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Strategic clarity, business strategy and performance

John A. Parnell School of Business Administration, University of North Carolina at Pembroke,

Pembroke, North Carolina, USA

Abstract

Purpose – This paper seeks to investigate the link between business strategy and performance, giving special attention to the composition of combination strategies.

Design/methodology/approach – A survey assessing business strategy and performance was completed by managers representing 277 retail businesses in the USA.

Findings – The combination strategy was associated with higher performance in some but not all instances. Strategic clarity – the extent to which a single strategy reflects the organization’s strategic intent – was also associated with organizational performance. Businesses with high and low strategic clarity outperformed those with moderate strategic clarity.

Research limitations/implications – This paper investigated US retailers and did not assess businesses in other industries or countries. Future research that seeks to replicate these findings is warranted.

Practical implications – Businesses can pursue either a single generic strategy (i.e. low cost or differentiation, prospector or defender or analyzer, etc.) or attempt to combine two or more strategies. Porter and others have warned that a combination strategy is suboptimal because of trade-offs inherent in “pure” strategies. While some businesses have pursued a combination strategy and performed poorly, others have done so with great success. Evidence presented in the paper attempts to resolve this conundrum, suggesting that high-performing businesses either concentrate on a single strategy along the Miles and Snow typology or combine all three equally. Those attempting intermediate combinations are more likely to perform poorly.

Originality/value – The paper proposes the notion of strategic clarity and provides evidence that supports a U-shaped link between strategic clarity and business performance.

Keywords Management strategy, Retail trade, Strategic groups, Corporate strategy

Paper type Research paper

The strategic group construct has contributed much to what is currently known about the business strategy-performance nexus (Capps et al., 2002; Leask and Parker, 2007; Mauri and Michaels, 1998; Panagiotou, 2007; Parnell, 2008; Phelan et al., 2002). The viability of combination business strategies represents a key business-level concern that can be assessed by invoking the strategic group level of analysis. Specifically, why do some businesses incorporating a combination strategy perform well – often much better than the industry norm – why do others perform significantly below their counterparts pursuing “pure” strategies? Does strategic clarity – the extent to which a business’ efforts coalesce around a single generic strategy – influence the strategy-performance relationship?

This paper examines the link between strategic group membership and performance among retailers in the USA with a specific interest in the complexity of the combination strategy-performance link. Toward this end, the remainder of the paper is divided into several sections. Following an historical overview of the

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1755-425X.htm

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Journal of Strategy and Management Vol. 3 No. 4, 2010 pp. 304-324 q Emerald Group Publishing Limited 1755-425X DOI 10.1108/17554251011092683

competitive strategy-performance literature, hypotheses that assess the strategy-performance relationship via two common typologies – one by Porter (1980) and the other by Miles and Snow (1978) – are tested. Findings, conclusions, and directions for future research are also addressed.

Competitive strategy Historical development The development of strategic management as a field – including the assessment of competitive strategies – can be traced to the branch of microeconomics known as industrial organization (IO) (Bain, 1956; Mason, 1939). Whereas a firm or corporate level strategy considers the broad direction of a firm – growth, stability, or retrenchment – a competitive or business strategy outlines how a business unit competes within its industry.

The IO perspective views profitability primarily as a function of industry structure. Characteristics of the industry – not the firm – are viewed the primary influences on firm performance. IO’s structure-conduct-performance model is considered to more appropriate for industries with uncomplicated group structures, high concentrations, and relatively homogeneous firms (Seth and Thomas, 1994). However, many scholars questioned IO’s ability to explain large performance variances within a single industry and the strategic group level of analysis was proposed as a middle ground between the industry and firm levels of analysis (Hergert, 1983; Porter, 1981).

Although each business employs its own unique strategy, strategic group assessments identify clusters of businesses employing a common generic strategy. Strategic groups are comprised of businesses in a given industry that seek to execute similar competitive strategies. Comparing outcomes between and among groups can help elucidate the strategic characteristics associated with high performance in a given industry without overemphasizing a single business unit.

Business strategy typologies are frameworks that identify several “generic” competitive strategies available to business units. Typologies were developed and used as a theoretical basis for identifying strategic groups across industries (Zahra and Covin, 1993). Although strategic groups are to some extent an industry-specific phenomenon, many strategic group researchers began to use approaches believed to be generalizable across industries.

A number of generic strategy typologies have been proposed (Veett et al., 2009; see also Venkatraman, 1989). Buzzell et al.’s (1975) typology identified three viable strategic approaches, building, holding, and harvesting. Utterback and Abernathy (1975) also identified three approaches, performance maximizing, sales maximizing, and cost minimizing. Abell’s (1980) approach emphasized the concepts of differentiation and focus/niche orientation. The typologies developed by Porter (1980, 1985) and Miles and Snow (1978, 1986) received much early scholarly attention, however. Others have since proposed various competitive typologies, some distinctive and others based on previously developed frameworks (see Garrigos-Simon et al., 2005; Goh, 2006; Kim and Mauborgne, 2005; Nwokah, 2008). Nonetheless, Porter’s and Miles and Snow’s original typologies remain among the most widely cited, tested, criticized, and refined (Bantel and Osborn, 1995; Veett et al., 2009).

According to Porter’s (1980, 1985) framework, a business can pursue superior performance by either establishing a cost leadership position (i.e. low costs) or

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differentiating its offerings from those of its rivals. Either of these approaches may be accompanied by focusing efforts on a given market niche.

Miles and Snow’s (1978) generic strategy typology identified four strategic types: Prospectors, defenders, analyzers, and reactors. Prospectors perceive the environment as dynamic and uncertain, maintain flexibility and employ innovation to address it, and often becoming the industry designers (Miles and Snow, 1986). Defenders perceive the environment to be stable and certain, and seek stability and operational control to achieve maximum efficiency. Analyzers attempt to stress both stability and flexibility. Reactors lack consistency in strategic choice and usually do not perform well (Brunk, 2003). Most published empirical work testing Miles and Snow’s (1978) typologies has been supportive (Allen and Helms, 2006; Conant et al., 1990; James and Hatten, 1995; Moore, 2005; O’Regan and Ghobadian, 2006; Slater and Olson, 2001).

The search for a universal typology remains elusive, however. While perspectives on classification schemes differ across scholars, few argue in favor of a single best typology, reinforcing the notion that strategic groups are a conceptualization of researchers (Veett et al., 2009).

Combination strategy debate As scholars began to study the relationship between strategy and performance, some studies concluded that only “pure” cost leadership or differentiation strategies were associated with superior performance, whereas others found that combining cost leadership and differentiation could be optimal for some businesses. Early studies addressed the combination strategy debate via Porter’s vernacular (cost leadership versus differentiation), but investigations quickly expanded beyond Porter’s work to that of Miles and Snow. Attempts to resolve this conundrum have been confounded by the fact that Porter’s approach does not allow for long-term viable combination strategies, whereas Miles and Snow’s typology allows for one via the analyzer. Wright et al. (1990) extended the Miles and Snow typology by proposing a high-performing combination strategy, the “balancer”. Whereas the analyzer has been viewed as a hybrid strategy, the balancer organization operates in three separate product-market spheres simultaneously.

The first perspective embraces Porter’s (1980) original contention that successful business units must seek either a low cost or a differentiation strategy and was supported by a number of early studies (Hambrick, 1981, 1982; Hawes and Crittendon, 1984). Dess and Davis (1984) examined 19 industrial products businesses, concluding that superior performance was achieved through the adoption of a single strategy. Hambrick’s (1983) investigation of capital goods producers and industrial product manufacturers yielded similar results. Most studies defending the single strategy position identified clear strategic groups, each with its own link to performance. Within this context, a competitive strategy represents a tradeoff between two or more alternatives.

This perspective is valid, at least to some extent. If a cost leadership business possesses cost advantages that cannot be duplicated easily by its competitors, it can benefit from below average pricing. A business whose basis of differentiation cannot be readily replicated by competitors may also perform well. In such cases, combining the two approaches could be detrimental for the business. Similarly, a strategy that emphasizes new product development costs the organization resources in research and

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development, expenses that must be recouped in higher margins or increased sales if the business is to be successful.

However, a second perspective considered the combination strategy to be both viable over the long run and in many cases associated with superior performance (Buzzell and Gale, 1987; Hill, 1988; Murray, 1988; Parnell, 1997; Parnell and Wright, 1993; Wright, 1987). Businesses successfully combining low costs and differentiation utilized synergies to overcome any tradeoffs. For example, to be successful, a manufacturer pursuing a strategy that emphasizes both first-mover advantages and production efficiency may emphasize the development of new products, which can be produced at lower costs than existing ones. Moreover, a single business might base its strategy on several facets of competitive advantage, although some combinations may be easier to implement than others.

Porter’s original perspective on the combination strategy is founded on the economic principle of tradeoffs, the idea that when executives choose to pursue one strategy they by definition choose not to pursue another. Scholars in the combination strategy school acknowledge this principle, at least to some extent. For example, the concept of tradeoffs may be accurate for relatively large industrial firms because of the emphasis in such organizations on the value chain (Fjeldstad and Haanaes, 2001). It may also be appropriate for industries with uncomplicated group structures, high concentration, and relatively homogeneous competitors (Seth and Thomas, 1994).

One could argue that the difference in perspective can be at least partially attributed to differences in research orientation. Miller and Friesen (1986) noted that studies lending support to the first school (e.g. Dess and Davis (1984) and Hambrick (1983)) considered only industrial markets, where buyers are typically better informed and more rational than consumers. Chen and Smith (1987) argued that databases utilized in many of the first school studies – including the PIMS database – do not necessarily constitute representative samples. Barney and Hoskisson (1990) and Ketchen and Shook (1996) questioned the validity of many strategy-performance studies that utilized cluster analysis, a technique commonly utilized by first school research. Others argued that the data collection techniques of second school studies, many of which utilize top executive and perceptual data, were not necessarily valid or reliable (Golden, 1992).

Strategic group research in context Scholarly work invoking the strategic group level of analysis has evolved in several key respects. While early strategic group research emphasized performance implications of group membership, later work began to examine behavioral distinctions, using group membership to explain competitive positioning, strategic behaviors, and rivalry patterns. Whereas performance-based research starts with the industry and works downward toward strategic groups, behavior-based studies tend to start with the organization-level data and work upward toward the strategic groups (Thomas and Pollock, 1999).

A frustration with strategic groups’ IO conceptual basis has led to a renewed and productive interest in the firm ( Jarzabkowski, 2003; Kim and Mauborgne, 2005; McDonald, 2006; Van de Ven and Johnson, 2006). Although much can be gleaned by examining groups of organizations within industries, several shortcomings associated with strategic group research should be noted. The existence of strategic groups – in

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general or in specific industries – has been challenged on both conceptual and empirical grounds (Barney and Hoskisson, 1990). Dranove et al. (1998) argued that strategic groups exist in a given industry if group effects on performance can be separated from organization and industry effects.

The notion of strategic groups assumes not only the existence of groups of businesses employing similar competitive strategies, but also the existence of clear and recognizable industries. This assumption might appear intuitive at first, but different industry conceptualizations can result in markedly different strategic groups because groups emphasize the relative strategic position of businesses in an industry. For example, fast-food restaurants might be considered as a cost leadership strategic group within the broader restaurant industry because they emphasize cost containment more than their casual and upscale competitors. If a narrow fast-food industry definition is invoked, however, some fast food establishments might still be considered as low cost leaders while others are more closely aligned with differentiation.

Toward the firm level of analysis Questions about these shortcomings and a general frustration with strategic groups’ deterministic underpinnings led to a continued shift away from the industry level of analysis, particularly in the late 1980s and early 1990s (Barney, 1991; Collis, 1991; Grant, 1991). An alternative paradigm drawing on the work of Penrose (1959) and Wernerfelt (1984) emerged, emphasizing unique firm competencies and resources in strategy formulation instead of industry characteristics (Kim and Mahoney, 2005; Knott, 2008). Although the resource-based view (RBV) embraces a firm level of analysis, it incorporates some of the same traditions as the broader perspective of organizational economics (Barney and Ouchi, 1986; Furrer et al., 2008).

RBV proponents studied firm-level issues such as transaction costs (Camerer and Vepsalainen, 1988), economies of scope, and organizational culture (Barney, 1991; Fiol, 1991). At the business level, RBV theorists examined such issues as competitive imitation (Rumelt, 1984), informational asymmetries (Barney, 1986), causal ambiguities (Reed and DeFillippi, 1990), and the process of resource accumulation (Dierickx and Cool, 1989). The nature of competitive advantage – when a firm implements a value creating strategy not simultaneously implemented by its rivals – began to gain renewed prominence within the RBV (Peteraf, 1993). Sustained competitive advantage exists when competitors are unable to duplicate the benefits of the strategy over time, and its development represents a key strategic objective.

Accepting the transitory nature of resources that lead to competitive advantage is a key concern for the RBV (Dess et al., 1995; Feurer and Chaharbaghi, 1994; Robins and Wiersema, 1995; Sheehan and Foss, 2007). The increasing pace of change and the notion of ephemeral competitive advantage have led to the development of dynamic strategy positioning models (Chung et al., 2006). Such models do not refute the tenets of IO, organizational economics, or the RBV, but challenges static assumptions in favor more flexible and adaptive approaches, especially where success depends on a constant flow of new offerings (Barnett, 2006; Feigenbaum and Thomas, 2004; Selsky et al., 2007). Businesses eschewing a traditional business strategy orientation in favor of a dynamic strategy approach may require greater strategic involvement from middle and lower level managers (Chung et al., 2006; Richter and Schmidt, 2005; Sorge and Brussig, 2003; Varadarajan, 1999).

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Although the two approaches may be viewed as somewhat compatible, the IO-resource-based theory debate ultimately concerns the relative importance of industry and firm factors in determining firm performance. McGahan and Porter (1997) found that industry factors accounted for 19 percent of the variance in profitability within specific industry categories, and that the difference varied substantially across industries. Powell (1996) suggested that industry factors account for between 17 and 20 percent of performance variance. Short et al.’s (2007) assessment of firms in 12 industries suggested that firm effects on performance are generally the strongest, but that strategic group and industry effects are also significant. According to Henderson and Mitchell (1997), however, resolving this conflict may not be possible because organizational capabilities, competition, strategy, and performance are fundamentally endogenous. Hence, any attempt to build on the merits of both the IO – including strategic groups – and resource-based perspectives must account for the varying degrees of influence of both industry factors and firm resources on performance (Claver-Cortes et al., 2004; Roquebert et al., 1996; Spanos et al., 2004).

Conceptual development has continued in the 2000s. A renewed interest in organizational economics, encompassing issues such as incentives, agency theory, transactions cost theory, authority and delegation, decentralization, and property rights theory, has built on both IO and the RBV (Fulghieri and Hodrick, 2006; Foss and Foss, 2005; Gibbons, 2003; Kim and Mahoney, 2005; Sheehan and Foss, 2007; Whinston, 2003). Strategic group research has continued during this time as well, often seeking to invoke firm-level concepts. DeSarbo and Grewel (2008) proposed the notion of hybrid strategic groups composed of businesses that combine strategic recipes from more than one pure group. Their conceptualization reinforces the strategic group concept, while allowing for groups of businesses pursuing various combination strategies. Likewise, DeSarbo et al. (2005) extended the Miles and Snow framework with a deeper examination of inherent strategic capabilities. The notion of distinctive or strategic capabilities can be traced to Selznick (1957) and Ansoff (1965). As such, an organization’s resources – including its assets and skills – represent the source of its foundation for sustainable competitive advantage (Aaker, 1989; Bowman and Ambrosini, 2003; Hussey, 2002; Lopez, 2005; Pandza and Thorpe, 2009).

Hypotheses Porter’s (1980) generic strategy typology is built on the economic concept of tradeoffs, the idea that successful businesses should avoid pursuing multiple strategic orientations that tend to be incompatible. As such, a business attempting to combine emphases on low costs and differentiation invariably finds itself “stuck in the middle” (Porter, 1980, p. 41), a notion that received considerable support in early studies (Dess and Davis, 1984; Hambrick, 1981, 1982; Hawes and Crittendon, 1984) but was later challenged extensively (Buzzell and Gale, 1987; Buzzell and Wiersema, 1981; Hill, 1988; Parnell, 1997; Phillips et al., 1983; Wright, 1987).

The combination strategy debate extended beyond Porter’s typology to Miles and Snow’s framework (Miller and Dess, 1993; Parnell and Wright, 1993). The fundamental premise of the argument is not about cost leadership and differentiation per se, but rather about the need for business units to make conscious choices that enable them to serve some markets more effectively, but at the expense of other markets. Most published work has supported the idea that businesses lacking a coherent and

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consistent strategic orientation (i.e. reactors within the Miles and Snow framework or those “stuck in the middle” within Porter’s conceptualization) are generally outperformed by others in their respective industries, but conclusions concerning the adoption of more than one strategic orientation simultaneously have been elusive ( Jusoh and Parnell, 2008; Parnell and Wright, 1993). Framed another way, a key concern is the relationship between strategic clarity and organizational performance. Along these lines, the present study tests three hypotheses:

H1. Reactor businesses will be outperformed by prospector, defender, and analyzer businesses.

H2. “Stuck in the middle” businesses will be outperformed by differentiation, low cost, and focus businesses.

H3. Businesses with high strategic clarity (i.e. a single, clear, preferred strategic orientation) will outperform those with moderate or low strategic clarity (i.e. no single, clear, preferred orientation).

The first two hypotheses seek to support the association between an incoherent strategic approach and poor performance that has been largely supported in extant literature (Dess and Davis, 1984; Hambrick, 1982; Jusoh and Parnell, 2008; Wright, 1987). Without such support, addressing the third hypothesis – a focal point of this study – is problematic. Within this context, strategic clarity can be conceptualized as the extent to which a business avoids a “stuck in the middle” position by concentrating its productive efforts on supporting a single generic strategy. In other words, a business with high strategic clarity would pursue either cost leadership or differentiation within Porter’s framework. It would pursue a prospector, an analyzer, or a defender strategy within Miles and Snow’s framework.

The third hypothesis is constructed from Porter’s perspective. Support would lend credence to the first perspective previously discussed, the idea that pure business strategies are by definition mutually exclusive. Rejection of this hypothesis would lend credence to the combination strategy perspective.

Methods Several previously validated scales were utilized in the present study. Zahra and Covin’s (1993) scale was utilized to categorize businesses along Porter’s typology. Several minor amendments suggested by Luo and Zhao (2004) were adopted, but the scale remained substantially unchanged. Shortell and Zajac’s (1990) self-typing scale was utilized to categorize businesses along the Miles and Snow typology. Minor wording changes – some adopted from James and Hatten (1995) – were made, but the integrity of the scale remains largely unchanged. A new follow-up question was added immediately after the Miles and Snow question to assess the extent to which respondents felt comfortable selecting only one of the strategies in the self-typing exercise:

To what extent is your business following more than one of the first three options (A, B or C) in the previous question?

A. A, B, and C in the previous question seemed to fit our business almost equally. It was a difficult decision, but I selected the option I thought was the best choice.

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B. Two of the three choices among A, B, and C in the previous question seemed to fit our business almost equally. It was a difficult decision, but I selected the option I thought was the best choice.

C. It was not difficult to select the best choice among A, B, and C in the previous question,

but at least one of the other two options partially fit our business.

D. The choice I selected in the previous question is clearly the best description of our

business. None of the other options fit at all.

Performance measurement was a key consideration, and the scheme selected for a particular study can influence the results substantially (Cavalieri et al., 2007; Jusoh and Parnell, 2008; Pongatichat and Johnston, 2008; Ramanujam and Venkatraman, 1987; Venkatraman and Ramanujam, 1986). Early strategy-performance studies focused on financial measures, and market-based measures of performance have also received considerable attention (Amit and Livnat, 1988; Kyriazis and Anastassis, 2007; Lubatkin and Rodgers, 1989;). There is a growing consensus, however, that other factors should be considered as well (Dutta and Reichelstein, 2005; Hillman and Keim, 2001; Jusoh and Parnell, 2008; Laitinen, 2002). Although specifics surrounding the measurement of organizational performance remain debatable, some scholars have suggested that different measures are appropriate for different strategies (Atkinson, 2006; Dye, 2004; Van der Stede et al., 2006).

Qualitative measures include subjective areas of performance such as the satisfaction of managers, customers and other stakeholders, as well as ethical behavior. Viewing performance through a qualitative lens can provide insight into organizational processes and outcomes that cannot be seen via financial measures (Parnell et al., 2006). The present study relied on management surveys that reference both objective (e.g. return on sales, revenue growth) and subjective (e.g. overall firm performance, competitive position) criteria, adopting the scale to assess relative competitive performance from Ramanujam and Venkatraman (1987).

Findings The survey instrument was administered to managers at a retail trade show held in a large Midwestern city in the USA. A total of 277 responses were received, representing all management levels. There were 35 non-managers (12.6 per cent), 79 lower level managers (28.5 per cent), 109 middle managers (39.4 per cent), and 54 top managers (19.5 per cent). There were more women (160; 57.8 per cent) than men (117; 42.2 per cent). The typical respondent had four years of management experience and five with the present organization. Businesses of various sizes were represented in the sample, as depicted in Table I.

Although previously validated, the cost, differentiation, focus, and performance scales were assessed prior to hypothesis testing. Results demonstrate integrity for all four scales, both in terms of factor loadings and coefficient alpha scores (see Table II). Factor scores (regression method) were calculated to serve as measures for each construct and were utilized in hypothesis testing.

The first hypothesis was supported. Prospectors modestly outperformed defenders and analyzers. Reactors represented the poorest performing group, slightly more than one-half of one standard deviation below the mean. Results are summarized in Table III.

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The second hypothesis was supported. Businesses were cluster analyzed along factor scores for cost leadership, differentiation, and focus. The five-cluster solution produced clear and definable strategic groups ranging in size from 30 to 99 (see Table IV). The fourth cluster – “stuck in the middle businesses” – reported levels of cost leadership, differentiation, and focus of approximately one standard deviation

Variable Range Median Mean Std dev.

Management experience (years) 0-22 3.72 3.64 Org experience (years) 0-21 4.75 3.44 Number of employees 2-150,000 75 2,529 11,643 Annual revenues ($000) 94-6,470,600 12,193 225,377 720,422

Note: n ¼ 277 Table I. Sample

Item Details Loading

Cost leadership (alpha ¼ 0.733) COST1 Efficiency of securing raw materials or components 0.640 COST2 Finding ways to reduce costs 0.660 COST3 Level of operating efficiency 0.693 COST4 Level of production capacity utilization 0.700 COST5 Price competition 0.779

Differentiation (alpha ¼ 0.854) DIFF1 Using new methods and technologies to create

superior products 0.721 DIFF2 New product development 0.779 DIFF3 Rate of new product introduction to market 0.762 DIFF4 Number of new products offered to the market 0.855 DIFF5 Intensity of advertising and marketing 0.675 DIFF6 Developing and utilizing sales force 0.678 DIFF7 Building strong brand identification 0.673

Focus (alpha ¼ 0.788) FOCUS1 Uniqueness of products in function or design 0.730 FOCUS2 Targeting a clearly identified segment 0.695 FOCUS3 Offering products suitable for a high price segment 0.836 FOCUS4 Offering specialty products tailored to a customer

group 0.864 Relative competitive performance (alpha ¼ 0.927) PERF1 Sales growth 0.752 PERF2 Growth in profit after tax 0.574 PERF3 Market share 0.864 PERF4 Return on assets (ROA) 0.863 PERF5 Return on equity (ROE) 0.845 PERF6 Return on sales (ROS) 0.890 PERF7 Overall firm performance and success 0.879 PERF8 Competitive position 0.879

Table II. Factor loadings

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below the mean. Performance was also about one-standard deviation below the mean. Ward’s cluster algorithm produced a second group that also includes businesses of relatively similar levels of cost leadership, differentiation, and focus emphasis. This cluster consisted of 57 businesses and produced high scores along the three strategic dimensions. Performance of this combination strategy cluster was the highest of the five.

The third hypothesis was supported. Respondents reporting high strategic clarity along the Miles and Snow typology (i.e. only one good fit) outperformed those whereby a second choice also represented a good or partial fit (see Table V). Interestingly, 37 businesses reported that three or more strategies represented good fits also performed well, although not as high as the first group. Graphically, the link between strategic clarity and performance can be viewed as a U-shaped curve with above average performance achieved by businesses with high or low strategic clarity (see Figure 1).

The link between strategic clarity and performance was assessed in greater detail by examining differences within strategic groups (see Table V). ANOVA results indicate significant differences in performance for defenders, prospectors, and reactors, but not analyzers. Defenders reporting only one strong fit (i.e. only the defender strategy) significantly outperformed all other defender groups. Prospectors reporting only one good fit or three good fits outperformed the other prospector groups. Small cell sizes rendered analysis of the analyzer strategic group unproductive. Although differences were significant across reactor groups, small cell sizes restrict interpretation as well.

Discussion Extant tests of both the Porter typology and the Miles and Snow typology have generated mixed results. While support for the viability of clear and coherent strategic approaches has been widespread, the combination strategy-performance link has remained tenuous. There appears to be a general consensus that a combination approach can be successful at least in some instances, but whether such an approach leads to superior performance or can be effective in all industry environments remains disputed. The present study does not resolve this conundrum, but it provides some critical insight.

Supporting Porter’s perspective on “pure strategies,” respondents reporting the highest degree of strategic clarity outperformed all other groups representing lower levels of clarity. This was true overall and also when the two viable strategic groups

Strategy n Mean Std dev.

Defender 70 20.021 0.764 Prospector 110 0.242 1.036 Analyzer 55 20.025 1.096 Reactor 42 20.544 0.955 Total 277 0.003 1.004 F-value 6.638 Significant 0.000

Table III. Analysis of variance

(ANOVA) – Miles and Snow strategy and

performance

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C lu

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lu st

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¼ 5 7 )

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3 0 )

A N

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D if

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0 8

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2 0 .2

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1 2

2 0 .1

2 2

0 .0

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9 9

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4 8

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Table IV. Competitive strategy clusters

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Combination Defender Prospector Analyzer Reactor Total

Only one good fit 0.391 0.653 0.460 20.150 0.418 n 20 18 7 7 52 One good fit and one partial fit 0.004 20.190 0.004 20.395 20.138 n 23 24 21 22 90 Two good fits 20.532 0.222 20.272 21.410 20.175 n 13 49 26 10 88 Three good fits 20.311 0.485 0.965 0.679 0.212 n 14 19 1 3 37 Total 20.050 0.248 20.035 20.520 0.000 n 70 110 55 42 277 F-value 6.037 2.745 1.181 7.083 5.421 Significance 0.001 0.047 0.326 0.001 0.000

Table V. Competitive strategy,

strategic clarity and performance

Figure 1. Strategic clarity and

performance

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where significance was found – defenders and prospectors – were assessed individually. This does not tell the entire story, however.

Moderate strategic clarity was negatively associated with performance across the board, with one possible exception. The lowest performing groups overall were those who reported that the there was one good fit along the Miles and Snow typology and one partial fit, and that there were two good fits. Defenders reporting an additional partial fit were outperformed by those reporting a single fit, but the former group performed slightly above the norm for defenders as a group. Hence, for defenders, incorporating a part of another strategy might be appropriate, depending on the situation. Attempting to blend a second or third strategy, however, appears to be suboptimal.

For prospectors, the “one good fit and one partial fit” group represented the lowest performers. Those with high strategic clarity performed the best, but those reporting three good fits also did well, performing at a level of about one-half of one standard deviation above the mean for the total group of businesses. This approach could be viewed as a combination strategy and a possible extension of the “balancer” strategy (Wright et al., 1990), lending support to the notion that businesses grounded in a prospector orientation can integrate other strategic dimensions effectively.

Results for analyzers are significant and support both high and low strategic clarity, but also remain inconclusive. With only seven businesses in the high group and one in the low group, it is difficult to assess the extent to which strategic clarity moderates the strategy performance relationship among analyzers. Conclusions concerning reactors are also inconclusive. Only one group of reactors – three businesses reporting three good strategic fits – performed well. It is difficult to assess the reactors except to note that their performance tended to be well below the mean, a finding consistent with most of the literature (Conant et al., 1990; James and Hatten, 1995; Moore, 2005; Slater and Olson, 2001).

Significant findings notwithstanding, it is always possible that the strategy assessments were influenced by the level of performance in the respondent’s organization. For example, those in high performing businesses could retrospectively assign high levels of attention to lost leadership, differentiation, or other strategic orientations. Likewise, respondents in poor performing businesses might have a tendency to perceive greater strategic confusion because of the performance level. It is difficult to account for such a possibility, but it should be recognized nonetheless.

Conclusion and future research directions The present study supported the integrity of the original typologies proposed by Porter, and Miles and Snow in the US retail industry. Businesses with incoherent strategies (i.e. reactors, or those “stuck in the middle”) performed poorly as a group. The nature of the combination strategy-performance link varied across strategic groups. Specifically, both the predominant generic strategy and the number and intensity of other strategies included in the combination influence performance.

This study also supported strategic clarity’s influence on the strategy-performance nexus. The graphical representation of the strategic clarity-performance linkage was that of a U-shaped curve, suggesting that businesses with either low or high strategic clarity outperformed those with moderate levels. This relationship was fairly consistent across all of the strategic groups, although some minor differences existed.

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A number of questions remain, suggesting at least three key opportunities for future research. First, replications of the present study are necessary to improve generalizability. Small cells such as those in Table V will be difficult to eliminate without large and/or cross-industry samples. Nonetheless, understanding the link between these cells and performance is critical to unlocking the nature of the combination strategy-performance relationship. Moreover, it is also important to examine different industries and firms outside of the USA. The use of different measures of strategy and performance are also germane.

Likewise, more work is needed to explain more fully the processes that underpin the notion of strategic clarity and the U-shaped curve, with special attention to differences across industries and strategic groups. For example, is high strategic clarity more important for prospectors than for defenders? Would an assessment of business strategy invoking Porter’s typology also generate a U-shaped curve? Is low strategic clarity appropriate for retailers but not for manufacturers? Questions such as these remain largely unanswered.

Second, most competitive strategy studies have assessed the link between strategy and performance over the fixed, relatively short time frame. High performing businesses generate profits and other positive outcomes over an extended period of time. Market sustainability reflects the extent to which a strategy’s success can achieve a desired level of financial performance while enduring current and potential change across competitors and markets. The extent to which sustainable competitive advantage is developed cannot be accurately assessed in a single iteration (see Barney, 1991). Hence, time lags may influence the nature of the combination strategy-performance relationship.

Finally, differences in strategic behavior between large organizations and SMEs are well founded in the literature (Ghobadian and O’Regan, 2006). The present study addressed both large and small firms and did not investigate prospective differences associated with size. Sample size considerations notwithstanding, organizational size might moderate the combination strategy-performance nexus.

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