How networks influence radical innovation: the effects of heterogeneity of network ties and crowding out - Feng, Yanan
Purpose – This paper aims to offer a novel set of insights to understand the role of network ties in pursuit of radical innovation. In this sense, the purpose of the study is to analyze how the heterogeneity in the content of network ties affects radical innovation. Design/methodology/approach – Based on a comprehensive review of existing literature, this paper conceptualizes how different types of network ties affect radical innovation by deriving five research propositions. Findings – Both buyer-supplier ties and peer collaboration ties are positively related to radical innovation, whilst the peer collaboration ties may be further affected by partner similarity. Compared to the two other types of network ties, equity ties have more of a moderating role on spurring radical innovation. Crowding out between network ties prevents firms from knowledge searching within an extensive network scope, reducing the opportunities of mixing-and-matching different types of knowledge required for radical innovation. Research limitation/implications – The study suggests a different way of launching marketing strategy by selectively integrating different sources of knowledge (market, supplier, or technology) needed for commercializing radical technologies, highlighting the importance of partner selection for radical innovation among different types of firms surrounding the current market. For managers, it is necessary to identify and select network ties that can be helpful for long-term business and strategic interests. Originality/value – This paper makes two main contributions. First, it addresses the question of how networks influence radical innovation by identifying three types of network ties and their effects —individually and in combination — on extension of the depth and breadth of knowledge and development of disruptive ideas. Second, it develops the existing literature by demonstrating the crowding out effect of network ties.