Cluster Regions A Social Network Perspective - SAGE Journals

Economic Development Quarterly Volume 22 Number 4 November 2008 345-352 © 2008 Sage Publications 10.1177/0891242408322719 http://edq.sagepub.com hosted at http://online.sagepub.com

Cluster Regions A Social Network Perspective Neil Reid University of Toledo, Ohio

Bruce W. Smith Michael C. Carroll Bowling Green State University, Ohio One ongoing debate in the cluster literature concerns methods of delineating the spatial footprint of industrial clusters. Some cluster regions correspond to political boundaries. Researchers have also used qualitative methods and various quantitative techniques including location quotients and spatial statistics to demarcate clusters. A common weakness of most approaches is that researchers do not incorporate collaboration among cluster participants. In this article, the use of social network analysis (SNA) is illustrated. SNA is not proposed as an alternative to other methods of cluster mapping. Instead, the authors suggest that it complements other methods. Because SNA focuses on networks of social or interpersonal relationships, it provides a dimension that techniques focusing on economic relationships do not capture. One advantage of SNA is that it enables the identification of critical nonindustry actors, such as politicians, economic development practitioners, and academic researchers. Keywords:

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cluster-based economic development; cluster mapping; greenhouse industry; Ohio

luster-based economic development (CBED) has received much attention as a regional development strategy in an era of globalization. In the academic literature, it has been extensively discussed by Porter (1998) and others (Feldman & Francis, 2004; Schmitz & Nadvi, 1999). CBED has also been embraced in the nonacademic community. As one example, the National Governors Association (NGA, 2007) published Cluster-Based Strategies for Growing State Economies and subsequently announced that it was launching a policy academy in which seven states would work on applying cluster analysis and innovation-based economic development strategies in their states. In Europe, Lagendijk and Cornford (2000) noted the widespread acceptance of clusters and learning regions among academics and regional development consultants. Regarding the popularity of clusters, Asheim, Cooke, and Martin (2006) observed, “Clusters, it seems, have become a worldwide craze, a sort of academic policy fashion item” (p. 3). Although CBED has become more ubiquitous, there has been much debate in the academic literature on various aspects of clusters. One ongoing debate concerns the appropriate definition of the spatial footprint of industrial clusters (Asheim et al., 2006; Boschma & Kloosterman,

2005; Martin & Sunley, 2003). Martin and Sunley (2003) observed that there is no agreement on the key indicators and methods for identifying and mapping clusters. Boschma and Kloosterman (2005) noted that most cluster studies lack sufficient data, and they differ in fundamental methodology, using differing spatial levels, key indicators, and the like. Batheldt (2005) observed that one should consider spatial proximity, economic interactions, and social relations in the delineation of a cluster region. The purpose of this article is to illustrate the utility of social relations in defining the spatial footprint of industrial clusters. In particular, we use social network analysis (SNA) to delineate the geography of the greenhouse industry cluster in northwestern Ohio. This approach provides a different perspective from more traditional methods. It does so by focusing on the collaboration among cluster participants including representatives of core firms, supplier firms, academia, and local government. The remainder of this article is organized as follows. First, existing methods of mapping clusters are addressed; second, a brief introduction to pertinent SNA ideas is Authors’ Note: This research was funded by a grant from the U.S. Department of Agriculture.

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provided; finally, an example of using SNA to delineate cluster regions is presented using the example of the northwestern Ohio greenhouse cluster.

Cluster Mapping The spatial boundaries of clusters are often defined politically. Within the United States, various cities and states have instituted CBED. These are largely contained within the borders of predefined political jurisdictions (Akundi, 2003). Numerous examples exist. As one specific instance, the governor of Texas announced in 2004 that economic development in Texas would be focused around six industry clusters (Texas Industry Profiles, 2004). Another example is in the San Diego, California, region, where 18 cities and county governments joined together and identified 16 industry clusters that play a major role in driving that region’s economy (San Diego Association of Governments, 1998). Other approaches have relied on qualitative strategies to define cluster footprints. For example, some have used expert opinions or key informants to isolate clusters (Colgan & Baker, 2003; Roberts & Stimson, 1998). Austrian (2000) argued that qualitative techniques, including case studies and cluster maps, are valuable methodological tools for delineating clusters. Quantitative approaches to cluster demarcation have been frequently employed. For example, Hill and Brennan (2000) used discriminant analysis to identify groups of similar industries in terms of their competitiveness, interindustry linkages, and export characteristics. Yet another approach has been to use location quotients to detect clusters (Miller, Botham, Martin, & Moore, 2001; Schoales, 2006). Feser and Bergman (2000) relied on a principal components analysis of the 1987 U.S. input–output accounts to derive 23 industrial clusters, which were templates for subsequent regional analyses. Yang and Stough (2005) considered both functional linkages and geographic proximity to identify clusters in the Baltimore metropolitan region. They used location quotients to identify “basic industries” and the suppliers of those industries. They then applied Ward’s clustering algorithm to group the basic industries and their suppliers into functional clusters. Finally, they computed the center of gravity of the clusters to evaluate the spatial structure of the functional clusters. More recently, researchers have applied spatial autocorrelation measures to the cluster definition issue (Helsel, Kim, & Lee, 2007). For example, Carroll, Reid, and Smith (2008) used Getis and Ord’s Gi* in combination with location quotients to isolate what they termed

potential cluster regions, which are areas that have a high potential for a successful CBED strategy.1 In isolation, none of these methods are ideal in terms of identifying the spatial footprint of clusters. All have some weaknesses, which is partly attributable to the lack of cluster theory to guide the implementation of empirical methodologies. For example, location quotients provide an index of specialization within a given areal unit, be it a county or metropolitan statistical area, but location quotients do not take into account the industrial structure of neighboring areal units. On the other hand, spatial autocorrelation measures incorporate industrial patterns in neighboring areal units, but the selection of an appropriate spatial weights matrix is a fundamental problem when using these techniques (Smith, Carroll, & Reid, 2007). Many studies have incorporated a firstorder nearest neighbor spatial weights matrix, which assumes that there is no spatial interaction between businesses in nonneighboring counties. Such an assumption is invalid for most economic activities. A common weakness of most of the existing approaches is that they do not incorporate collaboration among cluster participants. Our conception of CBED is that it is a network-driven economic strategy built on collaboration among the participants. CBED is an attempt to create and take advantage of the positive synergies that can result when business, academia, economic development agencies, and other community stakeholders strategically partner to address the competitive challenges facing a particular industry, which individual businesses, because they lack resources, cannot successfully address by themselves. Therefore, it is important to understand the social structure and interpersonal relationships among cluster members. Because SNA analyzes relationships within a network, it is well suited to measuring the collaborative relationships in a cluster. In the cluster literature, networks, implicitly or explicitly, are a fundamental concept (Karlsson, Johansson, & Stough, 2005). For example, Glückler (2007) suggested a network “represents the architecture through which productive resources, social values and economic interests circulate” (p. 631). Gordon and McCann (2000) identified three types of spatial industrial clustering, one of which is the social network model in which social networks of strong interpersonal relationships transcend firm boundaries. In Brökel and Binder’s (2007) conceptualization of the regional dimension of knowledge transfers, social networks are attributed an important role because networks define the spatial range of people’s search for new knowledge. For this reason, Brökel and Binder suggest that the geographic extent of social networks should

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be used to delineate regions suitable for analyses of socioeconomic processes. Despite the conceptual importance attributed to networks, relatively few researchers have used SNA in their study of industrial clusters. Notable exceptions include Boschma and ter Wal (2007), Giuliani and Bell (2005), and Giuliani (2007).

Figure 1 Illustrative Network G

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What Is SNA? SNA is a methodology for detecting, describing, and analyzing the relationships among a group of people or organizations (de Nooy, Mrvar, & Batageli, 2005). In SNA, the focus is not on individuals as discrete units of analysis; instead, it is centered on the relationships of those individuals. The underlying premise of SNA is that the behavior of people and organizations is affected by, and in turn shapes, the social networks in which they are involved. In other words, social context matters (Carrington, Scott, & Wasserman, 2005). Actors are viewed as interdependent entities instead of autonomous units whose behavior can be predicted solely by their characteristics (Wasserman & Faust, 1994). The primary input of SNA is relational data that link nodes. The nodes can be individuals or organizations. The relationships between nodes can be kinship ties, business interactions, informational networks, and the like. Most important, the observations are not assumed to be independent of one another. Indeed, SNA researchers focus on the interdependence among the observations under the premise that the behavior of an observation is affected by its ties with other observations. Consequently, the data set used in SNA is different from that typically used in statistical analyses by regional analysts, where the rows of a data matrix are observations and the columns are attributes of the observations. In the traditional statistical model, individuals are bundles of attributes, such as income, age, and size of business. The underlying premise of traditional analyses is that the attributes are thought to influence behavior. Moreover, the observations are assumed to be independent of one another. Figure 1 is a simplified illustrative network. It consists of 11 nodes, which could be individuals or organizations, and the ties among those entities. For example, Node A and Node C are directly linked. Node C is connected to Node L by two steps through Node A. Node J is an isolate in the network because it is not connected to any of the other nodes. Moreover, links can be characterized by the strength of the tie. For example, one could classify connections by the frequency of the interaction, such as weekly, monthly, quarterly, or annually.

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Various software packages are available to analyze social network data. Scott (2000) and Carrington et al. (2005) provide overviews of selected packages. In this article, InFlow 3.1 (n.d.) is used, but similar analyses could be conducted with a variety of software. Most software packages produce an aspatial graphic of the network displaying the nodes and the linkages. In addition, metrics describing the network are generated by the software. A large number of network metrics have been developed by researchers. de Nooy et al. (2005) provide an extensive glossary defining many of these metrics. The metrics provide insight into various attributes of the network structure and its component nodes and linkages. One of the more commonly used metrics is the centrality or connectivity of a node (person or organization), which is the number of connections that a node has to other nodes in the network. The influence of people in a network depends on their position in the network, which is in part measured by centrality (Clark, 2006; Llobrera, Meyer, & Nammacher, 2000; Pryke, 2005; Wasserman & Faust, 1994). According to Clark (2006), “The actor’s position in a network influences their access to resources such as goods, capital and information. This infers that economic activity is linked to social structures, which has led to the concept of social capital” (p. 4). Centrality can be measured by degrees out, which is based on the people themselves reporting those individuals to whom they are linked. Alternatively, centrality can be based on degrees in, which uses the number of times that a person is mentioned by other people in the network. Degrees in is preferable because it avoids the problem of self-reporting bias.


One virtue of an SNA is that it delineates the informal structure of relationships within an organization or region. Obtaining documentation of the formal structure of organizations is usually not difficult, but describing the informal linkages is more challenging because such relationships are transparent. As Cross and Parker (2004) stated,

solution to the boundary problem. Boundaries are delineated based on the subjects’ connections irrespective of their geographic location.

Whether as a manager presiding over a department or as a member embedded within one, we are all dramatically affected by information flows and webs of relationships within social networks. These networks are often not depicted on any formal chart, but they are intricately intertwined with an organization’s performance, the way it develops and executes strategy, and its ability to innovate. (p. 9)

The data for this article are based on the northwestern Ohio greenhouse cluster project officially launched in January 2005. Funding for the project was provided by the U.S. Department of Agriculture, with responsibility for cluster development given to academics with expertise in cluster-based economic development. The objective of the project is to help the local greenhouse industry, which is composed of small-scale, family-owned operations, to work collaboratively to address significant competitive challenges jeopardizing the industry’s viability. Those threats include increasing international competition, especially from southern Ontario, high and rising utility costs, a weak market presence, old production technology, and unfavorable “big box” stores’ purchasing agreements (LaFary, Gatrell, Reid, & Lindquist, 2006; Reid & Carroll, 2006). Figure 2 shows the six counties constituting the region in which the greenhouse cluster started. This area was selected in part because northwestern Ohio is a major greenhouse producing area in the nation (LaFary et al., 2006). More important, those counties roughly correspond to the congressional district of U.S. Representative Marcy Kaptur, who played a very influential role in the formation and continuation of the cluster project. In a real sense, the definition of the original cluster boundaries was politically inspired. In February 2007, an SNA of the participants in the greenhouse cluster was conducted. At that time, 111 people were identified with some affiliation with the cluster. The cluster is composed of representatives from greenhouses, suppliers, academia, government agencies, and economic development agencies. The data were collected using the roster-recall method (Boschma & ter Wal, 2007). In this approach, each of the respondents was given a list of all 111 members of the cluster project. They were asked to indicate those individuals with whom, during the past 12 months, they had collaborated on a project. In addition, space was provided in which each respondent could enter the names of people not on the list with whom they had collaborated during the previous 12 months. In effect, this becomes a snowball type of sample because one can then send the questionnaire to those people listed by respondents who were not on the initial roster. The results reported in this article are

Relying solely on key informants to generate information on informal networks can be problematic because people in a network are likely to be aware of activities in only a small segment of the network. Friedkin (1983) demonstrated that there is a “horizon to observability” (p. 70); that is, people are not likely to be aware of the performance of those who are more than two steps from them. SNA is a methodology for making those informal, and often invisible, connections visible. Establishing boundaries in an SNA can be problematic because the social relations of people do not correspond to political boundaries. For example, collaborations of business people in Silicon Valley will logically extend well beyond that local area. Marsden (2005) states there are three generic boundary specification strategies: (a) a positional approach, based on object characteristics or formal membership criteria, (b) an event-based approach resting on participation in activities, and (c) a relational approach based on social connectedness. In this analysis, the third approach is adopted. Boundaries are established based on the social connections of the nodes. Determination of appropriate boundaries is partially linked to data-collection strategies. One approach is sampling whole networks (Doreian & Woodward, 1994). A more common approach in the analysis of small networks is the use of snowball samples (Scott, 2000; Wasserman & Faust, 1994). In a snowball sample, one starts with a set of people within the study area and asks them to provide names of those people or organizations to whom they are connected irrespective of geographic location. The second phase of the data-collection process is to approach those people named but not surveyed in the first phase and so on through subsequent iterations. Although this approach has limitations (Frank, 2005), it does provide a practical method of data collection and a

SNA Cluster Region


Figure 2 Greenhouse Project Region and Cluster Participants

Figure 3 Centrality Indices

based on the responses received from the original list of respondents. A total of 74 people responded, for a 66.7% return rate. Those 74 respondents wrote in additional people, so a total of 138 different nodes (people) emerged from the survey. Although the data collected are aspatial, they can be converted to spatial data using the geocoding functionality of geographic information systems. Figure 2 displays the distribution of the nodes within Ohio. The cluster region defined by the SNA nodes is substantially larger than the six counties originally defined to be the focal area of the greenhouse project. When examining the distribution of nodes, it is important to keep in perspective the scale of operations of the greenhouses. In a 2005 survey, more than 75% of the growers in Ohio reported that more than 75% of their sales were within their home county (LaFary et al., 2006). Growers have a very limited geographic horizon in their operations, and therefore it is not surprising that most of the nodes are located in the six northwestern Ohio counties.

Taking into account the larger geographic area represented by the SNA nodes has various benefits. For example, restricting definition of the cluster to the sixcounty area excludes most suppliers who operate from locations outside the local region. The SNA revealed suppliers to be critical in the greenhouse network with high indices of centrality. In their day-to-day business activities, suppliers communicate with a wide variety of growers. Consequently, the suppliers are important in the transmission of information from grower to grower. This result is similar to Giuliani and Bell’s (2005) findings in the Chilean wine cluster, where they reported suppliers of machinery and materials, as well as consultants, seemed to be important sources of knowledge and technical change. Another benefit of the larger cluster region is related to the spatiality of centrality indices within the network. Figure 3 shows that some people with relatively high centrality indices are located outside the six-county area. As previously mentioned, centrality is a measure of the influence of people in the network. From a management perspective, it is important to keep the people with high


centrality involved, despite their peripheral location, because they will have a greater impact on the viability of the network than people with low centrality. Given that supply companies located on the periphery have customers in other parts of the state and beyond, they can provide links to other networks of greenhouses. They are, in essence, boundary spanners (Glückler, 2007). Also, they might serve a “pipeline” function, following the logic of Batheldt, Malmberg, and Maskell (2004). In terms of the spatiality of knowledge creation in clusters of economic activity, Batheldt et al. suggested that local buzz, or learning from people in the local area just by being there, is important. However, there are also advantages to having links outside the local community (pipelines) to access exogenous knowledge that can be a source of new ideas and information. In terms of setting boundaries, be it for an SNA or another type of study, one establishes the parameters of the study area by design and by default. Setting the limits by design means conscious decisions are made to eliminate certain groups or certain areas. For example, in the case of the greenhouse SNA, one may choose to exclude the academics because their views may not be pertinent given the context of the project. In addition, boundaries are set by default, where areas or groups of people are excluded without realizing who and what are being omitted. Given that the SNA captures a larger, more varied group of people and broader geographic coverage, the exclusions by default will likely be less prevalent. When using an SNA survey, the composition of the observations in the cluster will likely differ from a more traditional approach in which one would include only a sample of greenhouses, or even all the greenhouses, within the six counties of northwestern Ohio. For example, among the northwestern Ohio greenhouses, only 6.5% of the operations are solely owned by women, with a few others jointly owned by spouses. In the SNA survey, 28.2% of the respondents were female. Also, a notable percentage of the respondents were family members of the owners. From the perspective of decision making in that industry, it is important to include spouses and other family members. Because many greenhouses are multigenerational family operations, decision making extends beyond the owner to include spouses, adult children, and in-laws. In-depth discussions and focus groups with growers suggest that decision making in these small organizations often conforms to Yeung’s (2003) observation that “the firm is indeed a messy constellation of multiple identities, contestation of power, and shifting representations” (p. 451). Consequently, the ability to identify multiple actors who play a major role

in decision making at each greenhouse is an important advantage of SNA. Accurate definition of the spatial footprint of an industrial cluster also has important policy implications. A CBED strategy that does not reflect the spatial reality of a cluster is more likely to exclude key individuals who are located outside, say, the politically defined cluster region. SNA can provide valuable information to both economic development policy makers and practitioners who have a vested interest in developing and nurturing a robust and successful cluster. In general, SNA provides an alternative strategy for identifying the spatial footprint of a cluster. Its prime advantage is that it incorporates collaboration among the participants in the process. In addition, SNA surveys will yield a more varied sample population than will traditional surveys focusing on the core industry only. Although SNA provides a useful perspective on clusters, implementing an SNA presents some challenges. For example, collecting SNA data will be more expensive and time-consuming than relying on secondary data sources. Another problem is the absence of standard terminology and metrics. For instance, researchers define centrality or connectivity in a variety of ways. Rogers and Kincaid’s (1981) definition of connectedness is the number of connections between a node and other nodes, whereas Wasserman and Faust (1994) describe connectivity as a function of the graph remaining connected when nodes or lines are removed. Friedkin (2004) has noted the lack of established terminology. Complicating the lack of standard metrics is the fact that various SNA software packages produce differing metrics based on varying formulae. As previously discussed, SNA provides only a description of subjects’ positions in a social network, and, even then, it may be only a partial representation of reality. SNA does not provide insights into the motivation of people in various key positions in the network, nor does it provide insights into the nature of social relations among people in the network. To the extent that such information is important in a cluster study, it must be obtained through key informants or other qualitative methods. SNA can, however, identify the key people or organizations in a network that would be most appropriate for further study. Despite these limitations, SNA can be an invaluable complement to other methodologies in cluster research. Because humans and organizations are affected by the web of social relationships that surround them, consideration of those networks in cluster studies is worthy of investigation.


Conclusion In this article, we have presented SNA as a method to be used in the delineation of the spatial footprint of industrial clusters. We do not view SNA as an alternative to other methods of cluster mapping, such as key informants, location quotients, input–output relations, or similar methods. Instead, we suggest that SNA can complement the other methods. Because SNA focuses on the network of social or interpersonal relationships, it provides a dimension that techniques focusing on economic relationships do not capture. All existing methods of measuring the geographic extent of clusters contain some type of shortcoming, such as location quotients not accounting for the industrial structure of neighboring areal units. At best, one will gain only approximations of a cluster’s boundaries with each technique. Therefore, using a variety of methods is more likely to result in a better understanding of the sometimes complex geography of industrial clusters. One advantage that SNA has over other methods of cluster delineation is that it enables the identification of critical nonindustry actors, such as politicians, economic development practitioners, and academic researchers. Accurate definition of the spatial footprint of an industrial cluster can have important policy implications. It is important to have all actors who are needed to develop pertinent policies involved in the cluster. Traditional methods that focus on, for example, industry concentrations or input–output relationships may fail to identify these nonindustry players.

Note 1. Getis-Ord’s Gi* is a measure of local spatial autocorrelation. This statistic identifies statistically significant clusters in spatial distributions where areal units and their neighbors have similar high values on a variable (hot spots), or alternatively it identifies a cluster of low values (cold spots).

References Akundi, K. (2003). Cluster-based economic development, Part 1: A survey of state initiatives. Austin: Texas Economic Development, Business and Industry Data Center. Retrieved June 2007 from http://www.bidc.state.tx.us/Cluster%20Based%20EconDev%20P ART1.pdf Asheim, B., Cooke, P., & Martin, R. (Eds.). (2006). Clusters and regional development: Critical reflections and explorations. London: Routledge. Austrian, Z. (2000). Cluster case studies: The marriage of quantitative and qualitative information for action. Economic Development Quarterly, 14, 97-110.

Batheldt, H. (2005). Geographies of production: Growth regimes in spatial perspective (II)—Knowledge creation and growth in clusters. Progress in Human Geography, 29, 204-216. Batheldt, H., Malmberg, A., & Maskell, P. (2004). Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 28, 31-56. Boschma, R., & Kloosterman, R. (2005). Clustering, learning, and regional development. In R. Boschma & R. Kloosterman (Eds.), Learning from cluster: A critical assessment from an economicgeographical perspective (pp. 1-18). Dordrecht, Netherlands: Springer. Boschma, R., & ter Wal, A. (2007). Knowledge networks and innovative performance in an industrial district: The case of a footwear district in the south of Italy. Industry and Innovation, 14, 177-199. Brökel, T., & Binder, M. (2007). The regional dimension of knowledge transfers—A behavioral approach. Industry and Innovation, 14, 151-175. Carrington, P., Scott, J., & Wasserman, S. (Eds.) (2005). Models and methods in social network analysis. New York: Cambridge University Press. Carroll, M., Reid, N., & Smith, B. (2008). Location quotients versus spatial autocorrelation in identifying potential cluster regions. Annals of Regional Science, 42, 449-463. Clark, L. (2006). Network mapping as a diagnostic tool manual. La Paz, Bolivia: Centro Internacional de Agricultura Tropical—CIAT. Retrieved December 2007 from http://www.mande.co.uk/docs/ networkmapping_LC06.pdf Colgan, C., & Baker, B. (2003). A framework for assessing cluster development. Economic Development Quarterly, 17, 352-366. Cross, R., & Parker, A. (2004). The hidden power of social networks: Understanding how work really gets done in organizations. Boston: Harvard Business School Press. de Nooy, W., Mrvar, A., & Batageli, V. (2005). Exploratory social network analysis with Pajek. New York: Cambridge University Press. Doreian, P., & Woodward, K. (1994). Defining and locating cores and boundaries of social networks. Social Networks, 16, 267-293. Feldman, M. P., & Francis, J. L. (2004). Homegrown solutions: Fostering cluster formation. Economic Development Quarterly, 18, 127-137. Feser, E., & Bergman, E. (2000). National industry cluster templates: A framework for applied regional cluster analysis. Regional Studies, 34, 1-19. Frank, O. (2005). Network sampling and model fitting. In P. Carrington, J. Scott, & S. Wasserman (Eds.), Models and methods in social network analysis (pp. 31-56). New York: Cambridge University Press. Friedkin, N. (1983). Horizons of observability and limits of informal control in organizations. Social Forces, 30, 54-77. Friedkin, N. (2004). Social cohesion. Annual Review of Sociology, 30, 409-425. Giuliani, E. (2007). The selective nature of knowledge networks in clusters: Evidence from the wine industry. Journal of Economic Geography, 7, 139-168. Giuliani, E., & Bell, M. (2005). The micro-determinants of mesolevel learning and innovation: Evidence from a Chilean wine cluster. Research Policy, 34, 47-68. Glückler, J. (2007). Economic geography and the evolution of networks. Journal of Economic Geography, 7, 619-634. Gordon, I., & McCann, P. (2000). Industrial clusters: Complexes, agglomeration and/or social networks? Urban Studies, 37, 513-532. Helsel, J., Kim, H., & Lee, J. (2007). An evolutional model of U.S. manufacturing and services industries. In J. Gatrell & N. Reid

352 Economic Development Quarterly (Eds.), Enterprising worlds: A geographic perspective on economics, environments, and ethics (pp. 83-98). Dordrecht, Netherlands: Springer. Hill, E. W., & Brennan, J. F. (2000). A methodology for identifying the drivers of industrial clusters: The foundation of regional competitive advantage. Economic Development Quarterly, 14, 65-96. InFlow 3.1. (n.d.) Retrieved June 2008 from http://www.orgnet.com/ Karlsson, C., Johansson, B., & Stough, R. (2005). Industrial clusters and inter-firm networks. Cheltenham, UK: Edward Elgar. LaFary, E., Gatrell, J., Reid, N., & Lindquist, P. (2006). Specialized agriculture: Local markets & global competitors in Ohio’s greenhouse industry. In J. Gatrell & N. Reid (Eds.), Enterprising worlds: A geographic perspective on economics, environments, and ethics (pp. 57-70). Dordrecht, Netherlands: Springer. Lagendijk, A., & Cornford, J. (2000). Regional institutions and knowledge—Tracking new forms of regional development policy. Geoforum, 31, 209-218. Llobrera, J., Meyer, D., & Nammacher, G. (2000). Trajectories of industrial districts: Impact of strategic intervention in medical districts. Economic Geography, 76, 68-98. Marsden, P. (2005). Recent developments in network measurement. In P. Carrington, J. Scott, & S. Wasserman (Eds.), Models and methods in social network analysis (pp. 8-30). New York: Cambridge University Press. Martin, R., & Sunley, P. (2003). Deconstructing clusters: Chaotic concept or policy panacea? Journal of Economic Geography 3, 5-35. Miller, P., Botham, R., Martin, R., & Moore, B. (2001). Business clusters in the UK: A first assessment. London: Department of Trade and Industry. National Governors Association. (2007). Cluster-based strategies for growing state economies. Retrieved December 2007 from http://www.nga.org/Files/pdf/0702INNOVATIONCLUSTERS.PDF Porter, M. (1998). On competition. Boston: Harvard Business School Press. Pryke, S. (2005). Towards a social network theory of project governance. Construction Management and Economics, 23, 927-939. Reid, N., & Carroll, M. (2006) Collaborating to compete: The case of the northwest Ohio greenhouse cluster. In J. Gatrell & N. Reid (Eds.), Enterprising worlds: A geographic perspective on economics, environments, and ethics (pp. 41-56). Dordrecht, Netherlands: Springer. Roberts, B., & Stimson, R. (1998). Multi-sectoral qualitative analysis: A tool for assessing the competitiveness of regions and formulating strategies for economic development. Annals of Regional Science, 32, 469-494.

Rogers, E., & Kincaid, D. (1981). Communication networks: Toward a new paradigm for research. New York: Free Press. San Diego Association of Governments. (1998). San Diego regional economic prosperity strategy. Retrieved June 2008 from http://www.sandag.cog.ca.us/uploads/publicationid/publicationid_604_1005.pdf Schmitz, H., & Nadvi, K. (1999). Clustering and industrialization: Introduction. World Development, 27, 1503-1514. Schoales, J. (2006). Alpha clusters: Creative innovation in local economies. Economic Development Quarterly, 20, 162-177. Scott, J. (2000). Social network analysis: A handbook (2nd ed.). Thousand Oaks, CA: Sage. Smith, B., Carroll, M., & Reid, N. (2007). Potential cluster regions: The case of the U.S. floriculture industry. Papers of the Applied Geography Conferences, 30, 59-66. Texas Industry Profiles. (2004). Texas industry cluster initiative background. Retrieved December 2007 from http://www.texasindustryprofiles.com/PDF/twcClusterReports/ClusterInitiativeBackground.pdf Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press. Yang, G., & Stough, R. (2005). A preliminary analysis of functional and spatial clustering: The case of the Baltimore metropolitan region. In C. Karlsson, B. Johansson, & R. Stough (Eds.), Industrial clusters and inter-firm networks (pp. 303-320). Cheltenham, UK: Edward Elgar. Yeung, H. (2003). Practicing new economic geographies: A methodological examination. Annals of the Association of American Geographers, 93, 442-462. Neil Reid is director of the Urban Affairs Center and associate professor of geography and planning at the University of Toledo. His current research interests are in the theory and practice of cluster-based economic development. Bruce W. Smith is a professor of geography and a research fellow in the Center for Regional Development at Bowling Green State University in Ohio. His research focuses on local economic development and GIS applications. Michael C. Carroll is the director of the Center for Regional Development and associate professor of economics at Bowling Green State University. His current research interests are in regional economic development and social economics.