Firm, Strategic Group, And Industry

Strategic Management JournalStrat. Mgmt. J., 28: 147–167 (2007)

Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/smj.574

Received 9 January 2003; Final revision received 23 June 2006

FIRM, STRATEGIC GROUP, AND INDUSTRYINFLUENCES ON PERFORMANCE

JEREMY C. SHORT,1 DAVID J. KETCHEN, JR.,2* TIMOTHY B. PALMER3

and G. TOMAS M. HULT4

1 Rawls College of Business Administration, Texas Tech University, Lubbock, Texas,U.S.A.2 College of Business, Auburn University, Auburn, Alabama, U.S.A.3 Haworth College of Business, Western Michigan University, Kalamazoo, Michigan,U.S.A.4 Eli Broad Graduate School of Management, Michigan State University, East Lansing,Michigan, U.S.A.

A long-standing debate has focused on the extent to which different levels of analysis shapefirm performance. The strategic group level has been largely excluded from this inquiry, despiteevidence that group membership matters. In this study, we use hierarchical linear modelingto simultaneously estimate firm-, strategic group-, and industry-level influences on short-termand long-term measures of performance. We assess the three levels’ explanatory power using asample of 1,165 firms in 12 industries with data from a 7-year period. To enhance comparabilityto previous research, we also estimate the effects using the variance components and ANOVAmethods relied on in past studies. To assess the robustness of strategic group effects, we examineboth deductively and inductively defined groups. We found that all three levels are significantlyassociated with performance. The firm effect is the strongest, while the strategic group effectrivals and for some measures outweighs the industry effect. We also found that the levelshave varying effects in relation to different performance measures, suggesting more complexrelationships than depicted in previous studies. Copyright 2007 John Wiley & Sons, Ltd.

The determinants of firm performance have longbeen of central interest to strategic managementresearchers (Rumelt, Schendel, and Teece, 1994).Viewed collectively, research focused on explain-ing performance has emphasized determinants atthree primary levels of analysis: (1) firm; (2)strategic group; and (3) industry (McGee andThomas, 1986; Short, Palmer, and Ketchen 2003a).Research at the firm level focuses on how key

Keywords: firm performance; variance decomposition;strategic groups; firm effects; industry*Correspondence to: David J. Ketchen, Jr., College of Business,Auburn University, 415 W. Magnolia, Suite 401, Auburn, AL36849-5244, U.S.A. E-mail: [email protected]

within-organization features shape outcomes. Per-haps the most popular perspective guiding thiswork is the resource-based view of the firm, whichargues that a firm’s bundle of assets and capa-bilities drives its performance (e.g., Wernerfelt,1984). Strategic groups researchers argue that firmscoalesce around a limited array of competitiveapproaches, and that some approaches offer bet-ter performance than others (e.g., Fiegenbaum andThomas, 1990; Hunt, 1972; Porter, 1979). Draw-ing on economic inquiry, others have examined theextent to which the industries where companiescompete shape their performance (e.g., Rumelt,1991; Schmalensee, 1985).

Copyright 2007 John Wiley & Sons, Ltd.

148 J. C. Short et al.

A sizable literature examines the relative rolesof the industry and firm levels in shaping perfor-mance (e.g., McGahan and Porter, 1997; Rumelt,1991). When such studies do not integrate thestrategic group level, this limits the ability tounderstand performance (Short et al., 2003a). Fox,Srinivasan and Vaaler (1997) were the first to testfirm, strategic group, and industry levels of analy-sis using a random effects model. Using FTC lineof business data from 1974 to 1977, these authorstested stable and unstable industry and strategicgroup effects, corporate effects, stable firm effectsand unstable economy-wide (year) effects. Theyrelied on simulated annealing to first randomlyassign and then iteratively determine group mem-bership. They found that stable and unstable groupeffects accounted for approximately 40 percent ofthe variance in firm performance. In addition tothese supportive results, the strategic group levelis important because managers’ understandings ofgroups serve as strategic reference points (Fiegen-baum, Hart, and Schendel, 1996; Fiegenbaum andThomas, 1995) as well as shaping managers’ inter-pretations of the environment in which they oper-ate (Reger and Palmer, 1996).

In terms of theory, much of the study of firmshas proceeded from a systems theory perspective,where firms are viewed as structured collectiv-ities that are embedded in and dependent uponthe broader systems in which they operate (Scott,1998). Systems theory provides a useful founda-tion for practical strategic questions that extendbeyond the organization and tend to involve a wideempirical net (Hendry and Seidl, 2003). For exam-ple, systems theory has been used to study theimportance of resource constraints in the evolu-tion of organizational populations (Lomi, Larsen,and Freemen, 2005), product modularity withinorganizations (Schilling, 2000), and the applica-tion of complex adaptive models to strategic man-agement (Anderson, 1999). Additionally, schol-ars have called for the incorporation of systemstheory to examine complex phenomena such asthe hierarchical systems involving organizationswithin larger contexts (Morel and Ramanujam,1999; Schilling, 2000).

From a systems theory perspective, focusingnarrowly on individual components (such as firmsor strategic groups) of a performance-shaping sys-tem in isolation is much less valuable than simul-taneous examination of key components. Indeed,

understanding why some firms succeed while oth-ers fail requires diagnosis of the system’s com-ponent parts (Ashmos and Huber, 1987). Thus, ifa study includes only one or two of the levels,the resulting portrayal of the interwoven elementsthat collectively shape firm outcomes is incom-plete. Incorporating the strategic group level ofanalysis also adds additional information about theexistence and importance of strategic groups—twoissues that have long vexed strategic groupsresearch (Barney and Hoskisson, 1990).

The exclusion of one or two levels withina given study also creates empirical problems.For example, if groups and industries do in factinfluence performance, a study focusing only onfirm-level antecedents violates the assumption ofindependence of observations that underlies tra-ditional statistical techniques. Studies that focussolely on higher levels (such as the industry)overlook potentially meaningful lower-level vari-ance. Several studies have moved beyond sin-gle levels (e.g., Chang and Singh, 2000; Mauriand Michaels, 1998; McGahan and Porter, 1997;Rumelt, 1991; Schmalensee, 1985), but most haveignored the strategic group level. This is unfor-tunate, because a meta-analysis found that groupmembership accounts for a significant portion ofperformance variance (Ketchen et al., 1997). Thus,there is a need to further examine all three levelsin order to pursue the strategy field’s goal of diag-nosing performance antecedents (Dranove, Peteraf,and Shanley, 1998). Accordingly, this study’s pur-pose is to determine how much performance vari-ance the three levels account for, thereby pro-viding evidence about how much each ‘matters’(cf. Rumelt, 1991). Specifically, building on sys-tems theory, we develop and test hypotheses aboutthe levels’ roles and their ties to different per-formance measures. Our overall expectation wasthat all three levels would explain significant vari-ance in performance, but that each level’s effectwould vary across performance measures (cf. Ash-mos and Huber, 1987).

Using data on 1,165 non-diversified firms from12 industries across 7 years, we clustered firmsinto strategic groups using both deductive meth-ods (i.e., where a priori expectations exist aboutthe specific nature of groups) and inductive meth-ods (i.e., where there are no such expectations)(cf. Bantel, 1998; Ketchen, Thomas, and Snow,1993). Next, we used hierarchical linear model-ing (HLM) to assess the variance accounted for

Copyright 2007 John Wiley & Sons, Ltd. Strat. Mgmt. J., 28: 147–167 (2007)DOI: 10.1002/smj

Firm, Strategic Group, and Industry Influences 149

by the firm,1 strategic group, and industry lev-els across three different performance measures.As Hofmann (1997) describes, HLM’s nesting oflower levels within higher levels makes it wellsuited to assess hierarchically ordered systemssuch as those addressed by systems theory. WhileHLM is our main analytical technique, we alsoperformed our tests using the variance compo-nents techniques used in past studies. This wasdone to enhance comparability with previous stud-ies and to address the possibility of alternativeexplanations of the results. In summary, this studyoffers three key departures from past studies: inclu-sion of the strategic group level using deduc-tively and inductively defined groups, a relianceon systems theory to guide the inquiry, and theuse of hierarchical linear modeling as a power-ful methodology to address the ‘multiple levels’hypotheses.

THEORETICAL BACKGROUND ANDHYPOTHESES

Systems theory and multilevel influences onperformance

Systems theory seeks to understand scientific phe-nomena by considering the interdependence of net-works of entities within a larger system (Scott,1998). The theory strives to explain relation-ships by using a level of generality that maynot always be prevalent in particular sciences(Boulding, 1956). Some systems are character-ized by their predictable patterns (e.g., the rota-tion of the moon around the earth), while oth-ers involve adjustments to environmental stimuli(e.g., thermostats that adjust between the actualtemperature and a desired level of heating orcooling). Organizational theorists have used sys-tems theory to describe the prevalence of opensystems wherein environmental influences (e.g.,industry norms, government regulation) affect and

1 We follow Mauri and Michaels (1998) in using the term ‘firm’to represent members of our sample of single-business firmsthat compete in only one industry. As an anonymous reviewerpointed out, however, the term ‘business unit’ is much moreprominent in the literature. To facilitate comparison of ourresults with those of previous studies of multilevel influence onperformance, we note that our firm effect captures all explanatorypower originating within our firms. In this sense, our firm effectapproximates the combined roles of business unit and corporateeffects studied among diversified firms (e.g., McGahan andPorter, 1997; Rumelt, 1991).

are affected by firms and their leaders (Scott,1998).

Our contention is that systems theory offersa valuable vantage-point for understanding firmperformance, particularly as it applies to firm-, strategic group-, and industry-level effects. Anapplication of systems theory emphasizes that thedecisions managers make in an effort to lead theirfirm toward prosperity take place within a com-plicated milieu (Ashmos and Huber, 1987). Con-textual factors such as strategic groups and indus-try trends place certain constraints on the firm(cf. Rousseau and Fried, 2001). In turn, how-ever, the firm’s actions may reshape these exter-nal factors (Porter, 1980). For example, priceslashing by one firm may force direct competi-tors to match the lower prices, resulting in profitmargins being driven down across the industry.Through adjusting to other firms and the otherlevels (strategic group and industry), managerscan shepherd their firms toward enhanced suc-cess or toward ruin. Overall, a systems theoryview of performance suggests that firms, strate-gic groups, and industries are interdependent partsof a complex system that collectively influencesfirms’ fates. Only by examining each level in thecontext of the others can each level’s role bediagnosed.

Systems theory provides a conceptual basisfor tests of multiple levels of analysis, an areaabout which past researchers have expressed con-cern (Klein, Dansereau, and Hall, 1994; Rousseau,1985). This has led to calls for the adoptionof a meso paradigm that thoughtfully integratesphenomena at multiple levels (Hofmann, 1997;House, Rousseau, and Thomas-Hunt, 1995). Acareful focus on levels of analysis is needed whenincorporating an additional level, because not allperspectives provide effective bridges across lev-els (Rousseau, 1985). Because strategic groupsresearch shares the same industrial organizationeconomics heritage as research on firm and indus-try determinants, integrating them does not con-front such conceptual barriers (Michael, 2003).Indeed, scholars have noted that the use of a mesoperspective would provide a much-needed integra-tion to test the influences of the firm and strate-gic group levels (Joyce, 2003; Short, Palmer, andKetchen, 2003b). Below, we present argumentsdetailing why firms, strategic groups, and indus-tries are important drivers of performance.



Firm effects on performance

The idea that a firm’s attributes, possessions, andactions are driving forces behind performance haslong been central to the strategy field (Rumeltet al., 1994). The resource-based view of the firm(RBV) is a prominent reflection of this idea. A cen-tral tenet of the RBV is that resources help explainimportant outcomes (cf. Barney, 1991; Werner-felt, 1984). The products/services that can arisefrom any firm’s unique set of resources are like-wise unique (Wernerfelt, 2005). Meanwhile, cus-tomers prefer certain products/services to others.If possible, each firm would rush to match cus-tomers’ desires, but each firm is limited by itsresource set to providing a finite set of potentialoutputs. As a result, between-firm differences inoutcomes emerge. If the resources that provideadvantages are valuable, rare, non-substitutable,and inimitable, the duration of these performancedifferences can be lengthy (Barney, 1991).

Some resources that give rise to firm success areintangible, creating measurement hurdles (Godfreyand Hill, 1995). Despite this challenge, attemptsto validate the RBV are increasing (Barney andMackey, 2005). For example, scholars have inves-tigated the role of knowledge-based resources(Miller and Shamsie, 1996), brand name reputation(Combs and Ketchen, 1999), and qualities of topmanagement teams (Smith et al., 1994) in perfor-mance enhancement. Hence, a body of theoreticaland empirical research attests to the profit potentialthat is attributable to the firm.

A systems perspective can add to our under-standing of the role of the firm level in shap-ing firm performance. For example, researchershave argued that to fully assess the impact offirm resources, firm effects should be isolated fromstrategic group effects (Nair and Kotha, 2001;Rouse and Daellenbach, 1999). This suggests thata complete understanding of firm effects can onlyoccur within the appreciation of group influences.Others have advocated testing the RBV usinglarge-scale databases in a multi-industry context(Levitas and Chi, 2002), noting that ‘ultimately,the RBV will stand or fall not on the basis ofwhether its key constructs can be verified, butupon whether its predictions correspond to realityobserved for populations of firms’ (Godfrey andHill, 1995: 530; emphasis in original). Thus, mul-tiple perspectives expect that the firm level is an

important element of the system shaping firm per-formance. Stated formally:

Hypothesis 1: Performance within strategicgroups varies systematically with differences infirm-level characteristics.

Strategic group effects on performance

Strategic groups are naturally occurring subsets offirms that are more homogeneous in actions thanis found across industry incumbents in general(Cool and Schendel, 1988). Hunt (1972) coinedthe term ‘strategic group,’ and since the 1970sthere has been extensive research that has exam-ined whether these industry substructures exist,and, if they do, what their implications are forfirm performance (e.g., Cool and Schendel, 1987;Fiegenbaum and Thomas, 1990). While some firmsmore closely match a group profile than do oth-ers (Reger and Huff, 1993), group structure hasbeen found to be fairly stable and predictableover modest periods of time (Fiegenbaum andThomas, 1990). As described next, some of thekey issues surrounding the strategic groups con-cept include mobility barriers, the role of strate-gic groups as reference points, and methodologicalcontroversy.

The logic supporting an expectation that strate-gic groups vary in performance hinges on the con-cept of mobility barriers. Because opportunities arenot evenly distributed across an industry, someindustry segments offer better profit potential thanothers. Firms occupying one niche may be temptedto expand or to change strategies in order to exploitopportunities as they arise in other areas of theindustry. However, mobility barriers restrict suchopportunism. Specifically, shifting to a differentstrategic group can be risky because the neces-sary investment in developing the needed skills andproducts may be substantial, while the perceivedopportunities may be short-lived. Thus, firms gen-erally choose not to change groups because of therisk that the enhancements gained will be less thanthe costs incurred (Mascarenhas and Aaker, 1989).As a result, strategic groups occupying lucrativeindustry segments should outperform those in lessfertile areas. Likewise, members of less lucrativegroups are reticent to enter more attractive groupsbecause the very decision to enter a group addsoutput to group demand with a likely reduction



in group price and expected returns.2 Given thislogic and extant results linking strategic groupsand performance, an examination of the multi-level determinants of performance that excludesthe strategic group level cannot provide an accu-rate depiction of what drives performance. Such amodel would be conceptually underspecified andempirically incomplete.

As strategic groups research has advanced,scholars have focused their interest on the strate-gic group as a level of analysis that is as impor-tant for its influence on organizational actions andunderstandings as it is for its direct linkage to per-formance (Short et al., 2003a). Strategic groupsare important in and of themselves because man-agers’ cognitions are often based upon membershipwithin the context of a strategic group (Reger andHuff, 1993). Thus, understanding of group mem-bership can also act as an indirect link to perfor-mance because strategic actions are often based onconceptual maps of the competitive environmentthat involve group structure (Reger and Palmer,1996). Managers’ understanding of their firms’membership in a strategic group serves as refer-ence points when interpreting and responding totheir firm’s performance (Fiegenbaum et al., 1996;Fiegenbaum and Thomas, 1995) and can alsoshape a firm’s identity (Peteraf and Shanley, 1997).Recent studies have noted that strategic groupmembership is also important because of its influ-ence on competitive rivalry (Mas-Ruiz, Nicolau-Gonzalbez, and Ruiz-Moreno, 2005; McNamara,Deephouse, and Luce, 2003; Nair and Filer, 2003).

One source of controversy in the strategic groupsliterature is that considerable variance can be foundin the methods used to assess strategic group mem-bership. Porter (1979) used the relative size ofa firm as a proxy for membership. Subsequentstudies identified groups via the application ofclustering algorithms (e.g., Cool and Schendel,1988). The use of cluster analysis has been criti-cized because researchers’ choices have often beenless than ideal when implementing the technique(Ketchen and Shook, 1996). Such concerns haveled some researchers to examine strategic groupswithout relying on cluster analysis (Wiggins and

2 It is still possible that the returns could be higher than theexisting group but entering is, per se, adding to competition andreducing profitability of the participants in the group. We thankan anonymous reviewer for pointing this out.

Ruefli, 1995). Others have applied simulation tech-niques that randomly assign firms into groups andthen iteratively alter group structure to maximizevariance (Fox et al., 1997). Two recent tests havefocused on integrated mills vs. mini-mills as twonaturally occurring groups within the Japanesesteel industry (Nair and Filer, 2003; Nair andKotha, 2001).

We agree with the view that despite the prob-lems associated with its past use, cluster analy-sis provides a ‘valuable’ and ‘important tool’ fordiscerning strategic groups of firms (Ketchen andShook, 1996: 455). Cluster analysis enables exam-ination of groups defined both deductively (i.e.,where there are a priori expectations about thespecific nature of groups) and inductively (i.e.,where there are no such expectations), allowing theincorporation of the group construct from a num-ber of theoretical positions (Ketchen et al., 1993).Ketchen et al.’s (1997) meta-analytic review foundthat 8 percent of the variance in firm performanceis attributed to group membership, and that thisresult holds for both deductively and inductivelydefined groups. Despite these indications of strate-gic groups’ importance, examination of the relativerole of the strategic group level vs. the firm andindustry levels has been limited, and researchershave called for more robust tests using clusteringmechanisms (Fox et al., 1997).

Tests of deductively and inductively definedstrategic groups provide an important contributionto the literature for several reasons. Empirically,researchers have noted that future studies can max-imize the value of cluster analysis techniques byrelying on triangulation of multiple methods fordefining groups (Ketchen and Shook, 1996). Forexample, Nath and Gruca (1997) used clusteringtechniques to find convergence between competi-tive structures using archival measures, perceptualdata, and direct measures of competitors. Osborne,Stubbart, and Ramaprasad (2001) relied on clus-ter analysis of strategic intentions found in pres-idents’ letters to shareholders to establish link-ages between strategic groups, mental models, andfirm performance. Although theory testing is usu-ally associated with deduction and theory genera-tion linked to induction, both methods are usefulfor generating theory and most studies, in real-ity, involve both (Seth and Zinkhan, 1991). Like-wise, managers’ strategy creation processes are afunction of inductive and deductive thinking (Reg-ner, 2003). Thus, the application of inductive and



deductive clustering techniques to examine strate-gic groups allows the consideration of group mem-bership from a variety of statistical, theoretical,epistemological, and practical perspectives.

Systems theory provides a theoretical foundationfor the inclusion of strategic groups in a multilevelcontext. This inclusion builds on the strategic man-agement field’s historical incorporation of an opensystems approach and movement towards the addi-tion of developments from the industrial organiza-tion economics literature (Hoskisson et al., 1999).Scholars have called for the inclusion of systemstheories with such historical roots when examiningthe complexity of modern organizations (Ander-son, 1999; Burns, 2004). Additionally, scholarshave used systems theory to examine strategicchange (Hendry and Seidl, 2003), organizationaltransformation (Lemak, Henderson, and Wenger,2004), technology assessment (Rousseau, 1979),models of technology and structure (Rousseauand Cook, 1984), and total quality management(Manz and Stewart, 1997), and to examine the roleof dynamic resource constraints in the evolutionof organizational populations (Lomi et al., 2005).Based on previous findings and theory about strate-gic groups, we expect that the strategic group levelis an important element of the system shaping firmperformance. Formally, we predict that:

Hypothesis 2: Performance within industriesvaries systematically with differences in strate-gic group characteristics.

Testing this hypothesis allows us to shed light onan important, long-running controversy: whetherperformance differences exist between strategicgroups (Barney and Hoskisson, 1990). Recently,researchers have called for the use of state-of-the-art methods to assess the strategic groups–perfor-mance relationship (Ketchen, Snow, and Hoover,2004). Statistically, the addition of the group levelto random coefficient modeling software programssuch as hierarchical linear modeling allows for adirect test of the existence of performance dif-ferences among strategic group members. If thestrategic group level is found to play a significantrole vis-a-vis performance, this demonstrates thatgroups differed in their performance.3

3 We thank an anonymous reviewer for recognizing this value-added aspect of our study.

Industry effects on performance

Several theoretical perspectives recognize the im-portance of industry membership for firm per-formance. Economists have long theorized thatfirm performance is influenced by market structure(e.g., Schmalensee, 1985) as well as by changesin other structural elements such as industry con-centration, growth, and fluctuation of mobilitybarriers’ height (Bain, 1956). Similarly, environ-mentally based perspectives such as organizationalecology emphasize the power of environmentsover organizations (Hannan and Freeman, 1977).These theories suggest that one must analyze envi-ronments to understand firm performance.

Strategy researchers also acknowledge the influ-ence of industry characteristics. Indirectly, industrymembership affects performance through strate-gic perspectives (Sutcliffe and Huber, 1998) andactions (Slevin and Covin, 1997). More directly,performance appears to be impacted by industrytraits such as complexity (Zajac and Bazerman,1991), rivalry (Wiseman and Bromiley, 1996), andregulatory changes (Reger, Duhaime, and Stim-pert, 1992).

A key development in the strategic managementfield was the incorporation of an open systemsapproach that examined strategic processes beyonda single firm and allowed for stronger empiri-cal testing and greater generalization (Hoskissonet al., 1999). Subsequent to this shift, a series ofstudies have tested the influence of the firm andindustry levels and have concluded that indus-try effects play an important role in shapingfirm performance (Chang and Singh, 2000; Mauriand Michaels, 1998; McGahan and Porter, 1997;Rumelt, 1991). Recent studies have found that,even after controlling for outliers, approximately10 percent of the variance in firm performance isattributable to industry effects (McNamara, Aime,and Vaaler, 2005). Thus, considerable theory andevidence support the notion that industry mem-bership helps shape performance. This leads usto expect that the industry level is an importantelement of the system shaping firm performance.Stated as a formal hypothesis:

Hypothesis 3: Performance varies systemati-cally with differences in industry-level charac-teristics.



The three levels and different measures ofperformance

One key issue in testing the antecedents to perfor-mance is defining performance in a manner thatis meaningful across levels. Many multilevel per-formance studies have relied on return on assets(ROA) as the sole outcome. Recently, Hawaw-ini, Subramanian, and Verdin (2003) questionedthis reliance on accounting measures, noting thatsuch measures fail to reflect firms’ relative skillwith sustained value creation. This critique high-lights the potential value of including longer-termoutcomes. Thus, to capture the multidimensionalnature of firm performance, we examine a short-term accounting measure (ROA), a market mea-sure (Tobin’s Q), and a measure of default risk orbankruptcy propensity (Altman’s Z).

The latter measure warrants further explana-tion. The concept of risk has been emphasizedas an important theoretical consideration withinthe strategic groups literature (e.g., Fiegenbaum,McGee, and Thomas, 1987). Firms belonging tothe same strategic group have been found to havesimilar risk positions yet exhibit differences in per-formance (Cool and Schendel, 1988). Measuresincorporating risk have been used in the strategicgroups literature as key dependent variables as wellas measures of resource commitments (Fiegen-baum and Thomas, 1990, 1993, 1995). Whenexamining risk at the organizational level, the like-lihood of bankruptcy is an especially salient mea-sure (Eberhart, Altman, and Aggarwal, 1999) thatis of interest in a variety of country settings (Alt-man, Resti, and Sironi, 2004). As managers beginto rely more and more on bankruptcy as a strate-gic alternative and a dependent variable of inter-est, we agree with strategy scholars who advocateresearchers rely on Altman’s Z as an establishedmeasure of credit default risk (Miller and Reuer,1996).

Extant theory and evidence suggest that the threelevels may not have uniform roles in shaping eachperformance measure. Specifically, the firm levelmay have a stronger role with long-term measuresthan short-term measures, while the opposite holdstrue for the strategic group and industry levels.For example, research grounded in upper eche-lons theory suggests that although externalities arefactors in bankruptcies (D’Aveni and MacMillan,1990; Hambrick and D’Aveni, 1988), firm-levelfactors such as patterns of strategic actions and

the characteristics of the top management groupplay a greater role (D’Aveni, 1990; Hambrickand D’Aveni, 1988). Thus, bankruptcy is largely(albeit not exclusively) a function of bad man-agers and/or bad strategy within the firm. Firm-level issues are also central to market measures.Although the nature of a firm’s competitive contextis certainly reflected in a firm’s stock price, thisprice fundamentally represents the stock market’sexpectations about a firm’s ability to deliver futurereturns (Lubatkin and Shrieves, 1986). If manage-ment appears competent and is seen as servingshareholders’ goals, stock price is enhanced. Incontrast, the presence of self-serving, incompetentexecutives depresses stock price (Combs and Skill,2003). Based on extant theory and evidence, aswell as our expectation that the firm level mayhave a stronger role with long-term measures thanshort-term measures, we predict that:

Hypothesis 4: The firm level is associated withgreater variance in Tobin’s Q and Altman’s Zthan it is with return on assets.

The strategic group level is likely to have its great-est link with profits. For example, high barriers toentering the industry and relatively genteel com-petition within and between strategic groups havehelped pharmaceutical firms enjoy strong prof-its for decades (e.g., Cool and Schendel, 1987;McGrath and Nerkar, 2004). Cool and Schendel’s(1988) finding that groups of firms in the phar-maceutical industry constitute homogeneous riskclasses also suggests that groups are likely tobe associated with greater variance for profitabil-ity than risk measures. Other tests of the strate-gic groups–performance relationship have foundstronger relationships for profitability than for mar-ket measures (e.g., Mehra, 1996), or risk measures(Veliyath and Ferris, 1997). Based on extant theoryand evidence, as well as our expectation that thestrategic group level may have a stronger role withshort-term measures than long-term measures, wepredict that:

Hypothesis 5: The strategic group level is asso-ciated with greater variance in return on assetsthan it is with Tobin’s Q and Altman’s Z.

The industry level is also more likely to have itsgreatest links with profits. From the perspectiveof industrial/organization economics (e.g., Bain,



1956), as well as Porter’s (1980) related view,industry structure is a key underlying determinantof the amount of profit potential a firm can exploit.Defining industry attractiveness by profitabilityalone, however, has been problematic becausecharacteristics that create industry profitability maylead to contrary implications for any given indus-try member (Wernerfelt and Montgomery, 1986).As a consequence, multilevel studies that definethe importance of industry effects via profitabil-ity measures may conclude that industry has astronger effect on performance than other impor-tance organizational outcomes (Hawawini et al.,2003). Based on extant theory and evidence, aswell as our expectation that the industry level mayhave a stronger role with short-term measures thanlong-term measures, we predict that:

Hypothesis 6: The industry level is associatedwith greater variance in return on assets than itis with Tobin’s Q and Altman’s Z.

METHOD

Sample

Our sample was drawn from the COMPUSTATdatabase. COMPUSTAT includes data on over7,000 companies in more than 300 industries. Inconstructing our sample, we addressed two dilem-mas encountered in previous studies using thedatabase as described by Chang and Singh (2000).First, variance decomposition techniques’ resultsvary based on sample characteristics; specifically,they noted that previous studies have failed tocapture adequate variance in size. Second, COM-PUSTAT reporting methods lead to restrictions inthe reporting of diversified firms. In response, wepurposively sampled from manufacturing indus-tries dominated by single-business firms. Our sam-ple’s firms have considerable variance in sizeand include both large and small firms (i.e.,firms with less than 1% market share) (cf. Changand Singh, 2000). By focusing on single-businessfirms, COMPUSTAT’s reporting limitations (whichare problematic in diversified firms) are not anissue. Using single-business firms also avoids sta-tistical noise that would occur if firms operat-ing in multiple industries were included (Mauriand Michaels, 1998), and avoids confounding thatmight occur if diversified firms were placed intostrategic groups.

In addition to the single-business requirement,we restricted the sample to industries with a min-imum of 45 firms to provide the statistical powerneeded to detect a medium strategic group effect(Ferguson and Ketchen, 1999). There were 12four-digit SIC industries that met our selection cri-teria with sufficient data on the variables used inour analysis: pharmaceutical preparations (SIC =2834), in vitro/in vivo diagnostics (SIC = 2835),biological products (SIC = 2836), special industrymachinery (SIC = 3559), computer communica-tion equipment (SIC = 3576), computer peripheryequipment (SIC = 3577), television and telegraphapparatus (SIC = 3661), radio, television broad-casting, and communication equipment (SIC =3663), semiconductor-related devices (SIC =3674), surgical, medical equipment and apparatus(SIC = 3841), electromedical apparatus (SIC =3845), and prepackaged software (SIC = 7372).Our carefully selected sample was composed of1,165 firms.

Diagnostic tests

A lagged structure was used to improve the abil-ity to make causal inferences (cf. Palmer andWiseman, 1999). Strategic group traits were mea-sured with data from years 1991–95. For eachindustry, we performed ANOVAs along severalvariables (R&D intensity, capital intensity, sales,current ratio, and percentage of domestic sales)with year as the factor variable. None of thesetests were statistically significant, suggesting that1991–95 was a stable strategic time period for ourindustries; thus, examining strategic group traitsacross the period was reasonable (cf. Fiegenbaumand Thomas, 1990; Fiegenbaum, Sudharshan, andThomas, 1987). Performance was measured from1993 to 1997. Five years of performance data arenecessary to provide a stable measure of firm per-formance (cf. Keats and Hitt, 1988). Three years ofdata overlap were chosen because some attributesmay have an immediate performance effect, whileothers may require a number of years (cf. Palmerand Wiseman, 1999).

To assess the degree of differences between oursample and the samples used in previous studies,we drew on Short, Ketchen, and Palmer’s (2002)suggestions and compared our sample with avail-able data from the COMPUSTAT database dur-ing the time of our study along two variables.



First, we found no statistically significant differ-ence between the two sets in terms of return onassets (F7893 = 0.25). Our second variable is sales,a measure of size. We used the natural log of salesbecause the variable was not normally distributed.Our sample firms were smaller than the firmsin the COMPUSTAT database as a whole (meanln sales = 2.23 for our firms; mean ln sales = 4.15for the COMPUSTAT database; F7020 = 545.08,p < 0.01). Thus, our firms appear to be smallerthan publicly held firms in general. This is notsurprising because we examined single-businessfirms and COMPUSTAT includes a mix of sin-gle business and diversified firms. One implica-tion is that interpretation of the findings shouldbe done with caution for larger firms (and in par-ticular diversified firms); at the same time, repli-cation of our findings with larger firms would bevaluable.

Performance

As explained above, we used several measures tocapture the multifaceted nature of performance.First, we used return on assets (ROA) to indi-cate accounting-based (i.e., financial) performance.Other accounting measures such as return onequity are available, but using ROA enhancesour study’s comparability with the many pre-vious variance decomposition studies that haveused ROA. This short-term measure was aug-mented by measures that reflect longer-term con-cerns. Market-based performance was assessed viaTobin’s Q. Tobin’s Q is ‘the sum of the mar-ket value of equity, the book value of debt, anddeferred taxes divided by the book value of totalassets minus intangible assets’ (Thomas and War-ing, 1999: 739). Finally, we relied on Altman’sZ, a measure of bankruptcy propensity, to cap-ture prospects for firm survival (Altman et al.,1981).

Strategic group measures and clusteringprocedures

The evolution of strategic group analysis has pro-duced two distinct approaches (Bantel, 1998). Theinductive approach focuses on empirically derivedgroups that often vary considerably across indus-tries. In contrast, the deductive approach is atheory-driven approach that can be applied to a

wide variety of industry contexts (Ketchen et al.,1993). We test both approaches.

Deductively defined strategic groups

We rely on the deductive approach pioneered byBantel (1998) and Ketchen et al. (1993). Thisapproach relies on two theoretical perspectivesat the heart of organizational analysis: strate-gic choice and organizational ecology. The basicpremise is that firms’ strategies vary across twodimensions emphasized in both theories (Zam-muto, 1988). The first dimension relates to afirm’s method of developing competitive advan-tage; firms are expected to emphasize either beingfirst to market or exploiting previously existingopportunities. The second dimension focuses onbreadth of operations (i.e., narrow vs. broad).Combining these two dimensions results in fourquadrants that overlap in part with the Milesand Snow (1978) typology. The first quadrant isrepresented by defenders/K-specialists, who focuson existing opportunities in a narrow domain.Entrepreneurs/r-specialists pursue new opportuni-ties in a narrow domain. Analyzers/K-generalistsefficiently exploit existing opportunities in a broaddomain. Finally, prospectors/r-generalists pursuenew opportunities in a broad domain.

We used one measure of each of the two com-petitive dimensions as the basis for finding strate-gic groups via cluster analysis (cf. Bantel, 1998;Ketchen et al., 1993). To measure the methodof developing competitive advantage, we usedresearch and development (R&D) intensity (cf.Bantel, 1998). A firm that makes a significant, con-sistent investment in R&D has the capability toinnovate or be an early follower. Thus, we suggestthat high R&D denotes the pursuit of new oppor-tunities, while small investments are indicative ofa focus on existing opportunities. We measuredR&D intensity as the average R&D expendituredivided by sales for the years 1991–95 (Bierly andChakrabarti, 1996). To measure breadth of opera-tions, we used the number of trademarks the firmholds. Trademarks proxy for breadth of operationsbecause firms with many trademarks are likely tobe involved in the production of numerous prod-ucts, services, or devices (Hall, 1992). Conversely,firms that hold few trademarks are more likelyto focus their operations on a narrow niche mar-ket. Thus, trademarks offer a reasonable reflection



of competitive scope across the 12 industries and1,165 firms examined.

Inductively defined strategic groups

Strategic group analysis has traditionally basedmembership on profiles and characteristics thatinfluence competitive advantage (McGee andThomas, 1986). One popular approach focuseson two main competitive traits: scope of opera-tions and resource deployment methods (Cool andSchendel, 1987; Ferguson, Deephouse, and Fergu-son, 2000; Mehra, 1996). The choice of groupingvariables was informed by prior strategic groupstudies. Also, given our multi-industry sample, wefocused on variables that are available and relevantacross the settings included in our sample.

Scope of operations defines the number of nichesin which the firm operates and degrees to which anorganization sells products offered by the industry(Ferguson et al., 2000). We measured two aspectsof operations scope that are common to the indus-tries in our sample. To capture geographic scopewe used the percentage of domestic sales dividedby total sales (cf. Cool and Schendel, 1988). Acompany with a high percentage of domestic salesis less likely to have global operations, and morelikely to focus on domestic customers and suppli-ers of raw materials. The number of product typeshas been used as a measure of competitive scope inprevious strategic groups studies (e.g., Houthoofdand Heene, 1997). To provide a proxy for num-ber of product types across multiple industries weused the number of patents granted to the firmbetween 1991 and 1995, available from the CAS-SIS database from the Patent and Trademark Officeof the U.S. Department of Commerce (Penner-Hahn, 1998). Because patent protection providesthe owner with exclusive rights to make, use, andsell the patented invention for more than a decade,a patent represents the ability of a firm to achievea sustained competitive advantage through productscope (Hall, 1992).

We examined three resource deployment vari-ables. Physical resources encompass the firm’sphysical technology, plant and equipment, geo-graphic location, and access to raw materials (Bar-ney, 1991). To measure physical resources we usedcapital intensity, defined as capital expendituresdivided by sales. A firm that makes a consistentcommitment to capital expenditures is continu-ally building their property, plant and equipment.

Additionally, capital investment is a reflection ofstrategy that has been shown to have a strong rela-tionship with firm performance across studies (e.g.,Capon, Farley, and Hoenig, 1990). Liquidity ratiosare commonly used to identify a firm’s availabilityof financial resources (Chatterjee and Wernerfelt,1991). Available financial resources provide themeans for achieving strategic flexibility that canenhance organizational performance (Greenley andOktemgil, 1998). Following Chatterjee and Wer-nerfelt (1991), we used the current ratio to measurefinancial resources. The current ratio is calculatedby dividing current assets by current liabilities andrepresents a firm’s liquidity, or the ability to paybills and other immediate debts. Finally, organi-zational size is another variable that is expectedto be an indicator of relative scope of operations(Ferguson et al., 2000) as well as a measure ofresource commitments (Cool and Schendel, 1988).To measure size, we used the natural log of totalsales (Cool and Schendel, 1988).

Clustering procedures

For each approach, a two-stage clustering proce-dure was used to group firms. A two-stage processis valuable because it increases the validity of clus-ter solutions (Ketchen and Shook, 1996). For theinductive approach, we used hierarchical clustering(i.e., Ward’s method) to determine both the num-ber of groups and their cluster centroids. We usedthe largest percentage change in the agglomerationcoefficient to suggest the optimal number of groupsin each industry (Hair et al., 1998). For our deduc-tively defined groups, we again relied on Ward’smethod to find cluster centroids, but we relied onour a priori theoretical framework and imposeda four-group solution in each industry. For bothapproaches, the cluster centroids identified in thefirst step were used as the starting point for a non-hierarchical clustering procedure (i.e., K-means).Criterion validity was assessed through MANOVAsignificance tests with our performance variables(Ketchen and Shook, 1996).

Hierarchical linear modeling (HLM) analysis

We relied on HLM as the primary technique to testthe hypotheses. HLM was introduced to the strat-egy literature by McNamara et al.’s (2003) inves-tigation of variance within and between strategic



groups. The use of HLM provides for simultane-ous partitioning of variance–covariance compo-nents (Bryk and Raudenbush, 1992). Like othervariance decomposition techniques, HLM variancecomponents analysis allows for estimation of mul-tilevel influences without direct measurement ofvariables associated with each level. Specifically,firm characteristics are modeled as latent factors,while strategic group and industry attributes arecaptured using common latent variables shared bymembers of the same strategic group or indus-try. The use of HLM offers certain advantages.First, HLM’s mathematics recognize that the mem-bers of the lower level within a higher-level sys-tem (e.g., firms within a strategic group) maynot be independent from each other (Hofmann,1997). This accurately reflects systems theory’scontention that components interact in importantways. Also, at levels 1 and 2 (firm and strategicgroup in our study), HLM relies on a Bayesianestimation approach that improves the precision ofestimates relative to traditional approaches (Hof-mann, 1997). To model the effects of firm, strategicgroup, and industry levels on performance we useda three-level HLM technique offered in the HLM5 software package (Raudenbush et al., 2000).

Specifically, a three-level HLM model was usedto test the effects of firms (level 1) nested withinstrategic groups (level 2) nested within industries(level 3). This model represents how variation isallocated across the different levels. The level 1model represents the performance of each firm asa function of a strategic group mean plus randomerror using the following equation:

Performance ijk = π0jk + eijk

where Performance ijk is the average performancefor a single dependent variable (e.g., return onassets) of firm i in strategic group j and industryk; π0jk is the mean performance of strategic groupj in industry k; eijk is a random ‘firm effect’ thatmeasures the deviation of firm ijk ’s score from thestrategic group mean. These effects are assumedto be normally distributed with a mean of zeroand variance σ 2. The subscripts i, j , and k denotefirms, strategic groups, and industries where thereare i = 1, 2, . . . , njk firms within strategic groupj in industry k; j = 1, 2, . . . , Jk strategic groupswithin industry k; and k = 1, 2, . . . , K industries.

The level 2 model examines each strategicgroup mean, π0jk, as an outcome varying randomly

around an industry mean using the following for-mula: π0jk = β00k + r0jk, where β00k is the meanstrategic group performance in industry k; r0jk is arandom ‘strategic group effect,’ that is, the devia-tion of strategic group jk ’s mean from the industrymean. These effects are assumed to be normallydistributed with a mean of zero and variance τπ .

The level 3 model represents the variabilityamong industries. The industry mean, β00k, variesrandomly around a grand mean as presented inthe following formula: β00k = γ000 + u00k, whereγ000 is the grand mean; u00k is the random ‘indus-try effect,’ that is, the deviation of industry k’smean from the grand mean. These effects areassumed to be normally distributed with a meanof zero and variance τβ . This simple three-levelmodel partitions the total variability in the outcomePerformanceijk into its three components: (level 1)among firms within strategic groups, σ 2; (level 2)among strategic groups within industries, τπ ; and(level 3) among industries, τβ . This partitioningallows for estimates of the proportion of variationthat lies within strategic groups, among strategicgroups within industries, and among industries.Specifically, σ 2/(σ 2 + τπ + τβ) is the proportionof variance within strategic groups (i.e., firm dif-ferences); τπ/(σ 2 + τπ + τβ) is the proportion ofvariance among strategic groups within industries;and τβ/(σ

2 + τπ + τβ) is the proportion of varianceamong industries. This fully unconditional modelallows for an estimation of the variability asso-ciated with each of the three levels (i.e., firms,strategic groups, industries).

RESULTS

The inductive approach to strategic groups foundan average of 3.75 groups. The median number ofgroups was four, which was found in five indus-tries. Two groups were found in three industries,three groups were found in one industry, fivegroups were detected in three industries, and sixgroups were detected in one industry. As describedabove, a four-group solution was imposed on eachindustry for the deductive approach based on a pri-ori theory. Following verification procedures rec-ommended by Ketchen and Shook (1996), resultsof MANOVA significance tests using firm per-formance measures support the validity of clustersolutions. As shown in Table 1, the F -tests fromWilks’s lambda, provided by the MANOVA, show



Table 1. A comparison of deductive and inductive strategic groups

Industry SIC code Number Deductive analysis Inductive analysisof firms

Numberof

groups

MANOVAF -tests withperformance

Numberof

groups

MANOVAF -tests withperformance

Pharmaceutical preparations 2834 127 4 18.38∗ 2 40.83∗

In vitro/in vivo diagnostics 2845 65 4 8.48∗ 5 6.02∗

Biological products 2836 97 4 14.59∗ 4 18.21∗

Special industry machinery 3559 46 4 8.48∗ 2 17.47∗

Computer communicationequipment

3576 67 4 6.01∗ 4 4.27∗

Computer peripheryequipment

3577 64 4 3.57∗ 3 4.90∗

Television and telegraphapparatus

3661 77 4 6.34∗ 5 4.84∗

Radio, televisionbroadcasting, andcommunicationequipment

3663 71 4 7.00∗ 4 9.84∗

Semiconductor-relateddevices

3674 98 4 11.79∗ 2 20.95∗

Surgical, medicalequipment and apparatus

3841 54 4 9.98∗ 4 6.03∗

Electromedical apparatus 3845 95 4 8.61∗ 4 7.32∗

Pre-packaged software 7372 304 4 23.99∗ 6 15.69∗

∗ p < 0.01

significant differences in performance based ongroup membership for all industries in the sam-ple for both deductively and inductively definedtechniques (p < 0.01).

Table 2 displays the results that include thedeductively defined strategic groups using HLM.For ROA, 65.82 percent of the variance wasaccounted for by the firm level, 14.95 percent ofthe variance was at the strategic groups level ofanalysis, and 19.23 percent of the variance wasfound between industries. For Tobin’s Q, 91.08percent of the variance was associated with thefirm level, 2.44 percent was between groups, and6.48 percent was between industries. For Altman’sZ, firm effects were associated with 96.08 percentof the variance, while 2.59 percent was betweengroups, and 1.33 percent was between industries.Overall, significant variance was detected at eachlevel across all performance measures. Even thesmallest effect (1.33%) was statistically significant(p < 0.05).

Table 3 displays the results involving the induc-tively defined strategic groups using HLM. Over-all, the pattern of findings was consistent with the

pattern relying on the deductive groups. For ROA,78.97 percent of the variance was associated withfirm-level factors, 6.35 percent of the variance wasat the strategic groups level, and 14.68 percentof the variance was between industries. For bothTobin’s Q and Altman’s Z, the firm level wasassociated with over 90 percent of the variance.Significant variance was detected at the strategicgroup level across all performance measures. Sig-nificant variance was found at the industry levelfor ROA and Tobin’s Q, but the 0.98 percent ofvariance in Altman’s Z was not significant. In sum,the results strongly support the predictions that thefirm level (Hypothesis 1) and the strategic grouplevel (Hypothesis 2) are associated with signifi-cant variance in performance. In five of six tests,the industry level was significant as well, offeringsupport for Hypothesis 3.

Hypothesis 4 predicted that the firm level ofanalysis was associated with greater variance inTobin’s Q and Altman’s Z than it is with return onassets. In support, both analyses found that over 90percent of the variance in long-term measures wasassociated with the firm level of analysis, while



Table 2. Variance decomposition using deductively defined strategic groups

Source Return on assets Tobin’s Q Altman’s Z

Variancecomponent

Percentof total

Variancecomponent

Percentof total

Variancecomponent

Percentof total

Hierarchical linear modeling resultsFirm 747.71 65.82 3.15 91.08 73.19 96.08Strategic group 169.83 14.95 0.08 2.44 1.98 2.59Industry 218.51 19.23 0.22 6.48 1.01 1.33Total 1136.05 100.00 3.45 100.00 76.18 100.00

Variance components (maximum likelihood) resultsFirm 747.04 65.80 3.16 91.33 73.05 96.02Strategic group 169.81 14.95 0.09 2.60 2.02 2.65Industry 218.52 19.25 0.21 6.07 1.01 1.33Total 1135.37 100.00 3.46 100.00 76.08 100.00

ANOVA resultsFirm 743.10 71.77 3.16 91.86 72.85 95.64Strategic group 117.31 11.33 0.08 2.33 1.96 2.57Industry 174.96 16.90 0.20 5.81 1.36 1.79Total 1035.37 100.00 3.44 100.00 76.17 100.00

N = 1,165 at the firm level for ROA and Altman’s Z, and 614 for Tobin’s Q. Samples sizes at the strategic group and industrylevels are 48 and 12, respectively, for all dependent variables.

Table 3. Variance decomposition using inductively defined strategic groups


Variancecomponent

Percentof total

Variancecomponent

Percentof Total

Variancecomponent

Percentof total

Hierarchical linear modeling resultsFirm 787.53 78.97 3.21 90.68 70.79 92.44Strategic group 63.31 6.35 0.20 5.65 5.04 6.58Industry 146.36 14.68 0.13 3.67 0.75 0.98Total 997.20 100.00 3.54 100.00 76.58 100.00

Variance components (maximum likelihood) resultsFirm 787.69 78.98 3.15 92.65 70.79 92.42Strategic group 63.29 6.35 0.11 3.23 5.06 6.61Industry 146.30 14.67 0.14 4.12 0.74 0.97Total 997.28 100.00 3.40 100.00 76.59 100.00

ANOVA resultsFirm 789.81 76.28 3.25 92.59 71.12 92.96Strategic group 49.80 4.81 0.00 0.00 5.39 7.04Industry 195.76 18.91 0.26 7.41 0.00 0.00Total 1035.37 100.00 3.51 100.00 76.51 100.00

N = 1,165 at the firm level for ROA and Altman’s Z, and 614 for Tobin’s Q. Samples sizes at the strategic group and industrylevels are 45 and 12, respectively, for all dependent variables.

65.82–78.97 percent of the variance in ROA wasfound at the firm level.4 Hypothesis 5 predicted

4 We consulted several methodological experts (including atthe software company that provides HLM) in a search for asignificance test for comparing the variance explained across

different outcomes by one level (e.g., the firm level’s share ofROA vs. its share of the other measures). They were unable toidentify such a test; thus our findings for Hypotheses 4–6 shouldbe considered preliminary; additional testing using new samplesand/or more sophisticated techniques that may be developed inthe future is needed to verify the results.



that the strategic group level was associated withgreater variance in ROA than the variance associ-ated with Tobin’s Q and Altman’s Z. The deduc-tive analysis supported this hypothesis (14.95% vs.2.44% and 2.59%), while the inductive did not(6.35% vs. 5.65% and 6.58%). Finally, Hypothesis6’s prediction that the industry level is associatedwith greater variance in ROA than the varianceassociated with Tobin’s Q and Altman’s Z wassupported by both analyses (19.23% vs. 6.48% and1.33% for the deductive; 14.68% vs. 3.67% and0.98% for the inductive).

Supplemental analysis

In addition to the strengths listed above, HLMhas some limitations. HLM treats variables as ran-dom (i.e., not researcher-driven), but our deduc-tive groups were fixed. HLM assumes multivariatenormality—an assumption that is often violatedin organizational research. These issues highlightthe value of analyzing our data with alternativetechniques. To facilitate comparisons with previ-ous studies that have tested multilevel influenceson firm performance, we tested our sample usingtwo techniques prevalent in this line of research:variance components (e.g., Mauri and Michaels,1998) and ANOVA (e.g., Rumelt, 1991). Theseresults are presented alongside our HLM anal-ysis for ROA, Tobin’s Q, and Altman’s Z inTables 2 and 3. The pattern of results parallelsthose obtained using HLM (e.g., the firm level con-sistently accounts for over 90% of Tobin’s Q andAltman’s Z regardless of the analytical techniqueused), providing evidence that our findings werenot driven by our use of HLM.

To compare our specification to previous spec-ifications, we also tested our sample using athree-level HLM analysis without the strategicgroup level of analysis (i.e., firm, industry, anderror). This test most closely parallels Mauri andMichaels’ (1998) study of 264 companies in 69four-digit SIC industries that also tests firm andindustry effects of single-business firms (i.e., cor-porate level effects are excluded). Our results wereconsistent across the three sets of analyses (HLM,variance components, and ANOVA). In terms ofROA, the firm level accounts for 48.15–52.31percent of the variance, industry accounts for9.49–10.50 percent, and error accounts for 38.20–41.86 percent. Our results vary a bit from thoseof Mauri and Michaels (they found 36.9%, 6.2%,

and 56.9% of ROA across 5 years for firm, indus-try, and error, respectively). However, our resultsfor Tobin’s Q and Altman’s Z support the sameconclusion as Mauri and Michaels’ models—themajority of the variance is due to year-to-year vari-ation (i.e., error), firm effects account for the nextlargest amount, followed by industry effects. Thus,this post hoc analysis provides at least some evi-dence that our model specification offers parallelsto previous specifications.

DISCUSSION

There is a 20-year tradition of examining the mul-tilevel determinants of performance dating backto Schmalensee’s (1985) pioneering efforts. Ourstudy contributes to this research stream in threeways: the inclusion of the strategic group level, areliance on systems theory, and the use of hier-archical linear modeling. Below, we discuss theimplications of our findings for researchers and formanagers as well as the limitations of the study.

Implications for researchers

In Thomas Kuhn’s (1970) classic work, The Struc-ture of Scientific Revolutions, he argues that newtheories often replace old ones rather than build-ing upon them. This tendency can lead to theabandonment of valuable ideas. Strategy scholarsshould recognize the existence and the risks ofthis tendency relative to the theories surroundingour three levels. Research examining the resource-based view of the firm has grown at an astoundingrate since the publication of Barney’s (1991) semi-nal article. Indeed, according to the Web of Science,well over 2,000 articles have cited Barney (1991)as of this writing. At roughly the same time, strate-gic groups research was criticized on a number offronts, and some proposed abandoning the concept(Barney and Hoskisson, 1990). Research integrat-ing these levels has been relatively rare (see Foxet al., 1997; Nair and Filer, 2003; Nair and Kotha,2001, for exceptions) but is necessary to advanceour knowledge of the determinants of performancefrom a systems perspective.

Our study provides responses to two of theharshest criticisms of strategic groups research.Scholars have been critical of the strategic groupsconcept because research has relied largely ondifferences in performance at the group level of



analysis as evidence that strategic groups matter(Barney and Hoskisson, 1990; Hoskisson et al.,1999). In contrast, our study found that strate-gic groups were associated with firm-level perfor-mance across a number of measures. The HLMtests of bankruptcy risk (Altman’s Z), specifically,consistently found that the effects of group mem-bership were stronger than industry effects. Thisfinding is noteworthy because the risk of failureis a fundamental concern with wide applicabil-ity across borders (Altman, 1984), suggesting thatstrategic groups may be an important determinantof performance both in the United States as wellas globally. Also, our inclusion of strategic groupswithin a systems theory viewpoint provides aresponse to assertions that the strategic groups con-cept lacks theoretical support for defining groupswithin industries (Hoskisson et al., 1999).

Our incorporation of systems theory offers aconceptual base for research bridging micro andmacro inquiry (House et al., 1995). The impor-tance of context has seen increasing importance incontemporary organizational behavior (Rousseauand Fried, 2001). The field of human resourcemanagement, specifically, is one area with sig-nificant opportunity for bridging levels (Wrightand Boswell, 2002). For example, researchers haverecently investigated the extent that industry ‘mat-ters’ for the relationship between human resourcemanagement and labor productivity (Datta,Guthrie, and Wright, 2005). Given that strategicgroups are often defined in part by cost structures,the inclusion of the strategic group level of anal-ysis represents a fruitful area for inquiry into how

context shapes human resource practices and out-comes.

Our results have implications for a key theoreti-cal issue in the organizational sciences—the extentto which a firm’s fate is self-determined. We exam-ined three outcome measures. ROA is a short-termperformance measure and Tobin’s Q and Altman’sZ are long-term measures. We found that the firmlevel was associated with the largest variance inshort-term performance, but that the other two lev-els played major roles as well. For example, inthe results for the deductive approach to derivinggroups using HLM, the firm level accounted forapproximately 66 percent of the variance, with 15percent and 19 percent at the strategic group andindustry levels, respectively. Brush and Bromiley(1997) note that when interpreting variance com-ponents their relative importance can be assessedby examining the square root of the variance ateach level of analysis. In terms of ROA, the rel-ative importance of the firm level of analysis isroughly equivalent to the importance of context(the importance of firm and industry levels com-bined).5 For the long-term measures, however, thefirm level accounted for over 90 percent of thevariance and was the largest in terms of impor-tance (as shown in Table 4). This latter result

5 The square root of the 65.82 percent of the variance in the firmlevel of analysis is 0.81, the square root of the 14.95 percentof the variance in the strategic group level of analysis is 0.39,and the square root of the 19.23 percent of the variance in theindustry level of analysis is 0.44. Dividing these numbers bythe combined square root reveals that the 0.49 importance ofthe firm level of analysis is approximately the same as the 0.51importance of strategic group and industry levels combined forreturn on assets. We appreciate a reviewer pointing this out to us.

Table 4. Relative importance of firm, strategic group, and industry levels of analysis using HLM


% ofvarianceexplained

Squareroot

Relativeimportance


Squareroot

Relativeimportance


Squareroot

Relativeimportance

Deductively defined strategic groupsFirm 65.82 0.81 0.49 91.08 0.95 0.70 96.08 0.98 0.78Strategic group 14.95 0.39 0.24 2.44 0.16 0.12 2.59 0.16 0.13Industry 19.23 0.44 0.27 6.48 0.25 0.18 1.33 0.12 0.09Total 100.00 1.64 1.00 100.00 1.36 1.00 100.00 1.26 1.00

Inductively defined strategic groupsFirm 78.97 0.89 0.59 90.68 0.95 0.69 92.44 0.96 0.73Strategic group 6.35 0.25 0.16 5.65 0.24 0.17 6.58 0.26 0.20Industry 14.68 0.38 0.25 3.67 0.19 0.14 0.98 0.10 0.07Total 100.00 1.52 1.00 100.00 1.38 1.00 100.00 1.32 1.00



was very robust—it was consistent across induc-tive and deductive derivations, and across HLM,variance components, and ANOVA (as shown inTables 2 and 3), answering researchers’ calls toassess the robustness of different methods linkingstrategic group membership to differences in firmperformance (Fox et al., 1997).

One theoretical implication is that while the set-ting in which a firm competes has a significantinfluence on short-term performance (i.e., ROA inour study), a firm’s durability is based predomi-nantly on features of the firm itself. Our findingthat a firm’s survival is largely self-determinedshould reassure strategic management researchersthat our field’s contention that managers mat-ter is well founded. Viewed broadly, the resultshave implications for the long-running discussionabout strategic choice vs. organizational ecologyas explanations of firms’ fates (e.g., Hrebiniak andJoyce, 1985). To the extent that firm character-istics are the products of managerial decisions,our results suggest that strategic choices gener-ally offer greater explanatory power than ecol-ogy where survival is concerned. Profit, how-ever, is a roughly equal product of a firm andits context (i.e., its strategic group and indus-try).

The support we found for our predictions alsosuggests that systems theory is a valuable the-ory for viewing the multilevel drivers of perfor-mance. Perhaps the key implication here is thatthe critical issue of why some firms outperformothers can be better understood when importantcomponents of organizational systems are added.Our study takes a significant step in this direc-tion by adding the strategic group level. Simi-larly, Makino, Isobe, and Chan (2004) took a sig-nificant step by adding the country level to theresearch stream (they did not include the strate-gic group level, however). While the country levelwas beyond the scope of our investigation, thislevel as well as a geographic region level (e.g.,Scandinavia, Latin America, Oceania) could beeffectively included within a systems theory frame-work.

More broadly, our study encourages researchersto consider how levels of analysis issues mightinform other research streams. For example, therelationship between firm strategy and CEO payis unclear. This link could be clarified by includ-ing industry or other contextual characteristics

(Gomez-Mejia and Wiseman, 1997). CEO dual-ity research also might benefit from a multilevelperspective. The relationship between CEO dualityand firm performance is equivocal (Dalton et al.,1998); however, the strategic group level of anal-ysis has been absent from this research. Juxtapos-ing an organizational typology such as Miles andSnow’s (1978) might help reveal the efficacy ofthe CEO also chairing the board.

Implications for practitioners

Our study offers at least three useful insightsfor managers. First, there are currently two mainschools of thought about what level of analysisdrives performance that compete for managers’attention. The industrial/organization economicsschool stresses the role of industry structure, whilethe resource-based view emphasizes the impor-tance of firm attributes. For managers attempt-ing to discern which is stronger, our results sug-gest an adherence to resource-based view logic.Specifically, managers should focus their energymainly on firm-level concerns. However, the sig-nificant role played by industry suggests thatindustrial/organization economics logic cannot beignored when forming strategy. Our second insightis that the strategic group level is worthy of man-agers’ attention. Although most managers havebeen exposed at length to various concepts involv-ing the firm and the industry, the strategic grouplevel is much less publicized. Yet, our study indi-cates this level plays a role in outcomes, especiallyprofit. Our third insight is that the results supportpast calls for managers to move beyond finan-cial measures alone when evaluating their firm’sperformance (e.g., Kaplan and Norton, 1996).Reliance simply on return on assets would sug-gest that firm attributes explain about two-thirdsof the variance in performance. For the long-termmeasures, however, firm effects exceed 90 per-cent. The implication is that a focus on financialmeasures will understate the importance of strate-gic execution for achieving sustained competitiveadvantage.

Limitations and future research directions

Our study’s results should be viewed in light ofits limitations. While HLM has unique strengths,one limitation is its need for levels to be nestedwithin only one group. This limited our sample to



single-business firms, leaving us unable to measurecorporate effects. Subsequent inquiry could exam-ine corporations whose business units in differentindustries would be classified into strategic groups.Such a design would be very complex methodolog-ically, but it might provide results that would bevery revealing. Because our sample included onlymanufacturing firms in relatively high-tech indus-tries and because our firms appear to be smallerthan publicly held firms in general, our results maynot generalize to other sectors. Thus, replication inother samples (e.g., service firms, larger firms) isneeded.

Because we examined a variety of industries,we needed to identify measures that were rele-vant in all settings. This created a trade-off inthat these measures were less precise than wouldbe possible with a narrower sample. Our rela-tively coarse approach was consistent with prac-tice in the multilevel performance determinantsliterature. Looking to the future, researchers couldenhance precision by developing industry-specificmeasures, perhaps through primary data sourcessuch as interviews and surveys. Finally, a longertime frame than our 7-year focal period may haveshed additional light on the hypotheses. Firmschange strategies, industries mature and decline,and strategic group membership has been shownto shift; thus, one unexplored research issue isthe degree to which the relative roles of firm,group, and industry membership may change overtime.

CONCLUSION

Predicting performance is a cornerstone of strate-gic management research. Two research streamsfocused on explaining performance (multilevelperformance studies and strategic groups research)have developed independently. We found that,when examined together, the firm, strategic group,and industry levels each contribute significantly toaccounting for performance. This implies that if astudy includes only one or two of the levels, theresulting portrayal of the interwoven systems thatcollectively shape firm outcomes is incomplete.For managers, our findings suggest that achiev-ing superior performance is tied primarily to firmcharacteristics, but it also depends on appropriatepositioning within a strategic group and the indus-try.

ACKNOWLEDGEMENTS

The authors would like to thank Don Bergh,William Bloom, James Combs, and Bruce Lamontfor their valuable suggestions.

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