TECHNOLOGICAL CLUSTERS AND MULTINATIONAL ENTERPRISE R&D STRATEGY

Transcription

1 TECHNOLOGICAL CLUSTERS AND MULTINATIONAL ENTERPRISE R&D STRATEGY Ram Mudambi and Tim Swift ABSTRACT Economic clusters are global centers of excellence in particular industries or technologies. They consist of interlinked companies, specialized suppliers, support services, and relevant institutional actors in a specific field. Multinational enterprise (MNE) R&D strategy with regard to economic clusters is impacted by two contradictory forces. MNEs locating their R&D activities within economic clusters can benefit by availing of specialized resources and by capturing location-specific tacit knowledge. However, the risks of knowledge leakage can lead to adverse selection whereby clusters attract underperforming firms that have much to gain and little to lose. Further, general disagreement exists on the measurement of performance within economic clusters. We review the literature, assess the evidence, and suggest areas for productive future research. INTRODUCTION Firms can compete by striving to access the valuable knowledge required to develop commercially valuable technologies. Much of that knowledge is The Past, Present and Future of International Business and Management Advances in International Management, Volume, Copyright r 200 by Emerald Group Publishing Limited All rights of reproduction in any form reserved ISSN: -0/doi:0.08/S-0(200)

2 462 RAM MUDAMBI AND TIM SWIFT tacit in nature (Nelson, ; Arrow, 62; Nelson & Winter, 82; Rosenberg, 82; Zucker, Darby, & Brewer, 8). Newly created knowledge is also tacit. This tacit knowledge is known by relatively few people, is idiosyncratic or difficult for broad audiences to understand, and is not easy to share with others (Polanyi, 62; Zucker, Darby, & Armstrong, 2002). AU : Tacit knowledge is not transmitted easily; when it is transferable, it is transmitted most effectively in face-to-face settings (Cantwell & Santangelo, 2000; Sorenson, Rivkin, & Fleming, 2006). There is a large body of literature establishing the highly local nature of scientific knowledge flows (Jaffe, Henderson, & Trajtenberg, ; Henderson, Jaffe, & Trajtenberg, 8), underlining the importance of clusters in technology-intensive industries. Thus, firms that utilize high levels of tacit knowledge in their innovation efforts compete by accessing regional knowledge that resides in geographic centers of excellence. These firms create a presence within knowledgeintensive regions so that they can interact with locally embedded R&D subject matter experts (Cantwell & Janne, ; Cantwell & Santangelo, ). Such regions are relatively abundant in the resources that support R&D that is relevant to the firm s activities, such as an available supply of specialized knowledge workers. This line of reasoning underlines the importance of geographic areas known as technological clusters where R&D work that involves highly tacit knowledge can be performed by colocated R&D scientists. Clusters often represent worldwide centers of excellence in particular industries or technologies. Silicon Valley outside of San Francisco is a well-known technological cluster of software development; Boston s Route 28 is a wellknown biotech cluster, and Southern Germany has a renowned technological cluster for high-precision machinery (Saxenian, 4; Storper, ). Literature suggests many reasons for technological clusters, such as the existence large, incumbent firms in certain regions (Agrawal & Cockburn, 200), and the colocation of firms that are participating in different but complementary technological fields (Robinson, Rip, & Mangematin, 200). The most competitive firms seek to place some of their important R&D work inside technological clusters. Firms locate R&D resources in new geographic areas in order to gain access to locally embedded sectoral specialists (Cantwell & Santangelo,, p. 20). By interacting with R&D workers that are on the cutting edge of innovation, firms may gain a competitive advantage by applying this new, tacit knowledge in less competitive markets elsewhere. Clusters provide the location-specific supply base of technological and knowledge externalities that firms draw upon for their competitiveness

3 Technological Clusters and Multinational Enterprise R&D Strategy 46 (Amin & Cohendet, 2004) and clusters vary dramatically in terms of their comparative strengths and weaknesses (Mudambi, 2008). Thus, firms can enhance their competitive advantage by dispersing their creative endeavors, tapping into multiple centers of excellence and coordinating knowledge across geographic space (Lorenzen, 2004). For example, Canon U.S. Life Sciences Inc. is networked into the life sciences cluster along the U.S. eastern seaboard, thousands of miles away from its home-based R&D headquarters in Japan (Uchida, 2008). Clusters are particularly important for multinational enterprises (MNEs), which by their very nature are network firms. They are able to leverage their networks to effectively tap into a number of local clusters to assimilate and integrate knowledge (Mudambi, 2002; Foss & Pedersen, 2002). However, knowledge traffic is almost always two-way, so that clusters have much to gain from both intentional and unintentional knowledge outflows from MNEs. Thus, MNEs can also serve as conduits between clusters, so that their network knowledge contributes to the health of all the clusters in which they operate. Further, the relative value of knowledge inflows and outflows varies for different MNEs. In recent years, great progress has been made on the research of clusters. Nonetheless, significant work remains. Construct definition can improve; that is, our research methods can improve by developing common definitions of the key variables used in the field. Scholars have not reached consensus on several critical issues regarding the value and functioning of clusters. Although some research shows that firms exhibit a boost in innovativeness by locating R&D activities within clusters (Baptista & Swann, 8; Beaudry, 200), others argue that adverse selection operates within clusters, such that operating within clusters hurts the most innovative firms but benefits the least (Suarez-Villa & Walrod, ; Beal & Gimeno, 200). We have yet to reach agreement on whether changes to firm-level innovation within clusters are due to network ties or agglomeration economies (Bell, 200). The purpose of this paper is to address three key topics within the research on technological clusters. We wish to identify what we think we know. That is, we will provide a brief overview of extant research on clusters. We will offer our suggestions on what we need to know, or potential areas of improvement in our understanding of technological clusters and its importance to the study of international business. Finally, we encourage future researchers not only to evaluate countries and markets when forming international business research questions, but also to emphasize geographic clusters as the unit of analysis. For many companies, this is where the action is.

4 464 RAM MUDAMBI AND TIM SWIFT THEMES WITHIN THE RESEARCH ON CLUSTERS The Geographic Proximity of Industrial Activity Economists have observed that industrial activity clusters geographically, and that this clustering activity affects economic performance (Marshall, 20; Arrow, 62; Romer, 86). The more similar the activities of the firms in a cluster, the more likely that relevant (and valuable) knowledge will spill over from one firm to another. At the micro level of the firm, this appears as both a benefit and a cost. The knowledge inflows through learning are valuable to the focal firm while the knowledge outflows are likely to accrue to competitors. However, this is a static view. Over time, the focal firm perceives that while knowledge outflows today may benefit its competitors, this very effect improves its own potential future knowledge inflows. Thus, from the perspective of the cluster as a whole, both the knowledge inflows (benefiting the focal firm) and the knowledge outflows (benefiting its competitors) are positive outcomes. Empirically, sectoral growth in a cluster tends to be higher when concentration of that sector in the cluster is higher. These dynamic effects are known as Marshall Arrow Romer (MAR) externalities (Glaeser, Kallal, Sheinkman, & Schleifer, 2; Henderson, Kuncoro, & Turner, ). This type of analysis has become so mainstream that the U.S. Office of Management and Budget (OMB) has defined metropolitan statistical areas, and the U.S. Census Bureau has defined economic areas. Each U.S. metropolitan statistical area must have at least one urbanized area of 0,000 or more inhabitants, with adjacent regions with high levels of sociological and economic integration (U.S. Census Bureau, 200). The U.S. Bureau of Economic Analysis (BEA) tracks economic areas, which are the relevant regional markets surrounding metropolitan y statistical areas (U.S. Bureau of Economic Analysis, 200). Standard definitions AU :2 of these economic clusters were first defined by the U.S. federal government in 4 (U.S. Census Bureau, 200), and economic activity has been tracked using these definitions since that time. This provides an indication of the importance that analysts have placed historically upon geographic clustering of industrial activity. Krugman () refocused research on economic clusters with his seminal study on economic clusters. He showed that scale economies exist when manufacturing is colocated with demand, and at the same time population clustering depends upon the cost of accessing goods. Thus, consumers and

5 Technological Clusters and Multinational Enterprise R&D Strategy 46 industry tend to colocate. He found that the density of economic clusters depends upon the cost of transportation, economies of scale, and the relative size of manufacturing in a given economy. Porter provided in-depth theory and case study evidence suggesting that geographic, cultural and institutional proximity (Porter, 8) enhance the quality of knowledge flows within a cluster. He observed that while the old reasons for clustering, such as the cost of transmitting codified information, have diminished in importance over time, new reasons have emerged, such as the difficulty of transmitting tacit knowledge over longer distances (Porter, 2000). Porter observes that the economic and innovative performance of clusters in the United States varies markedly, and provides evidence that the interaction of different clusters drives regional economic performance (Porter, 200). The Geographic Proximity of Knowledge Flows A common view is that knowledge spillovers increase innovation and productivity growth (Griliches, 0; Nadiri, ), and that much R&D-related knowledge spills over locally (Jaffe, 86; Pavitt, 8; Jaffe, AU : 8; Acs, Audretsch, & Feldman, 2; Jaffe et al., ; Zucker et al., 8). Others have supported this research, finding that R&D activities and the outputs from innovative activities are geographically clustered (Feldman, 4; Audretsch & Feldman, 6). Jaffe et al. () find that patents are two to six times more likely to cite other patents that were filed in a geographically proximate area than patents that were filed farther away. Zucker et al. (8) support this by observing that the geographic location of biotechnology firms and star scientists are highly correlated for the years 6 to 8. This provides empirical evidence that basic scientific research provides commercial benefit, but provides no specific insights into knowledge management policies and R&D productivity in pharmaceuticals. Almeida and Kogut () find that R&D-based knowledge travels locally, and that such knowledge is embedded in local labor networks. Complementary research has showed that knowledge flows not only within an industry, but also between geographically proximate related industries (Jacobs, 6; Lucas, 88). Some new technologies can be adapted in multiple industries, creating greater returns to innovation (Scherer, 84; AU :4 Bairoch, 88; Porter, 200).

6 466 RAM MUDAMBI AND TIM SWIFT AREAS OF CONFLICT Despite the great progress made on cluster research, much work remains. Scholars disagree on key findings, multiple definitions exist for the same terms, and our performance measures vary greatly from study to study. What is a Cluster, Anyway? The formal definition of technological clusters is fairly straightforward. Extant research agrees that a cluster is a group of firms from the same or related industries located in the same or near geographic locations (Becattini, 0; Brusco, 0; Harrison, Kelley, & Gant, 6; Storper & Harrison, ; Bell, 200). However, approaches vary on how to identify technological clusters in an objective way. Baptista and Swann (8, ) identify a cluster as a region with the relatively highest levels of total employment. Porter (200) developed a composite measure of clustering based on private sector employment and wages, plus patents created by economic area. Bell (200) identified the city of Toronto as a cluster, and labeled any firm operating within the city limits as having a presence in the cluster. Lee (200, p. 6) asks survey respondents, In your vicinity (locality) is there a cluster (or group) of firms in your industry (including suppliers and/or users)? Zucker et al. (8) identified clusters as those geographic areas surrounding universities that employ star scientists. Aharonson, Baum and Plunket (2008) identified clusters by counting the number of biotechnology firms operating within each postal code in Canada. Baptista and Swann () observe the growth in the cluster itself as a measure of innovativeness of a geographic area. Finally, some researchers ask, why do clusters even exist? Robinson et al. (200) suggest that effective clusters contain entities that create both science and technological knowledge, and that the combination of these complementary fields of knowledge creates commercially valuable forms of innovation. Agrawal and Cockburn (200) test the anchor-tenant hypothesis, showing that the presence of a large, local, R&D-intensive firm enhances a cluster s ability to convert local university research into commercially valuable innovation. When is Geographic Proximity Really Value-Enhancing? The geographic proximity of research team members is not conducive to all types of innovative work. New technologies created by teams that operated

7 Technological Clusters and Multinational Enterprise R&D Strategy 46 using face-to-face interaction have a greater chance of successful commercialization (Almeida & Kogut, ; Agrawal, 2000; Jensen & Thursby, 200; Mowery & Ziedonis, 200). Gittelman (200) finds that geographically proximate R&D teams generate more commercially valuable innovations, while geographically dispersed R&D teams generate more high-impact scientific knowledge. Close proximity among R&D team members allows for close personal interaction, which fosters the creation and transfer of tacit knowledge. Increasing distance reduces the frequency of face-to-face interaction between team members. For example, prior research finds that communication between engineers falls dramatically if the work locations of the engineers are greater than 0 m apart (Allen, ). Further, long-distance communication often compels knowledge workers to rely on other modes of communication, such as and telephone conversations, which rely more heavily on codified forms of knowledge. However, there is also evidence that these deleterious effects of geographical dispersion can be minimized through the development of teamwork and socialization between members of different units within the firm (Mudambi, Mudambi, & Navarra, 200). Conversely, the labor market for science workers is geographically dispersed. Graduate students relocate for tenure-track positions in new cities, but maintain research relationships with colleagues at their alma maters. Prior research asserts that the social networks of scientists are geographically dispersed (Merton, ; Allison & Long, 8; Gittelman, 200). Thus, geographically dispersed teams generally engage in high-impact science, which has limited near-term commercial value (Gittelman, 200). Does Cluster Presence Really Enhance Firm Innovation? As discussed above, literature has long suggested that the agglomeration of industry conveys many benefits. Taken collectively, extant evidence does not demonstrate a clear benefit from participating in a cluster (Lee, 200). Research has shown an increase in innovating patenting activity for firms within a cluster (Baptista & Swann, 8; Beaudry, 200). Others do not find any increase in innovation (Suarez-Villa & Walrod, ; Beal & Gimeno, 200). There are two potential reasons that clusters may not be related to innovation. First, any observed increase in knowledge spillovers may be due to formal market mechanisms such as contracts or equity ownership between firms, and not any special effects of geographic proximity (Zucker et al., 8; Geroski, ). In addition, this research suggests that

8 468 RAM MUDAMBI AND TIM SWIFT the best knowledge that flows within clusters is protected by legal mechanisms; only the least valuable knowledge is freely available. Others argue that clustering increases R&D-based competition for resources, suppressing innovation (Beal & Gimeno, 200). Pouder and St. John (6) observe that a burst in innovative activity within clusters is often followed by spectacular collapse. They attribute this to the development of core rigidities (Leonard-Barton, 2) within these clusters that reduce firms ability to explore for new ideas and continue to innovate. Second, and perhaps most importantly, many researchers suggest that adverse selection can operate within clusters (Oakey & Cooper, 8; Shaver & Flyer, 2000a, 2000b; Cantwell & Santangelo, 2002; Fosfuri & Ronde, 2004). If clustering increases knowledge spillovers, intuitively we see that the most innovative firms will experience a net loss of knowledge (because they spill more knowledge than they can gain) while the least innovative firms benefit, gaining more knowledge than they disclose. Finally, others argue that firms within clusters are also simultaneously participating in global information networks. Thus, it is difficult to tease apart the benefits of the local and global network participation (Angel & Engstrom, ; Zeller, 200). Recent research finds that knowledge flows are quite strong between an inventor s prior location and the location to which he or she moves, suggesting that social relationships and coinvention networks play an important role in knowledge flows between clusters (Agrawal, Cockburn, & McHale, 2006; Breschi & Lissoni, 200). How Should We Measure Cluster Influence? We do not agree on how to measure the impact that clusters have on firm s innovative efforts. Some researchers have observed that firms that operate within clusters grow faster than firms outside the cluster (Henderson, 8; Baptista & Swann, 8; Glaeser et al., 2). Other researchers observe the number of patents firms within clusters create as a measure of innovative performance (Harrison et al., 6; Cantwell & Janne, ; Cantwell & Santangelo, 2000; Cantwell & Piscitello, 2002). Baptista and Swann (8) measure the actual number of innovations the firm has produced, whether patented or not. Lee (200) evaluates whether firms that operate within a cluster have higher R&D intensity, while others measure the adoption of new technologies (Breschi & Lissoni, 200).

9 Technological Clusters and Multinational Enterprise R&D Strategy 46 Benefiting the MNE vs. Benefiting the Local Subsidiary The MNE s local presence in a cluster is typically a subsidiary. The MNE subsidiary serves as its pod to access, transfer, integrate, and leverage localized cluster knowledge (Foss & Pedersen, 2002). However, there is evidence that the struggle for control of knowledge assets can increase the level of intrafirm tension in MNEs (Asakawa, 200). Therefore there are conflicting views with regard to the MNE s knowledge strategy at the subsidiary level. There is evidence that accessing cluster knowledge requires the subsidiary to embed itself in local knowledge networks (Andersson, Forsgren, & Holm, 2002). There is also evidence that subsidiary control of valuable local knowledge assets can reduce the ability of headquarters to control and leverage the knowledge (Mudambi & Navarra, 2004). Reconciling these two bodies of evidence implies that MNE subsidiary strategy must be nuanced to balance countervailing forces. Subsidiaries need to be given enough freedom to enter local knowledge networks; headquarters needs to maintain enough control to ensure that they remain firmly anchored within the MNE internal firm network. This dual-network view of the MNE subsidiary is recognized as critical to innovation success in MNEs. However, successful implementation of a strategy to lead to this outcome has proved to be a challenge for most MNEs (Birkinshaw & Pedersen, 2008). ASSESSMENT OF THE EVIDENCE: PRIVATE GOOD VS. PUBLIC GOOD ASPECTS OF KNOWLEDGE Our preceding review of the literature highlights two major issues. First is the public policy question; we must do more to determine the effect of cluster participation on the average firm, and the most innovative firms. Does cluster presence enhance innovation and productivity? Is the impact different between average firms and the best firms? The second issue is the strategic management question; we do not adequately understand how the presence of clusters should inform firm strategy. Under what conditions should a firm choose to locate its innovative activities within a cluster? Much of the recent empirical literature in economics and economic geography focuses on the first, public policy issue. Scholars have made great

10 40 RAM MUDAMBI AND TIM SWIFT progress in understanding and identifying MAR externalities, or intrasectoral spillovers (Marshall, 20; Arrow, 62; Romer, 86; Glaeser et al., 2; Henderson et al., ) and Jacobian externalities, or intersectoral spillovers (Jacobs, 6); they have presented widespread evidence that these phenomena are the main drivers of cluster performance. The strategic management literature is split between the market for lemons or adverse selection argument (e.g., Shaver & Flyer, 2000a) that holds that only inferior firms locate in clusters, and the more traditional Porter argument whereby cluster location yields greater benefits than costs. We can suggest a resolution whereby the deciding factor involves considering the type of industry (e.g., oligopolistic vs. competitive). The geographically concentrated nature of inputs to the innovative process (e.g., labor and knowledge) within clusters should attract firms. However, the incentive to the firm for locating its innovative activities within a cluster depends upon the structure of the industry in which the firm competes (McCann & Mudambi, 200). In a competitive market structure, there are a relatively high number of smaller firms; each of these firms has relatively small market share and profits. As such, these firms probably have little to lose from unintentional knowledge outflows and more to gain from inflows stemming from a strong clustered location. The public-good aspect of knowledge would appear to dominate here, with the local knowledge outflows being viewed as generally positive both for the firms themselves and for the local region (Jaffe et al., ; Saxenian, 4). In an oligopolistic industrial structure, relatively fewer players possess relatively larger shares of valuable knowledge. Thus, leakage of knowledge from one firm to a competitor can have significant impacts on the balance of power within a cluster comprised of fewer, larger firms. If the clustering of oligopolistic firms appears to jeopardize their proprietary knowledge assets by exposing themselves to the possibility of unintentional outward knowledge spillovers, such firms will decide not to locate in clusters, unless they can find a way of avoiding unintentional knowledge outflows. Thus, prior to selecting locations for conducting its R&D activities, firms must consider the structure of the markets within which they compete, and the composition of cluster. Larger, R&D-intensive firms colocating in technological clusters with other larger competitors can engage in a highstake games of knowledge exchange that can damage the firm s competitive position. Smaller firms engaging in more competitive markets are more likely to engage in the value-enhancing knowledge sharing that can take place within technological clusters (McCann & Mudambi, 200).

11 Technological Clusters and Multinational Enterprise R&D Strategy 4 NEXT STEPS FOR INTERNATIONAL BUSINESS AND MANAGEMENT RESEARCHERS Under what conditions is locating within a technological cluster beneficial to the firm? Are some clusters better than others? How can we measure the relative attractiveness of clusters? How should locating within the right cluster, under the right conditions, influence firm performance? What advantages accrue to MNEs that locate within clusters? These are a few avenues along which we can push the frontiers of research and provide practical advice to international business practitioners. First, researchers need better construction definition. Valuable progress in strategic management research was spawned by Porter s Competitive Strategy (80). By firmly rooting his Five Forces model in industrial organization economics, Porter provided strategic management researchers a consistent approach to developing testable hypotheses using reliable empirical methods. In the same way that rich theoretical frameworks have advanced management research in the past, research on technological clusters can advance by developing common definitions of clusters and cluster performance, and robust contingency theories that explain the conditions under which cluster location can aid in creating valuable knowledge. Second, studies can utilize multiple measures of performance when evaluating cluster impacts. Since disparate measures of performance have been used in the literature to evaluate cluster innovativeness, we lack a clear picture of which facets of economic geography impact which dimensions of firm performance. New studies can observe the multivariate relationships between location variables, firm profitability, firm value, knowledge creation, firm growth, and innovation. Third, longitudinal studies can shed light on whether the firm s innovative performance changes after the firm locates in a technological cluster. While several scholars have asserted that adverse selection may operate within clusters (Oakey & Cooper, 8; Shaver & Flyer, 2000a, 2000b; Cantwell & Santangelo, 2002; Fosfuri & Ronde, 2004), the empirical evidence is not conclusive since the existing studies have not tracked firms over time. New studies can observe firm performance before and after cluster entry, and evaluate whether the firm s relative knowledge endowment prior to entering the cluster influences the impact that cluster participation has on the firm s innovative performance. Finally and perhaps most importantly, more can be done to inform international business strategy. Seminal studies have led to the current view of the MNE as a global network of strategic business units (SBUs)

12 42 RAM MUDAMBI AND TIM SWIFT (Nohria & Ghoshal, 4), each with unique roles such as innovators, implementers and specialists (Gupta & Govindarajan, ). Innovator SBUs or competence-creating subsidiaries discover and integrate new knowledge (Gupta & Govindarajan, ), often creating new competencies for the MNE as a whole (Cantwell & Mudambi, 200). Clearly, such SBUs stand to benefit most from participation in the right technological clusters. However, within the international business literature, the industrial cluster is treated rather simplistically as a source of knowledge (Kuemmerle, ), so that important questions remain. First, what corporate structures provide MNE subsidiaries the best chance of successful participation within the cluster? Second, should MNEs colocate with its large competitors and risk losing competitively valuable knowledge? Third, how can MNEs identify new clusters that are creating the nascent knowledge that will be available in the future and avoid becoming entrapped within older clusters that are suffering technological lock-in (Leonard-Barton, ; Narula, 200; Pouder & St. John, 6)? CONCLUDING REMARKS: MNES AND TECHNOLOGY CLUSTERS The global innovation system is composed of knowledge processes that are spatially confined and that can be accessed only through local presence (what has been called buzz ) and processes that involve transferring such knowledge over distances (through what have been called pipelines or conduits) (Bathelt, Malmberg, & Maskell, 2004; Mudambi, 2002). MNEs are a critical part of the global innovation system since they incorporate both knowledge processes. MNE subsidiaries are embedded within and function as integral parts of technology clusters as they access and filter knowledge and generate spillovers. MNEs internal networks are de facto global pipelines and knowledge flows between MNE units are a significant portion of pipeline content. This approach recognizes the symbiotic nature of innovation in the cluster and in the multinational firm. Thus, the internal innovation system of the multinational and the cluster system of innovation each affect the evolution of the other. The MNE knowledge network can therefore be leveraged to generate two unique advantages: () transfer the use of knowledge created anywhere in the network at all other nodes of the network, and (2) integration the synthesis of knowledge flows from the parent, other subsidiaries, and from its host location (McCann & Mudambi, 200).

13 Technological Clusters and Multinational Enterprise R&D Strategy 4 It becomes apparent that the MNE s knowledge strategy (that underpins its competitive advantage) is based on optimizing the knowledge transfer and integration functions. These functions are determined, in large part, by the portfolio of clusters in which the MNE chooses to locate its knowledge sourcing operations, through subsidiaries or other entry vehicles. It is well known that MNEs operating within each industry can be hierarchically differentiated into leaders and laggards (Cantwell, 200). There is accumulating evidence that technology clusters in particular industries are also hierarchical, with the higher level ones being serviced by those at a lower level (Cantwell & Janne, ). Thus, the optimal portfolio of cluster locations chosen by an MNE is likely to differ depending on firm characteristics. This analysis suggests several important avenues for future research. Technology clusters themselves evolve over time. Much is yet unknown about this evolutionary process. Some clusters persist for long periods of time (Lorenzen, 200), renewing themselves as they survive the life cycles of several industries. Example include the clusters of radio producers in the United States that shifted to television (Klepper, 200) and the accordion cluster in Marche that incorporated electronics into its instruments as it renewed itself in this industry. Other clusters, like the Boston Route 28 computer and semiconductor cluster, are eclipsed even as their main industries continue to grow (Saxenian, 4). This implies that the MNE s strategy with regard to cluster location will evolve over time. In fact, the clusters in the MNE s portfolio are likely to be impacted by its location decisions, so that firms and clusters coevolve. Little is known about the nature of this coevolution as it has received little attention in the literature. Research on technological clusters is valuable. It provides value for researchers seeking to develop a better understanding of strategy and IB theory, for public policy makers seeking to maximize economic performance, and for managers seeking to capture lightning in a bottle, and convert cutting edge knowledge into commercially valuable innovation. All of these diverse constituencies would benefit from a better understanding of MNEs strategies with regard to technology clusters. UNCITED REFERENCES Tappi (200); U.S. Department of Commerce (200).

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