Innovation and network multiplexity : R&D and the concurrent effects of two collaboration networks in an emerging economy
Andrade Rojas, Mariana Giovanna
Solis, Edgar Rogelio Ramirez
Zhu, John JianJun
Date of Issue2018
College of Business (Nanyang Business School)
This study focuses on the multiplexity of firm R&D networks, and it investigates two types of boundary-spanning networks: the bipartite network between firms and government-sponsored institutions (GSIs), and the traditional firm–firm network. We apply a social network perspective to examine the effects that these kinds of networks have on firm innovativeness, in relation to the effects of the firm’s internal R&D efforts. We define the firm-GSI network as bipartite, and we investigate how the structural characteristics of this network (cohesion and centrality) affect innovativeness. We then decompose the innovational effects of firm–firm networks into two categories (intra- and inter-sector) to distinguish the effects of these collaboration networks. Furthermore, we investigate how these various external collaborative networks interact with a firm’s internal R&D efforts for driving innovativeness. Our empirical study of 420 manufacturing firms in Mexico evaluates evidence from surveys and secondary data. The findings indicate that the structural properties of both firm–GSI and firm–firm networks have positive effects on innovativeness, but firm–GSI network cohesion has a stronger negative interaction with R&D in influencing firm innovativeness. Moreover, intra-sector centrality in a firm–firm network has a stronger negative interaction with R&D than inter-sector centrality does in driving firm innovativeness. We contribute to the literature by integrating insights from the perspectives of network multiplexity, social embeddedness, and resource complementarity in regard to inter-organizational behavior. Our study also provides meaningful guidelines for both managers and policy makers. The study’s findings are robust to concerns of common method bias and alternative model specifications.
© 2018 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Research Policy, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.respol.2018.03.018].