Innovation networks help members develop new products at a faster rate with lower investment commitments. The R&D consortium named Semiconductor Manufacturing Technology (SEMATECH), with member firms such as Motorola, Texas Instruments, and others, is an example of such a network. In a study of this network, Lim (2000) found that the network members were able to develop an innovative copper-based semiconductor that rivaled a similar product developed by (at the time) an independently operating IBM. The SEMATECH consortium experienced a significantly abbreviated time line and collectively invested significantly less money than did IBM with almost identical results. Lim attributed the innovative success of SEMATECH to the “connectedness” of the firms. Researchers engaged in studies examining interorganizational alliances generally agree with the findings of Lim and others that innovation network alliances represent a potential solution to mitigate environmental uncertainty, in part through the sharing of information (e.g., Gulati & Gargiulo, 1999). Van de Ven (2005) refers to this strategy for dealing with environmental uncertainty as “Running In Packs.” The basic logic is that as a network grows in membership, the amount of information any individual firm can access grows, and the value of membership in that network grows. Consequently, firms engaged in networks typically realize superior economic gains from their increased access to information relative to independent or nonaligned firms (e.g., Carlsson, 2002; Van de Ven, 2005). Since organizations join networks to mitigate costs and uncertainties, the question of how network characteristics affect (or not) the transfer of information is relevant to both practitioners as well as researchers in knowledge management and/or organizational learning. For instance, some innovation networks are composed of members engaged in similar activities while other networks are composed of members engaged in very different activities. Some networks tolerate more competition among their members than others. Finally, some networks are more centrally governed than others. These differences in how an innovation network is formed and governed raises an important question—Given that firms embedded within organizational networks experience greater exchange of information relative to firms operating outside of a network, how do the different characteristics of these networks impact the movement of that information? In this chapter, we will first review the two primary factors that have been demonstrated to influence the transfer of information—absorptive capacity and causal ambiguity. We then review three characteristics of multi-organizational networks—governance structure, scope of operations, and intensity of competition—with particular attention to the issue of information transfer. We develop six testable propositions regarding how these network characteristics would be expected to affect absorptive capacity and causal ambiguity among networked firms. Finally, we discuss future and emerging trends related to the transfer of information among networked firms.
Innovation network characteristics and equipment manufacturing industry upgrading: based on the mediating effect of innovation ability
