The effect of social networks structure on innovation performance: A review and directions for research
By Eitan Muller, Renana Peres. International Journal of Research in Marketing, 2019
321 citations recorded by the source corpus.
Abstract Research on growth of innovations introduced to the market has gradually shifted its focus from aggregate-level diffusion to exploring how growth is influenced by a given social network structure's characteristics. In this paper, we critically review this branch of literature. We argue that the growth of an innovation in a social network is shaped by the network's structure. Borrowing from the field of industrial organization in economics, which defines itself as the study of the effect of market structure on market performance, we describe this new wave of research on growth of innovations as the effect of social network structure on innovation performance . Hence, social network structural characteristics should be incorporated into research on new product growth as well as into managerial marketing decisions such as targeting and new product seeding. We review how social network structure influences innovations' market performance. Specifically, we discuss (1) a networks' global characteristics, namely average degree, degree distribution, clustering, and degree assortativity; (2) dyadic characteristics, or the relationships between pairs of network members, namely tie strength and embeddedness; (3) intrinsic individual characteristics, namely opinion leadership and susceptibility; and (4) location-based individual characteristics, namely the degree centrality, closeness centrality, and betweenness centrality of an individual network member. Overall, we find that growth is particularly effective in networks that demonstrate the “3 Cs”: cohesion (strong mutual influence among its members), connectedness (high number of ties), and conciseness (low redundancy). We identify gaps in current knowledge, discuss the implications on managerial decision making, and suggest topics for future research.
Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/d359891670723a960b564de0e91f031657d6086e
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In Management Science and Operations Research
In Statistical and Nonlinear Physics
Citations in the corpus
- The Positive Effect of Not Following Others on Social Media (2020, background): “Consequently, researchers have proposed a variety of measures of network centrality (see Muller and Peres 2019 for a review), and studies show that influence is often more strongly associated with network centrality than commonly used self-reports of opinion leadership (Iyengar, Van den Bulte, and…”