Association for Public Policy Analysis & Management

An emerging area of research on knowledge sharing and research collaboration addresses faculty members&rsquo; decisionmaking regarding whether and with whom they share knowledge resources and collaborate on research. That is, how do faculty make &lsquo;knowledge partner selections&rsquo;. A large body of literature on knowledge management (KM) and research collaboration argues and empirically presents that such decisions are driven by instrumental motives (e.g., the calculation of benefits and costs), explicitly or implicitly assuming self-interested and rational agents. Another group of studies, on the other hand, claims that structural forces (e.g., social capital and homophily) influence individual researchers&rsquo; decisionmaking associated with knowledge sharing activities. Their research outlines particular patterns of knowledge sharing and research collaboration among knowledge agents, reflecting structure-level rules and principles. The structural perspective suggests that researchers may share knowledge and collaborate on research even when the costs of collaboration exceed its benefits. <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; We believe neither perspective can fully explain why or how knowledge sharing and research collaboration occurs in public research institutions. The economic perspective cannot explain knowledge partner selection for non-instrumental reasons or some structural regularities in knowledge networks sustained over time. The structural perspective ignores the the pursuit of self-interest. In this study we discuss how mechanisms inherent in both networks and individuals come into play when researchers in intramural networks choose their knowledge partners. Our work suggests a two-level knowledge partner selection framework that extends social capital theories and Herbert A. Simon&rsquo;s concept of bounded rationality. <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The empirical component of this study draws on qualitative and quantitative data collected from faculty (N=39) in three disciplines (i.e., computer science, information science, and public policy and administration) in a public research university in the United States. To analyze the data, we apply mixed methods; including exponential random graph models (ERGMs), network visualization, and conventional statistical techniques.&nbsp; <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; This study contributes to KM literature, generalizing KM theories across sectors and domains. The research setting was purposefully selected to test hypotheses mostly developed and proved significant in private research laboratories or firms. It also allows us to compare the target behavior in different disciplines and examine cross-discipline knowledge interaction between social and computational scientists in public research universities, which has not yet been addressed in literature. Finally, our discussions about the relationship between knowledge networks and human agents contribute to the structure-agency debate.