How networks influence radical innovation: the effects of heterogeneity of network ties and crowding out. Issue 6 (4th July 2016) - Hao, Bin
Abstract : 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 performance. Design/methodology/approach: Based on a comprehensive review of existing literature, this paper conceptualizes how different types of network ties affect radical innovation performance by deriving five research propositions. Findings: Both buyer-supplier ties and peer collaboration ties are positively related to radical innovation performance, whilst the peer collaboration ties may be further affected by partner similarity. Compared to other two types of network ties, equity ties act as more of moderating roles 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 kinds of knowledge needed for radical innovation. Research limitations/implications: The study suggests a natural 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 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 —individual 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.