资 源 简 介
The dynamics of knowledge transfer are a topic of major concern to scholars of organizations. Nevertheless, the systematic study of knowledge has been hampered by its context-dependence – for example, two firms possessing very different sources of expertise may be subject to similar knowledge-transfer dynamics. Text-based Bayesian inference techniques promise to advance organizational scholarship by enabling knowledge to be studied directly in a manner that is informed by text data from recorded dialogue. In this paper, we use such Bayesian techniques to study the dynamics of knowledge transfer in expert groups, applied to transcript data from the U.S. Food and Drug Administration"s Circulatory Systems Advisory Panel. We study how knowledge boundaries form among groups of expert decision-makers. Drawing from theory and research on expertise and knowledge transfer, we introduce the notion of boundary collapse, whereby knowledge boundaries first emerge and then disappear as the group
