Combinations of Strategy and Control: A Set-Theoretic Approach
Abstract
A significant amount of attention has been devoted to understanding the relationship between strategy and
control. While much progress has been made a number of important questions have so far received little
attention: (1) how do management controls combine to achieve effective control outcomes for different
firm strategies? and (2) are there multiple, equally effective control combinations in a given strategic
context? This study addresses these questions by drawing on the Miles and Snow (1978) typology to
hypothesize the control combinations expected to be effective for different strategic contexts.
Expectations are tested through a novel method termed fuzzy set qualitative comparative analysis
(fsQCA) using data obtained from a cross sectional survey. The study contributes to the literature by
providing evidence on how control attributes combine within and across strategic contexts and
demonstrating that effective control can be achieved through multiple configurations. In doing so the
study provides insight into the interdependence and relative importance of control attributes for achieving
effective control outcomes in different strategic settings.
Keywords: Management control, strategy, configurations, qualitative comparative analysis
1
Introduction
The interface between strategy and management control (MC) is one of the most enduring concerns in the
management accounting literature. Much of the research in this space builds upon strategic typologies,
such as prospectors/defenders (Miles & Snow, 1978), entrepreneurial/conservative (Miller & Friesen,
1982), build/harvest (Gupta & Govindarajan, 1984), and differentiation/cost leadership (Porter, 1980).
These typologies represent important developments as they allow researchers to empirically capture the
complex patterns of action and distinctive competencies that constitute the strategy of an organization
(Chapman, 1997; Dent, 1990). Although there are noted inconsistencies, the resulting body of research
has been insightful, identifying not only how strategy influences the choice of certain MC, but also
revealing how controls such as accounting might play a more active role in shaping the strategic direction
of the firm (Langfield-Smith, 2008).
Despite this progress there remain fundamental questions that have received little attention. First, while
the literature has been relatively successful in identifying the individual MC attributes associated with
higher performance for different strategic positions, there is little understanding of how these attributes
actually combine. This is somewhat surprising as many of the foundational theories of this research, such
as Miles and Snow (1978), argue that effectiveness is maximized only through specific configurations of
organizational attributes. The implicit assumption of much of the strategy-MC literature is that the
optimal combination is an aggregation of the individual MC attributes observed to have incremental
effects on performance. Yet, without empirical evidence it remains less than clear whether the MC
attributes studied in prior literature are in fact all necessary, or what the relative importance of each
attribute is, for achieving desired firm outcomes.
Second, the literature generally assumes that there is a single MC combination that leads to high
performance for a given strategy, ignoring the possibility that there may be multiple and equally effective
control arrangements available to an organization. The omission of choice in contingency models is
identified by Dent (1990, p. 10) as a possible explanation for the unrewarding results of early MC
research:
If organizations are only loosely coupled to their environments, there may be a range of viable
responses to specific contingencies […] and even in exogenously constrained situations there may
be degrees of freedom within which organizations operate.
2
This idea is captured by the term equifinality, which states that social systems are capable of achieving an
outcome through different structures and processes even when facing similar contextual conditions
(Gresov & Drazin, 1997; Katz & Kahn, 1978). Equifinality is a fundamental assumption of many
structural-functionalist and systems theories of social organization, from which contingency theory has its
origins (Ashmos & Huber, 1987). This is recognized by early theorists in the field such as Galbraith
(1973), Miles and Snow (1978) and Thompson (1967). But as contingency researchers began to move
away from firsthand accounts of organizations towards arms-length statistical analyses, the circumstances
originally conceived as the conditions that limit or constrain organizational choices became the factors
that essentially determine them (Galunic & Eisenhardt, 1994). Although prior research cites equifinality
as a possible reason for insignificant or unexpected results (e.g. Gerdin, 2005), there has been little
explicit attempt to investigate the potential for equally viable MC alternatives.
To explore these questions the study draws upon the strategic types of Miles and Snow (1978) and the
strategy-MC literature to hypothesize and test the MC combinations associated with effective control
outcomes. The initial expectation is that the most effective MC combinations will be those comprised of
the control attributes that theory and prior research indicates are individually associated with firm
outcomes conditional on strategic context. The theoretically derived combinations are then tested
empirically using a novel method termed fuzzy set qualitative comparative analysis (fsQCA). This settheoretic approach is uniquely suited to the research questions of this study as it explicitly examines how
attributes combine to produce an outcome of interest (Ragin, 2008). Furthermore, the method allows for
examination of the possible equifinality of multiple combinations, and unlike comparable techniques such
as cluster analysis and profile deviation analysis, it provides insight into the interdependence and relative
importance of attributes within a configuration for achieving an outcome (Fiss, 2007).
This study contributes to our limited knowledge on how MC attributes combine to achieve effective
control in different strategic contexts in a number of ways. First, the study extends the contingency MCstrategy literature by demonstrating that although many of the hypothesized associations are supported,
not all MC attributes need to be simultaneously present in an organization for effective control to be
realized. It is shown that there are multiple alternate and functionally equivalent ways to combine
different MC attributes in the same strategic context, providing empirical support for the notion of
equifinality in control system design (Gerdin, 2005; Sandelin, 2008). Second, it is widely assumed that
accounting and other control mechanisms are interdependent (Chenhall, 2003; Otley, 1980; Milgrom &
Roberts, 1995) but there is little understanding of how relationships between MC attributes might be
affected by firm context (Grabner & Moers, 2013). This study adds to the literature by showing how the
3
interdependence of MC attributes for achieving effective control is conditional on the strategic context of
the firm. Third, recent literature argues that firms pursuing mixed or joint strategies require control
systems that encourage managers to balance, rather than tradeoff, competing priorities (Gibson &
Birkinshaw, 2004), yet so far research in this area is limited to the choice and use of performance
measures (Lillis & van Veen-Dirks, 2008; Dekker et al., 2013). By examining a wider range of MC
attributes this study provides insight into how effective MC is achieved in this complex setting. Finally,
the study adds to recent literature that demonstrates the usefulness of set-theoretic methods for MC
research (Erkens & Van der Stede, 2013). In allowing examination of more complex combinations of
control the method holds particular promise for gaining insights into unresolved puzzles and gaps in the
literature. Specifically, this study speaks to concerns surrounding the effectiveness of tight accounting
controls in the context of innovation and the relationship between formal and social controls (Dent, 1990;
Langfield-Smith, 2008).
This study is organized as follows. The next section describes the configurational approach that underpins
the analysis of MC combinations and the concept of equifinality. The study then proceeds to develop a set
of hypotheses regarding effective MC combinations for different strategic contexts. Next the survey
design, set-theoretic method and measurement of constructs are described, followed by the results of the
analysis. The final section discusses the findings of the study and the limitations of the research.
Literature review and hypothesis development
Overview of management control combinations
The conceptual basis for understanding MC combinations stems from the configurational approach to
organizational analysis. This perspective suggests that organizations are best understood as complex
combinations of interconnected structures and processes (Fiss, 2007; Meyer et al., 1993). Organizational
outcomes are assumed to be dependent not only on the incremental contributions of individual
components, but on their overall arrangement. This logic underpins many of the foundational theories
used to investigate strategy-MC linkages. For instance, although the term is not explicitly used, the Miles
and Snow (1978) typology represents a configurational theory of organizational effectiveness (Doty &
Glick, 1994; Gerdin & Greve, 2004). The central contention is that there are a limited number of
arrangements that maximize fit and effectiveness; variations from these configurations result in lower
performance.1 Miles and Snow identify three ideal types, termed the defender, analyzer and prospector,
each representing a consistent and equally effective combination of organizational attributes.
1
Other researchers support a Cartesian approach to the analysis of organizational structures (see Gerdin & Greve,
2004). Donaldson (2001) argues that organizations are best viewed as consisting of multiple but independent
4
Despite the centrality of typological theories to strategy-MC research, there have been few attempts to
study how MCs actually combine, or the effectiveness of different combinations, for particular strategic
types. In a seminal investigation, Chenhall and Langfield-Smith (1998) use cluster analysis to investigate
the performance effects of different configurations of strategy and managerial practices, including
accounting. The study provides insight into the combinations of practices and techniques that are
frequently used when pursuing different strategic priorities. One limitation of their approach is the
inability to determine the relative importance of each mechanism, or whether all observed components are
actually necessary to achieve high performance (Ittner & Larcker, 2001).
Conceptually this concerns the distinction between the core and periphery of a configuration.
Configurational theorists distinguish core attributes in terms of the extent of interaction or connectedness.
These tightly integrated components are surrounded by peripheral attributes that reinforce but are loosely
coupled to the central core (Grandori & Furnari, 2008; Siggelkow, 2002). Recently Fiss (2011) has built
on these insights to define core attributes as those “for which the evidence indicates a strong causal
relationship with the outcome” and peripheral attributes as “those for which the evidence for a causal
relationship with outcome is weaker” (p. 398). This emphasizes the relative importance of components
within a configuration for achieving an outcome. While core attributes are necessary parts of a
configuration to lead to an outcome, they may not be sufficient by themselves unless combined and
reinforced with certain peripheral attributes. But as peripheral attributes are weakly connected,
organizations are able to substitute or interchange these, resulting in multiple permutations that are
potentially equally effective. This possibility is captured by the notion of equifinality.
Equifinality and management control
Equifinality, or functional equivalence, refers to the idea that a system can achieve the same final state or
goal “from different initial conditions and by a variety of paths” (Katz & Kahn, 1978, p. 30; von
Bertalanffy, 1968). In organizational analysis this has come to mean that the required performance of an
organization can be achieved through multiple strategic and structural alternatives despite facing the same
environmental imperatives (Gresov & Drazin, 1997). This is a central assumption of configuration
theories, such as Miles and Snow (1978, p. 260), who recognize that the pattern of adaptive response
selected by an organization is not wholly determined by exogenous forces:
structural dimensions that can be individually adjusted to external contingencies and rejects notions of
interdependence and equifinality. This study does not intend to make a claim that one perspective is theoretically
superior to another, but rather views such notions as open empirical questions (see e.g. Grabner & Moers, 2013).
5
It has been frequently noted that organizations adopt a variety of forms in response to apparently
similar environmental demands [...] Thus, there is evidence to refute a “functional imperative” of
organizational structure and behaviour.
While managers are noted to have strategic choice in deciding how their organization should be aligned to
the external environment, Miles and Snow (1978) contend that for each strategic profile there is a single,
optimal (ideal) structural solution, and variations from this arrangement will result in lower performance.
The theory of Miles and Snow (1978) therefore posits equifinality at a population or strategic level – that
organizations pursuing different strategies can be equally effective – but not at a structural level – only
one structural combination will be optimally effective for each strategy. However, as most research has
focused upon between-group differences in structural attributes and performance, there is little evidence
to reveal the within-group effects of employing different combinations (Zahra & Pearce, 1990; for
exceptions see Doty et al., 1993; Fiss, 2011).2
The understanding that there may be equally effective structures for a given organizational context is
largely derived from Merton’s (1968) critique of the central postulates of comparative sociology. In
particular Merton takes issue with “the postulate of indispensability” (1968, p. 87). Merton argues that
researchers frequently treat structures as having a single function that are necessary for the survival and
effective performance of the overall system.3 This assumption underpins conventional contingency theory
whereby “the independent variable [function] is a necessary and sufficient condition for the dependent
variable [structure]” (Schreyögg, 1982, p. 75). In effect, structures are assumed to be equivalent to their
functions as organizational contingencies dictate a single optimizing structure (Donaldson, 2001).4 The
central claim of Merton is that while a structure may fulfill a particular function, it is not synonymous
with that function. Rather, social systems are permissive of a “range of possible variation” in structural
components to effectively meet functional requirements (Merton, 1968, p. 106).
A challenge to empirically investigating equifinality is identifying a suitable basis for assessing the
equivalence of structural alternatives. Effectiveness or fit in MC-strategy literature is typically evaluated
in terms of financial performance (Langfield-Smith, 2008). While an obvious concern for most firms, the
2
A number of studies have, however, investigated the variation and effects of individual attributes (e.g. Abernethy
& Brownell, 1999; Auzair & Langfield-Smith, 2005; Olson et al., 2005; Simons, 1987).
3
Functions refer to the requirements necessary for the maintenance of a social system and structures to the
relational patterns or mechanisms that contribute to the achievement of functions (Gresov & Drazin, 1997).
4
An example is the reliance on accounting performance measures (RAPM) literature where accounting is
represented as having the single function of performance evaluation (Chapman, 1997; Hartmann, 2000).
6
connection with MC is largely implied, with Chenhall (2003, p. 132) noting that “there is no compelling
evidence to suggest that such links exist”. Instead it may be better to assess more proximate outcomes by
focusing on the functions of MC, that is, the control problems they are intended to solve (Grabner &
Moers, 2013). In this study the control problem relates to the alignment of organizational behavior to
various strategic objectives. The next section develops hypotheses for the MC combinations expected to
achieve effective control in different strategic contexts.
Hypotheses among strategy and MC attributes
The mechanisms available to top management to achieve effective control are potentially extensive
(Malmi & Brown, 2008; Merchant & Van der Stede, 2012). The concern of this study is, however, with
those MC attributes expected to vary in effectiveness within and between strategic orientations. In this
respect certain control elements have received greater attention in strategy-MC literature, presumably
because of their perceived importance for effective control for different strategic types (Chenhall, 2003;
Dent, 1990; Langfield-Smith, 2008; Simons, 1987). Most of this research adopts a contingency approach
to examine the association of individual MCs with firm outcomes. The general assumption is that MC
attributes have an additive effect, such that an organization will maximize performance by combining
individual attributes. The remainder of this section reviews the strategy-MC literature and identifies the
structural, accounting, incentive and social control attributes found or theorized to be associated with
improved firm outcomes for defender, prospector and analyzer strategic types.5 The MC combinations
hypothesized to lead to high MC effectiveness for each strategic type are then formally stated.
Structure
There is strong consensus in the literature that defender and prospector firms have quite different
structural requirements.6 Mechanistic structures, characterized by vertical chains of command, centralized
5
The Miles and Snow (1978) typology is well suited to this study for a number of reasons. First, it provides rich
descriptions of the most prominent strategic postures assumed by organizations in relation to their task environments
(Dent, 1990; Kabanoff & Brown, 2008). Thus, many of the contingent conditions upon which organizations are
structured are parsimoniously captured by this categorization (Chenhall, 2003). Second, it is the most widely applied
strategic typology, with a fairly extensive body of literature investigating the MCs used by firms pursuing different
product-market orientations (e.g., Fiss, 2011; Kober et al., 2007; Sandino, 2007). The typology is also comparable to
other strategic frameworks commonly applied in the literature such as Porter’s (1980) differentiation/low-cost and
the entrepreneurial/conservative distinction of Miller and Friesen (1982) (Kald et al., 2000; Langfield-Smith, 2008).
Finally, it retains robust empirical correspondence, is applicable to a wide range of industries, and has strong
theoretical relevance to contemporary concerns – speaking, for instance, to how organizations attempt to reconcile
the competing tensions of efficiency and innovation (Desarbo et al., 2005; Fiss, 2011; Hambrick, 2003; Kabanoff &
Brown, 2008; Simons, 1987, 1995).
6
Although empirical research tends to treat structure as a contextual variable, conceptually it is recognized as an
important control mechanism that can be designed and shaped by managers to influence the behaviors and
interactions of subordinates (Chenhall, 2003; Flamholtz, 1983).
7
coordination, standardization, and formal channels of communication, increase the effectiveness of
defenders by focusing organizational attention towards resource utilization and task efficiency (LangfieldSmith, 2008; Miles & Snow, 1978). As defenders tend to operate in relatively stable environments,
employ routine technologies and have limited interdependencies between sub-units, coordination can be
efficiently handled through standardized decision rules or hierarchical referral without the need for more
costly mechanisms (Galbraith, 1973; Miller, 1988). In contrast, the complex problems and uncertain
environments faced by prospectors require organic structures that emphasize autonomy, dispersed
authority, and lateral communication, to facilitate adaptation and mutual adjustment (Burns & Stalker,
1961; Damanpour, 1991). This is supported by prior research that reports positive associations between
organic structures and environmental uncertainty (Gordon & Narayanan, 1984), innovation (Chenhall &
Morris, 1995; Miller, 1987) and non-routine technologies (Covin et al., 1990). Although there is limited
empirical evidence, analyzers are expected to be most effective with a combination of organic and
mechanistic attributes. Facing dual pressures for stability and change, the required structure is one
characterized by balance (Dent, 1990). Lateral information flows must be interspersed with hierarchical
coordination to manage complex interdependencies. Elements of centralized decision-making and
standardized processes are necessary to maximize functional efficiency, while delegated authority and
mutual adjustment are required to rapidly respond to emerging market opportunities (Dent, 1990; Miles &
Snow, 1978).
Management Accounting Controls (MACs)
To understand the control implications of accounting it is necessary to identify what is measured, and how
that information is used by managers. The constructs of diagnostic, interactive and tightness are arguably
the most important in relation to how MACs are used by top managers (Merchant & Van der Stede, 2012;
Simons, 1995). Diagnostic controls are premised upon the desire for predictable goal achievement. This is
consistent with the emphasis on efficiency and standardization of operations in defender firms (Simons,
1990). Diagnostic controls also complement the mechanistic structure of the defender firm as they
emphasize existing hierarchical structures and vertical lines of communication and authority, thereby
reinforcing a focus on efficiency and productivity (Henri, 2006a). Prospector firms are expected to be
more effective when accounting information is used interactively to direct attention towards strategic
uncertainties and encourage the search for new opportunities (Simons, 1990). This is corroborated by
research that shows that a higher emphasis on interactive use of MACs (relative to diagnostic use) has
performance benefits for firms undergoing strategic change or pursuing product innovation (Abernethy &
Brownell, 1999; Bisbe & Otley, 2004). As analyzer firms face simultaneous pressures for flexibility and
efficiency, MACs are expected to be most effective when used both diagnostically and interactively. This
8
combined use fosters a dynamic tension that stimulates organizational debate, bringing opposing positions
to the surface and encouraging reconciliation of ideas or identification of alternative courses of action
(Henri, 2006a). In doing so, the organization is better able to balance competing demands for “innovation
and predictable goal achievement” (Simons, 1995, p. 153).
Whereas diagnostic and interactive use of MACs refers to the distribution of top management attention in
efforts to formulate and implement strategy, the tightness of MACs refers to the extent of individual
accountability (Simons, 1995).7 Tight accountabilities limit individual discretion and increase the
probability of predetermined actions being effectively implemented (Merchant & Van der Stede, 2012).
Consistent with prior findings in the literature, tight MACs are expected to be effective for defender firms
that have relatively predictable task environments (Auzair & Langfield-Smith, 2005; Miller, 1988; Van
der Stede, 2000). For prospector firms the literature is somewhat inconclusive. While the prototypical
prospector has loose control structures, there is empirical evidence indicating that tight financial controls
are associated with increased effectiveness in these firms (Chenhall & Morris, 1995; Simons, 1987). One
potential explanation is that tight MACs reduce innovative excess and limit exposure to risk (Dent, 1990;
Miller & Friesen, 1982). Alternatively it may be that interactive controls effectively encourage
opportunity search while filtering out unproductive efforts, with MACs remaining relatively loosely tied
to individual accountabilities (Simons, 1995). Analyzer firms are expected to require neither entirely tight
nor loose applications of MACs for effective control, but a combination of both (Miles & Snow, 1978).
Targets associated with operational efficiency and cost control are tightly emphasized, while accounting
information related to new product-market domains are applied more loosely.
Measurement diversity relates to the scope of performance dimensions captured by MACs. A narrow
focus indicates that attention is directed towards only a selective range of performance measures,
typically aggregated, historically oriented, financial measures (Henri, 2006b; Ittner et al., 2003). A broad
focus entails a wider scope of information on managerial behaviors and outcomes not encapsulated within
conventional financial or cost metrics (Van der Stede et al., 2006).8 Research shows that firms that face
uncertain environments, pursue differentiation strategies, have complex interdependencies and
decentralized authority structures, have higher measurement diversity (Abernethy & Lillis, 1995;
Chenhall & Morris, 1986; Gordon & Narayanan, 1984; Lillis & van Veen-Dirks, 2008; Van der Stede et
7
Simons (1995, pp. 161-2) notes that diagnostic and interactive controls are not inherently tight or loose and
organizations can apply these controls with varying levels of accountability and discretion.
8
Broad scope information increases the potential sphere of individual accountability, but even without tight
accountabilities, the very act of measurement serves a more subtle, ex-ante control function. Flamholtz (1983, p.
156) refers to this as the “process function” of accounting, whereby individuals tend to direct their efforts towards
areas that are measured.
9
al., 2006). Following this research, broad scope information concerning market, innovation, quality, and
employee performance is expected to be effective for both prospectors and analyzers. Control in defender
firms is achieved with a more limited range of metrics covering cost, productivity and financial
performance (Lillis & van Veen-Dirks, 2008). This encourages attention to be focused towards enhancing
the efficient performance of largely routine activities.
Compensation
Compensation systems function as a control when they motivate behaviors that are aligned to desired
organizational outcomes (Bonner & Sprinkle, 2002). The attributes that have gained the most interest
from strategy-MC researchers are the use of incentive based pay and the degree of subjectivity or
objectivity in incentive determination (Fisher & Govindarajan, 1993; Langfield-Smith, 2008). Prospector
firms are expected to be more effective when managers are provided greater bonus compensation than
those in defender firms (Fisher & Govindarajan, 1993). In the former, managers face relatively greater
uncertainty, risk, and possible courses of action. In firms attempting to balance multiple strategic
priorities the level of uncertainty and complexity is potentially even higher (Dekker et al., 2013). For both
analyzer and prospector firms the provision of performance-dependent rewards incentivizes managers to
pursue opportunities with higher payoffs and make appropriate trade-offs between competing objectives.
For defender firms operating in low uncertainty environments, managers have fewer critical decisions to
make, and therefore incentive based pay is a costly mechanism that has less impact upon the courses of
action taken to increase firm performance. These arguments are generally supported by prior literature
(Boyd & Salamin, 2001; Balkin & Gomez-Mejia, 1990; Montemayor, 1996).
The level of subjectivity or objectivity indicates whether the determination of incentive compensation is
derived through the subjective judgment of superiors or the use of predetermined formulas (Govindarajan
& Gupta, 1985). The higher the level of uncertainty and the greater the influence that uncontrollable
factors have in affecting task outcomes, the more emphasis managers will place on subjectivity in
determining compensation payments. Consistent support for this is provided by Govindarajan (1984),
Gupta (1987), Montemayor (1996) and Simons (1987, 1990). It follows that defenders will gain the
greatest benefit from formula-based compensation systems, whereas subjective (non-formula)
determination is better aligned to prospectors (Fisher & Govindarajan, 1993). Effective analyzer firms
which compete in both stable and dynamic market domains would use neither highly subjective nor
objective determination but a balanced combination of both.
Social Control
10
Given the stated importance of social controls for effective strategic control there is a surprising absence
of empirical investigation (Langfield-Smith, 2008). Auzair and Langfield-Smith (2005) find that cost
leaders use more bureaucratic and formalized control structures than differentiators, while Miller and
Friesen (1982) show that formal controls are negatively correlated with innovation in entrepreneurial
firms. Although neither of these studies directly measure social or cultural control, they suggest that
prospector and analyzer firms require strong social controls to be effective. For prospectors, strongly held
beliefs and values may be particularly effective in encouraging experimentation, creativity, open
exchange of information, resource sharing, and developing a tolerance for failure (Jassawalla & Sashittal,
2002), while the effective resolution of competing organizational tensions, such as flexibility and
efficiency present in analyzer firms, requires “a common strategic intent” and “an overarching set of
values” (O’Reilly & Tushman, 2008, p. 22). Common cognitive structures, values, and norms are also
important for organizational integration and coordination, particularly when tasks are non-routine or
require transmission of complex information (Barki & Pinsonneault, 2005; Ditillo, 2004; Perrow, 1967).
The more routine transactions of defender firms mean that coordination can be conducted centrally.
Although the effective functioning of hierarchical control arrangements is dependent upon the perceived
legitimacy of authority structures, shared agreement on broader values and beliefs is not essential (Bates
et al., 1995). As the mechanisms, such as selection and socialization, necessary to align individual norms
and values with organizational objectives are costly to the firm, defenders are expected to be most
effective without an emphasis on social control.
Configurations of strategy and control
The expectations outlined above lead to the following hypotheses:
H1: Defender firms that combine a mechanistic structure, MACs with a high diagnostic use, low
interactive use, tight accountabilities and narrow-scope measures, a low emphasis on incentive
pay that is based upon objective criteria, and a low emphasis on social control, have effective
management control.
H2: Prospector firms that combine an organic structure, MACs with a low diagnostic use, high
interactive use, and broad-scope measures, high emphasis on incentive pay that based upon
subjective criteria, and a high emphasis on social control, have effective management control.
H3: Analyzer firms that combine a structure with both organic and mechanistic traits, MACs with
both high diagnostic and high interactive use, with both tight and loose traits and broad-scope
11
measures, a high emphasis on incentive pay that is based upon both subjective and objective
criteria, and a high emphasis on social control, have effective management control.
Research method
Data
Data for the empirical analysis were collected through a cross-sectional survey. The sampling frame is the
member database of the Certified Practicing Accountants of Australia (CPAA). As the CPAA represents
the largest body of business professionals in Australia it provides an appropriate database from which to
draw the sample. A random selection of 1500 individual firms and respondents meeting the following
four criteria were identified from the database. First, respondents are members of the top management
team as they are likely to have the most comprehensive understanding of MC and firm strategy. 9 Second,
firms have a minimum of 100 employees and revenues of at least $20 million to ensure that formal
accounting and control systems are in place. Third, as the focus of the study is on business-level strategy
firms are independent organizations or strategic business units (SBU). Organizational forms such as
holding companies or headquarters of multi-divisional corporations are excluded as these are not
comparable in relation to either strategy or control requirements. Fourth, firms are for-profit. The framing
of a number of questions, notably strategy, is less applicable to non-profit organizations that pursue
fundamentally different objectives. Additional cross-checks of these criteria against Dun and Bradstreet
and Hoovers databases resulted in the removal of 107 firms, leaving a sampling frame of 1393.
Survey implementation was conducted over three months. Respondents were initially contacted by
telephone to create an interest in the research and to ensure that the firm characteristics and respondent
position and knowledge are suitable for the study. A mail-out package containing a cover letter, the
questionnaire and a prepaid return envelope were sent out within a week of contact to the 911 respondents
that agreed to participate. All respondents were sent a reminder postcard one and a half weeks after initial
mailing, while a second follow-up consisted of a phone call to those who had yet to reply (Dillman,
2000). In total 421 surveys (46.2 percent) were returned. Some of these responses are not able to be used
in the analysis. Responses are removed if they fail to meet the criteria of the study or contain significant
missing data (e.g. a page or more). Surveys that contained one or more values that appear to have been
missed inadvertently are retained, with values imputed using the expectation-maximization process (Hair
et al., 2006). The final sample contains 400 responses. Demographic information is provided in Table 1.
Consistent with prior literature top management team is defined as as the top two tiers of an organization’s
management structure (e.g. CEO, GM, COO, CFO, and the next highest level of management) (Henri, 2006a).
9
12
<Insert Table 1 about here>
Two checks for non-response bias are conducted. First, the construct means between the first and last 20
percent of surveys received are compared with no meaningful differences found. Second, the size and
industry of respondent firms are compared to the sample population. No significant differences are
identified. The extent of common-rater bias is assessed through Harman’s one-factor test. An unrotated
factor solution produces 12 factors with Eigenvalues greater than 1 that explains 70 percent of total
variance. The first factor explains 24.3 percent of variance suggesting that single-source bias is not a
significant concern (Podsakoff & Organ, 1986).
Method of analysis
The sample is split into groups based on the Miles and Snow (1978) strategic types using a self-typing
method. An unlabelled description of each strategic orientation is provided to the respondent, requiring
them to select the paragraph that best depicts their firm. This has been the primary method for empirical
classification when using the Miles and Snow typology (Abernethy & Brownell, 1999; Cadez &
Guilding, 2008; Kober et al., 2007; Olsen et al., 2005; Shortell & Zajac, 1990).10 This study uses the
instrument developed by Shortell and Zajac (1990). Minor changes to the descriptors are required as the
initial instrument was tailored to hospital administration. The essential meaning of each strategic type
remains unchanged. From the sample of 400 firms, 65 (16.25%) identified themselves as defenders, 188
(47.0%) as analyzers, and 112 (28.0%) as prospectors.11
To examine effective control configurations a set-theoretic approach termed fuzzy set qualitative analysis
(fsQCA) is conducted for each strategic group.12 As demonstrated in organizational literature (e.g., Crilly
et al., 2012; Fiss, 2007, 2011; Grandori & Furnari, 2008), and more recently in accounting (Erkens & Van
der Stede, 2013), set-theoretic methods are uniquely suited for configurational analyses. In contrast to
conventional econometric approaches that treat case attributes as independent explanations of variance in
a dependent variable, set-theoretic methods consider cases as unique configurations of multiple attributes.
In doing so the method allows for the analysis of “multiple conjunctural causation” (Ragin, 1987).
Miles and Snow (1978) self-typing instruments have been “subjected to considerable psychometric assessment”
(Abernethy & Brownell, 1999, p. 194). Furthermore, in an assessment of alternatives for measuring the Miles and
Snow typology, Shortell and Zajac (1990) report substantial congruence between perceptual and archival measures,
concluding that self-typing represents “a valid approach to measuring strategy” (p. 829) that could be applied by
researchers “with increased confidence in future work” (p. 830).
11
The remaining 35 cases (8.75%) are classified as reactors. As reactors are a residual category for firms that do not
have a consistent strategic pattern (Miles & Snow, 1978) these cases are excluded from the analysis.
12
The analysis is conducted using the software fs/QCA 2.5 available from
http://www.u.arizona.edu/~cragin/fsQCA/software.shtml
10
13
Multiple refers to the possibility that there may be more than one path to an outcome, while conjunctural
conveys the idea that attributes are often interdependent, that is several attributes jointly produce an
outcome and the effect of an individual attribute may change depending on the presence or absence of
other attributes.13
In set-theoretic methods the connection between attributes and an outcome is conceptualized in terms of
sets and set relations. Cases are assigned membership in sets that represent the outcome and the attributes
of interest, and the relationship between those sets examined. For example, to explain what configurations
lead to high MC effectiveness, the analysis examines those firms that are members of the set of high MC
effectiveness and identifies what attribute sets (if any) are associated with this outcome. These attribute
sets are then compared and, through Boolean algebra and set-theoretic algorithms, reduced to the
configurations that lead to the outcome (Fiss, 2011; Ragin, 2008).
The empirical application of fsQCA proceeds in three main steps (Erkens & Van der Stede, 2013).14 The
first involves variable calibration. Calibration refers to specifying the degree of membership a case (i.e. a
firm) has in a set (e.g. degree of membership in the outcome of high MC effectiveness) (Ragin, 2008). As
organizational characteristics are seldom dichotomous in nature, an advantage of fsQCA is that set
memberships need not be binary (crisp sets), rather cases may have partial membership (fuzzy sets).
Specification of threshold values on interval or continuous scale variables indicates full membership, full
non-membership and a cross-over point. Using these thresholds variables are rescaled from raw scores to
values between 0 (full non-membership) and 1 (full membership) based on set definitions.15 Variable
calibration is detailed in the proceeding section.
13
Although incorporation of variable interactions into regression-based models allows for an assessment of
dependencies between multiple attributes, these tend to be restricted to just two-way interactions as interpretation of
higher-order effects is notoriously problematic (Hartmann & Moers, 1999). Furthermore, regression models are
unable to account for equifinality as they estimate only a single path to an outcome (Fiss, 2007; Ragin, 2008). To
analyze multiple attributes researchers have instead used methods such as cluster analysis (e.g., Chenhall &
Langfield-Smith, 1998; Gerdin, 2005) and profile deviation analysis (e.g., Govindarajan, 1988; Selto et al., 1995).
While these techniques can provide useful insights neither is able to examine which of the observed attributes are
actually necessary or sufficient to produce an outcome or their relative importance (Fiss, 2007). Furthermore, cluster
analytic methods will always produce some degree of clustering, but as there is no test statistic the significance of
groupings is based upon interpretation.
14
For a more comprehensive guide to fsQCA see Fiss (2007), Ragin (2008), Rihoux and Ragin (2009) and
Schneider and Wagemann (2012).
15
Variable calibration follows the “direct” method outlined by Ragin (2008, pp. 85-104). Using the specified
thresholds variables are converted to fuzzy set scores through a logistic function. Following Fiss (2011) a constant
of 0.001 is added to all calibrated values equal to 0.5. This is necessary to prevent cases with attributes that fall on
the crossover point from being dropped in the analysis (Ragin, 2008).
14
In the second step a truth table is constructed. The rows of a truth table list all the logically possible
attribute combinations.16 Empirical cases are located in the truth table based on set membership scores.
Although with fuzzy sets cases have partial membership in all possible attribute combinations,
mathematically they can only have one membership score greater than 0.5, and it is to this set that the
case is assigned.17 The number of rows in the truth table is then reduced through specifying a minimum
solution frequency and a consistency threshold. Solution frequency refers to the number of empirical
instances of each unique combination. To avoid inferences from single observations frequency is set at
two (Maggetti & Levi-Faur, 2013), resulting in truth table rows with single or no empirical instances
being removed (Ragin, 2008).18 Consistency measures “the degree to which the cases sharing a given
combination of conditions agree in displaying the outcome” (Ragin, 2008, p. 44). With binary sets
consistency refers simply to the proportion of cases in a truth table row that exhibit the outcome. For
fuzzy set analyses, the consistency measure is modified so that minor deviations in membership scores
have small penalties with large penalties applied for major inconsistencies. Ragin (2008) suggests a
minimum consistency of 0.75, although recent literature recommends a threshold of 0.80 (Fiss, 2011;
Greckhammer et al., 2013; Maggetti & Levi-Faur, 2013). However, Schneider and Wagemann (2012, pp.
127-8) caution that researchers should not justify consistency thresholds solely through reference to those
used in prior research. One guideline they suggest that is applied in the literature is to select a threshold
that corresponds to a break in the distribution of consistency scores (Crilly et al., 2012). Following this
approach the applied consistency thresholds are as follows for defenders (0.82), prospectors (0.83) and
analyzers (0.85).19
The final step involves applying an algorithm based on Boolean logic to determine the commonalities
among configurations that lead to the outcome. For instance, if empirical evidence shows that the attribute
combinations of ABC and AB~C both lead to the outcome (where ~ denotes NOT and  indicates
16
With all possible combinations identified, the truth table will have a total of 2k rows where k is the number of
attributes in the analysis.
17
Attributes are combined using the logical operator AND which takes the lowest attribute value as the membership
score in a combination. For example, if A=0.8 and B=0.6, then the membership score in A•B is 0.6. Membership
scores in all other possible combinations will be less than 0.5 (e.g. membership in A•~B will be 0.4).
18
Selecting the appropriate frequency threshold involves a trade-off between the number of cases per configuration
and the proportion of the sample included in the analysis (Greckhamer et al., 2013). With a frequency threshold of
two, 72% of the sample is retained, which is close to the range of 75–80% suggested by Ragin (2008). The rows that
are removed in this process are called logical remainders (Schneider & Wagemann, 2012). Logical remainders that
lead to simplified solution terms are referred to as simplifying assumptions. These are incorporated into
counterfactual analysis.
19
Applying a standard threshold of 0.80 across all groups does not affect the results of the analysis. It should be
noted that in the analyzer group one additional truth table row would meet this lower threshold. However, the cases
in this row are problematic as none display membership in the outcome (i.e. Y<0.5). These logical contradictions
would typically be removed and hence the analysis would remain unchanged (Schneider & Wagemann, 2012).
Logical contradictions are explained further in the additional analyses.
15
AND), then the solution term can be simplified to AB (Erkens & Van der Stede, 2013; Ragin, 2008).
However, a central challenge to configurational research is that an analysis with even just a few elements
can lead to a large number of logically possible combinations with no empirical correspondence. This
problem of “limited diversity” is overcome in fsQCA by using counterfactual analysis (Ragin, 2008).
Counterfactual analysis incorporates theoretical expectations with empirical evidence to logically simplify
solution terms. The process results in a number of potential solutions. The most parsimonious solution
indicates the core attributes of a configuration that have the strongest evidence relating them to the
outcome, while the intermediate solution also includes peripheral components that display weaker
empirical support (Erskens & Van der Stede, 2013; Fiss, 2011).20
Construct measurement and calibration
Measures draw on existing instruments where possible. Refinements or development of new constructs
are made with reference to the measurement guidelines of Jarvis et al. (2003) and Rossiter (2002). The
survey instrument was subject to pre-testing with 10 senior managers and 9 academics to assess face and
content validity. The validity of reflective constructs is further assessed through (1) factor analysis, (2)
correlation analysis, and (3) Cronbach’s Alpha. Factor analysis using maximum-likelihood extraction and
oblique rotation supports unidimensionality for each construct. The correlation matrix, shown in Table 2,
presents plausible associations between constructs. Only one correlation is above 0.6, between interactive
and diagnostic MACs, but this is consistent with associations reported in prior literature (Henri, 2006a;
Widener, 2007). Cronbach Alpha’s are between 0.78 and 0.89 supporting construct reliability. The
validity of formative constructs is assessed through (1) principal component analysis (PCA) weightings,
and (2) variance inflation factors (VIFs) (Petter et al., 2007).21 PCA reveals all formative construct items
are positive and have weights above the recommended minimum of 0.30 (Hair et al., 2006). The
maximum VIF of 2.32 is well below the general threshold of 10 (Hair et al., 2006).
The intermediate solution is achieved by taking into account what are known as “easy counterfactuals” (Ragin,
2008). Easy counterfactuals refer to simplifying assumptions that are aligned to both theoretical expectations and
empirical evidence (Schneider & Wagemann, 2012). The parsimonious solution also includes “difficult
counterfactuals” which are consistent with empirical evidence but not theoretical expectations. Consider an example
where empirical evidence indicates that A•B•C results in the outcome, but there is no evidence either way for the
combination A•B•~C (it is a logical remainder). If theoretical knowledge links the presence, not the absence, of C to
the outcome, then the attribute would be retained in the intermediate solution but removed from the parsimonious
solution (Fiss, 2011).
21
There are as yet no generally accepted guidelines for statistically assessing the validity of formative constructs.
Some commentators argue that statistical tests are meaningless and formative constructs can only be evaluated in
terms of content validity (Rossiter, 2002). Petter et al. (2007) advise researchers to examine PCA weights and item
multicollinearity. Negative or very low weightings can suggest that included items are unrelated to the construct,
while highly collinear items may indicate that multiple items are tapping into the same construct dimension.
20
16
<Insert Table 2 about here>
Structure is assessed across five items that reflect a continuum from mechanistic to organic (Burns &
Stalker, 1961; Gordon & Narayanan, 1984; Covin et al., 2001). Items are derived from those used by
Chenhall and Morris (1995), Covin et al. (2001) and Leifer and Huber (1977). All items load significantly
(>0.60) on a single factor (Eigenvalue 2.64, Var. 52.7%, α = 0.77). For defender and prospector groups
set membership is calibrated with a value of 1 indicating full membership in the set of firms with a
mechanistic structure and fully out with a value of 7 (fully in the set with an organic structure). The scale
midpoint (4) is set as the crossover value. As the expectation for analyzer firms is that MC combinations
will be effective with a balanced structure (both mechanistic and organic traits), a different calibration is
required. For analyzer firms the scale midpoint (4) is set as full membership in the set of firms with a
balanced structure and the scale endpoints representing fully out (fully in the set with an unbalanced
structure). The crossover value is set as +/- 1 from the scale midpoint.
Diagnostic and interactive uses of MACs are measured through top management use of budgets and
performance measurement system (PMS). The focus on these two systems is appropriate for this study as
they have been found to have significant application at the top management level and are shown to be
empirically associated with variables relating to the strategic position of an organization (Abernethy &
Brownell, 1999; Bisbe & Otley, 2004; Henri, 2006a; Widener, 2007).22 Items for diagnostic and
interactive control use were responded to separately for budgets and PMS, with scores summated if the
firm used both systems. All items are anchored with “very low extent” (1) and “very high extent” (7).
Diagnostic control use is assessed through a reflective measurement model containing five items adapted
from the instruments used in Henri (2006a) and Widener (2007). The included items are selected to
correspond with the descriptions of diagnostic control by Simons (1995, 2000). Factor analysis reveals
loadings >0.75 on a single factor (Eigenvalue 3.48, Var. 69.6%, α = 0.89). The construct used for
interactive control is based on the formative measurement model outlined by Bisbe et al. (2007). They
identify five dimensions that constitute interactive control use. Each dimension is captured using a single
item. Items are adapted from prior literature where possible (Abernethy & Brownell, 1999; Bisbe &
Otley, 2004; Henri, 2006a; Naranjo-Gil & Hartmann, 2007; Widener, 2007).
22
In the survey instrument budgeting is defined as the preparation of budgets, variance analyses and the forecasting
of financials. Performance measures or performance measurement systems are defined as including both financial
and non-financial indicators that measure multiple dimensions of performance (Bisbe and Otley, 2004; Widener,
2007).
17
Unlike other MC constructs, calibration thresholds at the upper (7) and lower (1) bounds of the scale for
set membership are not meaningful for diagnostic control. Consistent with prior literature (cf. Henri,
2006a; Widener, 2007) the mean (5.51) and standard deviation (0.83) indicate that almost all firms in the
sample place a high emphasis on diagnostic control, with few firms located below the scale midpoint.
Given this, an empirically more meaningful distinction is to represent set membership in diagnostic use as
either “very high” or “not very high” (as opposed to “very high” and “very low”). Membership in the set
of firms with very high diagnostic use is coded as fully in for a value of 7 and fully out for a value of 4 for
all strategic groups. The cross-over is the halfway point of 5.5. To enable comparison interactive control
is calibrated with the same threshold values.
The measurement of tightness is based on Merchant’s (1985, 1998) conception of tight versus loose
control. Merchant outlines four defining attributes of tightness. A single indicator is used to capture each
of these dimensions, relating to target flexibility (anchored by “very flexible” and “very inflexible”),
communication of targets (“very infrequently – quarterly or longer”, “monthly”, and “very frequently –
daily”), monitoring of target variances and extent of evaluation based on target achievement (“very low
extent” and “very high extent”). These attributes are indicative of an emergent multi-dimensional
construct. They do not appear to share a common nomological net nor be driven by a higher order latent
construct, but are rather defining components of a tight control system. Therefore a formative
measurement model is applied. For defender and prospector groups membership in the set of firms with
tight MACs is coded as fully in at a value of 7 and fully out at a value of 1 (corresponding to fully in the
set of firms with loose MACs), with the scale midpoint (4) set as the crossover point. In the case of
analyzers, tightness is calibrated in the same fashion as structure, with the scale midpoint indicating full
membership in the set of firms with balanced MACs (both tight and loose traits), endpoints representing
fully out of the set, and the crossover point at +/- 1 from the scale midpoint.
To assess measurement diversity, respondents are asked to indicate the extent that measures relating to six
performance categories are used to evaluate subordinate performance. Categories are based on those
applied in prior studies of measurement diversity and performance measurement (Henri, 2006b; Ittner et
al., 2003; Scott & Tiessen, 1999). As performance dimensions are not necessarily interdependent the
construct is treated formatively. A single item is used to assess each dimension and is anchored by “very
low extent” and “very high extent”. All firms are coded as fully in the set of high measurement diversity
with a value of 7, fully out with a value of 1, with 4 set as the crossover point.
18
Incentive based pay is measured using an existing instrument from Chalos and O’Connor (2004). This
three item reflective scale is a modified version of the construct used by Shields and Young (1993).
Similar constructs have also been used by O’Connor et al. (2006) and Chow et al. (1999). Items are
anchored by “very low extent” and “very high extent”. Factor analysis returns a single factor with item
loadings >0.66 and satisfactory reliability (Eigenvalue 1.97, Var. 65.7%, α = 0.73). Membership of fully
in the set of firms with high incentive based pay is coded as 7, fully out with a value of 1 and a crossover
value of 4.
A single indicator is employed to measure incentive determination, based on the item used by Simons
(1987) and the definitions of Govindarajan and Gupta (1985). The indicator is a continuum, with one end
anchored by “determined subjectively (based on top management assessment)”, the other “determined
objectively (based on pre-determined formulas or targets” with the midpoint labeled “intermediate”. Set
membership thresholds correspond to these anchors, with defender and prospector firms calibrated as
fully in the set of objective determination with a value of 7, fully out of the set with a value of 1 (fully in
the set of firms with subjective determination) with 4 as the crossover point. For the analyzer group, the
scale midpoint is set as fully in the set of firms with balanced (both subjective and objective) incentive
determination, the endpoints as fully out, and +/- 1 from the midpoint as the crossover.
Social control is elicited using four items. Two items are adapted from the instrument used in Kober et al.
(2007). These relate to the extent of shared norms and expectations, and the extent of commitment to firm
objectives and values. The remaining items are formulated with reference to relevant literature on
organizational culture and social control (Pratt & Beaulieu, 1992; Schein, 2004; Wilkins & Ouchi, 1983).
Items for social control are anchored by “very low extent” and “very high extent”. Factor analysis returns
a single factor (Eigenvalue 2.92, Var. 73%, α = 0.87). Firms are coded as fully in the set of firms with
high social control with a value of 7, fully out with a value of 1, and a crossover point of 4.
No existing instrument is available to adequately capture MC effectiveness. Significant attention was
therefore given to the development of this construct. At a basic level MC is concerned with the
mechanisms and processes that lead to the achievement of organizational goals. In this sense, the ultimate
function of MC is the control (alignment) of individual behavior towards intended objectives. Flamholtz
(1983, p. 157) has alluded to the operationalization of this function, noting that it may be “useful to
conceive of ‘control’ as a variable, where the amount of control is a function of the configuration of
control system elements”. The utility of control as a first-order construct rests upon its definition. If
control is considered in cybernetic terms as the degree of regulated and predictable behavior (Fisher,
19
1995), then the overall amount of control managers perceive necessary is likely to vary between firm
contexts. However, as Simons (1995) and others highlight, the function or role of MC is not limited to
predictable goal achievement, but extends to other concerns such as experimentation and adaptation. In
this case the overall level of control may not vary between two firms even though their control
requirements could be quite different. These broader purposes of control are neatly articulated by Otley
and Berry (1980, p. 232) who define MC as:
[T]hose procedures which act to maintain viability through goal achievement, those concerned
with the coordination and integration of differentiated parts, and those which promote adaptation
to both internal and external change.
From Otley and Berry, three second-order control functions can be identified: goal alignment, adaptability
and integration. These functions relate closely to prior conceptualizations of control (Emmanuel et al.,
1990; Galbraith, 1973; Kloot, 1997; Lawrence & Lorsch, 1967; Simons, 1995).23 The conceptual
literature relating to these control functions provided the basis to develop an initial item pool. In total, 16
items were developed. The item pool was then subject to pilot testing with practitioners and academics.
Particular attention was given to the interpretation and meaning of each item. The general theme that
emerged from pilot testing was that many of the items had little substantive difference (e.g. items related
to coordination, integration and collaboration). The initial item pool was subsequently reduced to five
items that maintained sufficient conceptual coverage of the control functions.
As firms in different strategic contexts are expected to have different control requirements, respondents
were asked to indicate the importance of each control priority for their firm on a seven point scale
(anchored by “very low” to “very high”). Respondents were also required to score how effective their MC
structure is in relation to achieving each priority (anchored by “very low” and “very high”). A single
composite measure of MC effectiveness is obtained by weighting the effectiveness score by its relative
importance.24 Following recent literature (Erskens & Van der Stede, 2013; Fiss, 2011; Tochman et al.,
2013) firms are coded as having high MC effectiveness if they are in the 75th percentile or higher. Firms
23
Goal alignment refers to the desire for predictable and efficient achievement of organizational objectives (Duncan,
1973; Simons, 1995). Adaptability relates to the capacity of the firm to respond to variations in the external
environmental and to flexibly adjust to novel and innovative behaviors (Hrebiniak & Joyce, 1985; Simons, 1995).
Integration refers to coordination among different parts of the organization to accomplish collective tasks (Barki &
Pinsonneault, 2005; Lawrence & Lorsch, 1967; Van de Ven et al., 1976).
24
This measure is analogous to the multidimensional performance instrument introduced by Govindarajan (1984)
that is well established in the literature (e.g. Bisbe & Otley, 2004; Chenhall & Langfield-Smith, 1998; Govindarajan
& Gupta, 1985)
20
in the bottom 25th percentile are coded as having low MC effectiveness, with the median set as the
crossover point.25 As the construct for MC effectiveness is purpose developed for this study an additional
check for criterion validity is conducted. MC effectiveness is correlated with two additional items – (1)
the effectiveness of MC in achieving firm performance, and (2) overall firm performance relative to
competitors. Significant correlations of 0.65 and 0.31 respectively (p<0.01) provide additional validation.
Results
The results of the fuzzy set analyses for each strategic group are displayed in Tables 3 and 4.26
Presentation of results follows the notation of Ragin and Fiss (2008). Black circles () refer the presence
of a control attribute and circles with a cross () designate the absence of that attribute. Core attributes
are represented by large circles whereas small circles denote a peripheral attribute. Blank spaces indicate
a “don’t care” situation where the attribute may be either present or absent. Tables 3 and 4 also report
consistency and coverage measures. Consistency indicates the degree to which cases consistently produce
the outcome, while coverage reports the degree to which cases in the solution account for instances of the
outcome (Ragin, 2008).27 Raw coverage corresponds to all case memberships, while unique coverage
explains membership in the outcome not captured by any other solution term.
<Insert Table 3 about here>
<Insert Table 4 about here>
The results for the defender group reveal two configurations sufficient for effective control. The overall
consistency of the solution is 0.85, which is above recommended thresholds (Fiss, 2011; Ragin, 2008).
The overall coverage of 0.54 is also quite high, indicating that the solution covers more than half of the
set of defender firms with high MC effectiveness. Configuration 1a is mostly consistent with H1. The
core attributes of this configuration are a mechanistic structure and diagnostic use of MACs, that when
25
The analysis is also conducted with MC effectiveness calibrated at the 80 th (fully in), 50th (crossover), and 20th
(fully out) percentiles. The results remain unchanged suggesting that the analysis is not overly sensitive to the
specification of these thresholds.
26
The results presented in Tables 3 and 4 are based on tests for sufficiency. Here the individual attributes that make
up each configuration are known as INUS conditions – an insufficient but necessary part of a configuration which is
itself unnecessary but sufficient for the outcome to occur (Mackie, 1965; Schneider & Wagemann, 2012). Each
configuration is sufficient as they result in the outcome, but unnecessary as there are other paths to the outcome.
Individual attributes are necessary parts of a configuration as without that attribute the configuration may not
produce the outcome, but insufficient in themselves as each is dependent on the presence or absence of one or more
other attributes for the outcome to occur. Separate tests for necessity are reported in the additional analyses.
27
Consistency is similar to the p-values of regression coefficients while coverage is comparable to an R-squared
statistic (Erkens & Van der Stede, 2013).
21
combined with an objective incentive determination and MACs that are tight and not used highly
interactively, are sufficient for achieving high MC effectiveness. This presents a control structure
reminiscent of the machine bureaucracy (Mintzberg, 1979; Speklé, 2001). Recall that H1 indicated that
narrow-scope measures, low incentive based pay and low social control would also be required for
effective control. The “don’t care” results for these attributes indicate that they are not attributes that are
necessary as part of this configuration for the firm to achieve effective control. Like 1a, the second
configuration (1b) contains diagnostic MACs as a core attribute, and MACs that are not highly interactive
and objective incentive determination as peripheral components. However, neither tight MACs nor a
mechanistic structure are central to this configuration, while broad-scope measurement diversity is
revealed as a core attribute. This finding is inconsistent with H1. A potential explanation is provided by
Dent (1990). In commenting on the results of Simons (1987), Dent (1990) suggests that as defenders face
relatively stable environments tight financial control may not be necessary to generate efficiency. Rather,
effective control can be achieved through the monitoring of non-financial aspects, such as quality and
operational metrics (Dent, 1990). This broader focus may also necessitate a greater degree of autonomy
and informal communication implying that a mechanistic structure is not a necessary attribute in this
configuration.
Three configurations are associated with high MC effectiveness for prospector firms, with consistency
(0.85) and coverage (0.52) measures similar to the defender solution. All three prospector configurations
share the same core attributes of an organic structure and highly interactive MACs, social control as a
peripheral attribute, and diagnostic MACs as a “don’t care” attribute. The remaining four attributes vary
in how they combined, but are consistent with the expectations of H2. Configuration 2a, with loose
MACs that have broad-scope measurement diversity, combined with an emphasis on social control and
the core attributes of interactive control and an organic structure, is most closely associated to the ideal
prospector described by Miles and Snow (1978), suggesting a control logic consistent with the
exploratory archetype of Speklé (2001) and Mintzberg’s (1979) adhocracy. Compared to the first
configuration, 2b differs with the addition of incentive based pay and the removal of loose MACs as a
required attribute, while configuration 2c adds tight MACs, incentive based pay and subjective incentive
determination, with measurement diversity as a “don’t care” attribute. The results indicate that effective
control in prospector firms can be achieved with either tight or loose MACs when combined with an
organic and interactive control structure (Dent, 1990; Simons, 1987). Comparing solution 2c to both 2a
and 2b reveals that tight MACs may contribute to effective control only when combined with incentives
that are subjectively determined, allowing unanticipated changes to be taken into account when assessing
individual contributions to firm performance.
22
Four configurations are associated with high MC effectiveness for analyzer firms. The overall solution
exhibits acceptable consistency (0.87) and coverage (0.42). All analyzer configurations have the same
core component, highly interactive MACs, combined with the peripheral attributes of social control and
incentive based pay, consistent with H3. The remaining attributes vary as peripheral conditions.
Configurations 3c and 3d are most closely aligned to H3, as both have only one “don’t care” attribute,
whilst 3a and 3b have two. It is notable that the effective configurations for analyzers tend to require more
MC attributes for effective control than those for defender and prospector firms. This may reflect the
complexity of the control problem, balancing the dual pressures for stability and change and competing
demands of efficiency and innovation, in this setting (Dent, 1990; Miles & Snow, 1978).
Additional analyses
A number of additional checks are conducted to assess common criticisms and pitfalls in the application
of fsQCA and robustness of results. The first concerns the presence of logically contradictory cases
(Schneider & Wagemann, 2012). This occurs when a truth table row meets the consistency threshold of
the analysis, but one or more of the empirical cases that are members of this row (X>0.5) are not
members of the outcome (Y<0.5). Assessing the presence of logical contradictions requires visual
inspection of the members of each truth table row meeting the consistency threshold.28 No logical
contradictory cases were found to be present. A second concern stems from another form of logical
contradiction, whereby a truth table row can be found to be both sufficient for the presence and the
absence of the outcome (Cooper & Glaesser, 2011).29 To assess this possibility, the consistency scores for
truth table rows that are sufficient for achieving the outcome are compared to the consistency scores for
the negation (absence) of the outcome (Schneider & Wagemann, 2012). Comparisons indicate that the
consistency scores are significantly different, and importantly are well below the threshold of 0.80 when
the outcome is negated. The third concern relates to erroneous claims of necessary conditions (Schneider
& Wagemann, 2012). A cursory view of Tables 3 and 4 indicate that there are a number of attributes that
appear across all solutions of a strategic group, for example interactive control in analyzer and prospector
configurations. However, the results are based on tests of sufficiency, not necessity, and as coverage of
these solutions is less than 100 percent there may be configurations that achieve the outcome without this
28
This involves examination of an XY plot where X represents the degree of membership in the set of attributes (the
truth table row) and Y represents membership in the outcome. Logical contradictions appear in the bottom right
hand quadrant of the plot.
29
When membership in the set of attributes (X) is very small a case may pass the formal test of sufficiency (i.e.
X<Y) for both the outcome (Y) and the absence of the outcome (~Y) (Schneider & Wagemann, 2012). Consider a
case with values of X=0.2 and Y=0.7, and therefore ~Y=0.3. This case therefore satisfies the sufficiency relationship
for both the outcome (X<Y) and its negation (X<~Y).
23
attribute present. As noted by Ragin (2008) necessity is a stronger claim than sufficiency and should be
subject to a more stringent consistency threshold. Each attribute is assessed separately for necessity using
a consistency threshold of 0.90. No attribute is found to be individually necessary for any of the strategic
groups.
Discussion and conclusion
The purpose of this study is to understand how MCs combine to achieve effective control outcomes in
different strategic contexts. While the association between strategy and MC has been subject to extensive
investigation much of the literature is limited to the analysis of associations between individual attributes
of MC and strategy (Langfield-Smith, 2008). In examining how MC attributes combine in various ways,
this study shows that, on the one hand, existing contingency research remains highly relevant for
understanding the relationship between strategy and control, as many of the findings are consistent with
the expectations of prior research. On the other hand, however, conventional contingency analyses may
not reveal the full picture. Not all hypothesized attributes appear necessary for a configuration to lead to
effective control outcomes, and there are multiple, equally effective combinations for different strategic
types. The findings contribute to our understanding of the association between strategy and control, and
MC combinations more generally, in a number of ways.
First, in identifying the attributes that matter in a specific configuration for achieving effective control
outcomes, and those that are redundant, the study provides insight into MC complementarity.
Configuration theory assumes that organizations are characterized by complex patterns of interconnected
attributes, and it is these unique patterns that give rise to organizational outcomes. This is consistent with
the notion of complementarity as it implies that the effectiveness of one attribute is dependent on another
(Grabner & Moers, 2013). In this study the individual attributes in each configuration are necessary, but
insufficient, conditions for effective control, as they are dependent on the presence or absence of multiple
other attributes to be sufficient for the outcome to occur (Fiss, 2007; Jackson & Ni, 2013).
One observation is that configurations for prospectors and defenders do not share any common attribute
pairs. This is not unexpected as these strategic contexts give rise to quite different control problems, but
the implication is that interdependencies between MC attributes tend to be conditional on the strategic
context of the firm (Cassiman & Veugelers, 2006; Grabner & Moers, 2013). For instance, prospectors
with tight accountabilities (configuration 2c) require incentives to be determined subjectively for effective
control, but in defenders tight accountabilities need to be combined with objective incentive
determination (configuration 1a). Context specific interdependencies are also found in the analyzer group.
24
A stream of research investigating the levers of control framework suggests that interactive and diagnostic
uses of accounting systems are complementary (Henri, 2006a; Widener, 2007). This study extends this
literature by showing that whether or not these attributes have interdependent effects on control outcomes
is context dependent, specifically to those firms where the need to balance multiple and conflicting
strategic priorities is most apparent (i.e. analyzers).
Another observation relates to the origins of complementarity. Explanations for complementarity tend to
stem from the “similarity in logic” among organizational attributes or practices (Grandori & Furnari,
2013). This is consistent with defender 1a and prospector 2a configurations which are comprised of
attributes that share an underlying coherence; in the former attributes mutually reinforce a bureaucratic
logic of control, while in the latter attributes are consistent with an organic or exploratory control logic.
However complementarity may also arise from “differences in logic” (Grandori & Furnari, 2013). This is
evident in the analyzer configurations that have various combinations of opposing traits that assist an
organization to balance competing priorities, but complementarities from heterogeneous attributes emerge
in pure-type strategic contexts as well. In prospector 3c tight accountability is combined in an otherwise
organic control structure. As an individual attribute it may discourage innovation and restrict the capacity
for the organization to adapt in shifting environments. Yet when combined with various organic control
processes, tight accountability can act as a complement by counterbalancing tendencies for excessive
experimentation and exposure to risk, and facilitate learning through frequent monitoring of performance
(Chenhall & Morris, 1995; Dent, 1990). Future research should extend consideration to attributes whose
apparent inconsistencies function as sources of complementarity.
Second, the study shows that equally effective control outcomes can be achieved through different control
combinations in the same strategic context. This supports emerging evidence in the literature that it may
be “important not to assume automatically” that causal paths are necessarily represented by direct one-toone relationships because “different control mechanisms available in the control package may well
combine in different ways in a particular context” (Gerdin, 2005, p. 119; Fiss, 2011; Grandori & Furnari,
2008). This study also lends support to the recent idea of “neutral permutations” (Fiss, 2011). This
concept suggests there will be more than one possible arrangement of peripheral attributes surrounding
the core attribute(s) of a given configuration, and these permutations are equifinal regarding the outcome
of interest. Analyzer and prospector contexts exhibit neutral permutations with certain peripheral
attributes found to be substitutable in a variety of ways around a consistent core. This does not appear in
the case of defenders, where the viable configurations for high MC effectiveness exhibit both different
peripheral and core attributes. While the insights of Dent (1990) suggest that defenders can address the
25
same control problems with quite different MC configurations, the result may also be due to qualitative
differences in defender firm types. In comparing the frameworks of Miles and Snow (1978) and Porter
(1980), Walker and Ruekert (1987) suggest an additional distinction between the conventional low-cost
defenders, and differentiated defenders, which attempt to protect a market niche through limited
differentiation. The distinct control problems faced by differentiated defenders may explain the difference
in core conditions of the defender configurations found in this study.
Third, this study extends understanding of MC combinations in firms that pursue mixed or hybrid
strategies. Effective control in these firms requires MC arrangements that are able to effectively balance
multiple and contradictory objectives, for instance between flexibility and efficiency (Gibson &
Birkinshaw, 2004; Simons, 1995). So far research has been limited to the design of performance
measurement systems, with studies finding that firms following mixed strategies of differentiation and
low cost require an emphasis on both financial and non-financial metrics (Lillis & van Veen-Dirks, 2008;
Dekker et al., 2013). However, the results of this study suggest that firms can achieve effective control
without a diverse set of accounting metrics, as long as individual accountabilities are neither tight nor
loose, but balanced (configuration 3a). What seems important, though, is that each permutation contains
multiple attributes that counterbalance opposing tensions – i.e. diagnostic and interactive, organic and
mechanistic, loose and tight. This may relate to the specific properties of these attributes in
counterbalancing one another. Certainly there remains much to learn about the interaction of MC
attributes for achieving effective control in this complex setting.
Finally, the study adds to recent literature demonstrating the usefulness of set-theoretic methods for
penetrating the complexity of control combinations (Erkens & Van der Stede, 2013). As set-theoretic
methods are not restricted to the estimation of a single path to an outcome they allow for more finegrained understandings of how multiple attributes can combine in various ways to produce an outcome.
As such, this method holds promise for addressing some of the puzzles and gaps that persist in MC
contingency research (Chenhall, 2003; Langfield-Smith, 2008). One of these puzzles concerns the role of
tight accounting controls in contexts characterized by innovation and uncertainty. The results of this study
show that tight accountability can contribute to effective control in prospector firms (configuration 2c),
but it must be combined with an otherwise organic control structure (Chenhall & Morris, 1995; Simons,
1995). However, for some prospector firms (configuration 2a) loose MACs are necessary for effective
control, while for others it is a redundant condition (configuration 2b). This suggests a rather complex
association where the effectiveness of tight or loose accountabilities is dependent on how it is combined
with other control attributes. This finding is also consistent with Meyer et al. (1993) who note that
26
variables that are interdependent in one configuration “may be unrelated or even inversely related in
another” (p. 1178). Another gap in the literature concerns the association between formal and social
controls (Langfield-Smith, 2008). Current theory indicates that social controls act as substitutes for formal
control in complex and uncertain conditions (Ouchi, 1977, 1979). Instead it is found that prospectors and
analyzers combine social controls with formal control mechanisms, suggesting that social controls tend to
acts as complements, rather than as substitutes as is often assumed (Alvesson & Kärreman, 2004).
This study is subject to some limitations but also provides opportunities for further research. First, the
analysis relies on data obtained from subjective assessments of managers. While significant attention was
given to survey development, pre-testing and checks for construct validity, the measures may still contain
noise. Additionally, while diagnostic tests suggest that non-response and single-source biases are not
significant concerns, this cannot be entirely ruled out. As such, future research is necessary to verify and
extend the initial insights on effective MC combinations provided in this study. Second, the
configurations presented are unlikely to be exhaustive of all possible MC combinations. Although the
study examines MC attributes that prior literature indicates are some of the most important in relation to
the control requirements of common strategic orientations, inclusion of other MC attributes may reveal
additional configurations for achieving effective control. Third, the evidence of this study shows that
strategy does not dictate a single effective MC combination. However, this does not imply that
organizations have unrestricted agency in selecting their control structure or that these choices are without
causal antecedents. Although the Miles and Snow (1978) categorization has been found to relate closely
to factors such as environmental dynamism and technological routineness, there may be other contextual
dimensions that increase or decrease the relative costs and benefits of MC configurations across firms.
Future research should extend this study by incorporating the effects of other contingency dimensions on
restricting or expanding the range of viable MC combinations, and investigate the reasons why an
organization is observed with a particular MC configuration when there are viable alternatives (e.g. path
dependence, managerial preferences). Finally, while set-theoretic methods offer certain advantages over
techniques more common to MC research there are tradeoffs. Set-theoretic approaches emphasize
“difference in kind” (presence/absence) over “difference in degree” (increase/decrease) meaning
incremental effects are not easily observed. In contrast, econometric approaches, such as those described
by Grabner and Moers (2013), allow for more specific modeling of interdependencies between MC
attributes. However, such approaches tend to be limited to just two-way interactions. As the methods
make opposing tradeoffs between specificity and complexity, they are likely to provide complementary
insights for advancing the understanding of MC combinations.
27
Appendix A
Extract of structured questionnaire
Survey items
Structure
1. Indicate how control information is typically communicated in your
SBU
Anchors
Through highly structured, formal
channels of communication / Through
very open, informal channels of
communication
2.
Indicate the accessibility of operational information in your SBU
Highly restrictive access to important
operational information / Free flow of
important operational information
throughout the SBU
3.
Indicate the content of work-related communication between top
management and subordinates
Top management decisions and
mandates, instructional, direction giving /
Information and idea sharing,
consultative, advice giving
4.
In general, the operating management philosophy in my SBU
favours
Emphasis on giving the most say in
decision making to formal line managers
/ Emphasis on giving the most say to the
expert in a given situation even if this
means bypassing formal line authority
5.
In general, the operating management philosophy in my SBU
favours
Emphasis on specialisation and top level
coordination / Emphasis on initiative and
adaptation to the local situation
Diagnostic
To what extent does the top management team use budgets
(performance measurement systems) for the following
1.
Identify critical performance variables (i.e. factors that indicate
achievement of current strategy)
2.
3.
4.
Set targets for critical performance variables
Monitor progress towards critical performance targets
Provide information to correct deviations from preset performance
targets
5.
Review key areas of performance
Interactive
To what extent does the top management team use budgets
(performance measurement systems) for the following
1.
Provide a recurring and frequent agenda for top management
activities
2.
3.
Provide a recurring and frequent agenda for subordinate activities
Enable continual challenge and debate of underlying data,
assumptions and action plans with subordinates and peers
4.
Focus attention on strategic uncertainties (i.e. factors that may
invalidate current strategy or provide opportunities for new
strategic initiatives)
Very low extent / Very high extent
Very low extent / Very high extent
28
5.
Encourage and facilitate dialogue and information sharing with
subordinates
Tightness
The following questions relate to pre-established targets set for
subordinates of the top management team (e.g. senior managers
that report directly to a member of the top management team).
These targets or goals may be financial (e.g. budget targets) or
related to other performance dimensions.
1.
How flexible are subordinate performance targets once they have
been set? (Reverse coded)
Very inflexible / Very flexible
2.
How frequently are subordinates consulted about performance
target achievement? (Reverse coded)
Very frequently (daily) / Monthly / Very
infrequently (quarterly or longer)
3.
To what extent are written explanations for variances from target
performance levels required from subordinates?
Very low extent / Very high extent
4.
To what extent are subordinate evaluations predominantly based
on achievement of performance targets?
Very low extent / Very high extent
Measurement diversity
To what extent are measures related to the following dimensions
used to evaluate subordinate performance?
1.
2.
Customer (e.g. market share, satisfaction, retention)
Employee (e.g. employee satisfaction, turnover, workforce
capabilities and development)
3.
4.
Operational Process (e.g. productivity, safety, cycle time)
Innovation (e.g. R&D, new product/service success, development
cycle time)
5.
6.
Quality (e.g. product/service quality, defects, awards)
Social Responsibility (e.g. environmental compliance, community
impact, public image)
7.
Other Dimension (please elaborate)
Performance-based compensation
Please indicate the extent to which…
1. The financial rewards of subordinates increase as actual
performance increasingly exceeds targets
2.
Subordinates whose performance relative to targets is among the
top 25% are given larger financial rewards than those given to
managers among the bottom 25%
3.
Compensation contracts clearly specify how compensation is
related to subordinate performance relative to performance
targets
Incentive determination
1. What is the usual basis for determining performance-based or
bonus compensation for subordinates?
N/A / Very low extent / Very high extent
Very low extent / Very high extent
Determined Subjectively (based on top
management assessment) /
Intermediate / Determined Objectively
(based on pre-determined formulas or
targets)
29
Social control
To what extent…
1. Is there a sense of shared values, beliefs and expectations
among employees?
2.
Is there a consensus among employees on SBU objectives and
direction?
3.
Are employees committed to the values and objectives outlined
by top management?
4.
Does top management rely on the shared values and norms of
employees to provide direction when faced with uncertainty?
Management control effectiveness
Performance management systems are the combination of
systems and processes referred to in this survey that are used by
your SBU (e.g. planning, accounting, evaluation/reward systems,
structure, management processes, HR procedures and
organisational statements and values).
Very low extent / Very high extent
Very low / Very high
i) How important are the following priorities for your SBU?
ii) To what extent does your performance management system
contribute to achieving each priority?
1.
2.
3.
4.
5.
Improving efficiency
Being innovative
Adapting to changing business demands
Coordinating work between sub-units
Aligning subordinate actions to SBU goals
Strategic type
Please read the following descriptions of four types of firms. None
of the types is inherently “good” or “bad”. Using industry
competitors as a frame of reference, and considering your SBU
as a whole, which type best describes your SBU.
A.
-
B.
-
-
C.
-
-
Firm A maintains a “niche” within its market by offering a
relatively stable set of products/services.
Generally Firm A is not at the forefront of new
service/product market developments.
It tends to ignore changes that have no direct impact on
current areas of operation and concentrates instead on doing
the best job possible in its existing arena.
Firm B maintains a relatively stable base of
products/services while at the same time moving to meet
selected, promising new product/service market
developments.
The Firm is seldom “first in” with new products/services.
However, by carefully monitoring the actions of institutions
like Firm C (below), Firm B attempts to follow with a more
cost-efficient or well-conceived product/service.
Firm C makes relatively frequent changes (especially
additions to) its set of products/services.
It consistently attempts to pioneer by being “first in” in new
areas of market activity, even if not all of these efforts
ultimately prove to be highly successful.
Firm C responds rapidly to early signals of market needs or
opportunities.
30
D.
-
Firm D cannot be clearly characterised in terms of its
approach to changing its products/services or markets.
It doesn’t have a consistent pattern on this dimension.
Sometimes the Firm will be an early entrant into new fields of
opportunity, sometimes it will move into new fields only after
considerable evidence of potential success, sometimes it will
not make product/service or market changes unless forced
to by external changes.
31
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36
Table 1
Demographic data
Panel A: Firm size
Number of Employees
n
1-250
251-500
501-1000
1001-2500
2500+
184
116
54
32
14
Total
400
Panel B: Industry
Category
Agriculture, forestry, fishing
Mining
Construction
Manufacturing
Transportation, utilities
Wholesale
Retail
Finance, insurance, real estate
Services
Other
10
18
26
151
31
22
20
41
78
3
Total
400
37
Table 2
Descriptive statistics and bivariate correlations
Variable
Mean
Median
Std Dev
1
1. Structure
2. Diagnostic
3. Interactive
4. Tightness
5. Measurement diversity
6. Incentive based pay
7. Incentive determination
8. Social control
9. MCS effectiveness
4.12
5.51
4.78
4.32
4.26
4.56
4.63
4.57
24.27
4.20
5.60
4.90
4.25
4.33
4.67
5.00
4.75
24.20
0.95
0.83
1.03
0.98
1.06
1.42
1.75
1.03
7.61
0.07
0.21
–0.16
0.18
0.00
0.00
0.27
0.23
2
0.64
0.41
0.40
0.42
0.33
0.30
0.49
3
0.22
0.52
0.35
0.23
0.42
0.59
4
0.12
0.43
0.45
0.05
0.19
5
0.20
0.11
0.47
0.51
6
0.50
0.24
0.27
7
0.15
0.14
Correlations with an absolute value of 0.11 or higher are significant at p<0.05
38
8
0.44
Table 3
Results of fsQCA for high MC effectiveness for the defender and prospector groups
Defenders
Configuration
1a
Prospectors
1b
2a
2b
2c






Structure
Mechanistic  / Organic 


MACs
Diagnostic

Interactive

Loose  / Tight 







Narrow  / Broad 




Compensation

Incentive pay
Subjective  / Objective 





Cultural

Social control
Consistency
Raw coverage
Unique coverage
Overall solution consistency
Overall solution coverage
0.88
0.49
0.08
0.85
0.46
0.06
0.85
0.54
0.85
0.40
0.02

0.85
0.49
0.08

0.84
0.30
0.01
0.85
0.52
Black circles () refer the presence of a control attribute and circles with a cross () designate the
absence of an attribute. Core attributes are represented by large circles whereas small circles denote
a peripheral attribute. Blank spaces indicate that the attribute may be either present or absent.
39
Table 4
Results of fsQCA for high MC effectiveness for the analyzer group
Analyzers
Configuration
3a
3b
3c
3d


Diagnostic



Interactive





Tightness
Unbalanced  / Balanced 













Structure
Unbalanced  / Balanced 

MACs
Narrow  / Broad 


Compensation
Incentive pay
Incentive determination
Unbalanced  / Balanced 


Cultural
Social control
Consistency
Raw coverage
Unique coverage
Overall solution consistency
Overall solution coverage

0.87
0.37
0.01

0.88
0.41
0.04
0.89
0.26
0.01
0.88
0.26
0.01
0.87
0.42
Black circles () refer the presence of a control attribute and circles with a cross ()
designate the absence of an attribute. Core attributes are represented by large
circles whereas small circles denote a peripheral attribute. Blank spaces indicate
that the attribute may be either present or absent.
40