From Individual to Social Imitation: How Communities Expand Organizational Search

Generative artificial intelligence illustrates a broader organizational puzzle: young, resource-constrained firms can build on knowledge produced across a wider field to address problems that exceed their internal experience. We theorize one mechanism as social imitation: a distributed and recursive process through which communities observe practices and outcomes across organizations, distill recurrent elements into portable strategies, circulate them, and revise the collective repertoire as adoption produces new evidence. This perspective endogenizes the object of imitation. Organizations do not merely copy practices; communities collectively produce what becomes available to copy. We distinguish the source of a candidate (directly observed organizations or a collective) from its mode of adoption (blind or informed), and examine four forms of imitation in an agent-based model of organizational search. Collective sourcing alone does not improve performance. Its value depends on situated evaluation: assessing whether a candidate fits the adopter's configuration. As interdependence increases, informed social imitation improves population performance and the best solution discovered. As problems grow, it becomes more likely to outperform informed imitation from one or several directly observed peers because collective distillation sustains a broader candidate repertoire. Most gains arise from evaluating a small candidate set, and population performance can improve when only a minority of organizations possesses evaluative capability. Social imitation thus reveals a division of cognitive labor: communities expand the strategies organizations can consider, while organizations create value by determining which strategies fit. It explains how firms can mobilize knowledge beyond their boundaries, and why widespread access to collective knowledge does not produce equal benefits.