Abstract
Despite the widely recognized importance and the standardized process of project & program risk management, there are still difficulties in implementing and executing the process in practical situations. It is assumed that an underlying cause would be ”the difficulty of making trade-off decisions about risk events with uncertain conditions.” In this paper, we provide a new explanation of the practical difficulties of risk management from the point of view of transaction cost theory and prospect theory. We propose an integrated approach of machine learning and knowledge creation process, i.e., machine-in-the-loop knowledge creation process for the purpose of further enhancement of project & program risk management. Furthermore, we use GTA (grounded theory approach) for the analysis of the interview results of practitioners in product development organizations, and evaluate the effectiveness of the proposed method.
