STRATEGIC FIT AND PERFORMANCE: A TEST OF THE MILESAND SNOW MODEL
Rhys Andrews1
Cardiff University (UK)
George A. BoyneCardiff University (UK)
Kenneth J. MeierTexas A&M University (USA) and Cardiff University (UK)
Laurence J. O’Toole, Jr.University of Georgia (USA)
Richard M. WalkerUniversity of Hong Kong (PRC) and Cardiff University (UK)
Manuscript prepared for the Proceedings of Organizational Strategy,Structure, and Process: A Reflection on the Research Perspective of Miles and Snow,conference co-sponsored by Cardiff University and the Economicand Social Research Council, Cardiff, Wales.
December 2008
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1corresponding author: Dr Rhys Andrews, Centre for Local andRegional Government Research, Cardiff Business School, CardiffUniversity, Colum Drive, CF10 3EU, [email protected],+44(0)2920 875056
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STRATEGIC FIT AND PERFORMANCE: A TEST OF THE MILESAND SNOW MODEL
Miles and Snow (1978) provide one of the most comprehensive
generic models of strategy in the field of management
research. They suggest that strategy’s impact on
organizational success will be greatest when external and
internal factors are in alignment – when, for instance,
managerial prospectors in decentralized organizations operate
with incremental processes in an uncertain environment.
Although many studies have included one or more of these sets
of variables, to date no study has remained true to Miles and
Snow’s contention that optimal performance is a complex
interaction of all these factors. This study examines the
interactions between strategy, structure, process and the
environment with an appropriate set of statistical tests in
over one hundred public organizations during a four-year
period. Although the separate effects of strategy on
performance are broadly consistent with the Miles and Snow
model, the predicted moderating effects of structure,
processes and environments are not present in this set of
organizations.
3
INTRODUCTION
The argument that organizational strategy needs to be aligned
with organizational characteristics and the external
environment in order to achieve better outcomes has a
venerable status in the management literature. Strategic
management frameworks are predicated upon the notion that when
correctly aligned with the environment, certain strategies,
structure and processes are likely to improve organizational
performance. Miles
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and Snow’s classic (1978) typology of strategy is widely
acknowledged to be one of the most influential generic
strategic management theories of the past three decades due to
its parsimony, industry-independence and correspondence to
real-world situations (DeSarbo et al., 2005; Hambrick, 2003).
They posit four basic managerial strategies – prospecting
(innovative and exploratory), defending (narrow and focused),
reacting (waiting for environmental cues), and analyzing (a
mix of prospecting and defending) – and trace out the expected
contingencies for the core strategies of prospecting and
defending. Strategies work best, they argue, when they are
aligned with structure, process, and the environment. Within
their theory, this notion means that prospecting should be the
most effective strategy for decentralized organizations using
incremental processes in a turbulent environment, and
defending should be the preferred approach for centralized
organizations with rational planning approaches in a placid
environment.
To date, no study has taken the Miles and Snow variables
and operationalized them in a model that can test whether all
the contingencies they posit in theory hold up in practice.
5
Researchers have investigated whether the effects of strategy
in private firms are moderated by the environment (Davies and
Walters 2004; James and Hatten 1994), organizational
structures (Jennings and Seaman, 1994), and processes (Slater
et al. 2006). None of these studies, however, has sought to
capture the effects of the multiple contingencies postulated
by Miles and Snow. This study takes a first step in that
direction and examines each of the four variables regarded by
contingency theorists as critical elements of strategic
management – strategy, structure, process and the environment
– to determine if the alignments sketched by Miles and Snow do
indeed increase performance.
In the first part of the paper we introduce the ideas of
Miles and Snow and trace the contingencies in their theory of
strategic management, deriving a series of testable hypotheses
from their arguments. In the second part of the paper we
outline our data and empirical methods, demonstrating that the
appropriate test of Miles and Snow’s full argument is a
complex set of interactions. Third, we test this model in a
large sample of over a hundred public organizations during a
6
four-year period. Finally, we discuss the theoretical and
empirical implications of our findings.
STRATEGY CONTENT AND ORGANIZATIONAL PERFORMANCE
Miles and Snow (1978) consolidated prior strategic management
research by developing a typology of strategy content that
contained four “ideal types.” Prospectors are organizations
that focus on innovation and explore new markets and services.
They are often pioneers and “first movers” in their industry.
Defenders are organizations that take a conservative view of new
product development. They typically compete on price and
quality rather than on new products or markets, and stick to
their core business with a focus on improving efficiency.
Analyzers represent an intermediate category, sharing elements of
both prospector and defender. Reactors are organizations in
which top managers frequently perceive change and uncertainty
in their organizational environments but typically lack an
actual strategy. A reactor waits for cues or instructions
from powerful stakeholders in its environment.
Walker and Ruekert (1987) argue that Miles and Snow’s
notion of a defender strategy is too broadly conceived. In
7
particular, to successfully realize the goals of efficiency
and quality a defender is likely to require potentially
incompatible organizational structures and processes. This
leads Walker and Ruekert (1987) to distinguish between those
defenders (low cost defenders) that emphasise lower costs by
enhancing the efficiency of their operations and those that
focus on differentiating themselves from their competitors by
delivering superior product and service quality (differentiated
defenders). We investigate the impact on performance of the
strategic fit of each of these two types of defender strategy
to reduce the potential ambiguity surrounding Miles and Snow’s
defender archetype.
Conant et al. (1990) criticize strategy content research
that assumes organizations have only a single strategic stance
which can be easily observed, for example, a prospector or
defender. DeSarbo et al’s (2005) empirical test of the Miles
and Snow model found evidence of hybrid strategic stances
within organizations suggesting that strategic choice is messy
and complex rather than neat and simple. It is therefore
inappropriate to assign organizations to a single strategy
archetype; strategy variables are continuous, rather than
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categorical. This notion also implies that Miles and Snow’s
“analyzer” category is redundant because it is “essentially an
intermediate type between the prospector strategy at one
extreme and the defender strategy at the other” (Walker &
Rueckert, 1987, p. 17). All organizations are likely to
prospect and defend to some extent (although the priority
attached to these stances will vary). Consequently,
“analyzing” is not treated as a discrete strategy in our test
of the Miles and Snow model.
This modified version of the Miles and Snow model has
been tested in a number of settings. Private sector studies
have frequently supported Miles and Snow’s (1978) contention
that prospectors and defenders outperform reactors (Conant,
Mokwa & Varadarajan, 1990; Hawes & Crittenden, 1984; Shortell
& Zajac, 1990; Woodside, Sullivan & Trappey 1990). Recent
studies in public organization settings have examined the
relationship between strategy and organizational performance
(Andrews et al, 2006, 2008, 2009). The empirical results
reveal a hierarchy of strategy types: the impact of
prospecting is positive, defending neutral, and reacting
negative. Thus, controlling for the presence of other
9
strategic stances in an organization, prospecting is the best
option and reacting is the worst. Results are, however,
equivocal. For example, Meier et al.’s (2007) study of Texas
school districts suggests prospecting is not always the most
successful strategy. For school districts seeking to achieve
high test scores, defending may be a more successful strategy.
Defenders trump prospectors in some circumstance and
prospectors beat defenders in others (Evans & Green, 2000;
Hambrick, 1983). In highly regulated industries Snow and
Hrebiniak (1980) show that reactors outperform prospectors and
defenders. Nonetheless, based on the balance of the evidence
available from the public and private sectors, we hypothesize
that:
H1 Prospecting and defending strategies are positively related to
organizational performance
H2 Prospectors outperform defenders and reactors
H3 A reactor strategy is negatively related to organizational performance
STRATEGIES AND ORGANIZATIONAL CHARACTERISTICS
Miles and Snow (1978) argue that, to successfully align their
internal characteristics and environment, organizations face
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not only an “entrepreneurial” problem (which strategy to
adopt), but also an “administrative” problem (the selection of
structures and processes that are consistent with the
strategy). Prospectors and defenders operate with distinctive
structures – at least if their alignment is in order – whereas
reactors, lacking a coherent and stable strategy, are unlikely
to have consistent internal arrangements.
For defenders, “the solution to the administrative
problem must provide management with the ability to control
all organizational operations centrally” (1978, p. 41). To
maximize the efficiency of internal procedures decision-making
is centralized since “only top-level executives have the
necessary information and the proper vantage point to control
operations that span several organizational subunits” (Miles &
Snow, 1978, p. 44). By contrast, the prospector’s
administrative system “must be able to deploy and coordinate
resources among many decentralized units and projects rather
than to plan and control the operations of the entire
organization centrally” (Miles & Snow, 1978, p. 59). A
prospector spreads power much more widely amongst middle
managers and front-line staff so that they can apply their
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“expertise in many areas without being unduly constrained by
management control” (Miles & Snow, 1978, p. 62). Walker and
Rueckert (1987) argue that managers in differentiated
defenders are also likely to be permitted greater decision-
making autonomy in order to carry out environmental scanning
(at least within their limited domain). Differentiated
defenders require higher levels of flexibility than low cost
defenders to successfully protect their differentiated market
position in response to changing customer preferences.
Reactors, unlike defenders or prospectors, have no predictable
organizational structure: some may be centralized while
others are decentralized. Reactors “do not possess a set of
mechanisms which allows them to respond consistently to their
environments” (Miles & Snow, 1978, p. 93).
Turning from organizational structures to processes,
Miles and Snow (1978) distinguish between the extent of planning
associated with different strategies. In a defender, “the
planning sequence proceeds through a series of steps which
allows the organization to exploit current and foreseeable
environmental conditions fully. These steps mainly involve the
setting of output and cost objectives which are then
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translated into specific operating goals and budgets” (Miles &
Snow, 1978, p. 43). Slater, Olson and Hult (2006) argue that
this is especially so for low cost defenders, as wide-ranging
evaluation of alternative strategic choices would be expensive
and inefficient. Differentiated defenders, however, seek to
combine a focus on formal plans and procedures for
consistently delivering high quality, with greater
consideration of different approaches to doing so. As a
result, they may be less likely to rely as heavily on formal
planning processes as low cost defenders.
Prospectors, by contrast, are poised to continuously
expand or contract their activities, depending on the
opportunities or threats that they face, so the planning cycle
is seldom systematic or complete. Rather, planning is fluid
and shifts with new organizational directions. In a
prospector, “organizational objectives are allowed to coalesce
around current areas of prospecting and thus seldom achieve a
stable equilibrium. Unlike the low cost defender (in
particular), whose planning process is usually finalised
before implementation begins, the prospector must often
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directly engage a new problem or opportunity before detailed
planning can be completed” (Miles & Snow, 1978, p. 61).
Thus, both defenders and prospectors plan, but the former
do so more formally and precisely, whereas the latter are
predicted to follow a more informal and iterative process akin
to “logical incrementalism” (Quinn, 1980). In stark contrast,
reactors are not expected to maintain a consistent or
discernible approach to planning. In a reactor “management
does not fully shape the organization’s structures and
processes to fit a chosen strategy” (Miles & Snow, 1978, p.
93). The absence of a clear strategic vision and the reliance
on external pressures to shape strategy therefore makes
planning difficult if not impossible for reactors. These
arguments on the relationships between strategies and
organizational characteristics lead to the following
hypotheses on internal “fit” and performance:
H4 Decentralization is positively related to the performance of prospectors,
and incremental strategy processes will enhance this relationship
H5 Centralization is positively related to the performance of defenders, and
formal strategy processes will enhance this relationship
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H6 Centralization is unrelated to the performance of reactors, and strategy
processes will not affect this relationship
STRATEGIES AND ENVIRONMENTAL UNCERTAINTY
Although strategy-environment fit is an important element in
the Miles and Snow model, it receives comparatively less
attention than the alignment of strategy with internal
structures and processes. Nevertheless, it is clear that
Miles and Snow believe that organizations have the discretion
to adopt the strategy that is best suited to the circumstances
that they face. They follow Burns and Stalker (1961) in
claiming that an organic structure is required in an uncertain
environment, whereas a mechanistic structure is better in a
predictable and stable environment. Thus, prospecting (and to
a lesser extent differentiated defending) should work best in
an uncertain environment. Low cost defending, however, should
be an especially effective strategy in the presence of
environmental certainty.
Miles and Snow’s arguments suggest that reacting will not
be consistently linked to any set of external circumstances,
including uncertainty. However, while reactors “do not possess
15
a set of mechanisms which allows them to respond consistently
to their environments over time” (Miles & Snow, 1978, 93), a
dynamic and unpredictable environment may lead such
organizations to seek cues from other external actors about
the best way to respond to these circumstances. These
arguments lead to the following hypotheses on strategies and
perceived environmental uncertainty:
H7 Perceived environmental uncertainty is positively related to the
performance of prospectors, and decentralization and/or
incremental strategy processes will enhance this relationship
H8 Perceived environmental certainty is positively related to the
performance of defenders, and centralization and/or formal
strategy processes will enhance this relationship
H9 Perceived environmental certainty is unrelated to the performance
of reactors, and organizational structure and processes will not
moderate this relationship
DATA AND METHODS
We test our hypotheses in public service organizations:
English local governments. Public organizations make an ideal
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setting to test Miles and Snow’s industry-independent model
for a number of reasons. First, strategy in public
organizations is likely to be more persistent due to the
absence of meaningful exit options from difficult markets.
Indeed, the links between strategy, structure, process,
environment and performance may be more stable in the public
than in the private sector. Second, in recent times English
local governments have been exhorted by regulatory agencies to
align their internal arrangements with their service
objectives (Walker & Boyne, 2006).
The panel data set for the analysis consists of a maximum
of 142 English single and upper-tier local governments (county
councils, London boroughs, metropolitan districts and unitary
authorities), and includes measures taken from a five year
period (2001 to 2005). The dependent variable is drawn from a
secondary dataset developed by the Audit Commission, one of
local government’s key stakeholders. Data to measure
strategy, structure, process and the environment are taken
from a multi-informant survey of public sector managers.
Controls are drawn from the 2001 Census and UK central
government departments.
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Local authorities are elected bodies, operate in specific
geographical localities, employ professional career staff,
receive around three-quarters of their income from central
government and account for around one-quarter of English GDP.
These governments are multipurpose organizations providing
education, social care, regulatory services (such as land use
planning and waste management), housing, welfare benefits,
leisure and cultural services. In urban areas, unitary
authorities deliver all of these services; in predominately
rural areas, a two-tier system prevails with county councils
administering education and social services, and district
councils providing welfare and regulatory services. In this
study we do not include district councils because our
dependent variable is available only for the major authorities
(unitary and country councils).
Dependent variable
In England, central government performance classifications are
important (though contestable) means for assessing the
achievements of local governments. The major external
assessment of English local government performance carried out
by central government inspectors is the yearly Comprehensive
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Performance Assessment (CPA) conducted by the Audit
Commission. For four years during the period covered by this
analysis (2002-2005), this classified the service performance
of single and upper-tier local governments by making
judgements about their achievements in six key service areas
(education, social care, environment, housing, libraries and
leisure and benefits) together with their broader “management
of resources” (Audit Commission, 2002; 2003; 2004; 2005).
The services are given a score from 1 (lowest) to 4
(highest), based on a mixture of performance indicators,
inspection results and service plans and standards. Each
service score is then weighted to reflect its relative
importance and budget (children and young people and adult
social care = 4; environment and housing = 2; libraries and
leisure, benefits and management of resources = 1) and summed
to provide an overall service performance judgement. These
range from 15 (12 for county councils which do not provide
housing or benefits) to 60 (48 for county councils). Because
these scores are not directly comparable across all types of
authority, each government’s core service performance score
(CSP) is taken as a percentage of the maximum possible score.
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Independent variables
Strategy, structure, process and environment
A range of items from the survey were used to measure
strategy, organizational structures, processes and
environmental uncertainty. Table 1 lists the items and
provides Cronbach alphas. These data were derived from an
electronic survey of managers in English local governments
carried out each summer from 2001 to 2004. Email addresses for
the survey were collected from participating authorities and
questionnaires were delivered as an Excel file attached to an
email. Multiple informant data were collected from different
tiers of management to ensure that the analysis took account
of different perceptions within the local governments. Senior
and middle managers were selected because research has shown
that attitudes differ between hierarchical levels within
organizations (Aiken & Hage, 1968; Walker & Enticott, 2004).
In each participating government, questionnaires were sent to
at least three senior and four middle managers. In 2001, the
total sample consisted of 121 single and upper tier
governments, with a 56 per cent (1259) informant response
rate. In 2002 and 2003, the total sample was 77, with response
20
rates of 65 per cent (922) and 56 per cent (790) respectively.
In 2004, the total sample was 136, with a response rate of 54
per cent (1052).
[Position of TABLE 1]
Some cases could not be matched when the survey variables
and performance measures were mapped, due to missing data
within the respective datasets. As a result, the statistical
analysis was conducted on an unbalanced panel of 119 local
governments in 2001, 76 in 2002, 73 in 2003 and 132 in 2004.
Nonetheless, these cases are representative of the diverse
operating environments faced by English local governments,
including urban, rural and socio-economically deprived areas.
A prospector strategy was operationalised through five
measures of innovation and market exploration, as these are
central to Miles and Snow’s (1978) definition of this
orientation. The specific measures (see Table 1) are derived
from Snow and Hrebiniak (1980) and Stevens and McGowan (1983).
To explore the extent to which organizations displayed
defender characteristics, informants were asked two questions
assessing whether their approach to service delivery was
focused on core activities and achieving efficiency (Miller,
21
1986; Snow & Hrebiniak, 1980; Stevens & McGowan, 1983).
Reactors are expected to lack a consistent strategy and to
await guidance on how to respond to environmental change. We
therefore asked informants three questions about the existence
of definite priorities in their service and the extent to
which their behaviour was determined by external pressures. We
again based these measures on prior work (Snow & Hrebiniak,
1980). The prospecting index has an excellent Cronbach’s Alpha
internal reliability scores of .83, while the reacting index
has a reasonable score of .66 (Nunnally, 1978).
The extent of decentralization in organizational
structures was measured using an item focusing on the
distribution of decision-making. Centralization has been
defined as “the extent to which the locus of authority to make
decisions affecting the organization is confined to the higher
levels of the hierarchy” (Child, 1972, p. 164). Our measure of
decentralization was therefore based on a question evaluating
the degree to which control was devolved to middle managers
within the sample organizations (Hart & Banbury, 1994). To
explore processes of strategy formulation, informants were
asked two questions on logical incrementalism, both of which
22
are drawn from prior studies (Bailey, Johnson & Daniels, 2000;
Hart & Banbury, 1994; Miller, 1986; 1987; Snow & Hrebiniak,
1980; Stevens & McGowan, 1983). The key characteristics of
logical incrementalism are the production of broad goals and
ongoing negotiations with key stakeholders. These concepts
were separately captured in the questions we posed on logical
incrementalism. The resulting incremental process index has an
accceptable Cronbach’s Alpha score of .61 (Loewenthal, 1996).
Our survey items measuring environmental uncertainty
focused on managerial perceptions of the uncertainty of the
socio-economic and external political circumstances faced by
their organizations. These aspects of the environment are
especially likely to be important to the strategic stance of
English local governments, as they are increasingly expected
to be responsible for community well-being and are subject to
considerable political pressures to improve their performance
and management from a host of external stakeholders. The
alpha for this item is low (.54). However, for scales with a
small number of items and for new scales a smaller alpha is
considered permissible (Nunnally, 1978).
Control variables
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Organizational size. To identify the separate effects of consensus,
environmental uncertainty and structure it is necessary to
control for the potential effects of size on performance.
Local governments serving big populations can accrue economies
of scale by distributing fixed costs over more units of output
(Boyne, 2003). The relative size of local governments was
measured using population figures for each local area from the
2001 census.
Internal controls. English local governments receive the majority of
their budgets from the public purse—roughly three-quarters
from central government and the remainder from locally levied
property taxation. Total budget for each local authority is
determined by a formula that compensates local governments for
high service needs and/or a low tax base. However, this
equalization applies only to a “standard” level of service.
Local governments may deviate from this figure because they
have a surplus (or shortage) of “discretionary resources”
bestowed by historically high (or low) spending. A measure of
the financial slack available to each local government was derived
by dividing its net service expenditure by its Standard
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Spending Assessment (an index of service needs used by central
government to distribute grant funding to councils).
STATISTICAL MODELLING
To address potential methodological problems associated with
the use of panel data, random effects estimations are
reported. This approach controls for the possibility that
error terms across panels are time correlated, and for the
error term for one organization to be correlated with
another’s in the same year (Beck & Katz, 1995). The inclusion
of dummy variables for each year of the survey further reduced
the possibility of within panel autocorrelation.
The results for the statistical tests of “strategic fit”
on performance are shown below in Tables 3-5. We present four
models in the following sequence: model 1 (presented in Table
3) contains all the control variables and the base strategy
and contingency terms. We then show in Table 4 three models
including, in turn, each of the possible two-way, three-way
and four-way contingency configurations. This modelling
process can be best represented in notation form.
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Organizational performance (O) is a function of strategy
(S) decentralization (D), incremental processes (P), and
environmental uncertainty (U), and a set of environmental
controls (aside from uncertainty) that encompasses both
resources and constraints (X):
O = ß1S + ß2D + ß3P + ß4U + ß5X + e [1], where e
is a error term.
Equation [1], however, is not a comprehensive test of Miles
and Snow. It can show whether strategy is related to
performance but not whether a given strategy is the optimal
one in the context of organizational structures, process and
environment. For the sake of simplicity, let us start with
the idea that strategy should be contingent on
decentralization, thus including an interaction term for
strategy and decentralization (S*D, or SD) in equation [2]:
O = ß1S + ß2D + ß3P + ß4U + ß5SD + ß6X + e [2]
Estimating this equation and then calculating the impact of
strategy at different levels of decentralization can then
answer the question of whether the effectiveness of strategy
is contingent on organizational structure. To illustrate, if
we measure strategy as being a prospector, then we would
26
expect a positive relationship between prospecting and
performance when decentralization is high. This reasoning
suggests that the coefficient ß5 should be positive (and that
the addition of the interaction term improves the fit of the
model over the same model without the interaction term).
Equation 2, however, contains the interaction of only two
variables, strategy and decentralization. As more
contingencies are added, so the models become more complex.
To illustrate, let us set up the model for strategy as
contingent on both decentralization and incremental processes,
while ignoring for the moment contingencies related to
environmental uncertainty. Initially, one might start out
with the only slightly more complex model in equation [3] that
adds the strategy and process interaction (SP):
O = ß1S + ß2D + ß3P + ß4U + ß5SD +ß6SP + ß7X + e [3]
This model, however, does not allow for the possibility that
decentralization and incremental processes together have more
influence on the context of strategy than they do separately.
A more complete model requires a three-way interaction of
strategy, structure, and process:
O = ß1S + ß2D + ß3P + ß4U + ß5SD +ß6SP + ß7SDP + ß8X + e [4]
27
At this point, and even though we have yet to add
contingencies involving environmental uncertainty, it is
clear that interpreting the models becomes more complex.
To illustrate, in equation [4] the slope for strategy
becomes the following:
ß1 + ß5D +ß6P + ß7DP [5]
In other words, the impact of strategy on the performance of
the organization is a function of the value of
decentralization, the value for process, the product of the
values of decentralization and process, and a constant. This
complex relationship can be illustrated either via graphs or
with the use of representative values of the variables in
question. If the variables are coded in a consistent
direction (that is, strategy is a measure of prospecting,
decentralization takes on higher numbers as the organization
decentralizes, and incremental process takes on higher numbers
as the strategy process gets more incremental), then the sign
for the three-way interaction (ß7 in equation 4) must be
positive to be consistent with Miles and Snow’s hypotheses.
The models needed for assessing all four aspects of Miles
and Snow (strategy, structure, process, and the environment)
28
are even more complex. Since the theoretical formulation
offered by Miles and Snow is focused on strategy and its
degree of alignment with the three other variables, the full
model representing the theory requires interaction terms for
strategy with each of structure, process and the environment;
plus the full set of the more complex interactions with
strategy. Therefore, in addition to a four-way interaction
term, the complete model would also include three three-way
interaction terms so that all possibilities would be covered.
O = ß1S + ß2D + ß3P + ß4U + ß5SD +ß6SP + ß7SU + ß8SDP + ß9SDU
+
ß10SPU + ß11SDPU + ß12X + e [6]
STATISTICAL RESULTS
Table 2 presents the descriptive statistics for the
dependent and independent variables used to test the Miles and
Snow model.1 The figures in Table 2 show that local
governments were more likely to be low cost defending (mean
agreement = 5.12) and differentiated defending (4.75) than
prospecting (4.71), and least likely to identify themselves
with a reactor strategy (4.10). On average, respondents agreed
29
strongly with the survey statement on decentralization
(“control is devolved to service managers”). The figures also
show that respondents strongly identified their formulation
process with logical incremental procedures. By contrast, the
perceived environmental uncertainty scale is a little below
the midpoint of the item scale. Thus, managers in English
local government appear to believe that their organizations
have decentralized structures, an array of strategy processes
and comparatively low levels of environmental uncertainty, but
nevertheless are most likely to be low cost defenders.
[Position of TABLE 2]
The base model shown in Table 32 provides some support for
Miles and Snow’s hypotheses on the performance implications of
strategic stance. The coefficient for prospecting is positive
and statistically significant, while that for reacting is
negative and significant. Contrary to expectations, however,
the coefficients for low cost and differentiated defending are
statistically insignificant. Thus, prospecting organizations
appear to be reaping the benefits of more innovative
approaches to delivering services, while defending
organizations, at least for this set of organizations, are
30
gaining no tangible benefits from their inward focus on
enhancing service efficiency or quality. Only one of the
contingency variables is significant in this model: the
coefficient for logical incrementalism is positive at p.05. In
addition, the model shown in Table 3 suggests that
organizations spending within their service needs perform
better than their more profligate counterparts: the
coefficient for financial slack is negative and statistically
significant.
[Position of TABLE 3]
Joint f-tests revealed that the inclusion of the two-way
interactions made a (small) statistically significant
improvement to the explanatory power of the base model. This
finding highlights that it may be important to consider the
combined effects of two-way interactions between strategy and
each of structure, process and environment when investigating
the influence of strategic management on organizational
performance. However, the results for the two-way model shown
in Table 4 do not provide support for our hypotheses that
organizations achieving “fit” between two elements of the
Miles and Snow model will have better performance. There are
31
only three statistically significant coefficients, two of
which contradict rather than confirm elements of the Miles and
Snow model. For example, the interaction between prospecting
and decentralization is significantly negative rather than
positive, while that for reacting and environmental
uncertainty unexpectedly achieves statistical significance (in
a negative direction). Although the coefficient for differentiated
defending x decentralization provides a modicum of support for the
notion that differentiated defenders may require a similar
solution to the “administrative problem” as prospectors, this
single confirming finding represents a poor return on our
initial hypotheses. Indeed, nine out of the twelve coefficient
estimates are not statistically significant, indicating that
our results for the two-way interactions may be little more
than might be expected to occur at random.
The joint f-tests for the inclusion of the three-way and four-
way interactions indicate that they fail to make a
statistically significant contribution to the explanatory
power of the base model. Moreover, none of the twenty-four
interaction coefficient estimates in the three-way model or
the twenty-eight interaction coefficient estimates in the
32
four-way model achieve statistical significance. Thus, we find
that although our modelling process now captures more elements
of the Miles and Snow model, this has not led to a
corresponding increase in explanatory power.
[Position of TABLE 4]
Although we are unable to fully explore each of the
potential explanations for our statistical results using the
current data set, we nevertheless repeated our statistical
modelling using slightly different specifications to test the
robustness of our existing findings. First, we exclude all the
reacting interactions from the two, three and four way
interaction models. Since reactors are not expected to display
a consistent pattern of strategic fit, we expected that their
exclusion from the regression models would have little direct
effect on the findings in our fully specified model. Second,
we re-ran the two-, three- and four-way interaction models
including only the interactions for a single strategy
archetype (i.e. prospecting interactions only, low cost
defending interactions only, and differentiated defending
interactions only). This step enables us to isolate whether
simultaneously modelling different strategies and their
33
associated patterns of fit biased the coefficient estimates in
some way. The results of f-tests to determine whether
inclusion of the two, three and four-way interactions
influenced the explanatory power of these more parsimonious
model specifications are shown in Table 5.
[Position of TABLE 5]
The findings summarised in Table 5 indicate that excluding the
reacting interactions from our fully specified model made no
statistically significant difference to the base regression
model. When we enter the interactions for each strategy
archetype separately we find that only the addition of the
three-way interaction terms for low cost defending and
differentiated defending improved the explanatory power of the
base model. The pattern of the coefficients for both models
may signify that the inward focus of defenders can lead to
better performance when a tight fit is achieved between all
the internal characteristics of an organization. The two-way
interactions between (both types of) defending x decentralization and
defending x incremental process are significant and positive while
the three-way interaction between defending x decentralization x
incremental process is significantly negative in each case. Thus,
34
defenders that have centralized decision-making and formal
strategy processes appear to be achieving better performance.
Nevertheless, it must be emphasised that we find only two
statistically significant specifications out of a possible
nine.
CONCLUSION
This research confirms that the organizational strategies in
the Miles and Snow model have clear consequences for service
performance in the public sector. The results show that, when
we control for structure, process, environment and other
relevant variables, particular strategic stances have
important consequences for performance. However, our study
fails to provide confirmation of the contingency arguments
made by Miles and Snow. For example, prospectors are unlikely
to achieve additional performance improvement by
decentralizing or adopting incremental processes, even when
operating in an uncertain environment. In fact, it seems that
it does not make any difference how prospectors resolve their
administrative problem. The findings therefore suggest that
strategy matters, but that neither structures, processes nor
35
environment moderate its effects on organizational performance
to a meaningful degree. Indeed, the key lesson to be drawn
from our extensive modelling procedure is that while strategy
matters, it does so in isolation from other important
organizational characteristics. Whether prospectors
decentralize, adopt incremental strategy processes or seek to
do these things only when confronting an uncertain environment
seems to make little difference to organizational outcomes.
These findings have important theoretical and practical
implications.
Prospecting is associated with higher levels of
performance, while reacting is associated with lower levels of
performance. This finding reflects the chief proposition of
Miles and Snow (1978) that prospectors would outperform all
other strategic archetypes and reactors would do worst. These
results have also been found in private sector studies (Zahra
and Pearce 1990). While defending was not associated with
better performance, neither was it associated with lower
service achievements. Evidence to date suggesting that
defending has a neutral impact on organizational performance
has come from related areas of the UK public sector (Andrews
36
et al., 2006, 2008, 2009), but is not a consistent finding
across public agencies (Meier et al. 2007).
The non-significant results for our interaction models
are not as hypothesised. As we noted early, other studies have
only provided partial evidence on the array of contingency
effects specified by Miles and Snow. It is theoretically
possible that strategic fit (or misfit) simply has no
meaningful influence on performance. While our results could
be used to mount such an argument we prefer to point towards a
number of alternative explanations of our findings.
First, it is possible that the current formulation of the
model cannot handle hybrid strategies. As such it is
conceivable that the results provide evidence on the wider
implications of the argument that organizations pursue
multiple strategies. According to Boyne and Walker (2004),
Conant et al. (1990) and DeSarbo et al. (2005), organizations
vary in the relative priority that they accord to prospecting,
defending and reacting, because these are dimensional
constructs, not categorical. This may mean that attempting to
solve the administrative problem by pursuing a high degree of
fit between one strategy archetype and its “ideal” structure,
37
process and environment has negative consequences for
organizations wishing to simultaneously prioritise other
strategies. In an organization that simultaneously pursues
multiple strategies, better fit for one must entail poorer fit
for the others. Indeed, organizations are unlikely to be able
to adapt to alternative strategic choices by continuously
altering their core organizational characteristics in this
way. And they may have good reason for not doing so—Meier and
O’Toole (2008) show how stability is a powerful resource for
Texas school districts seeking to buffer themselves from
environmental circumstances. Indeed, the benefits of a
comparatively stable administrative system may be especially
great within the public sector, where organizations are often
required by legal statute to provide certain core services to
citizens.
This lead to our second point—perhaps industry effects
influence strategic choices and, in turn, fit. There have
been ongoing concerns that strategic management is not
applicable to public organizations because of the distinctive
political context (see Ring and Perry, 1985). Some public
agencies are likely to be highly constrained by political
38
oversight bodies, which may severely restrict their strategic
choices. However, many public organizations have substantial
autonomy, and theories of strategic management are clearly
applicable to them. As we noted earlier, public agencies in
many places are encouraged to align their internal processes
to their external circumstances. Indeed this, and our prior
research, demonstrates that public agencies take strategy
process and strategy content choices and these have
consequences for organizational performance. However, we
speculate that the rub may not be in the strategic management
decisions made by public service bodies, but in their internal
operations and their external circumstances. The environments
for public agencies can change rapidly (see Andrews et al.,
2009), however many changes are longer-term. It is also
argued that when reform is required to public agencies, it is
the “chairs on the deck” that are altered rather than more
fundamental issues such as strategy, goals and markets. One
interpretation of this point is that it is perhaps too easy to
make internal changes to public agencies, and that perhaps
these adjustments do not always align with the external
39
environment. A substantial research effort across multiple
industries would be require to validate this argument.
Third, the results may be a product of the organizational
level at which our analysis is conducted. The Miles and Snow
model is arguably applicable primarily to the behaviour of
Strategic Business Units (SBUs) (see DeSarbo et al. 2008). Our
study amalgamates data on the strategy, structure, process and
environment across the SBUs of a set of large multipurpose
public organizations. It is therefore possible that we have
been unable to capture all of the most relevant aspects of
strategic alignment within our data sample. Empirical
investigation of strategic fit and performance at the SBU
level within our data set could throw further light on this
issue. Future studies should address all of these themes in
more detail.
Clearly the limitations of our data, measures and samples
influence our results; and we are cognisant of their effects.
We have already noted the external validity question when
exploring industry effects. While we are able to benefit in
this investigation from a panel that employed lagged models,
it may be that patterns of fit emerge over longer time
40
periods. Our measures have limitations: a number are single
items, and where possible these should be replaced by indexes.
Where we do use an index, the newest of these indexes is
clearly problematic and wider validation and tests of
reliability are required. Finally, we are able to draw upon
an externally validated measure of organizational performance.
However, the CSP offers both strengths and weaknesses. By
measuring aggregate organizational performance, we cannot
examine nuanced relationships between fit and particular
dimensions of performance. Future research should, ideally,
attempt to fix all these issues.
The evidence in this paper leads us to conclude that the
Miles and Snow model is useful for conceptualising and
exploring the impact of strategic fit on organizational
performance, but that its formal requirements may place unduly
restricting demands on the strategic behaviour of
organizations. By considering the interactive effects of
strategy, structure, process and environment in one model we
have provided a more comprehensive assessment of the Miles and
Snow model than has been previously reported. However,
additional research is required to establish whether what we
41
find is merely an artefact of when and where our study was
conducted or if the presence of multiple (or hybrid)
strategies proscribes the successful application of this
contingency model.
Footnotes
1 Before running the statistical models, skewness tests were
carried out to establish whether each independent variable was
distributed normally. A high skew test result was found for
population (1.92). To correct for positive skew, a logged
version of population was created. To correct for negative
skew (-4.41), the financial slack measure was squared.
2 White’s (1980) test revealed that the data were
homoscedastic, so ordinary estimation of the regression
standard errors was used. The average Variance Inflation
Factor score for all the independent variables used in the
base model is about 1.9, with no single variable exceeding 5,
indicating that the results are not likely to be distorted by
multicollinearity (Bowerman & O’Connell, 1990). Inevitably,
though, the level of collinearity increases considerably when
interacted variables are added to the base model.
42
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49
Table 1 Variable items
Organizational characteristic
Item description
Prospecting (α = .83)
The authority/service is prepared to take risks where appropriateThe authority/service is at the forefront of innovative approachesProviding new services to new users is a major part of our approach to service deliveryProviding new services to existing users is a major part of our approach to service deliveryProviding existing services to new users is a major part of our approach to service delivery
Low cost defending Reducing the costs of service delivery was a majorpart of the approach to service delivery adopted in our service area
Differentiated defending
Focusing on key business areas is a major part of our approach to service delivery
Reacting (α = .66)
Pressures from auditors and inspectors were important in driving performance improvement in our serviceExternal pressure (e.g. the media) was important in driving performance improvementActivities of other authorities were important in driving performance improvement
Decentralization Control is devolved to service managers
Incrementalism(α = .61)
When we make strategy we produce broad goals and objectivesStrategy is made in consultation with our externalstakeholders
Perceived environmental uncertainty (PEU) (α = .54)
The socio-economic context the service operates inhas become more uncertain during the last financial year The external political context the service operates in has become more uncertain during the last financial year
50
Table 2 Descriptive statistics (2001-2004)
Mean Min Max s.d.PerformanceCSP 68.26 36.67 90.00 9.00StrategyProspecting 4.71 3.10 6.40 0.58Low cost defending 5.12 2.00 7.00 0.70Differentiated defending
4.75 1.75 7.00 0.79
Reacting 4.10 1.83 5.70 0.60Contingency variablesDecentralization 5.13 3.00 6.67 0.64Incrementalism 5.44 4.00 6.55 0.44Uncertainty 3.34 1.44 5.25 0.68Control variablesPopulation 348032 36700 1361300 258708Slack 1.15 0.05 1.38 0.09
51
Table 3 Strategy, structure, process, environment and performance
Independent variables Slope z-score
Constant 56.18 3.85**StrategyProspecting 2.45 3.04**Low cost defending -.40 -.75Differentiated defending -.23 -.47Reacting -1.73 -2.72**Contingency variablesDecentralization .77 1.25Incremental process 2.08 2.29*Uncertainty -.44 -.81Control variablesPopulation (log) .18 .17Slack2 -4.43 -1.90+2001 -.52 -.512002 .31 .362003 4.19 4.87**
F/Wald statistic 96.51**Within R2 .22Between .17Overall R2 .22N = 396
Note: significance levels: +p ≤ 0.10; *p ≤ 0.05; **p ≤ 0.01(two-tailed test).
52
Table 4 Strategic fit and performance
Independent variables Two-way Three-way Four-way
Slope z-score
Slope z-score
Slope z-score
Constant -45.02 -1.00 -23.49 -.46 -42.17 -.79Prospecting (P) 17.23 2.55* 15.72 .28 -
148.98-.76
Low cost defending (CD)
-.29 -.05 -29.44 -.61 -149.17
-.68
Differentiated defending (DD)
-4.53 -.80 -10.31 -.22 134.45 .75
Reacting 12.16 1.73+ 36.90 .74 224.33 1.17Decentralization (D) 8.41 1.32 3.28 .44 6.97 .86Incremental process (I)
2.94 2.21* 2.61 1.89+ 2.71 1.95*
Uncertainty (U) 16.91 2.66** 17.67 2.52* 17.12 2.42*Interaction termsP x D -3.01 -
3.01**-2.79 -.36 30.87 .84
P x I 1.00 .78 -1.46 -.12 13.93 .81P x U -1.29 -1.46 -.07 -.01 40.64 .74CD x D .24 .33 5.92 .75 27.91 .68CD x I .37 .35 4.44 .45 28.32 .65CD x U -.86 -1.29 2.55 .29 37.94 .58DD x D 1.27 1.76+ 2.23 .30 -25.46 -.75DD x I -.49 -.44 1.16 .12 -27.67 -.78DD x U .09 .16 .09 .01 -36.73 -.71R x D -.28 -.32 -4.44 -.55 -41.84 -1.10R x I -1.33 -1.06 -2.85 -.29 -40.39 -1.06R x U -1.76 -2.24* -7.45 -.78 -58.31 -1.07P x D x I .52 .34 -6.28 -.85P x D x U -.56 -.63 -9.13 -.88P x I x U .30 .15 -7.84 -.71CD x D x I -.76 -.50 -5.14 -.64CD x D x U -.52 -.64 -7.02 -.57CD x I x U -.19 -.11 -7.23 -.55DD x D x I -.28 -.20 5.21 .78DD x D x U .15 .21 7.28 .73DD x I x U -.16 -.12 7.20 .70R x D x I .12 .08 7.59 1.01R x D x U 1.04 1.13 11.29 1.04R x I x U .14 .07 10.40 .96P x D x I x U 1.71 .83CD x D x I x U 1.29 .53DD x D x I x U -1.42 -.72R x D x I x U -2.06 -.96
53
F/Wald statistic 119.25**
126.23**
127.55**
Within R2 .28 .30 .30Between .15 .19 .20Overall R2 .24 .25 .26F-test (sig. level) .06+ .73 .69N = 396
Note: significance levels: +p ≤ 0.10; *p ≤ 0.05; **p ≤ 0.01 (two-tailedtest). Control variables not reported.
54
Table 5 F-tests for alternative models
2-way 3-way 4-way
Excluding reacting interactions
N of interaction terms = 9p.14
N of interactions terms = 18p.64
N of interactions terms = 21p.79
Prospecting interactionsonly
N of interaction terms = 3p.09
N of interaction terms = 6p.27
N of interaction terms = 7p.29
Low cost defending interactionsonly
N of interaction terms = 3p.26
N of interaction terms = 6p.01
N of interaction terms = 7p.34
Differentiated defendinginteractionsonly
N of interaction terms = 3p.46
N of interaction terms = 6p.01
N of interaction terms = 7p.44
Note: p values indicate significance level of f-tests. T-scores for significant interaction terms in significantly improved models also shown.
55
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