Observational Learning in Networks of Competition: How structures of attention among rivals can bring interpretive advantage. Issue 2 (February 2023) - Prato, Matteo; Stark, David
Much of social network analysis has focused on learning in communication networks among collaborators in which actors can make direct inquiries to seek clarification about alters' behavior or views. But such inquiries are typically not possible among rivals. Learning among rivals occurs primarily in observational networks in which actors must make inferences of the logics guiding their competitors' behavior in markets. What promotes interpretive advantage in these networks of observation? We combine multimarket competition theory and structural hole theory to highlight the benefits of multiple exposure to disconnected competitors. In network-analytic terms we suggest that competitors' interpretive advantage lies in non-redundant dyadic closure, especially when dealing with uncertain market niches. Dyadic closure, measuring ego's exposure to her direct competitors in multiple markets, increases the ability to interpret competitors' observed behavior. Redundancy, measuring the extent to which ego's competitors are exposed to each other, reduces the diversity of views to which ego is exposed and hence the capacity to cope with uncertainty. We test our hypothesis by analyzing the network of competition created by securities analysts and the stocks they cover. We find that estimates issued by an analyst with multiple exposures to disconnected competitors are more accurate when confronted by more challenging, high risk, high reward, volatile stocks. Shifting the focus from direct social ties to the cognitive ties that link actors based on the objects, problems, or issues to which they pay attention, we develop a new approach to network analysis. Observation networks, we argue, operate neither as pipes nor as prisms but can be better conceived as scopes.