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, AndrewsR4@cardiff.ac.uk,+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.

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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.

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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

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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

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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

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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

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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

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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,

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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

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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

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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]

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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)

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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

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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

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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

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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