Exploring the Driving Mechanisms of Interorganizational Knowledge Sharing Based on the Bayesian Network Analysis

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.