26th International Association for Management of Technology Conference - IAMOT 2017

The advancement of digitalization in traditional industries confronts established companies with the need to adjust their current competence base and develop expertise in hitherto unfamiliar fields. Inter-organizational networks like strategic alliances, regional clusters or industrial associations provide member firms with an infrastructure for knowledge transfer and thus represent an important mean for meeting this task. Hence, knowledge transfer via networks plays a key role in innovation and technology management. While previous research yielded insights on why, when and how organizations choose to transfer knowledge, some important aspects of this phenomenon remain unaddressed. First, the exclusive focus of most studies on single types of knowledge (esp. technological knowledge) fails to adequately capture the complexities associated with innovation processes, particularly in the context of digital innovation. Second, academic knowledge on structural patterns of knowledge transfer in inter-organizational networks is still scarce. Nonetheless, identifying specific regularities on the dyadic level (e.g. the preference to acquire knowledge from a certain type of partner) or the extra-dyadic level (e.g. the tendency to form cyclic or transitive exchange structures between three or more firms) as well as finding explanations why they emerge bears significant potential for a better theoretical understanding of knowledge transfer processes. In this paper, we address both gaps by answering to the following research question: “In what way and why do knowledge transfer patterns in inter-organizational networks differ dependent on the type of knowledge being transferred?”. Following the assumption that the properties of transferred knowledge may implicate considerable barriers to exchange, we draw on previous theoretical perspectives highlighting codifiability and context-specificity as key impact factors on knowledge transfer processes. We argue that four types of knowledge relevant in the context of innovation (technological, market, managerial and regulatory) possess distinct characteristics which make them differ on these two dimensions. Enriching these considerations with insight from network theory, we deduce hypotheses on differences between the four knowledge types in the emergence of specific structural transfer patterns. In order to test the hypotheses, we investigate the knowledge transfer among the 92 member firms of the largest network of German municipal energy providers drawing on sociometric survey data. Structural differences between the four types of transfer networks were uncovered and tested for significance via exponential random graph models (ERGM). Concerning rather codifiable types of knowledge, our results provide evidence for a tendency of firms to select potential knowledge sources primarily based on superior resources (dyadic level). In line with this, the existence of in-stars, single firms which serve as knowledge source for many other firms, is significantly more probable than for rather tacit types of knowledge (extra-dyadic level). On the other hand, the results indicate an increased likelihood for reciprocal transfer (dyadic level) and transitivity (extra-dyadic level) if the type of knowledge transferred is highly context-specific. In all, our study refines and tests theory by explaining the emergence of structural transfer patterns for different types of knowledge. It hence contributes to a more thorough understanding of knowledge transfer in inter-organizational networks and provides a basis for offering implications for network managers on how to stimulate knowledge transfer among network members.