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Strategic Management JournalStrat. Mgmt. J., 25: 887–907 (2004)

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/smj.401

WHERE DO RESOURCES COME FROM? THE ROLE OFIDIOSYNCRATIC SITUATIONS

GAUTAM AHUJA1* and RIITTA KATILA2

1 University of Michigan Business School, Ann Arbor, Michigan, U.S.A.2 Department of Management Science and Engineering, Stanford University, Stan-ford, California, U.S.A.

In this paper, we examine the emergence of resources. Our analysis of technological capabil-ity acquisition by global U.S.-based chemical firms shows that the emergence of resources isinherently evolutionary. We find that path-creating search that generates resource heterogeneityis a response to idiosyncratic situations faced by firms in their local searches. Two such idiosyn-cratic situations—technology exhaustion and expansion beyond national markets—trigger firmsin our sample to create unique innovation search paths. We also find that along a given pathfirms experiment in order to find the correct investment—in fact, some organizations seem totake a step backward for two steps forward—further demonstrating the evolutionary nature ofthe resource creation process. Copyright 2004 John Wiley & Sons, Ltd.

The resource-based view of the firm is one of themost prominent theoretical perspectives in strate-gic management (Wernerfelt, 1984; Barney, 1991;Teece, Pisano, and Shuen, 1997; Eisenhardt andMartin, 2000; Helfat and Raubitschek, 2000). Cen-tral to this perspective is the idea that firms differin their resource positions, and that such resourceheterogeneity is a source of performance differ-ences across firms (Barney, 1991; Peteraf, 1993).In fact, resources are defined as those attributes ofphysical and knowledge-based assets that enablea firm to conceive and implement strategies thatlead to differences in performance (Wernerfelt,1984). Recent empirical work on resources hasbeen vibrant. For example, researchers have shownthat firms can sustain heterogeneous resource posi-tions over time (Helfat, 1994; Knott, 2003), andthat these heterogeneous resource positions explain

Keywords: innovation; resources; science; geographicboundaries*Correspondence to: Gautam Ahuja, University of MichiganBusiness School, 701 Tappan Street, Ann Arbor, MI 48109-1234,U.S.A. E-mail: [email protected]

why firms perform differently (Henderson andCockburn, 1994; Iansiti and Clark, 1994; Berman,Down, and Hill, 2002; Knott, 2003; Zott, 2003).

Yet, the research has provided only partial guid-ance on how these heterogeneous resource posi-tions are born. While factors such as initial endow-ments and prior commitments (Eisenhardt andSchoonhoven, 1990; Helfat and Lieberman, 2002),timing (Stinchcombe, 1965; Zott, 2003), and man-agerial capabilities (Knott, 2003) each provideintriguing explanations for heterogeneity, theseanswers still beg the question of how these posi-tions were initially acquired. The question thus stillremains: Where does resource heterogeneity comefrom?

In this paper we draw upon evolutionary the-ory to identify possible sources of the origins ofresource heterogeneity. We propose that hetero-geneity in resources can be created as a responseto idiosyncratic situations (Holland, 1975; Nelsonand Winter, 1982). We argue that firms respondto idiosyncratic problems and opportunities fac-ing them by embarking on new search paths. The

Copyright 2004 John Wiley & Sons, Ltd.

888 G. Ahuja and R. Katila

creation of such new paths is the cornerstone ofresource heterogeneity. While previous work hasexamined how inertia (Fredrickson and Iaquinto,1989; Helfat, 1994) and momentum (Miller andFriesen, 1980; Amburgey and Miner, 1992) sustainchange along pre-existing paths (path-deepeningsearch), we know much less about how thesepaths emerge in the first place (path-creatingsearch), and whether such created paths can pro-mote sustainable performance differences acrossfirms. Identifying the determinants of path-creatingsearch and understanding the performance implica-tions of the resultant resources are the two researchobjectives of the study.

We test our framework of resource heterogeneityin the context of the technological capabilitysearch activities by global U.S.-based chemicalsfirms. We identify two types of idiosyncraticsituations—technological exhaustion and marketexpansion—that lead to path-creating search.Firms that are inventing in arenas where thetechnology is well exploited are driven tosearch scientific knowledge to acquire fresh rawmaterial (science search). Similarly, firms thatare entering new international product marketsare driven to expand international researchto address local opportunities and problems(geography search). Second, we find that, atmoderate levels, resource heterogeneity that arisesfrom the science and geography search enhancessubsequent patenting of chemical firms. In sum, wepropose an integrated framework that demonstratesnot only the performance outcomes associatedwith resource heterogeneity, but also offers anexplanation of how the firms that benefited fromthese resources came by them in the first place.

Our results extend existing work on resources.The role of resource heterogeneity in underpinningperformance differences has been established inthe conceptual literature and has also been empir-ically validated to some degree (e.g., Hendersonand Cockburn, 1994). However, few researchershave studied how resource variations themselvesemerge. Thus, this paper responds to calls in theliterature for strategy researchers ‘to move beyondstudies of differential performance to more inte-grated studies which not only identify those factorsthat are correlated with superior performance butalso attempt to explore the origins and the dynam-ics of their adoption’ (Cockburn, Henderson, andStern, 2000: 1124). In this study we draw uponevolutionary theory to suggest that variations in

idiosyncratic situations faced by firms can even-tually lead to the development of resources thatimprove performance.

Our results also address intriguing questionsabout the dynamics of organizational change. Wefind that the acquisition of technological capabil-ities is inherently evolutionary: firms experimentto find the correct investment along a given searchpath, and frequently make mistakes, both under-shooting and overshooting the most productive lev-els of search. In fact, organizations that search thegeography space appear to take a step backwardfor every two steps forward.

We also contribute to the burgeoning litera-ture on evolutionary search (Nelson and Winter,1982; Helfat, 1994; Podolny and Stuart, 1995;Rosenkopf and Nerkar, 2001; Katila, 2002; Katilaand Ahuja, 2002). We extend prior work in thisstream in several ways. First, prior research onsearch has emphasized the role of local search; i.e.,new searches by firms are likely to be constrainedto the areas in the neighborhood of their currentsearches and eventually result in convergence ofsearch approaches (e.g., Cyert and March, 1963;Stuart and Podolny, 1996). In this study, we fol-low the lead of more recent literature that suggeststhat many successful firms proceed beyond localsearch to enhance their resource positions (e.g.,Rosenkopf and Nerkar, 2001; Hargadon, 2003).We identify situations that drive firms to breakaway from local search. Second, search researchershave focused largely on the degree to which firmssearch across the landscape of possible technolo-gies to develop knowledge-based resources (Nel-son and Winter, 1982). In this study we identifytwo additional dimensions that help firms proceedbeyond technologically local search: the degree towhich they search the science base, and there-fore cross the technology–science boundary (e.g.,Cockburn et al., 2000), and the degree to whichtheir search crosses geographic boundaries and isthus non-local in the spatial sense (e.g., Almeida,1996).

THEORETICAL BACKGROUND ANDHYPOTHESES

In this study, we examine the antecedents and con-sequences of resource heterogeneity by focusingon two dimensions of innovation search. One is

Copyright 2004 John Wiley & Sons, Ltd. Strat. Mgmt. J., 25: 887–907 (2004)

Where Do Resources Come From? 889

scientific. Firms search the science base to over-come the limitations of their current technologybase. The second dimension of search is geo-graphic. Firms search across geographical bound-aries to expand their technology base and to solvelocal technological problems. We discuss bothdimensions in more detail below.

Science is the establishment of facts and thedevelopment of quantitative rules or laws thatrelate those facts to each other (Allen, 1977). Thegoal of scientific activities is to enhance knowl-edge and understanding, or learning for its ownsake. Technology, in contrast, is concerned withincorporating such knowledge into physical arti-facts that benefit users. Although, for example,Nelson and Winter (1982: 229) recognize the roleof science in the process of innovation search,they model it as a sector-specific influence thatdetermines the likelihood of search resulting in thediscovery of new technologies in a given sector.Such an abstraction is useful, yet the treatment ofscience as an exogenous source of technologicalenrichment obscures an important reality. Scien-tific developments do not naturally and costlesslysweep across a sector and enrich the search effortsof all firms in the same manner. Just as firmsneed to search across the technological landscape,they need to actively monitor and exploit scien-tific developments (Henderson, 1994; Hendersonand Cockburn, 1994; Zucker, Darby, and Brewer,1998). To the extent that active exposure to thescience base does enrich innovation search efforts,such variations could have an impact on resourceheterogeneity and subsequent performance.

Geography search—the degree to which a firm’ssearch efforts span national boundaries (Almeida,1996; Hansen, 1999; Feinberg and Gupta, 2004)—also provides an opportunity to expand the vari-ety in the firm’s resource base. For many firms,technology search efforts are restricted to a singlenation. For others, such efforts may span severalnations. Such differences are important in the con-text of innovation because, increasingly, researchsuggests that the technological landscape is dif-ferentiated across geographic space (Freeman andSoete, 1997; Katila and Ahuja, 2002; Hansen andLovas, 2004). Idiosyncratic national circumstancesand institutions induce local, regionally distinctive,and partially insular development of technologyresources (Lundvall, 1988). Distinctive culturalinfluences and physical and institutional infras-tructures often impose their own pressures and

imprints on the avenues that are actively pursued intechnological development in any nation. Thus, theprecise elements of the technological domain thatare addressed and developed in a given nation mayvary from those that are developed in other nations(Porter, 1995). By crossing geographic boundariesfirms can obtain access to these variegated techno-logical trajectories.

In the hypotheses that follow we draw uponevolutionary theory to explain the sources ofresource heterogeneity in science and geographysearch. Specifically, we propose that path-creatingsearch that generates resource heterogeneity is aresponse to idiosyncratic situations (both problemsand opportunities) faced by firms in their currentlocal searches (Nelson and Winter, 1982). We thenexamine the second component of our integratedframework, that is, how these search variationschange the innovative performance of firms.

Antecedents of resource heterogeneity

Evolutionary researchers identify two circum-stances through which new search paths for re-sources are created. The first circumstance is theunexpected problems that arise during local searchefforts in the form of puzzles or boundary condi-tions (Nelson and Winter, 1982; Nelson, 1995).The second is the unforeseen opportunities thatarise from unexpected situations (Meyer, 1982;Martin and Eisenhardt, 2002). We examine bothproblems and opportunities in the context of sci-ence search first, and then proceed to geographysearch.

Science search

From the problem-driven perspective, firms willexpand science search when the current techno-logical area where they operate is reaching itslimits. Innovation often occurs through the com-bination and recombination of existing elementsinto novel artifacts (Utterback, 1994; Galunic andRodan, 1998; Hargadon and Douglas, 2001). How-ever, over time, unless the elements available forrecombination are increased in some fashion, thetempo of innovation must decline as the recom-binant search space is exhausted (Hargadon andSutton, 1997; Fleming, 2001). Thus, as the ele-ments in a given technological domain are increas-ingly exploited and their potential for subsequent‘new’ recombination declines, firms that are active



in that technological domain must look beyondtechnology to enrich and reignite the invention pro-cess. Science would then be a natural candidate forattention.

Exhaustion of the technological domain alsodrives resource heterogeneity in the form of anopportunity. While several authors have docu-mented that firms are reluctant to devote resourcesto science search (e.g., Henderson, 1994), idiosyn-cratic situations as described above can provideopportunities to justify such search. The observa-tion that idiosyncratic problems represent opportu-nities for firms to change is confirmed by resear-chers who have examined how idiosyncratic inci-dents, such as problems or surprises, can changesearch patterns of individuals and groups (Meyer,1982; Graebner and Eisenhardt, 2003). Duringthese ‘windows of opportunity’ that represent time-outs from normal activities, individuals are morelikely to become aware of alternative paths, andsee a legitimate opportunity to bring up new alter-natives (Tyre and Orlikowski, 1994; Okhuysen andEisenhardt, 2002). Based on these observations, wepropose that technology exhaustion represents alegitimate opportunity for firms to go beyond localtechnology search, and to search science.

The above arguments suggest that firms workingin well-exploited technological domains, i.e., indomains where current inventive efforts build onmany preceding inventions, are likely to searchthe science base more intensely in order to haveaccess to a more heterogeneous resource base.Accordingly, we hypothesize:

Hypothesis 1: The greater the degree of techno-logical exhaustion faced by the firm, the greaterthe intensity of its subsequent science search.

Geography search

Idiosyncratic problems and opportunities also driveinnovation search across geographic areas. First,firms that are active in multiple national marketsare likely to confront specific local problems ineach market. Variations in user needs, manufac-turing processes, or availability of materials mayrequire adapting technology to local contexts ordeveloping new technologies to solve local prob-lems (Abernathy and Utterback, 1978; Dunning,1992). For instance, variations in local environ-mental regulations or in resource availability may

make certain manufacturing practices and pro-cesses impossible or unviable in some countries.Similarly, certain products may need to be modi-fied to local specifications and needs. Raw materi-als may need to be substituted for and the resultantreformulation of the product may need extensivetesting and approvals in the new environment. Cul-tural, administrative, and institutional variationsmay also necessitate product and process adap-tation (Ghemawat, 2001). Supplying products ininternational markets may also entail the provisionof ongoing technical support. Finally, transfer oftechnologies developed in the domestic market tointernational subsidiaries often requires absorptivecapacity to manage and maintain that technology inthe foreign unit (Dunning, 1992; Nobel and Birkin-shaw, 1998). For all of these reasons, as a firm’sinternational product market presence broadens itis likely to promote the broadening of its interna-tional research presence.

Second, expansion to new product markets mayalso present an opportunity to engage in geogra-phy search. Presence in a market often increasesa firm’s awareness and appreciation of locallydeveloped technology, regional science and tech-nology networks, and of the potentially lower costsfor research activity in foreign sites (Granstrand,Hakanson, and Sjolander, 1992; Kuemmerle,1999). The firm may then respond by expandingtechnological operations to the international site.Thus, expanding a firm’s international product-market footprint translates to several stimuli—bothproblems and opportunities—that are likely toencourage the expansion of its international tech-nological presence. Accordingly, we hypothesize:

Hypothesis 2: Changes in a firm’s internationalproduct market presence will trigger changes inits international research presence.

Outcomes of resource heterogeneity

The second component of our framework focuseson the performance effects of science and geog-raphy search. A natural domain for the investi-gation of the effectiveness of a firm’s innova-tion search activities is its innovative performance.Innovativeness is naturally only one dimensionof performance, yet an important one for high-technology firms (Schoonhoven, Eisenhardt, andLyman, 1990).



From the evolutionary viewpoint, innovationsshare several common features. First, many inno-vations emerge because they are the locus of ameeting between a problem and its solution, evenwhen neither the problem nor the solution is itselfnew (Cohen, March, and Olsen, 1972). For exam-ple, innovations often result from the recombi-nation of known elements of various solutions(Holland, 1975; Galunic and Rodan, 1998; Har-gadon and Douglas, 2001). Their novelty stemsfrom the act of combination, not necessarily fromthe individual components that are combined (Hen-derson and Clark, 1990; Utterback, 1994). Second,innovations can also emerge because they repre-sent some unique genuine novelty; the solutionincludes components or elements that did not existearlier, and whose very existence is evidence ofnovel invention (Holland, 1975). Thus, innova-tions can emerge if they resolve existing problemsin new ways—either through recombination orby including novel elements; alternately they canemerge if they solve new problems, i.e., problemsthat were hitherto unidentified, but which onceidentified have a commercially relevant dimen-sion (Dougherty, 1992; Shane and Venkataraman,2000). Other things being equal, activities thatenhance a firm’s exposure to new combinationsof problems and solutions, or to novel problemsor novel solutions, represent paths to improve theproductivity of the firm’s innovation efforts. Belowwe examine how science and geography searchpromote proceeding down each of these paths, andthus enhance the innovativeness of firms.

Science search and innovativeness

Searching science can improve innovative pro-ductivity through at least two mechanisms. First,science can influence innovative productivity byincreasing the number of elements available forcombinations. The recombinant search space, orthe set of technological elements available forrecombination, is finite. If no new elements areadded to the search space recombination activityeventually exhausts the set of potential combi-nations (Kim and Kogut, 1996; Fleming, 2001).Using theoretical principles, science can identifynew, original building blocks (Merton, 1957) thatcan be combined to produce similar functionali-ties through completely novel routes. For instance,Einstein’s elucidation of the relationship betweenmass and energy identified the atom as a potential

energy source, completely distinct from all knownpaths to the generation of energy. Since new ele-ments that eventually lead to novel combinationsoften appear first in the scientific literature (Mer-ton, 1957; Rosenberg, 1990), science search canplay an important monitoring function (Prescottand Gibbons, 1993) for new ideas, and for newcombinations.

Second, science can improve the inventor’sunderstanding of cause–effect relationships andthus help identify the elements whose combina-tion is likely to be fruitful (Freeman and Soete,1997; Cockburn et al., 2000). For instance, in the1960s and 1970s pharmaceutical drug researchentailed large-scale screening of thousands of com-pounds in the hopes of discovering something newor effective. However, advances in the biomedi-cal sciences of physiology and biochemistry ush-ered in the era of ‘rational’ drug design, wherebiochemical and physiological principles are usedto focus on a few substances that are theoreti-cally likely to yield the desired outcomes (Hender-son and Cockburn, 1994). Scientific understandingof the cause–effect relationships between chemi-cal substances and physiological outcomes gainedthrough experience in science search reduced thenumber of combinations that needed to be tried forsuccessful innovation (Cockburn et al., 2000).

Although exploring science can provide theabove-mentioned benefits, excessive search of thescience base is likely to be counter-productivefrom the perspective of innovation. First, scan-ning scientific literature, attending scientific con-ferences, and other exploratory or knowledge-building activities all demand significant time andresources. Time devoted to such activities mustreduce the time available for actual integration andapplication of the knowledge elements obtained.Thus, excessive exploration to obtain new knowl-edge reduces the attention available for the task ofexploiting it (March, 1991; Levinthal and March,1993). At high levels, exploration tends to driveout exploitation altogether. An organization thatexcessively exposes potential innovators to sciencerisks their losing sight of the ultimate goal of cre-ating useful artifacts.

Second, since the success of science searchis much more uncertain than that of technologysearch (Rosenberg, 1990), extensive searches ofscientific knowledge may result in a random driftwhere a firm’s knowledge bases are altered fre-quently in uncertain directions (Lounamaa and



March, 1987). Such drift generates a selection ofunrelated discoveries that are costly to integrate(Levinthal and March, 1993), and may even createparalyzing anxiety about the future that stops inno-vation but still entails search costs (Eisenhardt andTabrizi, 1995). Innovativeness may also decreasesince individuals often have difficulties readjustingtheir mental models in the face of extensive changecaused by uncertainty (Barr, Stimpert, and Huff,1992). The above arguments suggest that someexposure to the science base can increase inno-vation output, but excessive search of the sciencebase may eventually decrease innovation output.Accordingly, we hypothesize:

Hypothesis 3: The intensity of science searchwill be curvilinearly (inverted U) related to thesubsequent innovativeness of the firm.

Geography search and innovativeness

Recombinatory search for new innovations occursonly among the knowledge elements that a firmis aware of (Galunic and Rodan, 1998). Natu-rally, under conditions of perfect information andunlimited rationality, the existence of knowledgewould be sufficient for all firms to be able touse it with equal facility, irrespective of location.However, when knowledge diffusion is imper-fect and cognition is limited, what matters isnot whether a piece of information exists in theworld, but rather whether it is part of the cog-nitive set or knowledge base of the firm whenit is needed (Hargadon and Sutton, 1997). Inter-national research units can be established specifi-cally to address these imperfections in knowledgetransfer.

Increasing international research presence, thatis, geography search, can improve innovative pro-ductivity through at least two mechanisms. First,with their idiosyncratic histories and technologi-cal pursuits, a presence in multiple nations canraise a firm’s awareness of the different areasof the knowledge landscape and thus provide avaried set of raw material for knowledge combi-nations (Pouder and St John, 1996; Kuemmerle,1999). Multinational researchers have identifiedthat rich communication channels that developinside multinational firms in the form of interper-sonal networks promote awareness of such special-ized local knowledge (Gupta and Govindarajan,2000; Almeida, Song, and Grant, 2002). Thus,

firms that are proximate to the innovation mayreceive not only more diverse but also more cur-rent information than firms without a presence inthe neighborhood.

Second, a research presence in multiple nationsalso links firms to multiple regional networks ofknowledge faster than market mechanisms do (DeMeyer, 1992). Knowledge is often held by localengineers and the identification, and the transferand combination with knowledge acquired fromother local contexts, is often made through networkconnections between such individuals (Almeidaand Kogut, 1999). Prior research also indicates thatgeographic co-location and short path lengths (in asense, small world structures created by multina-tional presence) facilitate the transfer of knowl-edge (Jaffe, Trajtenberg, and Henderson, 1993;Almeida, 1996; Hansen and Lovas, 2004).

However, scanning too widely across geographiccontexts can also be dysfunctional. Integrationproblems increase exponentially with an increasein the number of nodes across which the inte-gration is to be conducted. Distance, time zones,and national borders can exacerbate this prob-lem of coordination (De Meyer, 1992; Hansenand Lovas, 2004). Decentralization of knowledgealso intensifies the challenge of appropriating theorganization’s core knowledge from spillovers thatmay compromise the first-mover advantages innew technologies (Hood and Young, 1979; Katilaand Mang, 2003). As the complexity of handlingthis diversity grows and the limits of boundedrationality are approached, these monitoring prob-lems often overcome the benefits at high lev-els. Further, setting up research infrastructures inmultiple nations is likely to lead to suboptimalscale operations at extreme levels, as resourcesare spread too thin. Thus, beyond a point, inno-vation search across geographic boundaries willface diminishing returns. Accordingly, we hypoth-esize:

Hypothesis 4: The breadth of geography searchwill be curvilinearly (inverted U) related to thesubsequent innovativeness of the firm.

The above hypotheses summarize the relation-ships between the antecedents of resource searchand its consequences for firm innovativeness. To-gether these hypotheses present an integratedframework that explains both why search varies



across firms and the impact of such variations onfirm innovative performance.

METHODS AND MEASURES

Data

The hypotheses of the study were tested withlongitudinal data on the innovation activities ofthe leading U.S. chemical firms over the period1979–92. We identified the key players in theU.S. chemical industry from lists that are publishedannually by trade journals such as Chemical Weekand Chemical and Engineering News. To avoidany survivor bias we selected the sample fromthe lists at the beginning of the study period andattempted to obtain data on all firms over the entireperiod. However, the final panel is unbalanced assome of the firms were acquired by other firmsor restructured in a fashion that made comparisondifficult beyond a particular year. In such caseswe included the firm for the period before itsacquisition or restructuring.

The chemical industry is an appropriate settingfor this research for several reasons. It is a globalindustry with both market and technological activ-ity dispersed internationally. Market diversity andtechnological intensity also make it especially suit-able for our study. Market diversity in the chemicalindustry is due to local differences in downstreamproducts, customer bases, needs for technical assis-tance, and environmental regulations (Arora andRosenberg, 1998; Landau and Arora, 1999). Thechemical industry is also technology intensive(Klevorick et al., 1995). For example, in 1992 theU.S. chemical industry invested $16.7 billion inR&D, more than any other U.S. industry (Arora,Landau, and Rosenberg, 1998). Patenting is alsoimportant in chemical (Levin et al., 1987; Ahuja,2000a). Accordingly, there is significant potentialfor innovation search in this industry on the searchdimensions identified in this paper.

Measures

The theory presented in the previous section sug-gests several dynamic relationships between re-source search and its antecedents and consequen-ces. Obtaining the longitudinal data that are requi-red to test these hypotheses is, in general, very dif-ficult. However, patent data provide an opportunity

to examine the characteristics of innovation searchover a relatively long period of time (Katila andAhuja, 2002). Thus, patenting records are likelyto be good indicators of the underlying innovativebehavior of firms in our sample.

We use patent data from the United States Patentand Trademark Office database. To obtain patentprotection for an invention in the United States,the inventor is required to apply for a U.S. patent,even if the invention was conducted overseas.Since the United States is an important market formost industrial goods, almost half of the patentsissued in the United States reflect inventions cre-ated in foreign locations. Foreign subsidiaries ofU.S. firms that are active in research routinelyobtain patents for their overseas inventions in theUnited States. The U.S. patent data also give a con-sistent measure of the patenting activities of oursample firms, since all the firms are large, multi-national chemical firms for whom gaining patentprotection in the U.S. market is especially impor-tant.

To obtain the patent data, we prepared a list ofall the divisions, subsidiaries, and joint venturesfor each of the sample firms using Who OwnsWhom and The Directory of Corporate Affiliationsdirectories. Thereafter, each firm’s history wastraced through the study period to account for anyname changes and reorganizations, and to obtaininformation on the timings of events such as thefounding and dissolution of joint ventures. Thismaster list of firm names was used to identify allpatents issued to the sample firms. The grantedpatent carries the date of the original application,and we use this application date to assign patentsto appropriate years as is customary in the patentliterature (Griliches, 1990). In sum, there wereover 40,000 patents issued to the sample firms overthe study period.

Turning to the specific search measures, patentdata provide a useful indicator of a firm’s searchacross the science base (Carpenter, Cooper, andNarin, 1980; Narin, Hamilton, and Olivastro,1997). Since a patent confers a property righton the ‘new’ knowledge created by the patentto the assignee of the patent, U.S. patent lawrequires that all prior contributions on whichthe patent builds be documented on the bodyof the patent. These cited references record theknowledge already existing prior to the creationof the patent. Thus, they serve to identifyprecisely the new knowledge created by the



patent and delineate the domain on which thepatentee is actually granted an intellectual propertyright (Walker, 1995). The references in a patentare separated into two categories based on thesource of the prior knowledge used: (a) priorknowledge that was recorded in the form ofanother patent (patent references); and (b) priorknowledge that was recorded in other, non-patentsources such as scientific journal articles andcommercial literature (the non-patent references).In prior research patent references have beenused to relate patents to other patents andthus to identify linkages between technologies(Stuart and Podolny, 1996; Rosenkopf and Nerkar,2001; Katila and Ahuja, 2002). In contrast, non-patent references have been used to identifylinkages between technology and science (Narinet al., 1997). We exploit the information on non-patent references to identify links between theartifactual knowledge documented in a patent andthe non-patented knowledge recorded in scientificpublications.

Patent data also provide a good source for mea-suring the firm’s search across geographic areas.The location of the inventor is documented inthe patent and indicates the national origin of thepatent. Thus, firms obtaining patents in many coun-tries have a broader technological presence acrossnations than firms obtaining patents in a few coun-tries. We provide more details on these searchmeasures below.

Dependent variables

Science search

We used data included in the non-patent referencesto operationalize science search, i.e., the depen-dent variable in Hypothesis 1. We measure a firmi’s intensity of search across the science base inyear t + 1 (Science searchit+1) as the average num-ber of scientific non-patent references in the firm’spatents that year. To compute this variable we cal-culate the total number of scientific publicationscited by a firm’s patents in year t + 1 and dividethis number by the number of patents obtained bythe firm in the same year. For a firm that obtains100 patents a year and cites 50 scientific non-patent references in them, this variable has a scoreof 0.5. In order to separate scientific from non-scientific references, we followed procedures usedin prior work (Narin and Noma, 1985; Narin et al.,

1997). We first categorized the non-patent cita-tions (52,000 in our sample) into five categories:citations to scientific books, journal articles, con-ference proceedings, technical documents (such asforeign patent literature and standards), and com-mercial documents. A part of this categorizationwas accomplished through a computer programwritten for the purpose, but nonstandard input,name changes, and spelling differences forced usto do extensive hand-coding to complete the task.We then constructed the science search variablefor each firm yearly by using the citations in thefirst three categories only. Our final science searchvariable includes citations to scientific books, jour-nal articles, and conference proceedings; citationsto non-scientific documents such as commercialbrochures, patents, and standards are excluded.This variable is also used as an independent vari-able in equations predicting Firm innovation whereit is used in a count form as the total number ofjournals, books, and conference proceedings in theprevious year.

Geography search

The dependent variable in Hypothesis 2, breadth ofgeography search (Geography searchit+1), is mea-sured through the firm’s patenting across nationseach year. We use Blau’s (1977) index of diver-sity to construct this variable. The formula is1 − ∑c

j=1 pj2, where pj is the proportion of the

firm’s patents in country j , and c the total numberof countries. High scores suggest that the firm’ssearch is geographically more diverse. The Blauindex is widely used and highly correlated withalternative diversity indices (Bantel and Jackson,1989). Since the dependent variable in Hypothesis2 indicates changes in the breadth of geographysearch, we use the difference between the periodt + 1 and period t values of the Blau index to con-struct this variable. This variable is also used asan independent variable in predicting Firm inno-vation where the variable is used in its original(i.e., levels, not changes) form. To determine thegeographic location of each patent, we identify thelocation of the inventor documented in the patent.If the patent has many inventors, we follow theU.S. Patent Office and prior research convention(e.g., Trajtenberg, 2001) of using the first inventorto determine the country location.



Firm innovation

We measured the dependent variable in Hypothe-ses 3 and 4, Firm innovation it+1, as the num-ber of successful patent applications, or grantedpatents, for firm i in year t + 1. Using patents as ameasure of innovative output follows several pre-vious research efforts that have used patents asa measure of knowledge (Henderson and Cock-burn, 1994; Ahuja, 2000a; Ahuja and Katila, 2001;Rosenkopf and Nerkar, 2001). Empirical stud-ies find that patents are closely related to mea-sures such as innovation and invention counts(Achilladelis, Schwarzkopf, and Cines, 1987) andexpert ratings of corporate technological strength(Narin, Noma, and Perry, 1987). To measure inno-vative output we used the standard innovationproduction function approach (Griliches, 1979) inwhich the count of inventions is modeled as anoutcome of the total inputs (for example, R&D;see details below). For robustness we also supple-mented our analysis of patent counts with addi-tional analysis using citation-weighted patents.

Independent variables

Technology exhaustion

We captured the idiosyncratic triggers of Sciencesearch using the Technology exhaustionit variable(Hypothesis 1). This variable is measured as theaverage number of prior-art patents cited by a firmin its patents each year. Patents making more cita-tions to prior art exploit existing knowledge morethan develop new ideas (Podolny and Stuart, 1995;Ahuja and Lampert, 2001). Thus, firms that citemany patents as precursors of their own inven-tions are likely to be working in relatively well-exploited areas of technology and are more likelyto face technology exhaustion than firms that citeonly a few patents. Since technology exhaustioncan lead to increased scientific search with a lag,we constructed four lagged versions of this vari-able reflecting its period t , t − 1, t − 2, and t − 3values (Ahuja, 2000b).

Changes in international product-market presence

This variable is used as a measure of idiosyn-cratic triggers of Geography search (Hypothesis2). We first construct the international product-market presence variable as the number of coun-tries that each sample firm has a presence in

through either a subsidiary or an affiliate com-pany as recorded in the annual editions of the WhoOwns Whom directories (Shaver, 1998). This vari-able is collected yearly from 1982 to 1992 for eachcompany. Since in Hypothesis 2 we predict thatchanges in international product-market presencewill lead to changes in geography search, we thencompute the difference between the period t andperiod t − 1 values of the presence variable as thevalue of International product-market changeit . Asabove, since there can be a lag between changesin product-market change and its effect on search,we compute four alternative lagged versions of thevariable reflecting the t to t − 1, t − 1 to t − 2,t − 2 to t − 3, and t − 3 to t − 4 changes. Notethat including four lags significantly reduces oursample size as each lag implies a loss of a fullyear of data.

In predicting Firm innovation, Science searchit

and Geography search it are included as indepen-dent variables. Note that the study has a longitudi-nal design: the patents that form the basis for thedependent variables are distinct from the patentsthe independent variables are based on. The inde-pendent variables are based on patents from theprevious year(s).

Control variables

Science and geography search

To control for path-deepening search, which ispotentially an alternate driver of firm search behav-ior (e.g., Helfat, 1994), we include the one-periodlagged values of science and geography searchvariables (Science searchit , Geography search it ) inthe equations predicting Science and Geographysearch, respectively. Prior research on organiza-tional inertia has employed a similar strategy ofusing the relationship between current and pastvalues of the dependent variable as a measure ofinertial tendencies (e.g., Helfat, 1994). In sensi-tivity analyses we also used the two-prior-periodsaverage, the three-prior-periods average, and thefour-prior-periods average of these variables asalternate measures of path-deepening search.

Science–technology linkage

Since it is possible that firms are more likely tosearch the science base if they are active in areaswhere technological knowledge alone may be inad-equate for innovation, we include a control for



Science–technology linkageit in equations predict-ing Science search. We construct this variable byidentifying all technological classes that each sam-ple firm is active in, in a given year. We thencompute the average rate of non-patent referencecitations for all patents in the U.S. Patents Officein those classes in that year. Thus, if a firm wasactive in classes 2, 3, and 4 in a given year, weidentified all patents in these three classes in thatyear and computed the proportion of these patentsthat cited non-patent references as the value ofScience–technology linkage. High values of thevariable indicate increased linkage to the sciencebase.

Firm-operational controls

R&D, firm performance, firm size, and productdiversification are included in the equations ascontrols. We use logged R&D expenditures as con-trols in both the search and the innovation models(R&Dit ). Prior research also suggests that poorperformance can trigger changes in search behav-ior (Cyert and March, 1963; Audia, Locke, andSmith, 2000). Accordingly, we use return on assetsfor each firm yearly as a control for differencesin performance (Firm performanceit ). Firm size it

serves as an indicator of the resources availablefor the search activities, and is measured as thenatural log of the number of employees for eachfirm yearly. Finally, since diversified firms havemore possibilities to exploit new knowledge andmore possibilities to benefit from user innova-tion, we control for product diversification (Prod-uct diversification it ) using a Blau Index. Productsales in the 4-digit SIC codes for each firm yearlyare used to construct this variable. For some firm-years, R&D or product diversification data wereunavailable; in such cases we imputed these valuesbased on adjacent years.

Technological opportunity

The degree of technological opportunity can alsovary across technologies (Henderson and Cock-burn, 1994). Some firms may be active in relatively‘richer’ technological classes than other firms, andtherefore perhaps have higher subsequent patentingin those domains. To control for this possibility,we included a Technological opportunityit variable.For each firm we identified the technology classes

that it was active in, in any year. We then com-puted the number of total patents issued in theseclasses by the U.S. Patents Office and used thatnumber as an indicator of the relative richness ofthe firm’s specific environment. This variable is acontrol in the models predicting Firm innovation.

Technology breadth

We control for a firm’s breadth of technologicalsearch—the traditional dimension of innovationsearch (Rosenkopf and Nerkar, 2001)—throughthe Blau Index of the firm’s patenting across patenttechnology classes yearly. The U.S. patent systemidentifies almost 400 distinct technology classes.Each technology class reflects a specific area oftechnology in the same way that an SIC codereflects a certain product market area (Patel andPavitt, 1997). The broader the scope of a firm’stechnological activity, the more likely it is to patentin many distinct classes, and possibly the morelikely it is to innovate more. This variable is acontrol in the models predicting Firm innovation.

Technology age

The temporal dimension of search may also influ-ence innovative output—for instance, building onolder technologies may be less productive. Thus,we control for the age of the foundations ofeach firm’s current-period innovations. Technologyage it is defined as the average age of the prior-art patents that are cited in a given firm’s yearlypatents (Bierly and Chakrabarti, 1996; Katila,2002; Nerkar, 2003). The age of a cited patent wasthe time elapsed since its issue. This variable is acontrol in the models predicting Firm innovation.

We also include dummy variables for each yearto control for the possibility of period effects. Allindependent and control variables are lagged by1 year (or more, as in the case of the multipleyear lags in the search equations), relative to thedependent variable.

Model estimation

Our hypotheses on resource heterogeneity addressboth the antecedents and the consequences ofsearch. Accordingly, we estimate two sets of mod-els. The first set of models addresses the deter-minants of search (Science and Geography SearchModels). The second set of models examines the



impact of search on the innovativeness of the firm(Firm Innovation Model).

For the Search Models we use linear panelGLS regressions. In the Innovation Model thedependent variable is a count of innovation, Firminnovation it+1. Since this variable takes only dis-crete non-negative integer values, we use Poissonand negative binomial models that are appropriatefor such data. To address the possibilities of unob-served heterogeneity and autocorrelation we use

panel Poisson and negative binomial specifications(Hausman, Hall, and Griliches, 1984).

RESULTS

Tables 1, 2, and 3 present descriptive statisticsand correlations for all variables in the ScienceSearch, Geography Search, and Firm InnovationModels, respectively. The descriptive statisticsindicate that the firms are characterized by a

Table 1. Descriptive statistics and correlations (Science Search Model)

Mean S.D. Min. Max. 1 2 3 4 5 6

1 Science search it+1 1.19 1.01 0.00 7.002 Technology exhaustion it 8.19 4.46 0.00 51.67 0.293 Science search it

a 1.14 0.99 0.00 7.00 0.57 0.324 Science-technology linkage it 0.32 0.07 0.05 0.54 0.45 0.06 0.495 Firm size it

a 2.89 1.04 0.88 5.18 −0.08 −0.13 −0.04 −0.206 R&Dit

a 4.25 1.32 0.81 7.15 0.19 −0.02 0.23 0.22 0.767 Firm performance it 0.06 0.05 −0.13 0.24 0.01 0.08 0.04 0.05 −0.23 −0.03

a LogarithmN = 302

Table 2. Descriptive statistics and correlations (Geography Search Model)

Mean S.D. Min. Max. 1 2 3 4 5

1 Geography searchit+1 0.002 0.14 −0.56 0.562 International product-market changeit −0.26 2.99 −18.00 8.00 −0.133 Geography searchit 0.002 0.14 −0.56 0.56 −0.52 0.174 R&Dit

a 4.25 1.32 0.81 7.15 −0.01 0.01 0.045 Firm sizeit

a 2.89 1.04 0.88 5.18 0.04 0.02 0.04 0.756 Firm performanceit 0.06 0.05 −0.13 0.24 −0.04 −0.01 −0.01 0.006 −0.21

a LogarithmN = 216

Table 3. Descriptive statistics and correlations (Firm Innovation Model)

Mean S.D. Min. Max. 1 2 3 4 5 6 7 8

1 Firm innovationit+1 97.63 129.06 0.00 760.002 Science searchit (00s) 1.30 2.01 0.00 11.60 0.863 Geography searchit 0.09 0.11 0.00 0.56 0.24 0.274 Technological

opportunityit (00s)131.69 110.73 1.17 568.24 0.89 0.83 0.28

5 Technology breadthit 0.82 0.24 0.00 0.98 0.37 0.31 0.08 0.506 Technology ageit 10.75 2.59 2.75 29.71 −0.18 −0.13 −0.03 −0.20 −0.187 Product diversificationit 0.67 0.18 0.00 1.00 0.27 0.24 0.07 0.27 0.21 −0.228 R&Dit

a 4.25 1.32 0.81 7.15 0.78 0.72 0.33 0.79 0.43 −0.24 0.229 Firm performanceit 0.06 0.05 −0.13 0.24 −0.07 −0.03 −0.11 −0.10 −0.05 0.08 −0.16 −0.01

a LogarithmN = 281



significant diversity on variables such as Sciencesearch, Geography search, Firm innovation, R&D,and Firm size.

Table 4 presents the results predicting Sciencesearch it+1 using a panel random effects GLS spec-ification. Model 1 in Table 4 includes the controlvariables, and in Models 2, 3, 4, and 5 we addthe four lagged values, (t , t − 1, t − 2, t − 3)of the hypothesized effect (Technology exhaus-tion), respectively. Models 2–5 indicate that the1 period lagged and 3 period lagged values oftechnology exhaustion are positive and significant,as predicted. In Model 6 we enter all lags simul-taneously into the equation. Again the 1 periodand 3 period lagged values remain positive andsignificant. Thus, technological exhaustion, as pro-posed in Hypothesis 1, appears to prompt increasedsearch of the science base with a lag of 1–3 years.Among the control variables, the measure for path-deepening search, i.e., Science searchit , is consis-tently positive and significant. In summary, theseresults suggest that firms react to opportunities andproblems in their environments and, in the case ofscience search, with a fairly short lag.

The corresponding models that examine thepredictors of Geography searchit+1 are presentedin Table 5. Again, Model 1 includes the con-trol variables, Models 2 through 5 add the fourlagged values of the hypothesized effect (Interna-tional product-market change), respectively, whileModel 6 presents all four lagged variables includedsimultaneously in the equation. Models 2–6 sug-gest support for our prediction in Hypothesis2. Specifically, we find that firms do react tochanges in their international product-market pres-ence by changing their international research pres-ence; however, this occurs with a significant (4-year) lag. As Models 2–5 indicate, only the 4-yearlagged variable is significant. Model 6, in whichall lags are entered simultaneously, also indicatesthe same conclusion: there is a positive and signif-icant effect of international product-market changeon changes in international research presence after4 years. In Models 2–6, the variables reflect-ing path-deepening tendencies in search (i.e., thelagged value of Geography search) also presentan interesting story. These results suggest that,controlling for other factors, firms that undertake

Table 4. Random effects panel GLS regression predicting Science search it+1

Variable 1 2 3∧ 4∧ 5 6∧

Intercept −0.14 −0.47 −0.060.36 0.38 0.55

Technology 0.03∗∗ 0.03∗

exhaustion it 0.01 0.02Technology 0.01 −0.01exhaustion it−1 0.01 0.02Technology 0.03∗ 0.03∗

exhaustion it−2 0.01 0.02Technology 0.02 −0.005exhaustion it−3 0.02 0.02Science search it 0.47∗∗∗ 0.42∗∗∗ 0.45∗∗∗ 0.44∗∗∗ 0.42∗∗∗ 0.41∗∗∗

0.06 0.06 0.07 0.07 0.07 0.08Science–technology 1.99† 2.54∗ 2.16† 2.05† 2.07 2.53†linkage it 1.04 1.05 1.11 1.20 1.34 1.37Firm size it −0.17† −0.15† −0.18† −0.20† −0.23∗ −0.20†

0.09 0.09 0.10 0.10 0.12 0.12R&Dit 0.14∗ 0.14∗ 0.15∗ 0.16† 0.18† 0.17†

0.07 0.07 0.08 0.08 0.09 0.09Firm performance it −0.27 −0.53 −0.37 −0.43 −0.53 −0.45

1.25 1.25 1.33 1.40 1.51 1.49

R2 0.41 0.42 0.39 0.37 0.34 0.36N 302 302 279 256 233 233

† p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001 (one-tailed tests for hypothesized variables, two-tailed tests for controls).The table gives parameter estimates; standard errors are below each parameter estimate. Year dummies are included, but not shown.For models with lagged variables Stata randomly drops either the intercept or one of the year dummies. ∧ indicates models in whichthe intercept was dropped.



Table 5. Random effects GLS regression predicting Geography searchit+1

Variable 1 2 3∧ 4 5∧ 6

Intercept 0.01 −0.02 −0.01 −0.010.04 0.04 0.04 0.04

International product-market change it −0.002 −0.0010.003 0.003

International product-market change it−1 −0.0003 −0.00070.003 0.003

International product-market change it−2 0.0002 −0.00050.003 0.003

International product-market change it−3 0.01∗ 0.01∗

0.003 0.003Geography search it −0.53∗∗∗ −0.50∗∗∗ −0.52∗∗∗ −0.47∗∗∗ −0.39∗∗∗ −0.39∗∗∗

0.05 0.06 0.06 0.06 0.07 0.07Firm size it 0.002 0.01 −0.004 0.001 −0.00005 0.0005

0.01 0.01 0.01 0.01 0.01 0.01R&Dit −0.0003 −0.002 0.01 0.002 −0.001 −0.001

0.01 0.01 0.01 0.01 0.01 0.01Firm performance it −0.19 −0.23 −0.25 −0.25 −0.07 −0.06

0.18 0.20 0.20 0.19 0.22 0.23R2 0.31 0.32 0.33 0.32 0.27 0.27N 286 216 191 166 141 141

† p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001 (one-tailed tests for hypothesized variables, two-tailed tests for controls).The table gives parameter estimates; standard errors are below each parameter estimate. Year dummies are included, but not shown.For models with lagged variables Stata randomly drops either the intercept or one of the year dummies. ∧ indicates models in whichthe intercept was dropped.

significant expansions into new international re-search locations in one period are likely to par-tially reverse these actions in subsequent years. Wereturn to this intriguing finding in the discussionsection.

Finally, Table 6 presents the results predictingFirm innovation it+1. Control variables are includedin blocks in Models 1 and 2 (Firm-Operational andFirm-Technology controls, respectively). Models3 and 4 include the hypothesized variables, Sci-ence search and Geography search, entered sep-arately along with their squared terms. Model 5includes all variables, and provides support forboth Hypotheses 3 and 4. In Hypothesis 3 we pre-dicted a curvilinear relationship between sciencesearch and innovativeness with innovation outputincreasing with increasing science search up to apoint and then decreasing. This hypothesis wassupported. The coefficient on Science search waspositive and significant, while the coefficient on itssquared term was negative and significant. Simi-larly, in Hypothesis 4 we predicted an inverted Urelationship between geography search and innova-tiveness. This prediction was borne out as well, asthe coefficient on Geography search was positiveand significant, while the coefficient on its squared

term was negative and significant. Thus, to pro-mote innovation, firms should explore enough tocreate variety, yet not too much to lose control. Inboth these cases calculation of the point of inflec-tion on the respective curves indicated that thedownward part of the curve was indeed observedin the data. For science search, our variable rangesfrom 0 to 1160, and using the coefficients inModel 5 we can calculate the point of inflec-tion at approximately 737 [(0.0008)/(2*5.43e-07)],while on geography search the range was from 0to 0.56, and the point of inflection was at 0.17[1.42/2*4.16]. Thus, we observe firms on bothsides of the inflection points, both undershoot-ing and overshooting the optimal level of search.For the geography search variable both rising anddeclining parts of the curve seem to be identifiedwith some clarity—the inflection point is less thanone standard deviation from the mean, and at somedistance away from each tail. However, in the caseof science search, the downward part of the Ushape has significantly fewer observations than theupward part, and the point of inflection is morethan three standard deviations beyond the mean.Therefore, we cannot say with complete confi-dence whether the relationship is curvilinear or in


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fact simply exhibits diminishing returns. To distin-guish between these two possibilities we replacedthe original science search variables with a loggedvariable in unreported regressions. If the loggedvariable were to be a significantly poor fit, forinstance if it were to drop from significance, wecould be more confident that the curvilinear spec-ification was a significantly better fit. However,the logged variable was positive and significant(while other results remained unchanged) and thuswe were unable to conclusively resolve this issue.Thus, it appears that science search does impactinnovation output positively but with diminishingreturns. Whether these diminishing returns even-tually lead to a curvilinear (inverted U) shape orplateau off asymptotically could not be clearly dis-tinguished.

We also estimated a number of additional mod-els for robustness. For the Firm Innovation Model,we report generalized estimating equations (GEE)Poisson models with Huber/White robust standarderrors (Liang and Zeger, 1986) and panel nega-tive binomial regression in Table 6 in Models 6and 7. Furthermore, since patents can vary in theirvalue, we also estimated models where citation-weighted patents (forward citations that the firm’spatents receive after issue) were used as a depen-dent variable in place of simple counts of patents(Trajtenberg, 1990). For the Search Models, inunreported regressions we used alternative mea-sures of path-deepening search in the two searchmodels (lags of varying length from 1 to 4 priorperiods were averaged to create the variable).Since the search equations included lagged val-ues of the dependent variable as regressors wealso estimated both search models using an instru-mental variables regression approach. These addi-tional tests provided further support to the originalfindings.

DISCUSSION

In this paper we explored the question of the ori-gins of resource heterogeneity in the context ofglobal technological capability acquisition. Priorresearch has identified the importance of path-deepening searches through which firms’ inertiaor momentum drives them along certain paths thateventually lead to the building of specific resourceendowments (Amburgey and Miner, 1992; Karimand Mitchell, 2000). This literature has, however,

not explained what causes firms to initiate jour-neys along these diverse paths in the first place. Inthis study, we complemented the path-deepeningfocus of this prior research by drawing atten-tion to path-creating search behavior that initiatesresource heterogeneity. Specifically, we argued,and found empirical support for the argument thatresource heterogeneity can originate through path-creating search processes. Our support for thisthesis consisted of two parts. In the first part, wefound that firms respond to stimuli in the form ofproblems and opportunities in their idiosyncraticsituations. When faced with technological exhaus-tion, firms expanded their scientific activities. Sim-ilarly, when international expansion expanded thegeographic footprint of a firm’s product-markets,firms responded with an expansion of their inter-national research presence. In the second part ofthe study, we established that the variations inbehavior that constitute such path-creating activ-ities, the intensity of a firm’s science search orthe diversity of its geographic search, can indeedlead to performance-enhancing outcomes, at leastup to a point. We next turn to the theoreticaland research implications of these arguments andfindings.

Implications for theory and research

The resource-based view of the firm

One of the fundamental tenets of the resource-based view is that competitive advantage stemsfrom resource heterogeneity between firms andfrom the sustainability of this heterogeneity overtime. Yet, the resource-based view is less forth-coming on how such resource heterogeneity arises(but see Helfat and Lieberman, 2002, and Zott,2003, for work that has started to examine thisissue). Our focus on path-creating search high-lights one potential source of resource hetero-geneity: firms’ solutions to the idiosyncratic situa-tions faced by them can eventually transform intoperformance-enhancing capabilities. For instance,an expansion of research effort overseas that servesas a corollary to a product-market expansion caneventually become a source of enhanced innova-tive productivity. Thus, variety in the problems andopportunities faced by firms can translate to varietyin their resource bases.



Evolutionary search and change

Although our focus was on path-creating search,we also identified some intriguing results vis-a-vispath-deepening search. Specifically, we found thatwhile on average firms tend to persist in similaractivities (witness the positive effect of prior sci-ence search on subsequent science search), at themargin they are quite capable of reversing direc-tions (as indicated by the negative effect of priorgeographic search expansions on subsequent ones).While explaining this result formally is beyondthe scope of this study, the result and the evo-lutionary theorizing from which it draws raise anintriguing possibility. We noted in the second halfof the paper that, especially for geography searchand less definitively so for science, the relationbetween innovative and search activity was curvi-linear. For researchers to be able to statisticallyidentify a curve of this fashion, firms must bedistributed along the spectrum of search possibili-ties, some oversearching and some undersearching,but not all clustered around the optimal point. Ifthis is the case, one explanation of the negativerelationship between prior and subsequent geog-raphy search expansions could be quite simplythat firms find it difficult to identify the optimallevel of search (Nelson and Winter, 1982). Dur-ing the course of building their resource positions,firms make mistakes. For instance, a firm’s expan-sion of its international research presence mayfirst promote innovation as new paths are created,yet result in suboptimally high investments, andreversal in direction, as paths are extended exten-sively. These results thus highlight the value offinding a balance between exploitation and explo-ration (Tushman and O’Reilly, 1996; Katila andAhuja, 2002; Rivkin and Siggelkow, 2003), yetalso go further, to support recent theorizing onorganizations as complex adaptive systems (e.g.,Kauffman, 1993), and the idea that many innova-tive organizations live at the ‘edge of chaos’ wherethe system is inherently inefficient, ‘stumbling intothe wrong markets, making mistakes, and bouncingback’ (Brown and Eisenhardt, 1998: 8).

The results also answer, and perhaps also raise,interesting questions about the dynamics of orga-nizational change. Prior work generally arguesthat search behavior is both inertial (firms resistchange) and exhibits momentum (once change isinitiated, organizations keep changing in the samedirection). On the one hand, our results indicate

that idiosyncratic problems and opportunities caninitiate change—thus organizations may not be asinert as they are sometimes claimed to be (Han-nan and Freeman, 1989). On the other hand, ourresults show that once change is initiated it pro-ceeds down, but also up, a given path—thus wefind limits for the momentum argument (Miller andFriesen, 1980). In fact, organizations searching thegeographic space appeared to take a step backwardfor every two steps forward. These findings raiseinteresting issues for future work.

Innovation search

Our findings also have important implications forresearch on innovation search. The existing litera-ture on search has focused largely on search acrosstechnologies (Rosenkopf and Nerkar, 2001; Katilaand Ahuja, 2002), and found support for the the-sis that firms generally engage in local search, thatis, searching in the neighborhood of their exist-ing technologies (Helfat, 1994). In this paper, weexamined two types of behaviors that enabled firmsto go beyond this type of local search: spanning thetechnology–science boundary, and spanning thenational–international R&D boundary. Our pri-mary arguments, which also found empirical sup-port, were that even after controlling for variationsin the number of technologies explored by firms,the crossing of the additional boundaries, scientificand geographic, contributed to increases in inno-vative output.

Our findings on the drivers of search are alsointeresting. First, our results indicate that organi-zations need not necessarily be especially rich orpoor (Cyert and March, 1963) to embark on newsearch paths; they just need to recognize that theyare in the right place at the right time (i.e., recog-nize an idiosyncratic problem or an opportunity)to start a new search (cf. Shane and Venkatara-man, 2000). Second, past commitments can alsodrive search in unexpected ways. For example, wefind that organizations in the geography space fre-quently retract from search commitments, perhapsto correct past mistakes in a search—a driver ofsearch that deserves more attention in future work.

Going further into the individual dimensionsof search, our finding that searching the sciencebase enhances innovativeness is complementaryto another perspective on the science–technologyrelationship. Scholars have argued that firms maybenefit from engaging in science because that



allows firms to attract talented scientists at lowercost (Stern, 1999). Although our data do not allowus to speak to the cost side of this issue, the resultsdo indicate that supporting science may also bene-fit innovative productivity. On the geography side,our finding that international product-market pres-ence increases international research, and eventu-ally promotes innovation, draws attention to aninteresting aspect of multinationality. Although ourstudy focused on the two dimensions of non-localinnovation search, our results suggest that per-haps even relatively highly diversified internationalresearch presence can eventually be quite consis-tent with the idea of local search. The key to under-standing this lies in the recognition that the modernmultinational firm has potentially many geographicneighborhoods (Dunning, 1992). Even if each firmis constrained to search in only its immediate geo-graphic vicinity, the large multinational firm thathas wide product-market presence has the advan-tage of being ‘local’ to many neighborhoods, andhence has the ability to realize the benefits ofdiversified product-market presence by expandingits research presence to the locations where it isalready present—an advantage which may not beavailable to more geographically focused firms.

Limitations and future work

The study naturally also has limitations. First, oneinherent limitation of this study is the measure-ment of resource heterogeneity. While we wereable to show that the outcomes of path-creatingsearch processes were performance-enhancing andthus potential capabilities, we were limited in ourability, in the same way as previous authors havebeen (e.g., Cockburn et al., 2000), to specificallydemonstrate that we had been able to measurethe elusive concept of heterogeneous resources.Despite this limitation we made an important steptowards understanding the emergence of resources,and thus set the stage for future studies on resourcecreation. Empirically this study was also restrictedto inferring search processes from the paper-trailleft in archival documents. Direct observationand measurement of such processes (e.g., Graeb-ner, 2004) could immensely enrich and improveour findings. Unfortunately, the temporal sweeprequired to observe search behavior and its con-sequences is so large that accomplishing this on alarge statistical sample scale is likely to be pro-hibitive. Similarly, the restriction of this study

to a single nation, and to a single industry, isa mixed blessing. On the one hand, this contextlimits unobserved heterogeneity and makes pos-sible more systematic and unbiased comparisons,thereby enhancing internal validity. On the otherhand, this context limitation also suggests caveatsin terms of generalizability. Again, the intensityof data collection, especially regarding the sciencesearch variables that are extremely labor-intensiveto collect, imposed this constraint. Thus, one pos-sibility for future work is to examine other nation-industry contexts.

Conclusion

At the beginning of this study, we set out toinvestigate the emergence of resources. We notedthat the origins of resources were not well under-stood. Yet, the question is important if we wantto understand the processes through which firmscreate competitive advantage. Our results, foundin the context of technological capability acqui-sition, showed that the emergence of resourceswas inherently an evolutionary process: idiosyn-cratic, inertial; yet path-breaking on occasion; andprone to mistakes. Our results indicate that hetero-geneity emerged when the search paths that firmsfollowed were unique responses to idiosyncraticsituations they faced. We focused on two suchidiosyncratic circumstances: problems and oppor-tunities that firms faced in their local innovationsearches. As a response to these situations, firmscreated unique science and geography search pathsthat were likely to lead to the creation of het-erogeneous resources. We also found that firmsexperimented in order to find the correct invest-ment down each of these paths, and frequentlymade mistakes in undershooting and overshootingthe most productive levels, further demonstratingthe evolutionary nature of the resource creationprocess. We hope that these results lead to a morecomplete understanding of the variety of processesthat underlie resource heterogeneity.

ACKNOWLEDGEMENTS

We appreciate the insightful comments and sug-gestions of the Special Issue editors and the tworeviewers, and gratefully acknowledge the finan-cial support of the University of Texas at Austin,the University of Maryland, the University of



Michigan Business School, and the Stanford Tech-nology Ventures Program. The research was alsosupported in part by the National Science Founda-tion Grant (#0115147) for the second author. Sem-inar audiences at the University of Pittsburgh andDartmouth provided great feedback, and BenjaminHallen, Jennifer Marrone, and Qing Cao valuableresearch assistance.

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