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Article Excerpt
Introduction
Although research has long recognized the importance of interfirm networks in firm innovation (see Freeman 1991 for a review), much of this work has treated the network concept as a metaphor. Only recently have researchers begun to assess the formal structural properties of alliance networks and their impact on firm innovation. This research has focused on a firm's position within a broader network of relationships or the structure of its immediate network neighborhood rather than the structure of the overall network. Studies have examined a firm's centrality (Smith-Doerr et al. 1999), number of alliances (Shan et al. 1994), and local network structure (Ahuja 2000, Baum et al. 2000). To our knowledge, empirical research has not yet examined the impact of the structure of industry-level (1) alliance networks on member firm innovation. In a related study, however, Uzzi and Spiro (2005) examined the network structure of the creative artists who made broadway musicals from 1945 to 1989, and concluded that the large-scale structure of the artists' collaboration network significantly influenced their creativity, and the financial and artistic performance of their musicals. This raises the following questions: Does the structure of an industry-level interfirm network influence the rate of knowledge creation among firms in the network? If so, what structural properties will enhance firm innovation?
To address these questions, we examine the impact of two key large-scale network properties, clustering and reach, on the innovative output of members of the network. The dense connectivity of clusters creates transmission capacity in a network (Burt 2001), enabling large amounts of information to rapidly diffuse, while reach (i.e., short path lengths to a wide range of firms) ensures that diverse information sources can be tapped. We argue that networks with both high clustering and high reach will significantly enhance the creative output of member firms. We test this hypothesis using longitudinal data on the innovative performance of a large panel of firms operating in 11 industry-level alliance networks.
This research offers several important contributions for understanding knowledge creation in interfirm networks. First, we find empirical support for our argument that the combination of clustering and reach increases member firm innovation. To our knowledge, no other study has attempted to assess the effect of industry-level interfirm networks on the innovation performance of member firms. Although recent studies have examined the structure of largescale interfirm networks and the possible causes of these structures (Baum et al. 2003, Kogut and Walker 2001), little research has examined the consequences of large-scale network structure in an industrial setting (Uzzi and Spiro 2005 is a recent exception). Second, while most studies of network structure have examined a single industry, our study uses longitudinal data on 11 industries, which strengthens the generalizability of our findings.
We begin by describing two key structural characteristics of interfirm networks and their effect on information diffusion in the network. From this we develop a hypothesis about how the structure of interfirm networks will influence the innovative output of member firms. We test the hypothesis on a large, unbalanced panel of firms embedded in 11 industry-level alliance networks.
Large-Scale Interfirm Networks and Firm Knowledge Creation
We adopt a recombinatory search perspective in explaining the process of innovation (Fleming 2001). Innovation is characterized as a problem-solving process in which solutions to problems are discovered via search (Dosi 1988). Prior research suggests that search processes that lead to the creation of new knowledge, embodied in artifacts such as patents and new products, most often involve the novel recombination of known elements of knowledge, problems, or solutions (Fleming 2001, Nelson and Winter 1982) or the reconfiguration of the ways in which knowledge elements are linked (Henderson and Clark 1990). Critical inputs into this process include access to and familiarity with a variety of knowledge elements (e.g., different technological components and the scientific and engineering know-how embedded in them), novel problems and insights into their resolution, failed recombination efforts, and successful solutions (Hargadon and Fanelli 2002). Firms that have greater access to and understanding of these recombinatory resources should be advantaged in their innovation efforts.
As firms form and maintain alliances with each other, they weave a network of direct and indirect relationships. As a result, firms embedded in these networks gain access to information and know-how of direct partners and that of others in the network to which they are indirectly connected (Ahuja 2000, Gulati and Gargiulo 1999). The network of alliance relationships constitutes a conduit that channels the flow of information and know-how among firms in the network (Ahuja 2000, Owen-Smith and Powell 2004), with each member firm acting as both a recipient and transmitter of information (Ahuja 2000). The structure of these networks greatly influences the dynamics of information diffusion within the networks. Large-sample studies have found that direct alliance relationships facilitate knowledge flows between partners (Gomes-Casseres et al. 2006, Mowery et al. 1996) and enhance the innovative performance of firms (e.g., Deeds and Hill 1996, Stuart 2000). Research also shows that the extent to which a firm is indirectly connected to other firms in an alliance network enhances its innovativeness (Ahuja 2000, Owen-Smith and Powell 2004, Soh 2003).
Given the role of direct and indirect ties as channels for the flow of information and know-how, we argue that the structure of the interfirm network will significantly influence the recombination process. Two structural characteristics that have a particularly important role in diffusion are clustering and reach.
Clustering
Alliance networks tend to be highly clustered: Some groups of firms will have more links connecting them to each other than to the other firms in the network. A firm's clustering coefficient can be calculated as the proportion of its partners that are themselves directly linked to each other. The clustering coefficient of the overall network is the average of this measure across all firms in the network. Several mechanisms lead to clustering in interfirm knowledge networks, but two of the most common are linking based on similarity or complementarity. Firms tend to interact more intensely or frequently with other firms with which they share some type of proximity or similarity, such as geography or technology (Baum et al. 2003, Rosenkopf and Almeida 2003). This tends to result in a high degree of clustering.
Clustering increases the information transmission capacity of a network. First, the dense connectivity of individual clusters ensures that information introduced into a cluster will quickly reach other firms in the cluster. The multiple pathways between firms also enhance the fidelity of the information received. Firms can compare the information received from multiple partners, helping them to identify ways in which it has been distorted or is incomplete. Second, clusters within networks are important structures for making information exchange meaningful and useful. The internal density of a cluster can increase the dissemination of alternative interpretations of problems and their potential solutions, deepening the collective's understanding and stimulating collective problem solving (Powell and Smith-Doerr 1994). The development of a shared understanding of problems and solutions greatly facilitates communication and further learning (Brown and Duguid 1991, Powell et al. 1996). Third, dense clustering can make firms more willing and able to exchange information (Ahuja 2000). Sociologists (e.g., Coleman 1988, Granovetter 1992) have suggested that densely clustered networks give rise to trust, reciprocity norms, and a shared identity, all of which lead to a high level of cooperation and can facilitate collaboration by providing self-enforcing informal governance mechanisms (Dyer and Singh 1998). In addition to stimulating greater transparency, trust and reciprocity exchanges facilitate intense interaction among personnel from partnered firms (Uzzi 1997), improving the transfer of tacit, embedded knowledge (Hansen 1999, Zander and Kogut 1995). Thus, clustering enables richer and greater amounts of information and knowledge to be exchanged and integrated more readily.
When dense clusters are sparsely connected to each other, they become important structures for creating and preserving the requisite variety of knowledge in the broader network that enables knowledge creation. The internal cohesion of a cluster can cause much of the information and knowledge shared within a cluster to become homogeneous and redundant (Burt 1992, Granovetter 1973). The dense links provide many redundant paths to the same actors, and thus the same sources of information and knowledge. Cohesion can also lead to norms of adhering to established standards and conventions, which can potentially stifle experimentation and creativity (Uzzi and Spiro 2005). This limits innovation. Clusters of firms will, however, tend to be heterogeneous across a network in the knowledge they possess and produce due to the different initial conditions and causes for each cluster to form. The diversity of knowledge distributed across clusters in the network provides the requisite variety for recombination.
Clustering thus offers both local and global advantages. Firms benefit from having redundant connectivity among their immediate neighbors because it enhances the speed and likelihood of information access, and the depth of information interpretation. Firms also benefit from being embedded within a larger network that is clustered because the information a firm receives from partners that are embedded in other clusters is likely to be more complete and richly understood than information received from partners not embedded in clusters, and because information received from different clusters is likely to be diverse, enabling a wider range of recombinatorial possibilities.
Reach
The size of a network and its average path length (i.e., the average number of links that separates each pair of firms in the network) also impacts information diffusion and novel recombination. The more firms that can be reached by any path from a given firm, the more knowledge that firm can potentially access. However, the likelihood, speed, and integrity of knowledge transfer between two firms are directly related to the path length separating those two firms. The diffusion of information and knowledge occurs more rapidly and with more integrity in networks with short average path lengths than in networks with longer paths (Watts 1999). A firm that is connected to a large number of firms by a short average path can reach more information, and can do so quickly and with less risk of information distortion than a firm that is connected to fewer firms or by longer paths. To capture this we use distance-weighted reach.
A firm's distance-weighted reach is the sum of the reciprocal distances to every firm that is reachable from a given firm, i.e., [[summation].sub.j]1/[d.sub.ij], where [d.sub.ij] is defined as the minimum distance (geodesic), d, from a focal firm i to...
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