http://jom.sagepub.com/ Journal of Management http://jom.sagepub.com/content/33/6/959 The online version of this article can be found at: DOI: 10.1177/0149206307307645 2007 33: 959 Journal of Management Craig E. Armstrong and Katsuhiko Shimizu the Firm? A Review of Approaches to Empirical Research on the Resource-Based View of Published by: http://www.sagepublications.com On behalf of: Southern Management Association can be found at: Journal of Management Additional services and information for http://jom.sagepub.com/cgi/alerts Email Alerts: http://jom.sagepub.com/subscriptions Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://jom.sagepub.com/content/33/6/959.refs.html Citations: What is This? - Nov 29, 2007 Version of Record >> at PENNSYLVANIA STATE UNIV on February 20, 2013 jom.sagepub.com Downloaded from
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http://jom.sagepub.com/Journal of Management
http://jom.sagepub.com/content/33/6/959The online version of this article can be found at:
DOI: 10.1177/0149206307307645
2007 33: 959Journal of ManagementCraig E. Armstrong and Katsuhiko Shimizu
the Firm?A Review of Approaches to Empirical Research on the Resource-Based View of
Published by:
http://www.sagepublications.com
On behalf of:
Southern Management Association
can be found at:Journal of ManagementAdditional services and information for
A Review of Approaches to Empirical Researchon the Resource-Based View of the Firm†
Craig E. Armstrong*Department of Management and Marketing, University of Alabama, Box 870225,
155 Alston Hall, 361 Stadium Drive, Tuscaloosa, AL 35487
Katsuhiko ShimizuDepartment of Management, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249
This study reviews empirical studies of the resource-based view (RBV) and examines methodologicalissues and new directions that will help to clarify the value and boundaries of the RBV. Through ourcomprehensive review of the research design and operationalization of resource-based constructsused in 125 empirical studies, we (1) discuss key empirical issues particularly important to RBVresearch, (2) illustrate how researchers have or have not overcome some of these challenges, and (3)highlight two important approaches that offer promise for sharpening the boundary conditions of theRBV: an integrative framework for RBV research and utilization of nonsignificant results.
Keywords: resource-based view; operationalization of resources; sustainability; confoundingfactors; nonsignificant results
Since its introduction into the strategic management literature, the resource-based view(RBV) of the firm (Barney, 1986, 1991, 2001; Conner, 1991; Peteraf, 1993; Wernerfelt,1984) has earned great attention among scholars as a framework for explaining the conditionsunder which a firm may gain a sustained competitive advantage. Following Penrose (1959),Wernerfelt (1984) introduced the notion that firms should be analyzed from the resource sideat the level of the firm, not just from the product side at the level of the industry. Barney(1986, 1991) argues that a firm has the potential to generate sustained competitive advantage
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†The authors would like to thank Keith Brouthers, Michael Hitt, Ed Levitas, and Richard Priem for constructivecomments on an early version of this manuscript.
from firm resources that are valuable, rare, inimitable, and nonsubstitutable. These resourcescan be viewed as bundles of tangible and intangible assets, such as a firm’s managementskills, its organizational processes and routines, and the information and knowledge underits control (Barney, Wright, & Ketchen, 2001).
While the usefulness of the RBV as a theoretical framework is still being debated (Barney,2001; Hoopes, Madsen, & Walker, 2003; Priem & Butler, 2001a, 2001b; Williamson, 1999), agrowing number of empirical articles relating to the RBV are appearing in the literature.Researchers have performed empirical tests in which they have directly or indirectly invokedthe RBV as a key theoretical anchor and accumulated important empirical contributions(Barney et al., 2001; Wernerfelt, 1995). However, serious empirical challenges inherent in theRBV remain that must be confronted to contribute to further theoretical and empirical advance-ment of the RBV (Barney et al., 2001; Godfrey & Hill, 1995; Priem & Butler, 2001a, 2001b;Robins & Wiersema, 1995). Priem and Butler (2001b: 33), for example, observe that“researchers sometimes take a frequently researched strategy subject area, relabel the indepen-dent variable as ‘resources’ and the dependent variable as ‘competitive advantage,’ and usemeasures common to much cross sectional strategy research as operationalizations.”
Our intention is not so much to add another theoretical argument to the RBV debates or pro-vide meta-analytic review of empirical results (e.g., Barney & Arikan, 2001; Newbert, 2007)as to shed new light on the RBV by providing a comprehensive examination of the empiricalissues from the standpoints of research design and operationalization of resource-based con-structs. However, compared with the wealth of articles addressing the theoretical aspects of theRBV (see special issues of Journal of Management 2001 and Strategic Management Journal2003 for examples), there has not been a similar “taking of stock” of the RBV from an empir-ical perspective. Complementing the recent review by Newbert (2007), which compared thedegree of identifying statistically significant results by different theoretical approaches in test-ing RBV, we examine more specific issues in designing valid empirical tests when one has anRBV-related research question. Unless empirical design is well developed, we do not knowwhether nonsignificant results are attributable to the RBV or to methodological problems.Arguably, methodological ambiguity may be a primary reason why the models underlying theempirical literature remain seemingly “disjointed” (Hoopes et al., 2003: 890).
The purpose of this article, therefore, is to review the approaches to RBV empirical workfrom the last 16 years and discuss methodological issues and new directions that will help toclarify the value and boundaries of the RBV. Through our comprehensive review of 125 empir-ical studies, we intend to (1) review key empirical issues particularly important to RBV research,(2) illustrate how researchers have or have not overcome some of these challenges, and (3) high-light two important approaches that offer promise for sharpening the boundary conditions of theRBV: an integrative framework for RBV research and utilization of nonsignificant results.
Background
Resource-Based View
We begin our review with a brief discussion of the key discourses of the RBV. Resourcesare generally defined as “all assets, capabilities, organizational processes, firm attributes,
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information, knowledge, etc. controlled by a firm” (Barney, 1991: 101). The RBV helps toexplain the conditions under which a firm’s resources will provide it with a competitiveadvantage (Barney, 1991). In contrast with the emphasis on external analysis in traditionalindustrial–organization economics (Bain, 1959), the RBV emphasizes an internal analysis ofthe differences in resource endowments across firms (even within the same industry) andexplains how these differences can be a source of a sustainable competitive advantage(Barney, 1986, 1991; Wernerfelt, 1984). A firm is said to have a competitive advantage whenthe firm can produce more economically and/or better satisfy customer needs, and thus enjoysuperior performance relative to its competitors (Barney, 1991; Peteraf, 1993).
Resources contribute to these performance advantages to the extent that they are valuable,rare, costly to imitate, and non-substitutable. Resources are valuable when they help to improvethe firm’s efficiency and effectiveness (Barney, 1991). The conditions under which resourcesare valuable are context dependent (Barney, 1991, 2001; Conner, 1991; Priem & Butler,2001a). The value of a certain resource is determined in relation to such conditions as organi-zational strategy and external environments (Priem & Butler, 2001a). Resources also need tobe rare to provide competitive advantage; otherwise valuable resources only provide competi-tive parity (Barney, 1991). A valuable and rare resource can help sustain a firm’s competitiveadvantage to the extent that the resource is difficult to imitate (Barney, 1991). The sources ofinimitability include (1) unique historical conditions under which resource bundles are created,(2) a causally ambiguous relationship between the resources and resulting competitive advan-tage, and (3) social complexity of the resources (Dierickx & Cool, 1989; Lippman & Rumelt,1982). Finally, valuable, rare, and difficult-to-imitate resources can be a source of sustainedcompetitive advantage to the extent that there are no strategically equivalent resources (Barney,1991). When equifinality exists, a firm that possesses valuable, rare, and inimitable resourcesmay not be able to enjoy a sustainable competitive advantage (Barney, 2001).
More recently, three approaches have emerged to extend the RBV theoretically. One, the“dynamic resource-based view” of the firm (Helfat, 2000; Helfat & Peteraf, 2003), focuses onthe resource side of the firm. This dynamic resource-based view incorporates the notion cen-tral to dynamic capabilities that resources and capabilities are continually adapted, integrated,and/or reconfigured into other resources and capabilities (Eisenhardt & Martin, 2000; Teece,Pisano, & Shuen, 1997). In line with this dynamic view, more attention has been paid to therelationship between resources and strategy implementation (Hitt, Bierman, Shimizu, &Kochhar, 2001; Newbert, 2007). The realization of the potential value of resources is depen-dent on the strategy of the firm and how the strategy is implemented and resources are utilized(Barney & Arikan, 2001; Hitt et al., 2001; Newbert, 2007). Finally, some scholars (e.g.,Wiggins & Ruefli, 2002, 2005) have adopted Schumpeterian (1939, 1942) and hypercompeti-tion (D’Aveni, 1994) views to explain the persistence (or lack thereof) of sustainable compet-itive advantage among firms. These views stress that firms are increasingly finding it difficultto sustain strategic advantage over competitors and that sustained competitive advantage ismore a function of creating a series of competitive advantages over time (Wiggins & Ruefli,2005). The Schumpeterian and hypercompetition views complement the dynamic resource-based view in their assertions that firms can realize a sustained competitive advantage only tothe extent that they can create a series of temporary advantages (Brown & Eisenhardt, 1998;D’Aveni, 1994) through the continual adaptation and reconfiguration of resources.
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Empirical RBV studies have accumulated significant contributions (Wernerfelt, 1995), despitethe difficulties in dealing with intangible constructs inherent in the RBV (Godfrey & Hill, 1995;Robins & Wiersema, 1995). However, many challenges in empirically testing the RBV constructsstill remain. For example, Rouse and Daellenbach (1999) are critical of approaches to RBVresearch using cross-sectional analysis on large sample observations with secondary data, asthose approaches are unlikely to be able to disentangle the effects from such variety of sources asindustry, environment, and strategy, and fail to “isolate sustained sources of advantage” (488-489). They recommend using a detailed, field-based comparison of carefully selected firms touncover sources of advantage “buried” in organizational effects, yet others (e.g., Levitas & Chi,2002; Makadok & Walker, 2000) suspect that such approaches have their own limitations if thevalue of observable variables is ignored and efforts to verify conclusions are denied.
We agree that RBV researchers should not de-emphasize large-sample methods, but theyshould also find ways to creatively operationalize constructs and empirically measure theorizedoutcomes (Levitas & Chi, 2002) to advance the RBV. Extending their arguments, we examinethe extant empirical work and clarify methodological issues inherent in RBV research. Ourwork that focuses on specific methodological issues is different from the recent review byNewbert (2007). Newbert (2007) reviews 55 empirical tests of the RBV to assess its level ofempirical support and concludes that considerable variation exists regarding the level of sup-port across the theoretical approaches tested (e.g., capabilities and core competencies con-tribute to a firm’s competitive advantage to a far greater extent than do resources). He suggeststhat researchers should move away from “1991–vintage” RBV approach and toward the orga-nizing approach or dynamic capability approach (i.e., testing combinative effects of a resourceand either a specific organizational condition or a specific dynamic capability on performance).In this sense, Newbert’s (2007) study draws important insights from the levels of significancefound in past RBV studies. However, methodological shortcomings common in prior RBVwork suggest that the degree to which, for example, the 1991–vintage RBV actually explainssustainable competitive advantage can only be determined if future studies use more rigorousmethods (c.f., Priem & Butler, 2001a). Although Newbert (2007) touched on issues associatedwith measurement (p. 137), his main focus was on the statistical significance/nonsignificanceof the results. To the extent that we do not know whether statistical nonsignificance is attribut-able to the RBV and related theoretical approaches or to methodological problems, our workcomplements Newbert (2007) and identifies methodological improvements going forward thatwill help increase our confidence in the findings of future RBV work.
In summary, our review evaluates empirical RBV studies from a methodological perspec-tive and discusses both the contributions of the past research and issues that need to be morecarefully considered and addressed in the future. We elaborate on those issues and make a fewproposals that will facilitate further theoretical and empirical development of the RBV.
Review of Empirical Research
Scope of the Review
We limited the literature review to empirical studies published in the Academy ofManagement Journal, Administrative Science Quarterly, Journal of International Business
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Studies, Journal of Management, Journal of Management Studies, Management Science,Organization Science, and Strategic Management Journal. These journals share a high stan-dard of methodological rigor that fits the goal of this study. To be conservative, we identifiedstudies that included the phrase “resource-based” in the title, abstract, or listing of key wordsand presented and tested hypotheses on sample populations by operationalizing the con-structs in an empirical model. Using this approach, we identified 125 empirical RBV stud-ies that have appeared in the strategic management research literature between 1991 and2005. Because a key conceptual contribution of RBV theory is a framework for the relationbetween resources and sustainable competitive advantage (Barney, 1991), our review andcomments focus on the 125 studies that expressly test the relation between firm resourcesand performance-related outcomes. This means that our panel of empirical studies on theRBV did not include studies with other outcomes such as CEO compensation (Balkin,Markman, & Gomez-Mejia, 2000), sharing of fund managers (Drazin & Rao, 2002), andfirm learning from alliances (Simonin, 1997). Further, our selection criteria do not includeother studies related to the RBV, such as the knowledge-based, dynamic capabilities, andrelational views (cf. Acedo, Barroso, & Galan, 2006). We strongly believe, however, that ourintensive review of 125 studies provides a good starting point in understanding the issues inempirical tests of the RBV and other related areas.
As shown in Table 1, the number of empirical studies based on the RBV was steadilyincreasing until 2001 and has shown a saturating tendency during the last few years (Barney& Arikan, 2001; Wernerfelt, 1995). It is possible that this saturation is because of the method-ological rigor demanded from RBV researchers (Barney, 2001; Priem & Butler, 2001a). In thefollowing sections, we review our own panel of studies from a methodological perspectiveand discuss both the contributions of the past research and issues that need to be more care-fully considered and addressed in the future. While we acknowledge that some of these issuesare applicable to any good research, we elaborate on those issues specifically as they relate tothe RBV. Finally, we propose a framework that will facilitate further theoretical and empiri-cal development of RBV (c.f., Hoopes et al., 2003; Rouse & Daellenbach, 1999).
Overview
In evaluating a theory, Bacharach (1989) stresses two important criteria: falisifiability andutility. To test whether a theory meets these criteria, variables need to appropriately reflectconstructs, and causal relationships should be adequately examined (Bacharach, 1989). TheRBV essentially explains and predicts the relationships between the particular resources ofa firm (independent variables) and sustainable competitive advantage reflected by perfor-mance-related outcomes (dependent variable) (Barney & Arikan, 2001; Henderson &Cockburn, 1994; Rouse & Daellenbach, 1999). Accordingly, in applying Bacharach’s (1989)framework, we examine here (1) issues associated with operationalizing independent vari-ables (i.e., resources), (2) issues associated with operationalizing the dependent variable(i.e., performance), and (3) isolating relationships between resources and performance. Afterreviewing major topics in each of these issues, we summarize our assessment and recom-mendations in the following section.
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Issues in Independent Variables: Operationalization of Resources
An important challenge for researchers in empirically testing the RBV is isolation andoperationalization of constructs (i.e., resources). The key theoretical thrust of the RBV isinimitability of valuable resources that lead to sustainable competitive advantage and aboveaverage returns (Barney, 1991, 2001). Intangible and hard-to-observe resources are, by def-inition, inimitable. Therefore, measuring the inimitable resources is an inherent difficulty inRBV research (Godfrey & Hill, 1995; Zander & Kogut, 1995). In this section, we discussthree topics related to isolating and operationalizing resources: (1) incorporating a qualita-tive approach, (2) operationalizing the resources by survey methods, and (3) operationaliz-ing the resources by objective proxies.
Before discussing the issues associated with operationalization, we believe it is valuableto point out that the distinction between “variables” and “constructs” is important, but this isnot always an easy task in RBV research (Bacharach, 1989; Priem & Butler, 2001b). This ispartly because the concept of “resources” is “all-inclusive” (Priem & Butler, 2001a) and“extremely expansive” (Denrell, Fang, & Winter, 2003). In fact, Priem and Butler (2001b)argue that there are three levels in RBV tests: resources (“constructs”), specific resources(“lower-level constructs”), and variables that reflect theorized resources. As a result, whilemany researchers use lower-level constructs such as human capital in their hypotheses, oth-ers use more readily measurable variables such as “tenure” and “experience,” even if bothgroups of researchers similarly draw on the RBV as a key theoretical anchor. The latterresearchers hypothesize and test the relationship between tenure of managers and perfor-mance, rather than hypothesizing and testing the relationship between human capital andperformance. In our panel of empirical tests, 12 such studies relied exclusively on thisapproach, while another 29 studies used approaches that combined constructs and directlymeasurable variables. This approach of using readily measurable variables is certainly legit-imate, but, in our opinion, it offers limited contributions toward understanding the real valueof resource-based theory (Barney, 1991; Nelson, 1991; Zander & Kogut, 1995). To the extentthat key constructs of RBV are inherently unobservable (Godfrey & Hill, 1995), creativelydeveloping appropriate measures and accumulating those measures, as opposed to usingreadily available measures, will challenge and contribute to further development of RBV(Barney, 2001; Levitas & Chi, 2002).
Incorporating a Qualitative Approach
Levitas and Chi (2002: 960) call the difficulty of isolating unobservable resources “a fun-damental paradox”: reliable isolation is unattainable without full comprehension, but full com-prehension will enable outsiders to replicate what makes the firm unique. Thus, conductingfield studies will provide researchers more insights to isolate resources that are valuable andhard to imitate, since those will otherwise not be easily observed or comprehended (Barney,1991, 2001; Godfrey & Hill, 1995). Rouse and Daellenbach (1999) recommended thatresearchers should conduct inductive field interviews among high- and low-performing firmswithin the same industry. Godfrey and Hill (1995: 530) also recommend that researchers
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consider clinical work since “the description of the firm found in RBV is complex, deep, andhistorical.” We agree that understanding the richness of resources is important for RBVresearchers (Henderson & Cockburn, 1994). It is a prerequisite of this approach thatresearchers understand the limitations and potential problems of inductive approaches, such assubjectivity and personal and positional biases, and triangulate the information by accessingdifferent informants and sources (Huber & Power, 1985). In our review, 15 studies used qual-itative methods. McKevily and Chakravarthy (2002) provide a useful example. In their studyof firms in the adhesives formulation industry, they spent several months interviewing scien-tists and formulators to learn how adhesives are developed, then validated their interview datawith trade journals and two experts on adhesives technology. These qualitative approaches ledto the development of quantitative measures used to test the effects of knowledge complexity,tacitness, and specificity on the persistence of a firm’s performance advantages (McEvily &Chakravarthy, 2002). This approach can be especially useful in so-called “high-velocity” envi-ronments in which the rates of technological and competitive change make useful externalinformation unavailable or obsolete (Bourgeois & Eisenhardt, 1988). Given that external envi-ronments can change rapidly, we believe that researchers should consider using the qualitativeapproach more in applying the RBV to new areas or businesses.
Operationalizing Resources Using Survey
To conquer measurement problems associated with unobservability of resources, someresearchers use a survey methodology. A survey methodology is useful for obtaining directassessments about particular resources. Since it is difficult for researchers to objectivelyobserve such dimensions as value and inimitability of resources, developing an appropriatesurvey based on in-depth interviews with focal firms or experts in the industry should miti-gate the construct measurement problems in RBV research (c.f., Chen, Farh, & MacMillan,1993). For example, McEvily and Chakravarthy (2002) measure complexity, specificity, andtacitness using survey instruments to capture the dimension of inimitability. In our panel of125 studies, 57 studies used this approach for independent variables.
Besides general issues associated with using and developing a survey methodology, specialattention needs to be paid to the survey target in RBV research. This is precisely because theRBV deals with competitive advantages over competitors. Theoretically, then, dimensions suchas inimitability should be assessed by competitors or outsiders. The risks of overconfidenceand hubris among managers about their own resources and capabilities have been well estab-lished (e.g., Hayward & Hambrick, 1997; Zajac & Bazerman, 1991). While insiders maybelieve that a particular technology is valuable and hard to imitate, competitors may not thinkthat way. It may also be the case that resources that are taken for granted within a firm mayserve as a strong barrier against a competitor’s imitation efforts (Rouse & Daellenbach, 1999).Accordingly, it is beneficial for researchers to obtain assessments of resources from outsiders.Of the 57 studies that adopted survey instruments for independent variables, only four usedoutsiders (Bruton, Oviatt, & White, 1994; Combs & Ketchen, 1999; Silverman, 1999; Singh,1997). While it is probably difficult to directly ask competitors, utilizing industry experts suchas analysts should be more widely considered in future research (Chen et al., 1993).
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Operationalizing Resources Using Objective Proxies
Because of the difficulties inherent in measuring resources, some RBV researchers havefound ways to rigorously operationalize their constructs with observable proxies (Barney &Arikan, 2001; Wernerfelt, 1995). The logic behind this approach is that “theories that containunobservables should not be judged on the basis of their correspondence to reality, but insteadon their instrumental value as tools for generating predictions about the behavior of physical,natural, and social systems” (Godfrey & Hill, 1995: 520, also Bacharach, 1989). Thus, amongothers, Godfrey and Hill (1995: 530) point out that RBV researchers should “theoreticallyidentify what the observable consequences of unobservable resources are likely to be”(emphasis added). Since it is unlikely to find one proxy that reflects unobservable resources,researchers should use multiple variables to collectively represent latent constructs (Barneyet al., 2001; Boyd, Gove, & Hitt, 2005). Accordingly, we were surprised to find that manystudies rely on single indicators of resources (43 studies). Although parsimony is important,we believe that construct validity is particularly critical in RBV research when observableproxies are used (Bacharach, 1989; Boyd et al., 2005; Godfrey & Hill, 1995).
Another related issue is how the observable proxies should be selected. In our panel ofstudies we observed the use of R&D intensity, number of alliances, and/or years of experi-ence as proxies for knowledge development. However, this type of proxy represents “inputsto the creation of capabilities and indicates little if anything about resultant changes in capa-bilities” (Mowery, Oxley, & Silverman, 1996: 82). Further, using those simple proxies ofknowledge assumes that learning capabilities are homogeneous across firms, an assumptionwhich fundamentally contradicts the explanatory logic of the RBV. Heterogeneity of learn-ing capabilities plays an important role in creating heterogeneity in resources across firms(Anand & Khanna, 2000; Makadok, 1999). An alternative is to use output results as a proxyfor resources. For example, Miller and Shamsie (1996) used the number of Academy Awardsin measuring knowledge-based resources of Hollywood film studios. Mowery et al. (1996)and others used the number of patents as a proxy for R&D capabilities. Using output vari-ables, however, does not allow researchers to escape the criticism of not untangling the“black box” of firms (Priem & Butler, 2001b; Rouse & Daellenbach, 2002) unless accom-panied by a good theoretical justification and exclusion of alternative explanations. Thisissue did not attract enough attention in the articles we reviewed and needs to be examinedmore carefully in the selection of observable proxies (Barney et al., 2001).
Issues in the Dependent Variable: Sustainability
The central aim of strategic management has been to understand sustained competitiveadvantage and figure out how it can be systematically created (Meyer, 1991; Porter, 1980;Rumelt, 1984). If firms can achieve above-normal returns over a long time horizon, they canbe considered to have a sustained competitive advantage (Amit & Schoemaker, 1993;Barney, 1991; Conner, 1991). Although sustainability is such an important part of the RBV,attention to this issue from an empirical standpoint has been rather limited. One reason forthis inattention might be because of the infrequency of instances of firms that have obviouslyachieved sustained superior positions under competitive environments. For example, a recent
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study of 6,772 firms in 40 industries over 25 years showed that only four firms achieved 20years or more of persistent superior financial performance relative to their industry peersbased on the Tobin’s q metric, and only 32 firms achieved 20 years or more of persistentsuperior financial performance based on return on assets (Wiggins & Ruefli, 2002). Otherreasons include the difficulty in defining “sustainability” in terms of length or degree, as itmay change over time and be different across industries.
In our review, only four studies paid specific attention to this issue in their developmentof a sustainability-based dependent variable as shown in Table 2. Pettus (2001), for example,develops a resource-based perspective for predicting the sequencing of a firm’s resourcesthat best provides for firm growth. In doing so, he captures the development process of afirm’s resource base over time and relates this to sustainable growth. The dependent vari-ables of McEvily and Chakravarthy’s (2002) study of the U.S. adhesives industry expresslyfocus on persistence as “the difference between a firm’s development time and the speedwith which competitors replicate its performance (which) allows the focal firm to sustainsuperior product performance” (2002: 297).
Alternatively, some of the studies use such metrics as average return on assets (ROA) overa few years (e.g., three years) as a dependent variable. It is fair to say that average perfor-mance over certain time periods can partially capture the concept of sustainability. This met-ric, however, could permit a firm with a single year of extraordinary performance to beclassified as a sustained superior performer (Wiggins & Ruefli, 2002). We believe thatgreater use of longer-term performance metrics is needed to articulate the impacts ofresources on sustained competitive advantage. Alternatively, effects of a resource in a base-line year t may be examined by comparing performance in year t to years t+1, t+2, etc.Exploring such approaches will provide more information about the sustainability of theeffects of the focal resources.
Clearly defining and using sustainability-based dependent variables will also contributeto the understanding of inimitability of the resources (Dierickx & Cool, 1989). This isbecause, theoretically, even resources that are easy to imitate can provide “temporal” com-petitive advantages and favorable performance (Barney, 1986, 1991). Also, the definition of“sustainability” may vary based on industry or time. For example, one year of competitiveadvantage may be long in a high-tech industry, but not sufficient in the steel industry.Accordingly, we believe that the issue of sustainability needs to be more vigorously dis-cussed both at the hypothesis building stage by considering industry or other contextual fac-tors and at the stage of developing an empirical research design (Barney, 2001; McEvily &Chakravarthy, 2002; Pettus, 2001; Priem & Butler, 2001a).
In terms of research design, we believe it important to use a longitudinal setting—asopposed to a cross-sectional setting—to incorporate tests of the “sustainability” construct.By adopting time-series approaches, we can examine the dynamic relationships over timeand see how the conditions under which resources are developed or acquired in one periodaffect the strategic advantages of firms in subsequent periods (Barney, 2001). In a cross-sectional design, causality and dynamics, which are two key assumptions in testing sustain-ability, are hard to demonstrate. As discussed later, a longitudinal setting is also importantfor controlling systematic unobserved heterogeneity that can confound resource-competitiveadvantage relationships (Henderson & Cockburn, 1994). In our review, researchers’ use of alongitudinal research design is encouraging, but more studies need to adopt this approach. In
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our panel of 125 studies, 47 studies (38%) adopt a longitudinal approach, while the remaining78 studies adopt a cross-sectional setting.
Weick (1974) argued more than 30 years ago in his evaluation of systems theory researchthat more attention should be paid to the effects or dependent variables. We believe thatthe same arguments are applicable to RBV research now: more attention to the dependentvariable such as sustainable superior performance is needed to further develop RBVtheoretically and empirically.
Issues in Relationships: Control of Confounding Factors
When both independent and dependent variables are well specified, a study’s empiricaldesign needs to control for confounding factors. Otherwise, seemingly significant resultsmay be because of spuriousness embedded in the design. In this section, we first review theissue associated with consistency of level between the dependent and independent variables.We also examine how researchers have managed three major confounding factors: (1) indus-try effects, (2) parent effects, and (3) unobserved heterogeneity.
Level
Previous empirical research in the RBV has mostly focused on the effect of firm-specificresources on the overall performance of the firm (Barney & Arikan, 2001). However, firm-level performance may be an aggregated result of the different effects of different resources(Ray, Barney, & Muhanna, 2004). This aggregated level of analysis can obscure importantdifferences in performance between firms that have both superior and inferior resources andthose that lack both those resources. Accordingly, using firm-level performance takes the riskof confounding the effects of a certain resource. This risk is particularly critical when sam-ple firms have multiple businesses and compete simultaneously in several industries, repre-senting a condition in which the value of a particular resource will be different and/or effectsof certain resource are masked by other resources (Henderson & Cockburn, 1994).
In our review, only 20 out of 125 studies examined dependent variables at levels lower thanfirm-level performance, as shown in Tables 1 and 3. Using firm-level performance by itself,however, is not necessarily problematic since the RBV is a firm-level framework. Nonetheless,considering the potential problems resulting from differences in the level of dependent andindependent variables and controlling potentially confounding factors will provide more rigor-ous empirical contributions (Henderson & Cockburn, 1994; Ray et al., 2004). For futureresearch, it is worth considering using lower-than-firm-level performance data or incorporatingvariables that control for intraorganizational effects other than the focal resources.
Industry
In the debate between Priem and Butler (2001a) and Barney (2001), both parties agreethat “the value of a firm’s resources must be understood in the specific context within which
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Table 3Lower-Than-Firm-Level Dependent Variables in RBV Empirical Studies
Between 1991 and 2005
Citation Year Design DV from Hypotheses DV Level
Henderson & 1994 longitudinal drug discovery productivity research programCockburn
Pisano 1994 cross-sectional lead time between start of process development development project and projectits completion
Bergh 1995 longitudinal size and relatedness of unit sold subsidiaryWright, Smart, & 1995 cross-sectional importance coach places on individual
McMahan skills of recruitsZaheer 1995 cross-sectional profitability and evidence of trading room
liability of foreignnessKoch & McGrath 1996 cross-sectional labor productivity business unitAnand & Singh 1997 cross-sectional performance acquisitionAthanassiou & Nigh 1999 cross-sectional international business advisory top management team
network densityKlassen & Whybark 1999 cross-sectional plant manufacturing and plant
environmental performanceMakadok 1999 longitudinal market share relative to mutual fund family
competitorsGalunic & Anderson 2000 cross-sectional agent commitment to firm agentCarpenter, Sanders, 2001 cross-sectional CEO compensation level CEO
Ray, Barney, & 2004 cross-sectional performance of customer serviceMuhanna customer service
Ahuja, Coff, & Lee 2005 cross-sectional insider purchases individual
a firm is operating” (Barney, 2001: 52). Other researchers also suggest that organizationalresources co-evolve with industry or external environments through continuous feedbackprocesses (Levinthal & Myatt, 1994; Van den Bosch, Volberda, & de Boer, 1999). In other
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words, the value of a particular resource is frequently industry dependent and industryeffects need to be carefully controlled (Rouse & Daellenbach, 1999). While other contextualfactors such as country setting can influence the value of a given resource, we focus our dis-cussion on industry as a major contextual factor.
One approach researchers have frequently used is to confine the sample population to asingle industry. While resource value idiosyncrasy can limit the generalizability of single-industry studies (Barney, 2001; Priem & Butler, 2001a), the generalizability of the RBV maybe pursued by accumulating the results of multiple single-industry studies (c.f., Jensen,1983). In our review, about 47% (59 out of 125) of the empirical studies employed a single-industry approach. It is notable that some (10 of 59 single-industry settings, and 34 of the 66studies in multi-industry settings) of the studies define industry conveniently by using SICcodes, which may not sufficiently control for the uniqueness associated with a given indus-try (Barney, 2001; Robins & Wiersema, 1995; Rumelt, 1982). We elaborate on issues asso-ciated with the use of SIC codes later in this section.
The rest of the studies (66 out of 125) examine the RBV in multi-industry settings. Thisapproach helps researchers increase both sample size and generalizability (Dess, Ireland, &Hitt, 1990; Hoskisson, Hitt, Wan, & Yiu, 1999). Conducting RBV tests in multiple-industrysettings, however, requires researchers to control for industry effects. Controlling for indus-try effects is important because (1) the performance of firms is often influenced by generalindustry environments such as industry economic cycle (Dess et al., 1990; Rumelt, 1982,1991), and (2) the relationship between the performance and resources may be industry-dependent (Barney, 2001). Without controlling for industry effects, researchers may obtainerroneous results, such as support for opposite relationships, or unsupportable relationshipsat best. Alternatively, if “industry” is operationalized with one variable, industry may be usedas a moderating variable of the relationship between a resource and performance.
In our review of the 66 multi-industry studies, we found that 8 studies control for industryby adjusting the dependent variable (i.e., performance). This adjustment is important to controlfor the differences in levels of general industry environmental effects on performance (Desset al., 1990). However, adjusting only the level of performance does not control the idiosyn-crasy of the relationship between performance and resources across industries. Mathematically,subtracting a certain value (e.g., industry mean) from a linear relationship changes the y-inter-cept of the equation, but not the slope. The effects should be controlled by either includingindustry (dummy) variables, including variables that capture characteristics of industries, orstratifying the sample by such characteristics. For example, in advocating a rigorous method-ology in strategy research, Harrigan (1983) earlier suggests that sample industries should bestratified by such key variables as the degree of product differentiation, the degree of exit bar-riers, and the degree of need for buyer-seller integration. In our review, 27 of the 66 multi-industry studies control for industry effects using dummy variables for industry.
Finally, in relation to Harrigan’s (1983) point, the definition of “industry” needs to becarefully considered. In fact, both Rumelt (1982) and Barney (2001) admit that the defini-tion of an industry is subject to interpretation and often not very clear. Although many stud-ies use the SIC convention (as did 34 of the 66 studies that used a multi-industry sample inour review), and some research finds support for this approach (e.g., Hoskisson, Hitt,Johnson, & Moesel, 1993), more rigorous attention needs to be paid to this issue because
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controlling for industry effects is one of the core issues in RBV empirical research (Barney,2001; Robins & Wiersema, 1995).
The SIC system involves problematic assumptions such as the supposition that industriesare homogeneous within category levels (Robins & Wiersema, 1995). Another problem posedby use of the SIC code system in RBV research is that the system categorizes firms based onthe similarity of product outputs without regard to the similarity or dissimilarity of theresources firms within a given industry use to produce them. To overcome this limitation,researchers have begun to explore ways to categorize industries based on inputs. Coff (2002),for example, has categorized industries based on the similarity of human resource expertiseon which they draw. The Occupational Employment Statistics program of the Bureau ofLabor Statistics provides this data service. Future empirical research in the RBV should ben-efit from similar applications of such databases to improve the level of differentiation betweenindustries. Similarly, Farjoun (1998) adopts a skills-based approach rather than a product-based approach to provide a greater level of detail to the concept of industry relatedness. Inaddition to using objective differences (Harrigan, 1983), defining an industry based on man-agers’ cognitions may provide important insights since managers closely monitor and interactwith their competitors (Reger & Huff, 1993). In our review, however, this approach was notused in any study and thus should be considered as another future research opportunity.
Parent Effects
The issue of controlling for the effects of the corporate parent in diversified firms isclosely related to the issues with level of analysis described earlier. Whereas the value ofmost resources is industry-dependent (Barney, 1991; Priem & Butler, 2001a), diversifiedfirms may develop unique resources that can be transferable across businesses and contributeto competitive advantage in each of the businesses. For example, General Electric’s “SixSigma” quality program represents a valuable, rare, and costly-to-imitate corporate resourcethat the firm exploits across each of its lines of business. These resources are regarded ascore competencies that can be leveraged widely to many products and markets (Hamel &Prahalad, 1990). The importance of corporate parent effects has also been illustrated in thestudies of Rumelt (1991), McGahan and Porter (1997), and Brush, Bromiley, and Hendrickx(1999), which respectively show that corporate-parent effects account for 1–2%, 4%, and5–15% of aggregate variance in profitability.
In this regard, the structure of sample firms (e.g., whether sample firms are single busi-nesses operating in single industries or diversified conglomerates) needs special attention inRBV studies. Of the 125 studies we reviewed, 32 of the studies examined single-businessfirms in a single industry, 5 examined single-business firms in multiple industries, 6 exam-ined single businesses of diversified firms in one industry, 4 examined single businesses ofdiversified firms in multiple industries, and 18 examined diversified firms. While we wereable to determine if a study design used a single or multiple industries for its sample popula-tion for all our studies, we were not able to determine the structural unit of analysis in termsof single versus diversified entities for 54 of these studies. In other words, 54 of our 125 stud-ies may not have controlled for other businesses or parent effects. Future RBV studies should
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pay more attention to the parent effects and control the effects, for example, by including adiversification measure, if necessary. Controlling for overall firm effects in a comprehensivemanner is discussed in the next section.
Unobserved Heterogeneity
RBV research also needs to control for unobserved heterogeneity (beyond the focalresources) embedded in each firm that could bias the model estimation (Levitas & Chi, 2002).Researchers can do this by incorporating firm dummy variables (Anand & Khanna, 2000;Henderson & Cockburn, 1994). This issue is particularly important to RBV research for tworeasons. First, it is virtually impossible to select and measure every important resource. Whenresearchers adopt firm-level aggregated performance as a dependent variable and/or whendiversified firms are included in the sample, the effects on performance other than focalresources need to be controlled (Ray et al., 2004). Second, substitutability, one of the four keyconditions of resources that determine sustainable competitive advantage (Barney, 1991,2001), cannot easily be determined. Thus, it is possible that firms may employ a very differ-ent, yet strategically-equivalent resource as a substitute for a focal resource (Barney, 1991).Therefore, researchers need to control for this possibility. For example, firm dummy variableshave been shown to capture a variety of confounding effects or “noise,” such as systematicdifferences across firms in their propensity to patent and their accounting practices; thus,“omitting firm dummies gives puzzling results” (Henderson & Cockburn, 1994: 77).
In our review, only three studies (Henderson & Cockburn, 1994; Makadok, 1999; Park,Chen, & Gallagher, 2002) incorporated firm-level dummy variables to control for systematicunobserved heterogeneity across sample firms. While doing so requires panel data even in asingle-industry study (Anand & Khanna, 2000) and may make it more difficult to structureand collect data, we believe that future studies need to pay more attention to this issue. Hittet al. (2001), for example, use a least square dummy variable (LSDV) approach with dummyvariables for each firm and each year and estimated their model using generalized leastsquares regression.
Future Directions for RBV Empirical Research
Our review of empirical tests of the RBV up to this point has focused on three major issues:(1) operationalizing inherently hard-to-observe resources, (2) capturing “sustainability” in thedependent variable, and (3) control of confounding factors in the relationship betweenresources and sustainable competitive advantages and resulting performance outcomes. Ourdiscussion and recommendations for each of those issues are summarized in Table 4.
In this section, we integrate our discussion and recommendations for these issues and pro-pose two future directions to advance empirical research on the RBV. The first is a proposedintegrative framework for RBV research from a design and operationalization perspective; thesecond is the use of nonsignificant results as a valuable tool in RBV research. Our intentionis to help provide a response to the criticism that findings in RBV research are rather frag-mented and that this fragmentation has prevented the RBV from achieving an appropriate
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level of usefulness (Hoopes et al., 2003: 889, also Priem & Butler, 2001b; Rouse &Daellenbach, 1999).
Integrative Framework: Research Design and Operationalization
One main criticism of RBV research is that the broad definition of “resource” (e.g.,Barney et al., 2001) has resulted in a fragmentation of empirical studies and has distractedsome researchers from rigorously considering which methodological approach is mostappropriate (Denrell et al., 2003; Hoopes et al., 2003; Newbert, 2007). As we explain inmore detail below, we believe that this criticism can be overcome by clarifying researchinterests in terms of the types of resources in relation to specific operationalizationapproaches and by positioning the research within an overarching framework.
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Table 4Future Issues for RBV Empirical Studies
Issues Recommendations
Isolation and Operationalization of Resourcesa. Incorporating a Adopt a qualitative approach when industry or a focal
qualitative approach resource is new and/or unexplored.b. Operationalizing Include outside informants as assessors of the value and
resources using survey imitability of resources c. Operationalizing resources Use multiple indicators of a particular resource; it is unlikely
using objective proxies that one proxy will fully reflect an unobservable resourceTheoretically justify selection of indicators (e.g., input
variables vs. output variables) and exclude alternativeexplanations
Sustainability Pay attention to and select more appropriate dependentvariables that well reflect “sustainability,”
Use a longitudinal research design to see the long-termeffects of focal resources on the competitive advantage
Control of Confounding Factorsa. Level Maintain consistency of level between the dependent variable
and the focal resources (i.e., use of a dependent variable ata level lower than firm-level dependent variables should beexplored)
b. Industry Elucidate industry idiosyncrasies (e.g., value of a particularresource in the industry) by using either a single-industrydesign or a multiple-industry design with appropriateindustry controls
Consider incorporating industry into analyses as amoderating variable
c. Parent Clarify the degree of diversification of sample firmsControl for parent effects to account for variances in
performance contributed by diversity of the firmd. Unobserved heterogeneity Control for unobserved heterogeneity across sample firms by
using firm dummy variables (a longitudinal designis required)
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The key interest of the RBV is in what Denrell et al. (2003) call “complex resources” asopposed to the “commodity resources” that are typically available in markets and foundacross industries. Yet researchers have also demonstrated that, even among complexresources, there are some resources that can be important across industries and others thatare more important to a particular industry. When researchers want to examine relativelygeneral resources that are important across industries, it is natural to use a sample of firmsfrom various industries. By using a sample of firms derived from different industries, thegeneral value of the focal resources can be appropriately tested. In doing so, researchers needto consider two important issues. First, the actual effect size of a particular resource is con-text dependent. Thus, studies without a control variable for industry can be misleading.Second, the potential confounding effects on performance posed by unobserved heterogene-ity (Henderson & Cockburn, 1994) need to be controlled by collecting longitudinal data (i.e.,panel data). Controlling unobserved heterogeneity across firms is particularly important inRBV research (1) because the potential for substitution through use of other resources is oneof the key ideas of the RBV and (2) because researchers may have difficulty obtaining per-formance data below the aggregated organizational level, which may be contaminated by theeffects of resources embedded in the parent or other units within the organization (McGahan& Porter, 1997; Ray et al., 2003).
In the 45 studies in our sample that used a multiple-industry, cross-sectional design, theR-square attributable to the focal resources ranges from 0.005 to 0.367, with an average of0.06. This suggests that there is a substantial opportunity for researchers to explore theeffects of industry-specific complex resources. To do so, a single industry evaluated with apanel data approach will be suitable. Alternatively, when a dependent variable is appropri-ately specified in relation to the focal resource, with appropriate control of industry effects,a cross-sectional approach may be possible. A cross-sectional approach is sometimesinevitable when a survey methodology is used.
Although industry-specific resources can be theoretically isolated, it may often be difficultfor outside researchers to do so. In fact, in the 27 studies in our sample that use a single-indus-try, longitudinal design, the R-square explained by the focal resources ranges from 0.02 to0.47, with an average of 0.08. Accordingly, in exploring industry-specific resources or whenlarge unexplained variances persist, a qualitative approach such as use of interviews withindustry experts and insiders should prove effective for isolating important but under-exam-ined resources (Rouse & Daellenbach, 1999).
The four different approaches we describe here are not independent, but complementary.Studies of generally important resources, particularly when the R-square is low, can suggestthe need for exploring and examining more industry-specific resources. While some industry-specific resources may be theoretically driven, others may be identified only through in-depth interviews with insiders, particularly when an industry is changing or a new industryis emerging. When those resources that had not been examined are identified, a large sam-ple study in a single industry setting can validate the actual effects of the resources.Examining newly found resources in a multiple-industry setting may also provide a bound-ary of the effects of the resources (i.e., to what range or in which industries the effects exist).
We believe that a survey developed through in-depth interviews provides a finer-grainedoperationalization and assessment of focal resources. However, besides the risks of subjectivityand bias, it is very difficult for researchers to collect longitudinal data with a survey method, if
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not impossible (c.f., Miller, Cardinal and Glick, 1997). Thus, we believe that a survey methodis especially valuable in such focused research settings as a clearly defined single-industry set-ting or even a single-firm setting with specified dependent variables (e.g., Hatch & Dyer, 2004).
In contrast with a survey method, objective proxies are coarse, indirect measures ofresources. Nevertheless, objective proxies help researchers to examine the effects of theresource in a large sample in a longitudinal fashion. Moreover, the approach can be easilyreplicated or modified by other researchers. In this way, both survey and objective proxyapproaches are complementary (Godfrey & Hill, 1995; Levitas & Chi, 2002). We believethat researchers should strive to use output measures for proxies (Miller & Shamsie, 1996;Mowery, et al., 1996). Using input measures as a proxy of resources assumes that the dif-ference is determined by input only and the organizational capability to utilize the input ishomogeneous (c.f., Makadok, 1999). This assumption contradicts a key tenet of the RBV(Dutta, Narasimhan, & Rajiv, 2005).
Although the findings of empirical research on the RBV can be interrelated and integrated,we observed limited efforts to do so during our review. Recently, Newbert (2007) made a pre-liminary step by distinguishing four different theoretical approaches in designing empiricaltests of RBV. Future research can contribute to the literature by focusing not only on its ownparticular resource or context of interest, but also on how the results are related to others interms of such conditions as industry and time (Bacharach, 1989). Positioning their researchwithin the framework we propose here, researchers can see the next step to extend their RBVresearch avenue and to relate their research to others’. As a result, RBV researchers should bebetter able to break free from criticisms of “disjointed” results (Hoopes et al., 2003).
Use of Nonsignificant Findings (Null Hypothesis)
As discussed, RBV researchers have been working on testing the relationships betweenresources and sustainable competitive advantages and resulting performance outcomes(Barney, 2001: Barney et al., 2001; Levitas & Chi, 2002). In other words, the major concernof RBV researchers has been, “What resources are contributing to high performance?” Theflip side of this question is “what resources are not?” A clear distinction of resources or con-ditions that lead to sustainable competitive advantages from those that do not provides con-siderable implications for both managers and researchers (Cortina & Folger, 1998). Formanagers, this distinction will help them prioritize resources and optimally allocate invest-ments to develop a competitive advantage. For researchers, clarifying criteria or conditions inwhich particular resources do not result in competitive advantage provides the RBV withboundaries, which are a key component of a good theory (Bacharach, 1989). We are not rec-ommending that researchers should actually test resources that will not provide sustainableadvantage. We know there are many resources that will not contribute to sustainable compet-itive advantage. What we do not know is a clear boundary. If we could identify a context inwhich a seemingly valuable, rare, and hard-to-imitate resource does not provide sustainablecompetitive advantage and accumulate these findings, we can greatly extend RBV research.Accordingly, we recommend that researchers should utilize nonsignificant findings.
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Although unsupported hypotheses are generally regarded as of little value in managementresearch and often explained away (Cortina & Folger, 1998), we believe that the “non-significant” results that arise from a carefully crafted study design with high statistical powercan contribute to further developing our understanding of the value and boundaries of theRBV. While a few studies perform direct tests of null hypotheses (e.g., Ray et al., 2004), amore systematic examination and accumulation of “what is not” can emerge as an importantmissing piece in this stream of research. In fact, differences in the conclusions of Barney andArikan (2001) from those of Newbert (2007) are partly because of the differences in how tohandle or interpret nonsignificant results.
Empirically, justifying statistically nonsignificant results requires sophisticated researchdesign and methods, such as considering the power of a test, although the complexity of suchmethods has limited its application in strategic management research (Cohen, Cohen, West, &Aiken, 2003; Cortina & Folger, 1998; Ferguson & Ketchen, 1999). Statistical power is the prob-ability of correctly rejecting a null hypothesis, described as (1-β) where β is the probability of atype II error (accepting a false null hypothesis). Power is calculated in relation to effect size, sig-nificance level (α), and sample size. From a practical standpoint, empirical studies need to havea minimum sample size (Brock, 2003) to maintain an acceptable power, such as 0.8 (i.e., 80%probability of detecting an effect in the sample if it exists in the population). Because the effectsize in strategic management research is often small (Ferguson & Ketchen, 1999), a multiple-regression study that examines the incremental effect of one variable (i.e., a proxy of an unob-servable resource) over the effect of control variables may require a sample size as large as 400(Brock, 2003; Cohen et al., 2003) to assure a power of 0.8 and α=0.05. More stringent tests withpowers equal to 0.9 or higher need a larger sample size. Incorporating appropriate statisticalpower in RBV empirical research is important not only for correctly detecting the effects of par-ticular resources on competitive advantages or resulting performance, but also for understand-ing under what conditions hypothesized effects are rejected (Priem & Butler, 2001b). Althoughnot all studies will be able to obtain such large sample sizes, doing so and empirically demon-strating the validity of nonsignificant findings is a worthwhile endeavor.
Suggesting various important hypotheses that involve null effects in such areas as escala-tion of commitment (i.e., when commitment does not occur) and goal setting (when specificgoals do not increase performance), Cortina and Folger (1998) argue that researchers shoulddrop the “prejudice against the null” and consider the value of null hypotheses. They (1998)suggest two research designs that demonstrate sufficient effort to reject the null of primaryinterest and argue that null hypotheses that are still not rejected should be supported. Oneapproach is examining moderating conditions and showing that the effects are present in onecondition and not in another condition. This approach is consistent with using industry as amoderating variable as discussed in a previous section. Another approach is triangulatingresults by using multiple measures of the key variables. This idea is also consistent with thenecessity of multiple measures for operationalization.
Of the population of hypotheses offered in our panel of empirical studies, researchersfound support for 434 hypotheses at the p < .05 level. Within this same group of studies, 224hypotheses were not supported. Of these 224 unsupported hypotheses, there were 24 empir-ical tests that isolated the effect of a sole independent variable on the dependent variable in
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a regression model (i.e., the incremental effect of one variable beyond the effect of controland other variables). Among the isolated effects that did not receive statistical support, how-ever, none of the hypotheses was tested with sufficient statistical power to allow acceptanceof its null form. The absence of statistical power in these studies is unfortunate because itprecluded the opportunity to present more examples of conditions under which a particularresource had a significant effect and when it had a trivial effect on some outcome measurewith statistical assurance.
Other Issues that Need More Empirical Examination
There are some more issues that are potentially important but not discussed vigorously inthe RBV empirical studies. We would like to mention two of those issues briefly below.
Negative effects
While some resources do contribute to firm competitive advantage and positive performance,it is possible that other resources have negative effects on organizational competitiveness andperformance. While managers may recognize these “negative resources” and try to removethem, RBV logic suggests that it is not easy. Resources that contribute to competitive advantageare often accumulated over time and are complex, and causality is rather ambiguous (Dierickx& Cool, 1989; McEvily & Chakravarthy, 2002; Reed & DeFillippi, 1990; Zander & Kogut,1995). When environments change quickly and a firm needs to reformulate its strategy, thoseformerly valuable and inimitable resources are hard to identify and thus become a source of“core-rigidities” (Leonard-Barton, 1992). In situations where competitors are not burdened bythose resources, the focal firm is subject to a sustainable competitive disadvantage and result-ing negative performance. In other words, when the effects of those resources are examined ina longitudinal setting, earlier positive effects become negative after an environmental change.
While most RBV research has focused on the positive effects of resources on firm com-petitiveness and performance, demonstrating negative effects within the RBV frameworkwill cross-validate the theoretical value of the RBV. As the theory of organizational declineis relatively underdeveloped compared to the theory of growth (Sutton, 1990), the RBV mayalso be able to contribute to the decline literature. The potential negative effects of resourceswill also remind researchers of the fact that the value of resources is not context-free andmay change over time (Barney, 2001; Priem & Butler, 2001a).
Rarity and Nonsubstitutability
In empirical studies of the RBV, two important constructs, rarity and nonsubstitutabilityof resources, are scarcely discussed or examined. In fact, in his debate with Priem and Butler(2001a, 2001b), Barney (2001) acknowledges that more work is needed to clearly parame-terize the concept of rarity. In our observations, researchers seem to implicitly assume rarityin considering the uniqueness and inimitability of the resources. We speculate that rarity may
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be better positioned as a quantitative dimension of inimitability. While inimitability is future-looking and thus may not be determined simultaneously with rarity (Barney, 1991), the valueof the rarity dimension seems to be diminishing as result of technological development. Easyaccess to information makes it easy to replicate temporarily rare but “imitable” resources.
Regarding nonsubstitutability, it is impossible for us to demonstrate that a particularresource is nonsubstitutable, as we cannot demonstrate that a particular theory is right(Popper, 1959). Although Barney (2001) argues that nonsubstitutability is important to man-age the potential problem of equifinality, it is extremely difficult to identify or measure non-substitutability ex ante. In other words, whether a particular resource is substitutable or notmay eventually be an empirical question. From the perspective of research, thus, it is impor-tant to control for the potential effects of substitution by controlling unobserved hetero-geneity among sample firms (Henderson & Cockburn, 1994).
We have no intentions to discourage the theoretical development and empirical measure-ment of rarity or nonsubstitutability of resources. However, although deductive, the fact thatfew researchers have done so may indicate the need for theoretical modification or refinementof the RBV.1 In fact, Hoopes et al. (2003: 890) flatly conclude that “only value and inim-itability are ultimately important.” In relation to resource attributes, Barney (1991, 2001)implies that the four dimensions are multiplicative, not additive. However, we have observedno study that empirically tests this issue. More theoretical and empirical work is needed.
Conclusion
In this review, we focused our interest on empirical studies based on the RBV and clari-fied both areas of progress and unaddressed methodological issues in this stream of empiri-cal research. Whereas some of the topics, such as controlling for confounding factors, areimportant in any research, we have elucidated key empirical issues directly derived from the-oretical thrusts of the RBV and clarified fundamental challenges and contributions from theperspective of the RBV. Although we acknowledge that our list is far from exhaustive, webelieve that we are providing a good starting point to further develop the RBV from anempirical viewpoint.
Our review, although focused on empirical work, provides important implications for the-oretical development of the RBV. Specifically, our review suggests that the RBV needs toclarify its boundary conditions as required for any good theory (Bacharach, 1989; Priem &Butler, 2001a). For example, if the value and sustainability of resources are defined by exter-nal conditions (Barney, 2001), RBV researchers need to pay more attention to incorporatingmoderating conditions such as industry and time (Bacharach, 1989) and to the effects of con-sumer preferences on resource value (Priem, 2007). It is our tentative conclusion that RBVresearch is at the stage of “interim struggles” (Weick, 1995: 385) through which the RBVcan advance by further interaction between theoretical refinement and empirical develop-ment. The quality of the empirical articles reviewed in this article suggests that researchershave taken steps to address the criticisms leveled against the RBV. We hope that our reviewand suggestions on the empirical issues stimulate further discussion and development of theRBV and other related research.
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1. In our review, only one study (Schilling & Steensma, 2002) attempts to capture the effects of rarity (unique-ness) of resources and no study expressly attempts to capture the effects of nonsubstitutability.
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Biographical Notes
Craig E. Armstrong is an assistant professor of management at the University of Alabama. He received his PhDfrom the University of Texas at San Antonio. His research interests include decision making under uncertainty andorganizational learning.
Katsuhiko Shimizu is an associate professor of strategic management at the University of Texas at San Antonio. Hereceived his PhD from Texas A&M University. His research interests include decision change and decision imple-mentation under uncertainty, learning from mistakes, and managing cultural challenges in international contexts. Hisother work appears in such outlets as Academy of Management Journal and Strategic Management Journal.
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