Pipes, pools, and filters: How collaboration networks affect innovative performance

Abstract

Research summary: Innovation requires inventors to have both new knowledge and the ability to combine and configure knowledge (i.e., combinatory knowledge), and such knowledge may flow through networks. We argue that both combinatory knowledge and new knowledge are accessed through collaboration networks, but that inventors' abilities to access such knowledge depends on its location in the network. Combinatory knowledge transfers from direct contacts, but not easily from indirect contacts. In contrast, new knowledge transfers from both direct and indirect contacts, but is far more likely to be new and useful when it comes from indirect contacts. Exploring knowledge flows in 69,476 patents and 89,930 unique inventors reveals evidence that combinatory knowledge from direct contacts and new knowledge from indirect contacts significantly affects innovative performance.Managerial summary: Inventors often combine ideas to create innovations. To do this, they need ideas to combine and they need the ability to combine those ideas. Inventors can get ideas to combine as well as the ability to combine ideas through prior co-workers. Prior co-workers can share ideas that may be relevant for the inventor's project and can tell the inventor about other things that other people are working on, especially people the inventor may not know. This can help inventors easily learn about ideas from friends-of-friends. The ability to combine ideas, however, is much harder to pass on. Prior co-workers must carefully work with the inventor to teach him or her the complex processes of combining ideas. This means that it is very hard to learn how to combine knowledge from a friend-of-a-friend, but it may be possible to learn from prior co-workers. We explore this phenomenon in the social relationships of software inventors.

Technological proximity and recombinative innovation in the alternative energy field

“…To mitigate the simultaneity and endogeneity brought by reverse causality, we adopt other scholars' approaches (Singh et al, 2015;Tether and Bascavusoglu-Moreau, 2012), introducing the longitudinal design, wherein independent variables measured in a prior period with dependent variables in the subsequent period. We utilize alternative energy patents granted in the period from 2003 to 2007 to measure the independent variables and use alternative energy patents granted between 2008 and 2012 to calculate the dependent variable.…”

Resource congestion in alliance networks: How a firm's partners’ partners influence the benefits of collaboration

“…Alliances are a key channel for knowledge sourcing (Aggarwal & Wu, 2019;Alvarez & Barney, 2001;Beckman & Haunschild, 2002;Dyer & Singh, 1998;Rosenkopf & Almeida, 2003;Rothaermel, Hitt, & Jobe, 2006;Villalong & McGahan, 2005), enabling the expansion and renewal of a firm's knowledge base (Agarwal & Helfat, 2009;Capron & Mitchell, 2009;Hoang & Rothaermel, 2010;Sosa, 2011). R&D alliances in particular allow firms to learn, to gain access to information they may not otherwise be able to access, and to develop crucial "know-how" that is of use in the innovation process (Ahuja, 2000;Hamel, 1991;Kale, Singh, & Perlmutter, 2000;Schilling & Phelps, 2007;Singh, Kryscynski, Li, & Gopal, 2016;Vasudeva & Anand, 2011). These benefits span not only the context of the particular relationship in which firms are engaged, but spillover to the broader set of firm activities-that is, knowledge acquired in one context can facilitate knowledge creation efforts more broadly (Doz, 2017;Gambardella & Panico, 2017;Kale, Dyer, & Singh, 2002;Khanna, Gulati, & Nohria, 1998).…”

Network Resource Munificence, Geographical Dispersion and Inventor Performance

“…Rodan & Galunic, 2004). A recent study by Singh et al (2016) supports this claim and shows how different knowledge types available from direct and indirect network contacts influence individual innovative outcomes. This means that network structural characteristics alone do not fully explain innovation outcomes at the ego-level.…”