Cognition, Network Structure, and Learning in Top Managers’ Interpersonal Networks

Abstract

In an uncertain, complex and competitive business landscape, top managers’ ability to quickly and effectively learn from others in their interpersonal networks could give their companies a competitive edge. We propose an integrative cognitive framework to explain how their interpersonal learning outcomes might be shaped by the interplay between cognitive attributes, such as impatience and conservatism, and the degree to which extremely well-connected individuals, or hubs, is probable in their networks. Using agent-based simulation models, we find that impatience and conservatism may lead to poor interpersonal learning outcomes, particularly if top managers belong to less “hubby” networks. In addition, distorted information has the potential to compromise learning, regardless of the level of impatience or conservatism and network “hubbiness”. Moderate to high levels of initial knowledge variety may also hamper interpersonal learning if top managers are highly impatient or conservative, and operate in networks with a certain degree of “hubbiness”. Although the adoption of less-than-ideal standards for judging the accuracy of beliefs may expedite learning, it is generally costly in terms of low learning performance levels. However, more impatient or conservative top managers may improve their learning performance by primarily targeting hubs, or some mix of hubs and nearby contacts.