Vicarious Learning Without Knowledge Differentials

The benefits of vicarious learning are usually conceptualized in terms of a mechanism for learners to utilize the superior knowledge of others. Building on the fact that vicarious learning typically co-occurs and interacts with individual learning-by-doing, we propose an alternative mechanism—one in which vicarious learning is useful because it corrects for certain well-known limitations of individual learning-by-doing. Using computational agent-based models, we show that, under this mechanism, vicarious learning can be beneficial, even without any ex ante differential knowledge to exploit. Our analysis contributes to a deeper understanding of the microfoundations of vicarious learning, which is a vital component of organizational learning. We draw implications for empirical analysis and managerial practice.