the negative long term debt versus industry level discretion scatter plot (see
Figure 1, Panel G) as a possible outlier, two clusters of values also merited investigation
on the receivables versus industry level discretion scatter plot (see Figure 1, Panel D).
On closer examination the two clusters could not be excluded as outliers as they
represented industry attributes.
Describing the Bivariate Outliers
On the long term debt to industry level discretion scatter plot (Figure 1, Panel G) the
case of the Hospital and Medical Service Plans industry (SIC 6324) in the year 1992 was
observed as a bivariate outlier. This industry-year case is the only representation from
that industry across any year of the sample, no other years are available and so
comparisons with similar industry cases were not possible. Some indication to the
location of this case as an outlier may be present in its makeup. Of the 23 annual reports
making up this case, 13 (57%) reported nil long term debt. The calculated mean value
for the remaining observations is 0.049, which, if taken as the average value for the case,
would position it with the other cases. Sample wide however, 36 percent of cases report
nil long term debt. This industry specialises in medical insurance in the form of service
plans for members and other subscribers where business models involve investing rather
than borrowing. Removing this case as an outlier is reasonable because of its uniqueness
and that industry members rarely have any long term debt.
68
-3.00 -2.00 -1.00 0.00 1.00
ILD
0.10
0.20
0.30
0.40
0.50
0.60
A1
-2.00 -1.00 0.00 1.00
ILD
0.05
0.10
0.15
0.20
0.25
A2
-2.00 -1.00 0.00 1.00
ILD
-1.00
-0.50
0.00
0.50
1.00
1.50
2.00
A3
Panel A. Available Slack -
Retained Earnings (A1) vs. ILD
Panel B. Available Slack -
Negative Dividend Payout (A2) vs. ILD Panel C. Available Slack -
Working Capital – Cash (A3) vs. ILD
-2.00 -1.00 0.00 1.00
ILD
0.00
0.05
0.10
0.15
0.20
0.25
0.30
R1
-2.00 -1.00 0.00 1.00
ILD
0.00
0.10
0.20
0.30
0.40
0.50
0.60
R2
-2.00 -1.00 0.00 1.00
ILD
0.20
0.40
0.60
0.80
1.00
R3
Panel D. Recoverable Slack -
Working Capital – Recievables (R1) vs. ILD
Panel E. Recoverable Slack -
Working Capital – Inventory (R2) vs. ILD Panel F. Recoverable Slack -
Sales,General and Admin. (R3) vs. ILD
-2.00 -1.00 0.00 1.00
ILD
-0.30
-0.20
-0.10
0.00
P1
1.000.00-1.00-2.00-3.00
To
tal S
lac
k
2.00
1.50
1.00
0.50
0.00
-0.50
Panel G Potential Slack -
Negative Long Term Debt (P1) vs. ILD
Panel H Total Slack
(All Measures Summed) vs. ILD
Figure 1 - Industry Average Slack and Industry level Discretion
ILD
69
Normality
Figure 2 contains the SPSS generated histograms and approximate normal curve from
the final industry level sample with outliers removed. The industry level sample data is
also provided in Table 10. Table 8 reports that industry level discretion and a number of
the slack measurements are marginally skewed with skewness statistic values greater
than one in absolute terms. The same slack measures had kurtosis values greater than
one, while the long term debt measure had a kurtosis statistic less than negative one.
These values support the observation from the histograms of minor non-normality (De
Vaus, 2002).
Linearity
Industry level hypotheses testing used Pearson’s r which captures only linear
correlations. The present study is concerned with understanding the associations
between variables irrespective of linearity or otherwise. As the scatter plots (see Figure
2) generally represent monotonic relationships, the application of a linearity assumption
for further correlation analysis is adequate for describing relationships. However the
correlation coefficients should be interpreted with this in mind and with the scatter plots
in view (Tabachnick & Fidell, 2007).
70
Panel A. Available Slack
(Retained Earnings)
Panel B. Available Slack
(Negative Dividend Payout) Panel C. Available Slack
(Working Capital - Cash)
Panel D. Recoverable Slack
(Working Capital - Receivables)
Panel E. Recoverable Slack
(Working Capital - Inventory) Panel F. Recoverable Slack
(Sales, General & Admin.)
Panel G Potential Slack
(Negative Long Term Debt)
Panel H Industry Level Discretion Panel I Total Slack
(Summation of Slack Measures)
Figure 2 - Slack and Industry Level Discretion Frequency Histograms
Total Slack2.001.501.000.500.00-0.50
12.5
10.0
7.5
5.0
2.5
0.0
Mean =0.62Std. Dev. =0.52N =38
Freq
uenc
y
-2.50 -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00
ILD
0
5
10
15
20
Mean = 0.07Std. Dev. = 0.1N = 82
Freq
uenc
y
-0.30 -0.20 -0.10 0.00Potential Slack (Negative Long Term Debt)
0
5
10
15
20
Mean = -0.13Std. Dev. = 0.11N = 82
0.20 0.40 0.60 0.80 1.00 Recoverable Slack (S,G&A)
0
10
20
30
Mean = 0.35Std. Dev. = 0.18N = 82
Freq
uenc
y
0.00 0.10 0.20 0.30 0.40 0.50
Recoverable Slack (Working Capital – inventory)
0
5
10
15
20
25
30
Mean = 0.11Std. Dev. = 0.11N = 82
Freq
uenc
y
0.00 0.05 0.10 0.15 0.20 0.25
Recoverable Slack (Working Capital – Recievables) 0 2
4
6 8
10
12
14
Mean = 0.16 Std. Dev. = 0.07N = 82
Freq
uenc
y
-0.50 0.00 0.50 1.00 1.50
Available Slack (Working Capital – Cash) 0
5
10
15
20
25
30
Mean = 0.07Std. Dev. = 0.39N = 82
Freq
uenc
y
0.05 0.10 0.15 0.20 0.25
Available Slack (Negative Dividend Payout)
0
2
4
6
8
10
Mean = 0.13Std. Dev. = 0.049N = 40
Freq
uenc
y
0.10 0.20 0.30 0.40 0.50 0.60 Available Slack (Retained Earnings)
0 2
4
6 8
10
12
14
Freq
uenc
y
Mean = 0.25 Std. Dev. = 0.093 N = 38
Freq
uenc
y
71
Table 10 - Average Slack and Industry Level Discretion by Industry Target Year
SIC-TY Slack Measures a Total
Slack
SIC-TY Slack Measures a Total
Slack ILD A1 A2 A3 R1 R2 R3 P1 ILD A1 A2 A3 R1 R2 R3 P1 1311 1991 -1.78 -0.31 0.20 0.02 0.15 -0.25 4213 1993 -0.07 -0.14 0.11 0.01 0.17 -0.251311 1992 -1.96 -0.19 0.19 0.06 0.18 -0.29 4213 1994 0.20 0.13 0.13 -0.16 0.12 0.01 0.16 -0.24 0.351311 1993 -2.28 -0.18 0.21 0.01 0.13 -0.28 4213 1995 0.25 0.19 -0.14 0.12 0.01 0.17 -0.22 0.381311 1994 -2.34 0.56 0.14 -0.16 0.21 0.02 0.15 -0.31 0.61 4213 1996 0.23 0.16 0.21 -0.14 0.12 0.01 0.17 -0.23 0.531311 1995 -2.12 0.41 0.09 -0.50 0.21 0.01 0.15 -0.26 0.11 4812 1995 -0.85 -0.17 0.12 0.05 0.49 -0.321311 1996 -2.12 0.44 0.09 -0.20 0.25 0.02 0.11 -0.29 0.42 4812 1996 -0.84 -0.16 0.16 0.07 0.81 -0.301311 1997 -2.05 0.39 0.06 -0.79 0.22 0.10 0.11 -0.33 -0.24 4813 1996 -0.36 0.23 0.18 0.01 0.49 -0.213571 1990 -0.39 0.02 0.19 0.15 0.38 -0.10 4813 1997 -0.11 1.09 0.20 0.00 0.67 -0.103577 1990 0.41 -0.06 0.20 0.17 0.26 -0.07 4911 1990 0.00 0.27 0.08 -0.31 0.09 0.07 0.23 -0.30 0.133577 1991 0.39 -0.09 0.17 0.15 0.24 -0.04 4911 1991 -0.19 0.27 0.07 -0.32 0.08 0.06 0.23 -0.33 -0.133577 1992 0.34 0.08 0.18 0.14 0.26 -0.01 4911 1992 -0.40 0.28 0.07 -0.28 0.08 0.07 0.25 -0.32 -0.253577 1993 0.50 0.08 0.20 0.17 0.26 -0.02 4911 1993 -0.58 0.26 0.06 -0.23 0.09 0.06 0.26 -0.31 -0.393577 1994 0.71 0.02 0.20 0.15 0.24 -0.03 4911 1994 -0.41 0.28 0.07 -0.22 0.08 0.06 0.26 -0.32 -0.23577 1995 0.77 0.13 0.18 0.14 0.24 -0.07 4911 1995 -1.06 0.29 0.07 -0.26 0.09 0.06 0.25 -0.31 -0.873577 1996 0.86 0.19 0.14 0.07 0.17 0.16 0.27 -0.05 0.95 4911 1996 -1.11 0.24 0.06 -0.23 0.09 0.05 0.24 -0.31 -0.973577 1997 0.90 0.01 0.19 0.25 0.35 -0.04 4923 1991 -2.20 0.17 0.09 -0.34 0.10 0.07 0.25 -0.28 -2.143661 1991 0.09 0.07 0.22 0.19 0.40 -0.07 4923 1992 -2.01 0.20 0.10 -0.30 0.10 0.08 0.22 -0.29 -1.93661 1992 -0.06 0.09 0.18 0.14 0.29 -0.04 4923 1993 -2.03 0.22 0.11 -0.30 0.11 0.07 0.22 -0.28 -1.883661 1993 0.16 0.02 0.17 0.15 0.29 -0.05 4923 1994 -1.50 0.24 0.11 -0.28 0.09 0.06 0.22 -0.29 -1.353661 1994 0.23 0.04 0.20 0.18 0.27 -0.02 5812 1990 0.39 0.16 0.17 -0.16 0.02 0.02 0.37 -0.16 0.813661 1995 0.28 0.10 0.19 0.17 0.24 -0.03 5812 1991 0.56 0.15 0.20 -0.10 0.01 0.02 0.39 -0.16 1.073661 1996 0.32 0.25 0.21 0.16 0.26 -0.05 5812 1992 0.49 0.15 0.14 -0.03 0.02 0.03 0.40 -0.17 1.033661 1997 0.24 0.11 0.20 0.17 0.39 -0.04 5812 1993 0.35 0.15 0.15 0.02 0.02 0.02 0.33 -0.12 0.923663 1994 0.87 0.81 0.22 0.37 0.47 -0.05 5812 1994 0.42 0.14 0.14 -0.07 0.02 0.03 0.41 -0.15 0.943663 1995 1.08 -0.13 0.25 0.25 0.26 -0.07 5812 1995 0.36 0.13 0.14 -0.10 0.01 0.02 0.35 -0.17 0.743663 1996 0.82 0.13 0.21 0.21 0.25 -0.05 5812 1996 0.34 0.11 0.12 -0.04 0.01 0.02 0.40 -0.16 0.83663 1997 0.99 0.07 0.20 0.22 0.22 -0.07 5812 1997 0.49 0.03 0.02 0.02 0.45 -0.203674 1991 0.29 0.25 0.16 0.04 0.17 0.17 0.21 -0.07 0.93 6324 1992 -1.59 0.01 0.06 0.00 0.173674 1992 0.55 0.25 0.17 0.24 0.17 0.17 0.21 -0.05 1.16 7372 1990 0.87 0.03 0.22 0.03 0.49 -0.083674 1993 0.75 0.28 0.19 0.22 0.16 0.14 0.20 -0.04 1.15 7372 1991 0.81 0.31 0.24 0.09 0.24 0.03 0.47 -0.04 2.153674 1994 0.78 0.29 0.20 0.11 0.17 0.14 0.21 -0.06 1.06 7372 1992 0.69 0.22 0.15 0.08 0.22 0.03 0.47 -0.02 1.843674 1995 0.80 0.25 0.14 0.14 0.17 0.15 0.17 -0.08 0.94 7372 1993 0.73 0.27 0.18 0.20 0.23 0.02 0.49 -0.02 2.13674 1996 0.76 0.30 0.18 0.31 0.15 0.17 0.19 -0.04 1.26 7372 1994 0.84 0.24 0.15 0.19 0.22 0.03 0.51 -0.02 2.163674 1997 0.61 0.34 0.20 0.15 0.17 0.16 0.26 -0.05 1.23 7372 1995 1.00 0.20 0.11 0.35 0.26 0.01 0.61 -0.03 2.513845 1991 1.05 0.17 0.38 0.20 0.26 0.42 -0.05 7372 1996 0.97 0.21 0.11 0.62 0.25 0.04 0.60 -0.03 2.773845 1992 1.02 0.91 0.23 0.37 0.51 -0.06 7372 1997 1.08 0.39 0.26 0.02 0.68 -0.033845 1993 1.05 0.76 0.23 0.30 0.68 -0.05 7373 1994 0.76 0.12 0.23 0.12 0.40 -0.023845 1994 1.11 0.86 0.24 0.29 0.81 -0.04 7373 1995 0.79 0.06 0.24 0.07 0.40 -0.063845 1995 1.03 1.02 0.22 0.42 0.85 -0.05 7373 1996 0.69 -0.18 0.25 0.10 0.39 -0.063845 1996 0.88 0.84 0.22 0.55 0.73 -0.06 7373 1997 0.76 0.10 0.22 0.06 0.42 -0.033845 1997 0.83 1.78 0.23 0.40 0.90 -0.04 8071 1994 0.30 -0.21 0.22 0.01 0.35 -0.15a: A1 = Retained Earnings/Sales A2 = Negative Dividends / Net Worth A3 = Cash & Securities less Current Liabilities/ Sales
R1 = Accounts Receivables / Sales R2 = Inventory/ Sales R3 = S,G&A / Sales
P1 = Negative Long Term Debt / Net Worth
72
Correlations among Slack Measures
Table 11 shows significant and non significant correlations between the measures of
slack. Among the measures of available slack, the only significant relationship was
found between negative dividend payout and cash and securities less current liabilities.
Among the measures of recoverable slack, three significant and weak to moderate
correlations suggest different aspects of this slack type are being measured.
The cash and securities less current liabilities measure of available slack, which is the
cash component of working capital, had a significantly and moderate to strong
correlation with inventory, S,G&A (recoverable slack), and negative long term debt (
potential slack). These correlations across slack categories may be evidence that the cash
component of working capital has an underlying relationship or common accounting
inputs with the other financial ratios (White, Ashwinpal, Sondhi & Fried, 2003).
Potential slack had a significant positive and moderate to strong relationships with
measures of available and recoverable slack which might also be expected as the levels
of the internal sources of slack influence and are influenced by the same task
environmental forces as potential slack. The highest correlation for potential slack was
with negative dividend payout which Bourgeois and Singh (1983) categorised as one of
the more available sources of slack. This suggests that dividend policy effects or is
affected by an organisation’s level of potential slack or indeed that both are
independently influenced by forces in the task environment.
Total slack, the simple summation of all seven measures showed significant and
moderate and strong correlations with five slack measures which may be evidence of
their magnitude in the summation. Overall, among the seven measures of slack no
correlation is above r = .73 which gives no cause for suspicion of the ability of
individual measures to validly measure different aspects of slack (Sekaran, 2003).
73
Table 11 - Pearson Correlations among Slack Measures
Available
Slack
Recoverable
Slack
Potential
Slack
Retained Earnings
/ Sales
Negative
Dividends
/ Net Worth
Cash & Securities
less Current
Liabilities / Sales
Accounts
Receivable
/ Sales
Inventory
/ Sales
S,G&A
/ Sales
Negative
Long Term Debt
/ Net Worth
Available Slack
Negative Dividends
/ Net Worth -0.13a
(0.42)
Cash & Securities less
Current Liabilities / Sales -0.21 a 0.56 b
(0.21) (0.00)
Recoverable Slack
Accounts Receivable / Sales
0.60 a 0.14 b 0.40 d
(0.00 (0.38) (0.00)
Inventory / Sales
0.17a 0.17 b 0.63 0.40 d
(0.31) (0.29) (0.00) (0.00)
S,G&A / Sales
-0.44a 0.20b 0.73 d 0.28 d 0.37 a
(0.01) (0.23) (0.00) (0.01 (0.01)
Potential Slack
Negative Long Term Debt
/ Net Worth -0.22 a 0.70 b 0.60 c 0.51 c 0.49 c 0.37 c
(0.18) (0.00) (0.00) (0.00) (0.00) (0.00)
Total Slack All measures summed
0.01 a 0.62 a 0.94 a 0.55 a 0.26 a 0.58 a 0.92 a
(0.96) (0.00) (0.00) (0.00) (0.12) (0.00) (0.00) a: n = 38, b: n = 40, c: n = 81, d: n = 81
74
Industry Level Data Analysis
With the industry level sample described, the bivariate relationships of interest
graphically illustrated and the relationships among slack measures examined, hypotheses
were then tested formally with Pearson’s correlations calculated using SPSS which are
reported in chapter four.
Sub-Industry Sampling and Data Preparation Moving from the industry level to the sub-industry level of analysis, this section
describes the approach taken to divide the industry sample into ‘within-industry’ sub-
samples by each slack measure. The hypothesised negative curvilinear slack to
discretion relationship requires testing of slack values at a minimum of three positions
on an industry’s spectrum of values. Figure 3 illustrates that at point A and C the value
for slack and discretion is low while at point B it is relatively higher, hence suggesting a
curvilinear relationship.
Figure 3 - Hypothesised Executive Discretion versus Slack within Industries
Organisational
Indu
stry
leve
l
B
A C
75
Industry sub-groups were categorised to achieve within industry comparisons of
organisations from the industry level data previously described. Empirical studies
categorising organisations by slack are few, one such example is Marino and Lange
(1983) wherein organisations were categorised as high or low slack organisations
relative to the industry median. The approach adopted by the present study originates
form Keegan and Kabanoff (2007) who divided industries into thirds, according to the
percentile position of organisations with long term debt.
Industry level data were grouped into industry thirds by the retained earnings measure of
available slack and the sales, general and administration expense measure of recoverable
slack measures while Keegan and Kabanoff’s (2007) results were used for the potential
slack analysis. Low, medium and high slack groups were created for available and
recoverable slack which positioned organisations between the zero, 33 1/3, 66 2/3 and 100
percentiles for their industry in the rolling five year period centring on a target year.
Appendix B provides more information in a rationale of the thirdtile approach.
The Sub-Industry Level Sample Annual reports were assigned to thirdtile groups for each industry target five year
period. While all of these cases were used for the purpose of industry percentile
calculation and thirdtile group allocation, only the thirdtile groups that contained 20 or
more president’s letters were considered for textual analysis. The result of this was a
usable sample of 140 ( x ¯ = 38.9, s = 20.1) thirdtile group level cases for the available
slack measure and 156 ( x ¯ = 37, s = 19.1) thirdtile group level cases for the recoverable
slack measure. This compares favourably to a sample of 70 ( x ¯ = 33.4, s = 6.8) thirdtile
group level cases for potential slack, relying on Keegan and Kabanoff’s (2007) long
term debt results from the same research database. The usable data for both measures
was from nine industries over all eight target years.
Table 12 shows the number of president’s letters in each of the sample’s five year
industry-year period for the available and recoverable slack.
76
Table 12 - Thirdtile level Sample of President's Letters by Industry-Target Year
Available Slack Recoverable Slack
SIC_TY Low
Thirdtile
Middle
Thirdtile
High
Thirdtile
Low
Thirdtile
Middle
Thirdtile
High
Thirdtile 4911 1990 39 27 21 21 29 37 5812 1990 40 20 22 27 23 32 7372 1990 32 31 25 30 29 29 3577 1991 25 22 23 3674 1991 28 29 21 28 27 23 3845 1991 27 20 21 4911 1991 48 30 26 24 36 44 5812 1991 53 25 27 33 31 41 7372 1991 46 40 36 42 38 42 3577 1992 32 20 26 20 22 3661 1992 22 24 22 20 20 3674 1992 38 38 28 37 36 31 3845 1992 36 27 26 29 21 4911 1992 57 36 31 29 46 49 4923 1992 21 5812-1992 64 32 31 40 39 48 7372 1992 59 53 43 51 51 53 1311 1993 27 23 22 26 20 3577 1993 31 22 21 25 3661 1993 27 25 21 25 20 24 3674 1993 52 47 40 51 48 40 3845 1993 45 33 32 36 24 4911 1993 54 32 31 27 45 45 4923 1993 21 5812 1993 62 32 27 38 41 42 7372 1993 76 68 57 63 66 72 1311 1994 33 30 33 33 26 30 3577 1994 32 20 23 26 3661 1994 31 29 25 31 24 27 3674 1994 58 50 49 59 52 46 3845 1994 46 34 33 37 28 4213 1994 22 4911-1994 46 29 33 27 42 39 5812 1994 60 35 24 39 42 38 7372 1994 100 87 71 82 91 85 1311 1995 40 30 39 39 35 34 3577 1995 31 22 20 26 23 3661 1995 31 31 25 30 27 27 3663 1995 21 22 21 3674 1995 59 52 52 60 56 47 3845 1995 41 32 21 30 35 26 4213 1995 22 4911 1995 31 24 29 26 5812 1995 52 29 23 34 36 34 7372 1995 121 104 88 100 111 102 7373 1995 23 23 1311 1996 37 28 35 38 33 30 3577 1996 24 20 23 3661 1996 27 26 22 26 29 22 3663 1996 21 22 3674 1996 50 40 46 50 47 39 3845 1996 32 26 24 28 22 4911 1996 22 22 5812 1996 39 24 28 28 25 7372 1996 107 95 77 88 102 89 7373 1996 23 23 1311 1997 33 32 33 29 30 3661 1997 21 21 21 26 3663 1997 20 3674 1997 40 31 39 41 38 31 3845 1997 23 20 5812 1997 28 21 20 7372 1997 94 82 70 79 89 78
77
Describing the Sub-Industry Thirdtile Groups For each slack thirdtile group the lexical commonality, lexical density and the length of
the positive extra use list was extracted from presidents’ letters. At the thirdtile group
level, correlations between these sub-measures were all very strong and significant (see
Table 13) across both measures of slack, supporting the appropriateness of factor
analysis to produce a single measure of attentional homogeneity.
Table 13 - Thirdtile level Sub-measures of Attentional Homogeneity
Sub Measure Lexical Commonality Lexical density
Available Slack a
Lexical density .83**
+25 Extra Use .84** .86**
Recoverable Slack b
Lexical density .81**
+25 Extra Use .80** .80**
** Correlation is significant at the 0.01 level (2-tailed). a: n = 144 b: n = 156
Table 14 shows that factor scores capture a large amount (89.24% and 86.96%) of the
total variance for the three sub-measures across the two slack measures. This result was
congruent with that of Keegan and Kabanoff (2007) who reported 86.5% of variance
captured when using the long term debt thirdtiles. Following this, the Comprehensive
Exploratory Factor Analysis (CEFA) computer program by Browne, Cudeck, Tateneni,
and Mels (2004) was specifically used to provide standard errors and 95% confidence
interval estimates around point estimates of factor solutions. The use of confidence
intervals around industry thirdtile group level discretion point values was suggested by
Keegan and Kabanoff (2007) to defend criticisms of the use of point estimates in
exploratory factor analysis with small to medium sample sizes. This method is also used
to support model fit in the light of representativeness of the convenience sample (De
Vaus, 2002; MacCallum, Widaman, Zhang, & Hong, 1999; Velicer & Fava, 1998).
Simply stated, the use of confidence intervals improves model fit and richness although
analytical complexity increases.
78
Table 14 - Summary of Thirdtile level Discretion Sub-measure Factor Analysis
Factor matrix
Factor score
coefficients
Communalities
Sub-Measure Initial Extraction
Available Slack
Lexical density 0.92 0.35 0.77 0.84
Lexical commonality 0.90 0.29 0.75 0.81
Extra Usage +25% 0.93 0.39 0.78 0.86
Eigenvalue 2.52
Proportion of total variance 89.24
Bartlett's Test of Sphericity 377 (df 3, Sig 0.00)
Kaiser-Meyer-Olkin 0.77
Recoverable Slack
Lexical density 0.90 0.36 0.72 0.81
Lexical commonality 0.90 0.34 0.72 0.80
Extra Usage +25% 0.89 0.33 0.71 0.80
Eigenvalue 2.41
Proportion of total variance 86.96
Bartlett's Test of Sphericity 353 (df 3, Sig 0.00)
Kaiser-Meyer-Olkin 0.76
79
Sub-Industry Data Analysis Method
Factor analyses produced upper and lower 95% confidence interval estimates of average
discretion for each thirdtile group case. Taking these values of the sub-industry sample,
sub-industry hypotheses were tested using paired sample t-tests. Appropriately, the
upper confidence interval boundary of one thirdtile group was tested against the lower
confidence interval boundary of the paired thirdtile group. Statistically significant
differences between the confidence intervals of paired thirdtile group means would
constitute evidence supporting a curvilinear observation. The effect size statistic Eta
Squared (Fisher, 1925) was used to explain the magnitude of the effect observed by the
t-test which summarised the differences (or movement effect) between thirdtile group
mean values of discretion (Cohen, 1988; Wilkinson and Inference, 1999). Cohen’s d was
also calculated.
Chapter Summary
This chapter began by introducing Keegan and Kabanoff‘s (2007) recent work as a
example of the method used by the present study. Operational measures of executive
discretion and organisational slack were detailed before an explanation of the industry
level data sampling process. The resultant sample was then examined for the
assumptions of industry level hypothesis testing using correlation analysis. The results
of which are reported in the next chapter. The industry level samples of between 38 and
82 cases had some outliers removed at the organisational level and one at the industry
level. All samples were found to be marginal skewed after outlier removal. Two of these
industry level samples were used as an input to sub-industry sampling; one each for
available and recoverable slack. The sub-industry sampling process and data preparation
methods resulted in a sample grouped in thirds by industry slack percentile. Industries
were divided into their high, middle and low slack thirds so that a test for curvilinearity
could be conducted using paired sample t-tests which produced the sub-industry results
reported next chapter.
Chapter Five – Results of Industry and Sub-industry Analyses
The previous chapter detailed the methods and the sample used to investigate the
interactions between organisational slack resources and industry level discretion. This
chapter reports the results of two studies which applied those methods to test the
industry level (Study A) and sub-industry level (Study B) hypotheses.
The results of the industry level correlation analysis, for seven of Bourgeois and Singh’s
(1983) measures of organisational slack, are reported before an account of the results of
the sub-industry analysis. Due to the time constraints of the present study and the
complexity of textual analysis, one measure for available slack, and one for recoverable
slack were chosen to test the sub-industry hypotheses. The results of these two tests are
reported along with a short reinterpretation of Keegan and Kabanoff’s (2007) sub-
industry results in the present study’s context to test the potential slack sub-industry
hypothesis.
The Statistical Package for Social Sciences (SPSS) version 12.0.1 was the primary data
analysis tool used to prepare these results supported by Microsoft Excel 2003 and
specialised textual analysis and factor analysis programs.
81
Study A – Associations Between Slack and Discretion
across Industries To examine the associations between slack and discretion at the industry level, three
hypotheses were used to envelop Bourgeois and Singh’s (1983) composite ‘ease of
recovery’ theoretical construct. The set of three hypotheses 1(a), 1(b) & 1(c) propose
associations between industry level discretion and industry average available,
recoverable and potential slack respectively. Hypothesis 1(a) was tested using three
measures of available slack, hypothesis 1(b) was tested using three measures of
recoverable slack and hypothesis 1(c) was tested using one measure of potential slack.
Pearson’s correlations for industry average organisational slack and industry level
discretion are presented in Table 15, along with the relevant pair wise sample sizes. All
but one measure of slack is observed to correlate positively and significantly with
industry level discretion with correlations ranging from small (r = .22) to large (r = .80).
Only one measure of available slack, retained earnings/sales, had a significant negative
association with industry level discretion (r = - .47). Additionally, the correlation
between the total slack measure and discretion is moderately large in the predicted
direction (r =.71). Overall, the findings indicate strong support for the general
proposition of a positive relationship between slack and discretion.
While the Pearson correlations are supportive, it is useful to also undertake a detailed
examination of the scatterplots and industry groupings to identify spurious or potentially
more informative interpretations of the associations. The result of each of the seven tests
between slack and industry level discretion are described next to test the three industry
level hypotheses. These tests are followed by an exploration the relationship between
total slack (as a composite of the seven measures) and industry level discretion.
82
Table 15 - Pearson Correlations between Slack and Industry Level Discretion
Slack Category
Measure Industry Level
Discretion
Available slack
Retained Earnings/Sales -0.47a
(0.00)
Negative Dividends/Net Worth 0.61 b
(0.00)
Cash & Securities less Current Liabilities/ Sales 0.56d
(0.00)
Recoverable Slack
Accounts Receivable / Sales 0.22 d
(0.04)
Inventory/ Sales 0.42 d
(0.00)
S,G&A/ Sales 0.46 d
(0.00)
Potential Slack Negative long Term Debt/ Net Worth 0.80 c
(0.00)
Total Slack 7 Measures Summed 0.71 a
(0.00)
a: n= 38, b: n= 40, c: n= 81, d: n= 82
83
Available Slack and Industry Level Discretion
Hypothesis 1(a): At the industry level, available slack is positively associated with
executive discretion.
Industry Average Retained Earnings / Sales vs. Industry Level Discretion
The scatterplot for industry average retained earnings/sales versus industry level
discretion presents a generally negative yet somewhat spread relationship (see Figure 1,
Panel A). A minority of points show cases high in retained earnings with low industry
level discretion may be evidence of a curvilinear relationship. However, the absence of
high retained earnings points at the high industry level discretion end does not allow
such an inference. On further examination, these four points represent all of the cases
available from the sample for the Crude Petroleum and Natural Gas industry (SIC 1311)
and may indicate a special type of industry relationship. Available slack has a negative
relationship to industry level discretion however four points from one industry influence
this finding.
The Pearson’s correlation coefficient of this hypothesised relationship (r = -.47, two
tailed p-value = .00, n = 38) reflects the linear aspects that can be seen on the scatter
plot. A statistically significant negative linear relationship was found in the presence of
cases skewed marginally to the higher end of the range of industry level discretion
(skewness = - 1.2, s = 0.27) and the lower end of the range of retained earnings values
(skewness = 1.14, s = 0.38). This evidence of non-normality, along with a small sample
size may contribute to the appearance of curvilinearity on the scatter plot for this test.
The hypothesised positive relationship between available slack and industry level
discretion was not supported by this test.
84
Industry Average Negative Dividends / Net Worth vs. Industry Level Discretion
The scatter plot for industry average negative dividend payout as a proportion of net
worth (see Figure 1, Panel B) presents a generally positive and possibility curvilinear
relationship. As in the previous test, cases from the Crude Petroleum and Natural Gas
industry (SIC 1311) occupy an influential position on the scatter plot with low discretion
but moderate levels of negative dividend payout. With skewness and kurtosis values less
than one in absolute value, the sample was considered to approximate the normal
distribution. A statistically significant positive linear relationship was indicated by the
Pearson’s correlation coefficient (r = .61, two tailed p-value = .00, n = 40).
Cash and Securities less Current Liabilities / Sales vs. Industry Level
Discretion
A positive linear relationship can be observed on the scatter plot for industry average
cash component of working capital as a proportion of firm sales versus industry level
discretion (see Figure 1, Panel C). Whilst two influential points appear not to fit neatly
to the general trend of the other points, they were not considered outliers as they lie
within three standard deviations of the sample mean for both variables and in broad
terms do not contravene the pattern of the other cases. Both variables were moderately
skewed (available slack: sk = 1.70, industry level discretion: sk = -1.23) while the slack
measure was notably peaked (kurtosis = 4.65). A statistically significant positive linear
relationship was indicated by the Pearson’s correlation coefficient (r = .56, two tailed p-
value = .00, n = 82).
85
Summary of Hypothesis 1(a) Tests Two of the three measures found a statistically significant positive relationship between
industry average available slack and industry level discretion in the presence of minor
non-normality. Although these results cannot claim absolute success in supporting
hypothesis 1(a), the unexpected direction of the retained earnings measure highlights the
need to judiciously select available slack measures. The strong influence of the Crude
Petroleum and Natural Gas industry (SIC 1311) on the results also suggests that industry
contextual influence can be important.
86
Recoverable Slack and Industry Level Discretion
Hypothesis 1(b): At the industry level, recoverable slack is positively associated with
executive discretion.
Industry Average Accounts Receivables / Sales vs. Industry Level Discretion
The scatter plot for industry average accounts receivable as a proportion of sales versus
industry level discretion offers the possibility of a positive linear or curvilinear
relationship. Two peripheral clusters of cases can be seen in Figure 1, Panel D.
Investigating these clusters further informed the hypothesis by their unique features and
their influence on correlation analysis. These influential points, although not considered
bivariate outliers, may influence the inferences drawn from the correlation statistic. The
highly positioned cluster with low industry level discretion (values below -0.15) and
high industry-year average accounts receivable as a proportion of sales (greater than
0.18) represents the Crude Petroleum and Natural Gas industry (SIC 1311) across all
available years (1991-1997). The lowly positioned denser cluster with industry level
discretion values between zero and one and low industry-year average accounts
receivable as a proportion of sales ( less than 0.03) represents the Eating Places industry
(SIC 5812) across all available years (1990-1997). These cases were the only ones of
their industry in this sample.
The Crude Petroleum and Natural Gas Industry, a low discretion industry (Hambrick &
Finkelstein, 1987) may be expected to hold high levels of receivables as a proportion of
sales because of the nature of the products market. From a different position, the Eating
Places industry typified by restaurants and fast food outlets would be expected to hold
zero or very low levels of accounts receivables as their sales transactions are largely
cash (including credit card transactions).
87
With both clusters removed the correlation between industry level discretion and
industry-year average accounts receivable as a proportion of sales increased
considerably from r = 0.22 (two tailed p-value = .04, n = 82) to r = 0.78 ( two tail p-
value = .00, n = 67) in the presence of normality of the slack variable and marginal
skewness of the industry level discretion variable (sk = 1.2). Further investigation of the
clusters is not possible because of limited sample size. Overall, this test supports a
significant, moderately strong relationship between recoverable slack and industry level
discretion, with industry contextual effects.
Industry Average Inventory / Sales vs. Industry Level Discretion
The scatter plot for industry average inventory as a proportion of firm sales versus
industry level discretion (see Figure 1, Panel E) offers the possibility of a positive linear
or curvilinear relationship. Although the scatterplot indicates a positive relationship, five
industry level discretion points extend vertically to high values of average inventory on
sales. These points, represent all the available years for the Electromedical and
Electrotherapeutic Apparatus industry (SIC 3845). As a manufacturing industry
specialising in high cost medical equipment it is reasonably expected that the monetary
value of the inventory holdings of such firms would be particularly high.
Excluding the Electromedical and Electrotherapeutic Apparatus industry from the
analysis saw a decrease in the hypothesised relationship strength from r = 0.42 (two
tailed p-value = .00, n = 82) to r = 0.35, (two tailed p-value = .02, n = 75). Normality
improved however with this treatment, showing a skewness value of 1.55 decreasing
marginally to 1.01 and a kurtosis value decreasing from 2.72 to 1.01. The results of this
test show a significant, moderate relationship between recoverable slack and industry
level discretion, with the notable influence of one industry.
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Industry Average S,G&A / Sales vs. Industry Level Discretion
The scatter plot for industry average sales, general expenses and administration as a
proportion of sales versus industry level discretion presented the possibility of a positive
linear or curvilinear relationship (see Figure 1, Panel F).The scatter plot shows four
points from the related Radiotelephone Communications and Telephone
Communications industries (SIC 4812 and 4813) lying in a middle zone of industry level
discretion with values between minus one and zero extend vertically to high values of
industry average sales and general expenses as a proportion of sales. These four points
are all of the measurements that were available from these two related industries and
represent three target years. Relatively high levels of sales expenses for these middle
discretion industries may be expected by the nature of their product. This scatter plot
was also influenced by the Electromedical and Electrotherapeutic Apparatus industry
(SIC 3845) which dominates the vertical tail of high values industry average sales and
general expenses for high discretion industries. The five highest of six measured values
belong to this industry. As a specialist high cost medical equipment industry it is
reasonably expected that industry average sales expenses would be high.
Accepting the special influence of these three industries on the hypothesised
relationship, the Pearson’s correlation coefficient (r = .46, two tailed p-value = .00, n =
82 represents the linear aspects of the hypothesised relationship that can be seen in the
scatterplot. A significant, moderate to strong positive relationship was found in the
presence of marginal non-normality indicated by an industry level normality statistics
reported above and a slack measure skewness value of 1.20, while the kurtosis value was
1.00. The correlation was most influenced by the removal of the Radiotelephone
Communications and Telephone Communications industries as the Pearson’s r increased
to 0.52 (two tailed p-value =.00, n = 80) with a corresponding increase in skewness of
1.26 and kurtosis equalling 1.3. The correlation also excluding the Electromedical and
Electrotherapeutic Apparatus industry was 0.50 (two tailed p-value = .00, n = 73) while
normality improved with skewness and kurtosis both falling below 1.00. Overall, this
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test supports of a significant, moderate to strong linear relationship between recoverable
slack and industry level discretion with a notable industry influence.
Summary of Hypothesis 1(b) Tests All three measures indicate a significant, positive relationship between industry average
recoverable slack and industry level discretion. The descriptive power of each individual
measure is evidenced in these results by the differing aspects of slack revealed which
reinforces the need to employ all three to create a composite view of recoverable slack.
There is also evidence of some specific industry effects on individual slack measures
which is consistent with the task environment of the industries concerned.
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Potential Slack and Industry Level Discretion
Hypothesis 1(c): At the industry level, potential slack is positively associated with
executive discretion.
Industry Average Negative Long Term Debt/Net Worth vs. Industry Level
Discretion
The single measure used to operationalise potential slack here is reverse signed industry
average long term debt as a proportion of firm total assets similar to Keegan and
Kabanoff (2007). Whereas their measure of debt discipline aggregated industry total
debt and industry total assets the present study calculated financial ratios at the firm
level before measuring the industry average. The seemingly subtle change in method
provided different industry average potential slack values to those of Keegan and
Kabanoff (2007).
As was Keegan and Kabanoff’s (2007) observation, the scatter plot for this test of
hypothesis 1(c) suggested the prospect of a linear or curvilinear relationship (see Figure
1, Panel G). While Keegan and Kabanoff (2007) set out to describe the relationship
between these variables in detail, the research question here requires simply the
recognition of associations. Excluding the single outlier identified and removed prior to
analysis, the scatter plot exhibits a very strong positive linear relationship between
negative long term debt as a proportion of total assets and industry level discretion. A
paucity of values in the vicinity of -1.00 industry level discretion may be an indication
of a curvilinear relationship.
The Pearson’s correlation coefficient of r = .80 (two tailed p-value = .00, n = 81)
supported the relationship strength observed on the scatter plot. A strong positive
relationship between negative long term debt as a proportion of total assets and industry
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level discretion was found in the presence of marginal skewness on the industry level
discretion variable as reported above and with a kurtosis value of -1.3 on the potential
slack variable. The corresponding skewness and kurtosis values were below one.
Total Slack and Industry Level Discretion
Summed Slack vs. Industry Level Discretion
Total slack, a simple arithmetic composite of the seven measures used by the present
study, is revealed as having a positive relationship to industry level discretion (see
Figure 1, Panel H). The Pearson’s correlation coefficient of r = .71 (two tailed p-value =
.00, n = 38) suggests a significant positive relationship. These results contrast to those of
Bourgeois and Singh (1983) who, at the organisational level, could not show empirically
a significant relationship between overall (summed total) slack and their other
organisational variables. The seven slack measurements were submitted to factor
analysis however, contrary to Moses' (1992) results, produced no interpretable results.
The summed total measure of slack contributes to the description of the associations
between slack and industry level discretion by supporting the notion of a generally
positive relationship.
Summary of Study A The industry level analysis suggests a significant positive association between industry
average organisational slack resources and industry level discretion but also found
evidence suggesting the association is affected by industry context at least for some
indicators of slack. This hypothesised relationship was tested using seven measures of
organisational slack and one measure of industry level discretion. This result was formed
on the observation of scatter plots and developed with hypothesis tests using correlation
analysis which were reported along with descriptions of the data to support validity.
Next, the associations measured between organisational slack and industry level
discretion as it was observed within industries are reported.
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Study B - Slack and Discretion within Industries Keegan and Kabanoff (2007) demonstrated that long term debt has a negative
curvilinear relationship with industry level discretion within industries. Restating this
from the perspective of the present study, their study pointed to a curvilinear association
between potential slack and industry level discretion within industries. The present study
extends Keegan and Kabanoff’s (2007) findings by testing the associations between
available and recoverable slack, and industry level discretion within industries.
To examine the association between organisational slack resources and industry level
discretion at the sub-industry level of analysis the three hypotheses used previously are
restated to reflect the within-industry enquiry. Hypothesis 2(a) and 2(b) propose that
within industries, industry level discretion decreases as firms move away from their
industry’s target for available and recoverable slack respectively. Hypothesis 2(c)
proposes a similar relationship for potential slack although, strictly speaking, it is not a
hypothesis as it was not tested by the present study. The two sub-industry hypotheses
were examined by selecting one measure of available, recoverable and slack. Keegan
and Kabanoff’s (2007) findings were interpreted by the present study for potential slack.
As with the first study, the sub-industry results are presented first graphically to aid their
analysis and hypothesis testing that follows.
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Available Slack/Recoverable Slack and Executive Discretion
within Industries
Following Keegan and Kabanoff’s (2007) method, the 95% confidence interval values
of low, middle and high slack thirdtile group level discretion were graphed for both
available and recoverable slack by ascending industry level discretion. Figure 4 allows
the reader to see for available slack (negative dividends/net worth), the estimated
confidence intervals across thirdtile groups showing some differences at lower levels of
industry level discretion and a tendency to converge at or just above zero industry level
discretion. Those cases with lower industry level discretion of each pair wise thirdtile
group confidence interval comparisons suggest the possibility that the low and high
slack thirdtile groups exhibit higher discretion than the middle thirdtile group. Simply
put, this result suggests a curvilinear relationship but oppose to that hypothesised. Figure
5 shows mixed differences across thirdtile groups for recoverable slack (SG&A/Sales)
suggesting no clear curvilinear relationship exists for recoverable slack and discretion
within industries. No differences are observable between low and middle recoverable
slack thirdtile groups while the high thirdtile group has lower industry level discretion
than both middle and low thirdtile groups.
Although the cross thirdtile group comparison of cases graphs of industry thirdtile group
level discretion and available and recoverable slack ordered by industry level discretion
revealed some differences, attempts to conduct paired sample t-tests did not yield any
significant results. These results were not able to show any curvilinearity as Keegan and
Kabanoff (2007) did. Their results suggest that long term debt and therefore potential
slack may be more influenced by task environment forces than the internally sourced
available and recoverable slack.
Low and Middle Available Slack Thirdtile Group by Ascending Industry Level Discretion
-3.5-3
-2.5-2
-1.5-1
-0.50
0.51
1.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
Indu
stry
Lev
el D
iscr
etio
n (IL
D)
LT_95%CI_Upper LT_95%CI_Lower MT_95%CI_Upper MT_95%CI_Lower
(ILD=0)
Middle and High Available Slack Thirdtile Group by Ascending Industry Level Discretion
-3.5-3
-2.5-2
-1.5-1
-0.50
0.51
1.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Indu
stry
Lev
el D
iscr
etio
n (IL
D)
MT_95%CI_Upper MT_95%CI_Lower HT_95%CI_Upper HT_95%CI_Lower
( ILD =0)
Low and High Available Slack Thirdtile Group by Ascending Industry Level Discretion
-4-3.5
-3-2.5
-2-1.5
-1-0.5
00.5
11.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Inm
dust
ry L
evel D
iscr
etio
n (IL
D)
LT_95%CI_Upper LT_95%CI_Low er HT_95%CI_Upper HT_95%CI_Low er
(ILD=0)
Figure 4 - Available Slack Thirdtile Group Executive Discretion ordered by Industry Level Discretion
95
Low and Middle Recoverable Slack Thirdtile Group by Ascending Industry Discretion
-4-3.5
-3-2.5
-2-1.5
-1-0.5
00.5
11.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48Indu
stry Lev
el D
iscretion (IL
D)
LT_95%CI_Low er LT_95%CI_Upper MT_95%CI_Low er MT_95%CI_Upper
(ILD=0)
Middle and High Recoverable Slack Thirdtile Group by Ascending Industry Level Discretion
-4-3.5
-3-2.5
-2-1.5
-1-0.5
00.5
11.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
Indu
stry
Lev
el D
iscr
etio
n (IL
D)
MT_95%CI_Low er MT_95%CI_Upper HT_95%CI_Low er HT_95%CI_Upper
(ILD=0)
Low and High Recoverable Slack Thirdtile Group By Ascending Industry Level Discretion
-4-3.5
-3-2.5
-2-1.5
-1-0.5
00.5
11.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
Indu
stry
Lev
el D
iscr
etio
n (IL
D)
LT_95%CI_Low er LT_95%CI_Upper HT_95%CI_Low er HT_95%CI_Upper
(ILD=0)
Figure 5 - Recoverable Slack Thirdtile Group Executive Discretion ordered by Industry level Discretion
96
Observable in all the graphs of Figure 4 and Figure 5 were series of cases containing
four or five points appearing to exhibit industry similarity. Indeed, when the industry
was identified for each point, such similar cases were of the same industry over several
years. To investigate this observation, the average discretion was calculated for the eight
industries across all target years. Cases were reordered by ascending industry average
discretion, irrespective of year which showed that the differences across thirdtile groups
are apparent for some industries and not for others. This suggested that curvilinearity of
the relationship between available or recoverable slack and industry level discretion was
possible for some industries and not for others. The analysis was halted at this point
because the reordered data highlighted that only two industries (across four to six target
years) fell below zero industry average discretion while six industries were above. This
offered little prospect of achieving any further meaningful t-test results. Although
grouping industry year cases by industry level discretion was not considered tenable
given the previously observed pattern, reordering by industry was also unsuccessful in
identifying testable differences across industry slack thirdtiles.
Summary of Hypothesis 2(a) and 2(b) Tests Where Keegan and Kabanoff (2007) identified and statistically tested differences
between slack thirdtile mean confidence intervals of three industries below zero industry
average discretion and four above (extracted from five industries), the present study
found no such differences. Thus, Hypotheses 2(a) and 2(b) cannot be supported. This
occurred for two reasons: firstly that the differences were not visually apparent when
cases were ordered by industry average discretion and secondly, that when ordered by
industry, the sample size was too small to conduct paired sample t-tests.
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Association between Potential Slack and Executive Discretion
within Industries
Hypothesis 2(c): Inside industries, executive discretion will decrease as firms move
away from their industry’s target range for levels of potential slack.
Keegan and Kabanoff’s (2007) results, viewed from the Bourgeois and Singh (1983)
slack resources perspective, offer support for Hypothesis 2(c) in that, within low
discretion industries, executive discretion will decrease as firms move away from their
industries target range for levels of long term debt (potential slack). In industries with
industry level discretion values greater than one, potential slack is not influenced by
industry level discretion.
Although the present study captured more industries than Keegan and Kabanoff (2007)
(eight, vis-à-vis their seven) the curvilinear hypothesis for available and recoverable
slack could not be supported. Their analysis of long term debt showed support for
hypothesis 2(c) with significant t-test results. The difference in the two sets of results
may be explained by the nature of the source of slack. Potential slack is more closely
associated with task environmental influences on executive discretion than available and
recoverable slack. A possible cause is that the level of executive discretion and
subsequently the executive’s success in gaining long term debt is affected by industry
determinants external to the organisation (industry level discretion) more than their
executive discretion over, say S,G&A expenses and dividend payout.
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Study B Summary The sub-industry level analysis does not find support for a curvilinear relationship
between available and recoverable slack resources and industry level discretion. This
section began by describing the sub-industry sample in terms of the results of slack
measurement and discretion measurement. By graphing available and recoverable slack
thirdtile groups versus executive discretion, the prospect of a curvilinear relationship
similar to that reported by Keegan and Kabanoff (2007) was examined. Examination of
graphed data and hypothesis testing of the mean values of discretion taken from the sub-
industry thirdtile groups using t-tests does not support the hypothesised relationship.
Chapter Summary This chapter reported tests conducted to examine the associations between
organisational slack and executive discretion at the industry and sub-industry level. It
began by reporting the results of the industry level correlation analysis, using seven
measures of organisational slack before providing an account of the results of the sub-
industry analysis which centred on t-tests of 95% confidence interval estimates of sub-
industry slack groups executive discretion.
The hypothesised positive relationship between industry average available slack and
industry level discretion received support when tested with the negative dividend payout
measure and cash component of working capital measures. The retained earnings
measure of available slack found a statistically significant negative relationship.
The hypothesised positive relationship between industry average recoverable slack and
industry level discretion received support from all three measures. The accounts
receivables measure of recoverable slack highlighted two industries as clusters that when
investigated did not fit the hypothesised model because of their industry specific
attributes. With these two industries removed the reported correlation results improved
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markedly from indicating a weak relationship to indicating a strong relationship. The
inventory measure of recoverable slack supports a moderate association to executive
discretion and the sales and general component of working capital supports a strong
hypothesised positive relationship.
The hypothesised positive relationship between industry average potential slack and
industry level discretion received strong support. This test revealed significant support
for a strong relationship between negative long term debt and executive discretion which
supports Keegan and Kabanoff’s (2007) similar finding.
At the sub-industry level, organisations were grouped into industry thirds by available
slack and then recoverable slack. By forming groups and labelling them ‘low’, ‘middle’
and ‘high’ for both slack types, it was possible to measure average executive discretion
within industries using textual analysis. The hypothesised curvilinear relationship
between slack and executive discretion was tested by comparing the low-to-middle-to-
high pairings. Unlike Keegan and Kabanoff (2007) the present study found little support
for the hypothesis that as firms moved away from their industry’s target range for slack,
their executive discretion decreases. While not statistically testable, it was observed that
an industry pattern exists for available and recoverable slack.
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Chapter Six – Conclusions and Implications This final chapter recaps the purpose and process of the present study before discussing
its distinct contribution to the body of knowledge in terms of theory and method. First,
the research results are discussed in the context of the two fields of literature. The
discussion then turns to a review of the limitations to the present study. The thesis then
closes by suggesting future research opportunities, notably: further examination of the
interactions between organisational slack and executive discretion, and the development
of alternative measures for slack.
A Short Recap of (the purpose and process of) this
Study Bourgeois and Singh (1983), George (2005), Sharfman et al. (1988) and Sharma (2000)
have referred to organisational slack as having a discretionary dimension but until now
this assumption was untested. From the executive discretion perspective, slack is seen as
contributing to resource availability and therefore increases executive discretion
(Hambrick & Finkelstein, 1987). To date, the two fields have not met in a published
empirical study. Articulating the purpose of the present study, chapter one concluded by
posing the research question: What are the associations between organisational slack
resources and executive discretion? By examining and measuring some of their
interactions, the present study contributes to an understanding of both slack and
discretion.
Using a systems approach, the exploration of Bourgeois’ (1981) organisational slack
construct showed it to be recoverable from multiple sources, to have various forms, and
to have different functions. Exposing the facets of slack also provided an opportunity to
apply the executive discretion construct and to clarify slack as both a dependant and
independent variable. Abrahamson and Hambrick’s (1987) model of industry level
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discretion highlighted the forces of the task environment, including resource availability,
that act on executive discretion where the slack construct could be applied. Associations
between the two constructs were examined by measuring industry level discretion and
industry average slack using annual report data.
Discussion Chapter two hypothesised that, at the industry level, slack is positively associated with
executive discretion. The results support this by showing that industries with higher
levels of slack have greater industry level discretion. However the associations between
different slack types and executive discretion are not uniform. This latter finding shows
that all measures of slack are not equal: they tap into different aspects and types of the
underutilised resources. This suggests that the discretionary dimension of slack
purported to be captured by Bourgeois and Singh’s (1983) model of slack recoverability
is influenced by the industry level determinants of executive discretion.
Associations between Slack and Discretion
Available slack, those slack resources not yet absorbed into the organisation, are the
most liquid form of slack. It comes as no surprise then that strong positive correlations
were found to suggest that industries with higher available slack are higher discretion
industries. Executives in higher discretion industries, irrespective of their individual or
organisational level discretion are endowed with greater latitude of action by the task
environment, including action over gaining and deploying slack. Equally, from the
executive discretion perspective, in industries with greater available slack, executives
may experience less industry constraint because available slack functions as a strategic
facilitator and reduces the executive’s reliance on the task environment (Bourgeois,
1981). With less reliance on the task environment, executives are less constrained to
conform to industry expectations (Hambrick & Finkestein, 1987).
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Recoverable slack, those slack resources absorbed into the organisation which can be
recovered with some effort in structural redesign or reconfiguration, are also positively
correlated with industry level discretion. However the strength of relationship is less
than for available slack which is contrary to Bourgeois and Singh’s (1983) ‘easy of
recovery’ concept whereby available slack is more accessible than recoverable slack
which is more recoverable than potential slack. One explanation for is this is that the
three measures (accounts receivable, inventory and SG&A expense) are of internal
resources and therefore less subject to the forces of the task environment than available
slack. This type of slack is ‘locked up’ in programmed organisational routines and
industry recipes whereas the more cash like, available slack, is more open to external
scrutiny. Recoverable slack may be seen by the industry to have the useful internal
maintenance functions (particularly as a workflow buffer) and powerful stakeholders
concede more executive discretion over this type of slack because it is more causally
ambiguous than cash (Hambrick and Finkelstein, 1987).
Potential slack refers to those slack resources absorbed into the organisation that can be
recovered over the longer term with significant effort. Using reputation, evidence of past
performance or similar, organisations with potential slack can generate additional capital
or debt from the external environment. The positive correlations between potential slack
and executive discretion found by the present study agree with the results of Keegan and
Kabanoff (2007). However they also found that negative long term debt (potential slack)
is positively associated with average-to-high levels of industry level discretion but high
usage of long term debt is ubiquitous where industry level discretion is low. They
reasoned that industry level discretion is a contextual variable that influences the
disciplining effect of debt. Recasting their results show that potential slack is low where
industry level discretion is low, only increasing at a threshold point. Low discretion
industries have little potential to recover slack (by increasing increase debt) from the
task environment while middle to high discretion industries can access debt
proportionately to the constraining forces of industry level discretion. With potential
103
slack comes discretion, but only for those industries where the task environment allows
debt.
Total slack, the summation of the multiple measures of slack as conceived by Bourgeois
and Singh (1983), was also shown to increase with industry level discretion. This result
contrasts to Bourgeois and Singh’s (1983) result that failed to find statistically
significant relationships with political behaviour and strategic discord (organisational
contextual variables) until the total slack measure was disaggregated. One explanation is
that the composite measure of slack, aggregated to the industry averages here, is
capturing industry effects shared by the measures which were not captured by Bourgeois
and Singh’s (1983) organisational level examination of 24 firms. Total slack, as a
composite measure offers the prospect of capturing slack more widely as it has been
shown to agree with all of the positive individual measures of slack.
Just one measure of slack did not support the hypothesised positive industry level
association. The significant negative relationship between industry average retained
earnings as a proportion of sales and industry level discretion is contrary to Bourgeois
and Singh’s (1983) model which expects slack recoverability to increase with liquidity.
Indeed available slack is expected to be of the most recoverable by executives (i.e.
highest level of discretion). An alternative view, albeit weaker, could be taken by
suggesting that this test only captured executive discretion at the industry level which is
independent of the level of executive discretion within the organisation which is used to
access this type of slack. However, dividend policy sets retained earnings at the
organisational level where the social forces of shareholders and capital markets exert
considerable industry level influence (Baker & Wurgler, 2004). This latter view suggests
an enhancement to Bourgeois and Singh’s (1983) model by introducing the idea of
industry influence on the source of available slack. The influence of social forces on
dividend policy and the empirical results of the present study suggest that this type of
available slack (retained earnings) is not as recoverable as other types of slack.
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The scatter plot for retained earnings versus industry level discretion exhibits a cluster of
four influential points from one industry – Crude Petroleum and Natural Gas. By
isolating the influence of this industry, the remaining 34 cases suggest that retained
earnings are independent of industry level discretion (although this was not tested
statistically). Although the influence of sample size can not be discounted, this
significant result can be interpreted as evidence of industry contextual influence (Johns,
2006). For the Crude Petroleum and Natural Gas industry, high levels of available slack
in the form of retained earnings are experienced with low industry level discretion. This
capital intense industry could be expected to retain profits independently of executive
discretion. However, Hambrick and Finkestein’s (1987) discretion model would suggest
that because the industry level discretion of this industry is low, retained earnings are
part of an industry recipe. This industry’s recipe for, or ‘zone of acceptance’ for, levels
of retained earnings would seem to be different to other industries.
Discretionary Dimension of Slack
Correlations of the three slack types show that discretion and slack are not uniformly
related with the traditional liquidity view, on which Bourgeois and Singh’s (1983) based
their ‘ease of recovery’ approach. Slack is a multi-dimensional construct as Bourgeois
and Singh’s (1983) suggested, but their ‘ease of recovery’ dimension overlooked the
influence of industry context. Slack that has its source in the task environment could be
expected to correlate most strongly with industry level discretion whereas slack from
internal sources would experience a lower industry effect. The results here suggest that,
the more that slack recoverability is influenced by industry forces rather than its source,
determines its ‘ease of recovery’. In part this is because industry social forces act to
maintain managerial actions within their ‘zone of acceptance’ for industry recipes which
focus on the deployment of slack across its various functions (Hambrick & Finkestein,
1987). Neither the present research, nor any extant study has examined specifically the
105
social control of slack: such an examination would inevitably need to consider the
source, form and functions of slack and the task environment determinants of executive
discretion.
The discretionary dimension of slack may not be as simple as Sharfman et al.’s (1988)
high and low discretion slack, which attaches discretion to the inanimate resource. It is
influenced by the context of its source, form and functions, of which, the industry level
determinants of executive discretion are an element. Specifically, discretion from any
perspective is an attribute of a decision maker in his or her environment.
Slack and Executive Discretion within Industries
Chapter two also hypothesised that within industries, available, recoverable and
potential slack are positively associated with executive discretion up to a point at which
it becomes negatively associated. The results of the present study could not support this
curvilinear hypothesis however Keegan and Kabanoff’s (2007) findings were relied on
to find support for the hypothesised relationships for potential slack. They identified and
successfully tested visual differences between potential slack (negative long term debt)
thirdtile mean confidence intervals of three industries below zero industry average
discretion and four above (extracted from five industries). Keegan and Kabanoff’s
(2007) results showed a clear and testable difference between industry thirdtiles for
cases with industry level discretion values greater than one. The present study could not
replicate their outcome for available or recoverable slack because the thirdtile
differences were not visually apparent and ultimately not able to be tested statistically.
Keegan and Kabanoff’s (2007) findings suggest a positive curvilinear relationship
between potential slack and industry level discretion. The limited results of the present
study reflect problems with the method previously identified, but also may be an
indication of a shallow curvilinear relationship which will, by its nature will be more
106
difficult to test with paired sample t-tests of confidence intervals. Recasting this in the
light of the industry level findings, available and recoverable slack may have a weaker
curvilinear relationship than potential slack has with industry level discretion. Put
another way, industry norms, for available and recoverable slack are more causally
ambiguous and the ‘zones of acceptance’ are broader than the clear norm for potential
slack within industries. This further reinforces the importance of industry context for the
slack and discretion relationship. Although industry constraints on executive discretion
mould industry recipes to have a tight ‘zone of acceptance’ or norm for potential slack,
available and recoverable slack are gained from internal sources with less industry
constraints and so a broader curvilinear relationship is possible (Keegan & Kabanoff,
2007).
Contributions The present study contributes to the theory and method of both organisational slack and
executive discretion by bringing them together empirically, possibly for the first time.
A summary of the contributions of this thesis are provided in Table 16 and explained
below.
Bourgeois and Singh’s (1983) three category conceptualisation of slack has been relied
on for a number of decades without scrutiny of its ‘ease of recovery’ dimension.
Similarly, the suggestion by George (2005), Sharfman et al. (1988) and Sharma (2000)
of a discretionary dimension of slack has not been tested. The present study is a first step
in showing how industry context and task environmental forces affect, and are affected
by slack. Slack has a source, form and function. It was clarified here that forces effect
and are affected by these elements of slack. One of the contextual variables associated
with slack is executive discretion. Hambrick and Finkestein’s (1987) model suggested
that discretion is determined by context including the abundance of slack which endows
options. The present study contributes to their theory by showing the strong association
of executive discretion with slack at the industry level. By showing how the different
107
types of slack are associated with industry level discretion, the importance of industry
context is highlighted.
The present study has added to the understanding of the measurement of organisational
slack by measuring it simultaneously with industry level discretion. The method used
here is an example of the appropriate use of the absolute measurement of slack. As
Marino and Lange (1983) suggested, this measurement technique suited the research
question. By measuring slack concurrently with executive discretion, the proximity in
time is maintained between an executive’s authoring of a letter to shareholders and the
slack resources before them. The present study measured the industry average and also
industry thirdtile values for organisational slack. The present study demonstrates that it
is possible to measure slack at the organisational level and to apply the results to
industry level analysis.
Table 16 - The Contributions of This Study
Contributions to Theory
Construct Contribution
Slack ‘Ease of recovery’ (Bourgeois & Singh,1983) and Discretionary slack (Sharfman et.al. ,1988)
Industry context influences slack recoverability via discretion
Industry Level Discretion –Task Environmental determinants (Hambrick & Finkestein, 1987)
Industry level discretion affects/is affected by organisational slack
Contributions to Method
Method Contribution
Slack measurement (Bourgeois & Singh, 1983)
Judicious use of absolute slack measurement Application of organisational slack to industry level analyses
108
Limitations At the outset of the present study a number of limitations were identified that centred on
attributes of the sample and the timeframe of and resources available to the researcher.
Whilst the study was carefully designed, other limitations emerged that could not be
avoided. The sub-industry thirdtile level analysis of available and recoverable slack’s
interaction with industry level discretion was halted because of small samples. Even
though the research database contains almost 22,000 annual reports, the necessary
disaggregating of industry data into industry thirds by slack reduced the sample of
available presidents’ letters considerably. Either a revised method or a larger or more
complete database may be required to effectively take this research further. These
limitations are acknowledged as part of the research process but do not detract from the
conclusions of the present study. Indeed they provide opportunity for further research,
which is discussed next.
Opportunities for Further Research The present study offers opportunity for future research to examine the associations
between organisational slack and executive discretion at the organisational and
individual level, indeed all levels concurrently. Bourgeois and Singh’s (1983)
organisational slack construct is targeted at the organisational level and at those who
manage slack. Theorists and practitioners would benefit from a deeper understanding of
how discretion influences and is influenced by the gaining, losing and deploying slack.
This would also benefit executive discretion scholars as it would see the individual and
organisational levels of Hambrick and Finkestein’s (1987) model applied to slack.
Many studies have used financial measures of slack however few studies validating
slack measures have been published. The readily accessible archives of financial data
from annual reports are a ready source for slack researchers to examine the relationships
109
between the multiple slack measures and to test them against non-financial measures
(Kabanoff, 1997). Although a factor analysis of slack measures was explored for the
present study, constrains precluded any fruitful outcome. Indeed the present study
accepted the weight of the published examples that have used Bourgeois (1981) and
Bourgeois and Singh’s (1983) composite measures. By applying multiple measures
concurrently it was made apparent that the measures of slack would benefit from further
development. Building on the present study, future research should clarify the efficacy
of operationalising slack with the well used financial measures.
The research database used here was limited to larger U.S. publicly listed firms between
1988 and 1999. Opportunity exists for the present study to be replicated using data from
smaller publicly listed or privately owned firms where slack and discretion, and the
associations between, would be expected to differ greatly (George, 2005). Equally, by
applying a similar research question to a database other than from U.S. firms would
explore the influence of a different industry context. A study using a different period
would also explore macrosocial effects on slack and discretion (Hambrick et al., 2005)
110
Thesis Summary The purpose of the present study was to examine the associations between organisational
slack and executive discretion. To do this, a positive association was hypothesised and
tested at the industry level and a curvilinear association at the sub-industry level.
Evidence suggests that different types of slack, available, recoverable and potential have
different levels of associations with industry level discretion. This finding has
implications for both slack and discretion theories. For slack it exposes the
recoverability dimension of slack in greater detail by introducing industry context to the
mix. For discretion theory it stands as an example of the operationalisation of the
construct and an exploration of one of the determinants of discretion - the task
environment. The present study could not replicate Keegan and Kabanoff’s (2007)
findings at the sub-industry level. This limitation offers opportunity for further research
as does the casting of the same research question onto organisational and individual
level studies of different cohorts of firms and industries. Future efforts should also
scrutinise the measurement of the slack construct. To do so, in the light of the findings
here, would serve to develop the financial and non-financial measures of slack and both
constructs.
111
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Appendix A
Illustration of Lexical Commonality Measure of
Attentional Homogeneity Abrahamson and Hambrick (1997) illustrated the method of calculating lexical
commonality with a hypothetical example. Mirroring this, Table 1A adapts Abrahamson
and Hambrick (1997) example to reflect the method used here. Letter one, two and three
depict three organisations that have a shared lexicon of four words in the left side
column. Having measured the occurrence of the four words in each letter we can see in
the right most column the percentage of letters in which the word is used, its
commonality. For example the word ‘assets’ is used in just one of the three letters of the
sample and so it has a commonality of 33% while the word ‘costs’ is used in all letters
of the sample and so has a commonality of 100%. The word commonality for each letter
as part of the sample can be calculated by summing the product of each word’s
commonality and the number of times it is used. For example letter 2 uses the words
‘sales’ 10 times and ‘costs’ two times both of which are used by all three letters and so
have a word commonality of 100%. Letter two also uses the word ‘margin’ three times
but it is only used by two letters having a word commonality of 66%. The letter 2
commonality is therefore;
[(10 uses of ‘sales’ x 100%) + (2 uses of ‘costs’ x 100%) + (3 uses of ‘margin’ x 66%)] / (10+2+3) = 93
This number represents the extent to which the words used in letter 2 are shared with the
sample’s lexicon. By averaging the letter’s commonality for the sample a measure of the
entire sample is produced. As this represents the extent of words shared within the
sample group it is called the lexical commonality measure.
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Table 1A - Lexical Commonality Calculation
Word Letter 1 Letter 2 Letter 3 WORD’S
COMMONALITY
Sales 3 10 1 100%
Assets 4 33%
Costs 5 2 1 100%
Margins 3 1 66%
Calculation of letter’s
communality
3 x 100 + 10 x 100 + 1 x 100 +
4 x 100 + 2 x 100 + 1 x 100 +
5 x 100 3 x 66 1 x 66
/(3 + 4 + 5) /(10 + 2 + 3) /(1+ 1 +1)
Letter’s commonality = 78 = 93 = 89 Average of letters’
commonality
= 86
Abrahamson and Hambrick (1997) p521
The lexical commonality measure of attentional homogeneity effectively allows for
letters of different lengths and variety. This can be seen in the Table 1A example where
the letter 3 only contains three words from the lexicon of 30 occurrences but it can be
compared to other length letters with different word combination.
Illustration of Lexical Density Measure of Attentional
Homogeneity Raw lexical density is a measure of the actual number of times a word is shared. From
the Table 1 B example the word ‘asset’ can be seen to occur in only one letter and so it
is shared zero times while the word ‘costs’ is shared three times. The maximum number
of times a word can be shared with other letters of the sample (N) is N x (N - 1) times.
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As a word is shared by two presidents’ letters so it may be counted twice. By dividing
by 2 the number of binary combinations removing double ups is calculated. Perfect
linguistic homogeneity occurs when all of the different words of the lexicon are shared
in all of the letters. Using the notation W to represent the number of different words as
did Abrahamson and Hambrick (1997), total linguistic homogeneity occurs when W x N
x (N - 1)/2 words are shared across the sample. Raw lexical density is measured by the
proportion of times all words are shared to the maximum number of times possible. The
range of values of raw lexical density is from zero to one. Using the Abrahamson and
Hambrick (1997) example, reproduced in Table 1B, the raw lexical density is calculated
to illustrate the method used by the present study.
Table 1 B - Raw Lexical Density Calculation
Word LETTER CONTAINS WORD?
WORD SHARING Letter 1 Letter 2 Letter 3
(Times shared)/2
Sales Yes Yes Yes (6)/2 3
Assets Yes (0)/2 0
Costs Yes Yes Yes (6)/2 3
Margins Yes Yes (2)/2 1
Total actual number of times words are shared 7
Maximum number of times words could be shared W x N x (N - 1)/2
4 x 3 x (3-1) /2 12
Raw Lexical Density 7/12 0.58
Abrahamson and Hambrick (1997) p521 example
With raw lexical density calculated for each group of letters (in the example of
Abrahamson and Hambrick (1997) this group in an industry) a regression of these values
on the reciprocal of the number of letters (1/N) allows the residual for each to be
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produced. Originally Abrahamson and Hambrick (1997) regressed using N however the
reciprocal (1/N) was used here. Keegan and Kabanoff (2007) found, the reciprocal to be
significantly correlated with the residuals of the raw lexical density values. The end
product is lexical density measure of attentional homogeneity corrected for the number
of presidents’ letters.
Illustration of Extra Use Measure of Attentional
Homogeneity Extra usage is a measure of attentional homogenety which calculates the extra use of a
word in one industry compared to its general use in all industry. For example, the
general use percentage of commonly used words such as ‘the’ and ‘and’ would be
expected to effectively have 100 percent general use or that they are used in 100% of
sampled letters. However all words are not used as commonly as Abrahamson and
Hambrick (1997) found in their illustration that the general use percentage of the word
‘prices’ was 22 percent. By measuring the use of each word in the sample’s sub-set (in
the Abrahamson and Hambrick (1997) illustration this was an industry as a subset of all
sampled industries) the percentage of use can be assigned for that word in that subset.
Again relying on the Abrahamson and Hambrick (1997) illustration, the word ‘prices’
was less commonly used in the oil and gas industry sub set with an industry use of 93
percent. By subtracting the general use percentage from the sub set percentage the level
of word use above or ‘extra’ to the whole sample is measured. In the Abrahamson and
Hambrick (1997) example the word ‘prices’ had an extra use of 71 percent which is
greater than zero and so reflects the extra use of the word ‘prices’ in the industry over
the general use in all industries. Had this value been negative then it would be concluded
that the word is used less in this industry than in the sample generally.
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Appendix B
Rationale of the Thirdtiles Approach To test the curvilinear hypothesis, the industry sample was grouped into high, middle
and low industry thirds by slack percentile. It was expected that for a curvilinear
relationship the middle group would have higher levels of industry level discretion than
the high or low groups.
Other approaches to grouping industry thirds by percentile were considered. For
example, Maritan and Schnatterly’s (2002) matched trio comparison grouped
organisations in bands of the lowest 15 percent, median 15 percent and highest 15
percent of the financial ratio of interest before extracting relevant text for analysis.
Another option considered was grouping around the first, second and third quartiles
excluding cases in zones between groups in an attempt to differentiated group means.
The need to maximise the number of comparisons of textual measurement across groups
motivated an evaluation of the various approaches.
To investigate the various approaches, 10 tests were undertaken. Each industry’s
organisational level ratio measures of slack were divided into thirds around the quartiles,
discarding cases at test one and progressing to a pure thirds approach at test 10. For each
test the number of president’s letters available for each industry third was recorded. The
number of cross group comparisons available (with a minimum of 20 presidents letters)
increased with no loss of differentiation as the groups expanded to three full thirds. The
term ‘thirdtile’ taken from the term ‘quartile’ is designed to convey that full thirds are
separated at the 33 1/3 and 66 2/3 percentiles, the first and second thirdtiles.
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Appendix C
SEC Narrative of 17 Sampled Industries by 4 Digit SIC Code 1311 Crude Petroleum and Natural Gas
3571 Electronic Computers 3577 Computer Peripheral Equipment, Not Elsewhere Classified
3661 Telephone and Telegraph Apparatus
Establishments primarily engaged in operating oil and gas field properties. Such activities may include exploration for crude petroleum and natural gas; drilling, completing, and equipping wells; operation of separators, emulsion breakers, desilting equipment, and field gathering lines for crude petroleum; and all other activities in the preparation of oil and gas up to the point of shipment from the producing property. This industry includes the production of oil through the mining and extraction of oil from oil shale and oil sands and the production of gas and hydrocarbon liquids through gasification, liquid faction, and pyrolysis of coal at the mine site. Also included are establishments which have complete responsibility for operating oil and gas wells for others on a contract or fee basis. Establishments primarily engaged in performing oil field services for operators on a contract or fee basis are classified in Industry Group 138.
Establishments primarily engaged in manufacturing electronic computers. Electronic computers are machines which: (1) store the processing program or programs and the data immediately necessary for execution of the program; (2) can be freely programmed in accordance with the requirements of the user; (3) perform arithmetical computations specified by the user; and (4) execute, without human intervention, a processing program which requires them to modify their execution by logical decision during the processing run. Included in this industry are digital computers, analog computers, and hybrid digital/analog computers. Establishments primarily engaged in manufacturing machinery or equipment which incorporate computers or a central processing unit for the purpose of performing functions such as measuring, displaying, or controlling process variables are classified based on the manufactured end product.
Establishments primarily engaged in manufacturing computer peripheral equipment, not elsewhere classified, including printers, plotters, and graphic displays. Establishments primarily engaged in manufacturing modems and other communications interface equipment are classified in Industry 3661.
Establishments primarily engaged in manufacturing wire telephone and telegraph equipment. Included are establishments manufacturing modems and other telephone and telegraph communications interface equipment. Establishments primarily engaged in manufacturing cellular radio telephones are classified in Industry 3663.
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3663 Radio and Television Broadcasting and Communications Equipment
3674 Semiconductors and Related Devices
3845 Electromedical and Electrotherapeutic Apparatus
4213 Trucking, Except Local
Establishments primarily engaged in manufacturing radio and television broadcasting and communications equipment. Important products of this industry are closed-circuit and cable television equipment; studio equipment; light communications equipment; transmitters, transceivers and receivers (except household and automotive); cellular radio telephones; communication antennas; receivers; RF power amplifiers; and fixed and mobile radio systems. Establishments primarily engaged in manufacturing household audio and video equipment are classified in Industry 3651; those manufacturing intercommunications equipment are classified in Industry 3669; and those manufacturing consumer radio and television receiving antennas are classified in Industry 3679.
Establishments primarily engaged in manufacturing semiconductors and related solid- state devices. Important products of this industry are semiconductor diodes and stacks, including rectifiers, integrated microcircuits (semiconductor networks), transistors, solar cells, and light sensing and emitting semi-conductor (solid-state) devices.
Establishments primarily engaged in manufacturing electromedical and electrotherapeutic apparatus. Establishments primarily engaged in manufacturing electrotherapeutic lamp units for ultraviolet and infrared radiation are classified in Industry 3641.
Establishments primarily engaged in furnishing "over-the-road" trucking services or trucking services and storage services, including household goods either as common carriers or under special or individual contracts or agreements, for freight generally weighing more than 100 pounds. Such operations are principally outside a single municipality, outside one group of contiguous municipalities, or outside a single municipality and its suburban areas. Establishments primarily engaged in furnishing air courier services for individually addressed letters, parcels, and packages generally weighing less than 100 pounds are classified in Industry 4513 and other courier services for individually addressed letters, parcels, and packages generally weighing less than 100 pounds are classified in Industry 4215.
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4812 Radiotelephone Communications 4813 Telephone Communications,
except Radiotelephone 4911 Electric Services 4923 Natural Gas Transmission
and Distribution Establishments primarily engaged in providing two-way radiotelephone communications services, such as cellular telephone services. This industry also includes establishments primarily engaged in providing telephone paging and beeper services and those engaged in leasing telephone lines or other methods of telephone transmission, such as optical fiber lines and microwave or satellite facilities, and reselling the use of such methods to others. Establishments primarily engaged in furnishing telephone answering services are classified in Services, Industry 7389.
Establishments primarily engaged in furnishing telephone voice and data communications, except radiotelephone and telephone answering services. This industry also includes establishments primarily engaged in leasing telephone lines or other methods of telephone transmission, such as optical fiber lines and microwave or satellite facilities, and reselling the use of such methods to others. Establishments primarily engaged in furnishing radiotelephone communications are classified in Industry 4812, and those furnishing telephone answering services are classified in Services, Industry 7389.
Establishments engaged in the generation, transmission, and/or distribution of electric energy for sale.
Establishments engaged in both the transmission and distribution of natural gas for sale.
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5812 Eating Places 6324 Hospital and Medical Service
Plans 7372 Prepackaged Software 7373 Computer Integrated Systems
Design
Establishments primarily engaged in the retail sale of prepared food and drinks for on-premise or immediate consumption. Caterers and industrial and institutional food service establishments are also included in this industry.
Establishments primarily engaged in providing hospital, medical, and other health services to subscribers or members in accordance with prearranged agreements or service plans. Generally, these service plans provide benefits to subscribers or members in return for specified subscription charges. The plans may be through a contract under which a participating hospital or physician agrees to render the covered services without charging any additional fees. Other plans provide for partial indemnity and service benefits. Also included in this industry are separate establishments of health maintenance organisations which provide medical insurance. Establishments providing these services through their own facilities or employed physicians are classified in Major Group 80.
Establishments primarily engaged in the design, development, and production of prepackaged computer software. Important products of this industry include operating, utility, and applications programs. Establishments of this industry may also provide services such as preparation of software documentation for the user-installation of software for the user; and training the user in the use of the software. Establishments primarily engaged in providing preparation of computer software documentation and installation of software on a contract or fee basis are classified in Industry 7379, and those engaged in training users in the use of computer software are classified in Industry 8243. Establishments primarily engaged in buying and selling prepackaged computer software are classified in Trade; those providing custom computer programming services are classified in Industry 7371; and those developing custom computer integrated systems are classified in Industry 7373.
Establishments primarily engaged in developing or modifying computer software and packaging or bundling the software with purchased computer hardware (computers and computer peripheral equipment) to create and market an integrated system for specific application. Establishments in this industry must provide each of the following services: (1) the development or modification of the computer software; (2) the marketing of purchased computer hardware; and (3) involvement in all phases of systems development from design through installation. Establishments primarily engaged in selling computer hardware are classified in Wholesale Trade, Industry 5045, and Retail Trade, Industry 5734; and those manufacturing computers and computer peripheral equipment are classified in Manufacturing, Industry Group 357.
8071 Medical Laboratories Establishments primarily engaged in providing professional analytic or diagnostic services to the medical profession, or to the patient on prescription of a physician.