| description abstract | Despite extant studies having widely identified and explored the effects of different factors on interorganizational knowledge sharing (IKS) in interorganizational projects (IOPs), the complex interrelationships between factors and their joint effects still remain vague. We employed the Bayesian network (BN) methods to establish an IKS-BN model to fill in the gap. Sixteen factors in knowledge, organization, and context dimensions were identified through a combination of literature reviews and focus group discussions to construct a qualitative IKS-BN model. Then, questionnaire surveys were conducted with 240 valid respondents to quantify the model. The findings revealed that the top influential factors were interorganizational trust, project incentive mechanisms, communication infrastructure, organizational distance, absorptive and sharing capacity, and tacitness of knowledge. Further, the joint effect of controlling various factors on improving the efficiency of IKS was greater than the simple factor, achieving the highest probability (74%) of good IKS efficiency among all five-factor scenarios and 88% among six-factor scenarios. Three basic factors should be carefully controlled among joint scenarios: interorganizational trust; sharing; and absorptive capacity. Scenarios combined with multidimensional factors could contribute to a high level of IKS efficiency. Our proposed IKS-BN model provides effective decision support techniques to improve IKS efficiency in IOPs. Knowledge has become one of the most critical resources for successfully delivering interorganizational projects. Interorganizational knowledge sharing (IKS) has garnered significant attention from scholars and practitioners, as no single organization can possess all the knowledge required for a project. Instead, knowledge must be shared beyond organizational boundaries to create innovation ecosystems. While previous qualitative and quantitative research has extensively examined the influence of various factors, the interrelationships among these factors and their combined effects on IKS have yet to be fully explored. Current studies do not provide a comprehensive understanding of the dynamic mechanisms that enhance IKS efficiency. This study, using Bayesian network analysis, identifies interorganizational trust, project incentive mechanisms, communication infrastructure, organizational distance, absorptive and sharing capacity, and knowledge tacitness as the most influential factors for IKS. Additionally, it highlights the importance of controlling these factors together to improve IKS efficiency. For instance, a joint scenario of interorganizational trust, sharing, and absorptive capacity predicts a 42% probability of achieving a high level of IKS efficiency. Scenarios involving five factors increase this probability to 74%, while six-factor scenarios reach 88%. Project managers can tailor their inputs based on the specific project context to select the most effective scenario to optimize their IKS efficiency. | |
