The Interplay of Experiential and Social Learning Mechanisms in Firm Exit Decisions

Abstract

This study examines how organizations private information obtained from performance feedback is moderated by social learning mechanisms. Building on inferential learning and market categorization literatures, my theory suggests that even when organizations experience multiple performance failures, they are not likely to exit the market when a substantial number of peers have entered the industry. On the contrary, they are more likely to exit the market when a relatively large number of peers have exited. The latter relationship is weakened when the average number of industries with which exiting peers engage is greater. This is because decision makers regard the exits of those who are involved in diverse categories as involuntary actions rather than signaling a negative market situation, and they rely less on such signals. I test my theory using longitudinal data on the exit decisions of 1,405 U.S. private venture capital firms from 1987 to 2014.