The current study provides a comprehensive analysis and integration of the literature on the social network correlates of individual innovation. Reviewing the extant literature, we cluster existing network measures into five general properties—size, strength, brokerage, closure, and diversity. Using meta-analysis, we estimate the population effect sizes between these network properties and innovation. Results showed that brokerage had the strongest positive relation to innovation, followed by size, diversity, and strength. Closure, by contrast, had a weak, negative association with innovation. In addition, we offer a path-analytic integration of the literature proposing and testing the direct and indirect effects of the five properties on innovation. We suggest that network size and strength impact innovation through a web of relations with the more proximal features of brokerage, closure, and diversity. Our path-analytic integration considers the two dominant perspectives on the effects of social networks—brokerage versus closure—simultaneously allowing us to establish their relative efficacy in predicting innovation. In addition, our model highlights that network strength can have both negative and positive effects (via different direct and indirect pathways) and thus inherently involves a tradeoff. We discuss the implications of these results for future research and practice.
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