INTRODUCTION

Business strategy has been discussed from many differing perspectives.^{1, 2, 3} A common and useful conceptualisation put forward by Miles and Snow⁴ focuses on a firm's strategic environmental adaptation or aggressiveness towards the market. Much research over the years has investigated differences among the four strategic types regarding a variety of internal factors, including innovation, management characteristics, organisational performance, and organisational design. The outlined Defender, Analyser, Reactor, and Prospector firms are suggested to be distinct in their actions, with each strategic group enacting consistent decisions and activities across a variety of organisational areas.^{5, 6, 7, 8, 9}

Recent studies have supported and extended the findings of earlier works regarding Miles and Snow's typology: the strategic groups are found to differ among each other on a variety of factors, including implementation and usage of market research, organisational performance, and environmental perceptions.^{10, 11, 12, 13} In particular, one main proposal of Miles and Snow⁴ is that the four strategic types vary according to their efforts at innovation and related activities, with Prospectors the most aggressive followed in order by Analysers, Defenders, and Reactors.^{6, 14} The level of innovation, however, does not always translate into profitability or better performance, due to cost factors or market factors. In order to improve the chances of success, a company might match its strategy with an appropriate structural form to achieve higher performance.

The purpose of this study is to determine which type of organisational structure is implemented by the four strategic orientations. Also, of interest is whether these combinations of strategy and structure influence performance or, rather, the main effects of strategy and structure are the drivers of performance. The author uses a sample of managers from credit unions to investigate these relationships.

THE MILES AND SNOW STRATEGIC ORIENTATIONS

Miles and Snow introduced a typology in 1978 that purported to classify the strategic orientation of firms based on specific strategic actions. Firms within any industry could be classified into one of four groups as Prospectors, Defenders, Analysers, or Reactors. Firms are classified into the first three strategic types if they have a formal and implied strategic orientation and as Reactors if they do not have a formalised strategic orientation. Zahra and Pearce¹⁵ summarise the four types: 'It is sufficient here to observe that Defenders emphasize a narrow domain by controlling secure (and often premium) niches in their industries. They engage in little or no product/market development and stress efficiency of operations. Prospectors constitute the other end of the continuum, constantly seeking new opportunities and initiating product development. Analysers exhibit characteristics of both Defenders and Prospectors. Finally, Reactors do not follow a conscious or consistent strategy and are viewed as a dysfunctional organizational type'. Most of these ideas are held up in research into a variety of industries over the years.

Regarding performance of the strategies, Miles and Snow⁴ originally proposed that Reactors will be the lesser performers, while the other three types could perform equally well, depending on the situation and the industry. In most cases, empirical evidence supports these proposals.^{16, 17, 18} Somewhat contradictory results are found in a few instances regarding the performance of each strategy type: such as no impact on performance or the presence of successful Reactor firms.^{19, 20, 21, 22, 23, 24} Plus, it is not uncommon for the ordering on performance to differ from the suggested Prospector, Analyser, Defender, then Reactor.^{5, 25, 26, 27} These variations can oftentimes be explained by abnormalities of a given industry or possibly by the varying methods used to classify the companies.¹⁵ Given the generally consistent findings over time, it, however, might be expected that one of the Prospector, Analyser, or Defender strategies will be the most successful and that Reactor firms will underperform the other strategies.

STRUCTURAL FORM

Numerous structural characteristics are common in the literature. Four major structural dimensions are, however, prevalent: (1) formalisation, (2) integration, (3) centralisation and (4) complexity.^{2, 28, 29, 30, 31, 32, 33, 34, 35} Centralisation refers to the degree to which the right to make decisions and control activities is concentrated.³³ In other words, a high degree of centralisation within an organisation means that the critical decisions are made at the top management level. Formalisation can be defined as the extent to which an organisation uses rules and procedures to prescribe behaviour such as the details on how, where, and by whom tasks are to be performed.³³ Formalisation restricts the activities of employees to those proscribed in advance. Complexity describes the many, usually interrelated, parts of an organisation.³³ This can refer to the number of hierarchical levels, the span of control, or the geographical dispersion of operating sites, among others. Structural integration refers to the coordination of activities among the different specialisations within the firm.² Highly integrated firms allow contacts between the experts within each department and also with the top level decision-makers.

The relationship between organisational structure and performance can best be summarised as inconsistent, since the relationships between key structural dimensions and performance are not strongly supported.³¹ Centralisation seems to have a negative association to performance at times, and a positive association at other times.³¹ The findings on the associations of both complexity to performance and formalisation to performance do not offer a consistent pattern, oftentimes being positive and other times being negative.^{31, 35} Finally, concerning integration and performance, the directionality is also in question.^{31, 35, 36}

Although the inconsistency of the impact of structural dimensions on performance is known, it is, however, widely accepted that specific structural forms do indeed influence performance in some way.³⁵ In particular, it may be that the 'fit' between organisational structure and organisational strategy is the key criteria in a given situation.^{37, 38} Miller³⁵ finds that integration and formalisation are relevant to performance for specific strategic types. Therefore, it may be that certain structural dimensions must be present with given strategies in order for the firm to achieve high performance levels.³⁹ This would suggest that a 'fit' or, alternatively, an interaction between strategy and structure is relevant to performance.

Perhaps, the driver of performance is not the structural dimensions independently, but rather the combination of structural dimensions: referred to as structural configuration. Porter¹ claims that organisations require a high degree in all of the structural dimensions in order to implement generic strategies. Thus, the use of consistent structural configurations may lead to better performance. Mahajan and Vakharia⁴⁰ support this empirically in a dynamic environment, where higher performing firms are found to have constant or similar levels across all or most of the structural characteristics. This might suggest that it is the structural configuration that has an impact on performance, with firms choosing between a low-structure or a high-structure or even a mixed-structure configuration.

INDUSTRY/SAMPLE DESCRIPTION

A sample of executives from credit unions is taken in the financial services industry. Data for the study are gathered from a statewide survey in Florida of all the credit unions belonging to the Florida Credit Union League (FCUL). Membership in the FCUL represents nearly 90 per cent of all Florida credit unions and includes 325 firms. A single mailing was directed to the president of each credit union, all of whom were asked by mail in advance to participate. A four-page questionnaire and a cover letter, using a summary report as inducement, were included in each mailing. Of those responding, 92 per cent were presidents and 8 per cent were marketing directors. This approach yielded 125 usable surveys, a 38.5 per cent response rate. A chi-squared test of the respondents versus the sampling frame indicates that the responding credit unions are significantly different from the membership firms based on asset size (chi-sq=20.73, d.f.=7, p<.01). Further analysis of the sample indicates that the smaller asset groups are under-represented.

MEASURES

The Miles and Snow strategy typology (STRATOR) is measured using self-classification, as is common in the literature.¹⁶ The respondents are asked to check the box which best describes their firm's strategy. They could choose from four descriptions that follow. One hundred and nineteen respondents answered the question with Defenders being 38 per cent (45/119), Prospectors being 5 per cent (6/119), Analysers being 44 per cent (53/119), and Reactors being 13 per cent (15/119).

Defenders: We attempt to locate and maintain a secure niche in a relatively stable market environment. We try to protect our markets by offering high-quality, well-targeted services. We are not at the forefront of industry developments.

Prospectors: We typically concentrate on many diverse markets, which we periodically help to redefine. We value being first-in with new services and in new markets even when these efforts are not highly profitable initially. We respond rapidly to most new opportunities.

Analysers: We attempt to maintain a stable and secure position in the market while at the same time moving quickly to follow new developments in our industry. We are seldom first-in with new services or in new markets, but are often second-in with better offerings.

Reactors: We appear to have an inconsistent approach to our markets and services and are often indecisive. We are not aggressive in attacking new opportunities, nor do we act aggressively to defend our current markets. Rather, we take action when we are forced to by outside forces such as the economy, competitors, or market pressures.

The firms' structural characteristics are measured using a 12-item scale, ranging from (5) true to (1) not true where respondents are asked to circle the number which best describes their firm. From these 12 items the three structural configurations used in this study will be derived, as follows: (i) High-Structure, (ii) Low-Structure, and (iii) Mixed-Structure. The 12 structure variables are subjected to a factor analysis using principal factors followed by a varimax rotation. Two of the 12 items were eliminated due to inconsistent loading. This analysis resulted in three dimensions that explain 61.9 per cent of the original variance: (1) formalisation — four items, (2) integration — three items, and (3) centralisation and complexity combined — four items. The remaining ten measurement items are as follows: (F1) decision making is highly controlled, (F2) most jobs are highly specialised, (F3) decision making follows formalised procedures, (CC1) the proportion of administrative personnel is high, (CC2) services production is highly mechanised, (CC3) there are many levels of management, (CC4) the ratio of clerical to all personnel is high, (I1) committees are used extensively in decision making, (I2) divergent views are reconciled systematically through discussions, and (I3) task forces are used to assess uncertain decisions. Summated scales are used for each of the three components to derive overall indicators of the structural dimensions themselves. Reliability, as measured by coefficient alpha is as follows: 0.791 for formalisation, 0.696 for centrality/complexity, and 0.642 for integration.

In order to derive the three structural configurations (Low, Mixed, High), first a median split is used to divide each of the individual structural dimensions into high or low categories. Thus, each of the 119 usable respondent firms is now classified as having either high or low levels of formalisation, high or low levels of integration, and high or low levels of centralisation/complexity. The structural configuration indicator (STRUCFN) is then derived in the following manner. Firms which exhibit high levels across all of the structural dimensions are categorised as High-Structure (26 per cent, n=31). Firms which exhibit low levels across all of the structural dimensions are categorised as Low-Structure (15 per cent, n=18). Firms which exhibit inconsistent levels across the structural dimensions are categorised as Mixed-Structure (59 per cent, n=70).

Regarding firm performance, both market share and profitability indicators are included in the study. Both perceptual and accounting variables are included in the study, which should alleviate some of the problems associated with each type of measure.^{41, 42, 43, 44} It is possible that objective measures may lead to different results than perceptual measures.⁴⁵ Also, market share and profits are two distinct goals, each with their own demands on the firm. The inclusion of both objectives in the study should greatly add to the findings, especially since different strategies may affect share but not profits, or vice versa.⁴⁵

Market Share (PERFMS) is a perceptual indicator measured using a five-item scale, ranging from (1) poor to (5) excellent, as regards five baselines of market share: (1) vs competitors, (2) vs goals/expectations, (3) vs previous years, (4) vs firm potential, and (5) growth. A principle axis factor analysis indicates that the five items load highly on a single dimension explaining 66.4 per cent of the original variance. The overall indicator of market share performance, PERFMS, is constructed by summing the five. PERFMS is the market share indicator used in the analysis. A reliability of 0.872 is found using coefficient alpha. PERFMS ranges from five to 25 with a mean of 14.64 and a standard deviation of 3.56.

Overall Profitability (PROFCMB) includes a perceptual indicator (PROFP) gathered from the respondents, as well as two variables derived from government-mandated accounting reports (ROA, ROI). The ROA variable has a range from 0 to 5 per cent, a mean of 2.20 per cent, and a standard deviation of 0.98. The ROI variable has a range from 1 to 17 per cent, a mean of 7.77 per cent, and a standard deviation of 2.26.

The perceptual indicator of profits (PROFP) is derived from five questions. In particular, respondents are asked about their profit performance on a scale from (1) poor to (5) excellent, relative to five profitability baselines: (1) vs competitors, (2) vs goals/expectations, (3) vs previous years, (4) vs firm potential, and (5) growth. A principle axis factor analysis indicates that the five items load highly on a single dimension explaining 66.1 per cent of the original variance. An overall indicator of PROFP is constructed by summing the five. A reliability of 0.870 is found using coefficient alpha. PROFP ranges from five to 25 with a mean of 16.06 and a standard deviation of 4.35.

The three profitability indicators (ROI, ROA, PROFP) are then factor analysed using principle axis factoring to determine if there is evidence for an overall construct. The factor analysis results in a single dimension that explains 45.1 per cent of the total variance in the three profit items. The overall indicator of profit performance (PROFCMB) is derived using factor scores for the three items. PROFCMB ranges from -4.43 to 6.80 with a mean of -0.06 and a standard deviation of 2.28. PROFCMB is the profitability dependent indicator used in the analysis. It is noteworthy that the perceptual profit component (PROFP) is negatively related to PROFCMB while the two objective components are positively related to PROFCMB.

Two control variables are included in the analyses. The first, Environmental Dynamism (DYNA), is included as a proxy for external influences on the firm and its performance. This study uses the perceived level of environmental dynamism, described as the amount of change occurring in an industry environment.^{35, 46} The respondents are asked to evaluate their perceptions of the environment on a bipolar scale from (1) to (5) across three items: (1) stable/unstable, (2) variable/not variable, and (3) volatile/not volatile. The factor analysis indicates that the three items load highly on a single dimension explaining 57 per cent of the original variance in the three items. An overall indicator of dynamism (DYNA) is constructed by summing the three items. A reliability of 0.639 is found using coefficient alpha. DYNA ranges from three to 15 with a mean of 7.35 and a standard deviation of 2.43. The second control, asset size (SIZE), is included as a proxy for organisational characteristics.⁴⁷ Firms are self-classified by marking the box next to the appropriate asset size category and then re-classified into large vs small firms by median split. Firms with asset holdings up to &10 million are considered small credit unions, while those with holdings greater than $10 million are considered to be large in size. This produces 59 small credit unions and 65 large credit unions.

ANALYSIS/RESULTS

To investigate which of the strategic configurations is implemented by the strategic types, a cross-tabulation is performed. This is shown in Table 1. The chi-square test statistic reveals a significant relationship between strategy and structure (p=0.011). A closer investigation of the numbers indicates that most firms have a mixed-structural form, with fewer firms being either high-structure or low-structure. Defenders are more likely than expected to be of mixed-structural form and less likely than expected to be high-structure. Prospectors and Analysers are more likely than expected to implement a high-structure configuration. Finally, Reactors show little variation from the expected distribution, but appear more likely than expected to exhibit low-structure.

Table 1 - Strategic orientation vs structural configuration.

Full table

To investigate the impact of the main factors on performance, the general linear model is utilised to perform univariate analyses of variance. Table 2 reveals this analysis regarding market share (PERFMS). As noted in the table, the analysis shows a significant impact on market share ( p=0.00), explaining 31 per cent of the adjusted variance. Neither the structural configuration (STRUCFN), the firm size (SIZE), nor the interaction of strategy and structure significantly influences market share. Environmental dynamism (DYNA), however, exhibits a negative impact on share ( p=0.03), while the Miles and Snow strategic orientation (STRATOR) also shows a main effect ( p=0.00). Post hoc tests using Least-Squared Differences reveals that Reactor firms have the lowest perceived market shares, being significantly smaller than Prospectors ( p=0.00), Analysers ( p=0.00), and Defenders ( p=0.00). Also, Defender firms have significantly smaller perceived market shares than Prospector firms ( p=0.05). Not shown in the table is the ordering of market share (range: 5–25, mean: 14.6) for each strategy types: Prospectors (mean=17.4), Analysers (mean=15.5), Defenders (mean=14.1), and Reactors (mean=10.6).

Table 2 - Regression with market share (PERFMS).

Full table

Regarding profitability (PROFCMB), Table 3 shows this analysis. As noted in the table, the analysis shows a significant impact of the predictors on profits ( p=0.05), explaining 9 per cent of the adjusted variance. Neither the Miles and Snow strategic orientation (STRATOR), the firm size (SIZE), the perceived environment (DYNA), nor the interaction of strategy and structure significantly influences profitability. The structural configuration (STRUCFN), however, exhibits a main effect ( p=0.03). Post hoc tests using Least-Squared Differences reveals that the low-structure firms are more profitable than either the high-structure firms ( p=0.00) or the mixed-structure configuration firms (p=0.03). Not shown in the table is the ordering of profitability (range: -4.4–6.8, mean: -0.06) for each structural configuration: Low-Structure (mean=1.70), Mixed-Structure (mean=-0.10), High-Structure (mean=-0.85).

Table 3 - Regression with profitability (PROFCMB).

Full table

DISCUSSION

The study is attempting to answer a number of questions. First, do firms with different strategic orientations (Miles and Snow) implement different structural configurations (Low, Mixed, High)? Secondly, which of strategic orientation, structural configuration, or their interaction is more relevant to performance and in what ways? Finally, are the effects of these strategic variables the same for both profitability and market share?

The statistics reveal that most credit unions implement either a Defender or Analyser strategic orientation, as well as a mixed-structure configuration. Evidence is, however, provided that there is a relationship between the strategic orientation of firms and the structural configuration which the firms implement. In order to better understand the strategic configurations of each of the strategy-types, the mean profiles of structural dimensions are shown for each strategic-type in Table 4. The profiles suggest that Porter¹ may have been correct with his claim that a high degree across the structural dimensions is necessary to implement any specific type of strategy.

Table 4 - Structural profiles (means) of strategic orientations.

Full table

Defenders are found more likely than expected to be of mixed structural form and less likely to be highly structured. This is as expected since a Defender must be ready to adapt to demands within its well-defined area of focus. Either of a too-structured or a too-unstructured organisation might prevent defenders from carrying out the strategy in environments which may require a mixture of structural dimensions in order to succeed. The structural profile in Table 4 reveals that the average Defender firm implements nearly average levels of formalisation, integration, and centralisation/complexity. The emphasis of structure for the average Defender is definitely on integration and formalisation, rather than towards centralisation/complexity.

Prospectors are found more likely than expected to be highly structured. This is not surprising, since Prospector firms are suggested to be more aggressive and bold and need to have a structure that provides quick and correct information to support this strategy. We might expect Prospector firms to implement a highly structured organisation with the abilities to carry out these demands of being a first-mover. The structural profile in Table 4 reveals that the average Prospector firm implements fairly large above average levels of formalisation, integration, and centralisation/complexity. The emphasis of structure for the average Prospector seems to be a highly structured organisation across all three dimensions.

Analysers are also more likely than expected to be highly structured. This is not surprising, since Analyser firms are suggested to be more conservative and process information prior to making major decision. We might expect Analyser firms to implement more developed rules and procedures into their organisation, providing well-thought out decision-making to eliminate any major mistakes that might be made by other strategic types. The structural profile in Table 4 reveals that the average Analyser firm implements slightly above average levels of formalisation, integration, and centralisation/complexity. There does not appear to be an emphasis of structure for the average Analyser, other than being consistent across the dimensions.

Reactors show little variation from the expected distribution. Since Reactor firms are said not to implement a definite long-term strategy, the need to utilise a specific structural configuration, which might offer more or less structure, to support that strategy would be weaker. The structural profile in Table 4 reveals that the average Reactor firm implements below average levels of formalisation, integration, and centralisation/complexity. There does not appear to be an emphasis of structure for Reactors, other than being slightly below average.

Regarding the influences of the variables on performance, it is shown that the effects differ across performance objectives. For market share, it appears that strategic orientation is the primary driver of share. In that regard, Reactor firms are the lesser performers vs the other three types. Defender firms are also on the low side regarding market share, as might be expected due to their focus strategy. Thus, Analysers and Prospectors might be considered the two best strategies to achieve large market shares. For profitability, it appears that structural configuration is the primary influence on profits, although dynamic environments do have a negative impact as well. Surprisingly, the lesser-structured firms are shown to be more profitable in credit unions than either the mixed-structure or highly structured firms. It may be that increased levels of structure lead to more emphasis on procedures and internal factors to the firms rather than to the important market factors outside the firm. In this way, time and money and focus may be overused on matters that may simply be termed 'pushing paper' and not on true cost-savings or revenue-generating items. Therefore, it may be that looser controls result in more profits.

The question remains unanswered as to whether increased or large market share performance (and similarly asset size) precedes strategy or follows it. For instance, Do Prospectors (the largest share performers) enact this strategy after gaining large share because they then have the financial means to be aggressive. Or, on the other hand, does using a Prospector strategy lead to long-term increases in share. Similar questions can be asked about the other three strategic types. The current study is not designed to answer those specific questions.

Additional analysis of the asset size for the strategy groups, however, shows that Prospectors (6/6) and Analysers (35/53) are definitely more likely to be larger firms by asset size than expected, Defenders (31/45) are more likely than expected to be small in asset size, while Reactors are equally likely to be large or small firms ( p=0.001). Thus, it appears that Defenders defend a smaller share, Reactors are equally likely to end up large or small in size depending on fate or luck as much as management strategy, while Prospectors and Analysers somehow reach larger sizes, at least in the credit union industry.

Also regarding performance, no evidence was provided to support an interaction of strategy and structure on either market share or on profitability. Contrary to what might be expected, it appears that the fit between strategy and structure is not as relevant to this industry as to others.^{35, 39} Thus, the recommendations are straightforward here: if market share is the goal, then focus on the strategy; whereas if profits are the goal, then focus on the structure. It is not necessary, at least in credit unions, to fit the structure to the strategy so much as it is to emphasise the correct factor for the specified objective.

CONCLUSIONS/LIMITATIONS

The author investigates the structural configurations implemented by the firms classified by the Miles and Snow⁴ strategic orientation. The majority of the firms under study is either Defenders or Analysers and is of mixed-structural configuration. It appears that Defenders are more likely than expected to implement mixed-structures, while Prospectors and Analysers are more likely than expected to implement highly structured configurations, although these interactive effects do not impact performance. In the absence of interactions (towards performance) between strategy and structure, the managers can focus on one area, depending on the objective. If market share is the goal, then the focus of the firm should be on strategy. If profitability is the objective, then structural configuration should be emphasised. Since large asset holdings may also be an important goal of the firm, the appropriate strategies most likely to achieve this result are the Analysers and the Prospectors.

As structural configuration appears to be a vital ingredient to profitability, organisational design must be a focus for credit union managers. Contrary to what might be expected in this type of industry, the more structured firms were not the best performers. In fact, the least structured firms were the most profitable in this study, when compared to mixed and/or highly structured companies. It may be an aberration that 15 per cent of the sample (18/119 firms) exhibiting low-levels of structure are uncharacteristically high performers: this result might not be consistent across a larger or different sample. Or it may be that fluidity and freedom in the structural design of a firm allows more freedom to tackle the day-to-day challenges faced by these firms. This might eliminate wasted efforts on unnecessary paperwork, meeting, policies that oftentimes take away from productivity.

It is worth noting a possible limitation in the sample with the small number of Prospector (5 per cent) and Reactor (12 per cent) firms. These small percentages, however, might be a general characteristic of this particular industry. The thrift industry serves a generally conservative consumer base and is often considered one of the more conservative segments of the financial services industry in general, especially when compared to investment houses and international banks. Thus, it is not surprising to see small numbers of Prospector firms. Also, theory would suggest that Reactor firms are not a viable long-term strategy and thus it is not surprising to see the small numbers. It must, however, be noted that the Reactor firms are competing and surviving in this industry. Remember, the statistics show no differences in profitability among the different strategies. Therefore, it may be possible that a Reactive strategy is more likely to be successful in this, more conservative type, industry than in other settings where.

Additionally, it should be noted that a possible indicator of market share in this industry would be asset size, which is used here as a covariate. One must be aware that these credit unions are stand-alone retail outlets situated in different communities. Few are in the same town or general area of a larger city. Thus, they do not really compete with each other directly and, so, using asset size would not really be a correct indictor of share in this instance. The author, however, realises that asset size may still be related to share closely enough to confound the results of the share analysis. To test for this, asset size was removed as a covariate and the results are unchanged: only significant strategy differences ( p=0.00) and dynamism ( p=.03) effects on market share. Thus, it can be concluded that asset size has not acted to confound the market share regression.

It should be noted that the reliabilities for the initial structural dimensions are on the low end of acceptability. These low reliabilities might lead to minor errors in classifying firms into the low–mixed–high structural groups. Additionally, the study should not be generalised to smaller-sized credit unions due to their underrepresentation in this study. It should be noted that credit unions exist in an environment that is more protected than other financial institutions, such as banks, and therefore any generalisations might be suspect. Stremersch and Tellis⁴⁸ suggest that successful strategies differ from country to country. Thus, repetition across regions and cultures might offer new insights. Future studies might investigate these relationships in banks, savings and loans, and other financial services industries. Future studies might also apply this framework to products industries in both the business-to-business and consumer products area to further test the findings. Any future studies might also look at other control variables or other strategies.

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