Title: A Network Filter for Social Learning
Description: When are decision makers able to learn from others? I argue that actors occupying network positions that enable social learning gain a competitive advantage. I show that the accuracy of security analysts' earnings forecasts improves when the coverage network readily conveys information about competitors' decision-making context. The benefits of social learning are most pronounced in unstable environments, measured by firms' forecast dispersion. Causality is established using a natural experiment: surviving analysts' network positions -along with their forecasting accuracy -deteriorated to the extent that their coverage overlapped with analysts who perished in the 9/11 attacks on the World Trade Center. The importance of social learning in the analyst profession goes well beyond improving forecasting accuracy. I show that analysts' clients recognize narrow expertise on those stocks where the analyst is ideally positioned for social learning. This article contributes to organizational theory by specifying network positions providing a superior view of competitors' information environment and to strategy research by identifying conditions under which these positions confer a competitive advantage.