The Effects of Knowledge Distance and Knowledge Complexity on Learning From Hiring

Abstract

Hiring employees from high-performing rivals is a common channel for transferring knowledge and enhancing firm’s capabilities. But while the literature on learning-by-hiring posits that new knowledge can be a powerful source of improvement and rejuvenation for a firm’s knowledge stock, evidence for whether firms actually benefit from hires with knowledge that is very distant from their own is mixed. With the help of a computational model, we explore how the knowledge distance of a new hire shapes the benefits of learning-by-hiring. The analysis of the model allows us to reconcile opposite findings and predictions found in the literature and provides further nuance to our understanding of this important phenomenon. Specifically, we show that knowledge complexity has a critical impact on the benefits of hiring an employee with distant knowledge. We also show that the level of refinement of the hiring firm’s knowledge shapes benefits from hiring, and we identify the mechanisms responsible for these outcomes.