Collective Intelligence of Market-Categories in Entrepreneurial Ecosystems: Evidence of Population-Level Learning in Mobile Applications

In this paper, the authors examine how groups of organizations engage in population-level learning in entrepreneurial ecosystems. The authors specifically analyze how within- and across-group characteristics of organizational groups shape learning, and thereby extend the literature on population-level learning that has focused mostly on within-population leaning. The authors argue that a group of new organizations may develop collective intelligence, in which population-level learning stemming from both the audience of consumers and fellow producers enhances innovation in groups. These persistent differences are facilitated by active feedback from within the audience (within-group learning sources) and member organizations’ diversification into other groups (across-group sources), but the effect of latter decreases after a certain point, indicating a curvilinear relationship. Their longitudinal analysis of Apple’s mobile application ecosystem supports these claims. Different market-categories indicated a significant, persistent heterogeneity in the aggregated performance across several different criteria, and this heterogeneity was facilitated by active audience feedback. Diversification of member organizations also showed a positive influence on group-level heterogeneity, yet its effect decreased after a threshold. This paper contributes to the literature on population-level learning by showing the importance of examining across-group learning sources, and by introducing a new collective intelligence framework for analyzing organizational groups. In addition, the authors provide a framework through which persistent heterogeneity in entrepreneurial ecosystem innovation can be understood.