文献类型：期刊文献

英文题名：Exploring the Driving Mechanisms of Interorganizational Knowledge Sharing Based on the Bayesian Network Analysis

作者：He, Hui[1];He, Qinghua[2];Chan, Albert P. C.[3];Feng, Xiaowei[1];Dong, Shuang[4]

机构：[1]Shanghai Inst Technol, Coll Urban Construction & Safety Engn, Shanghai 201418, Peoples R China;[2]Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China;[3]Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong 999077, Peoples R China;[4]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China

年份：2025

卷号：151

期号：8

外文期刊名：JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT

收录：;EI(收录号：20252118471818);WOS:【SCI-EXPANDED(收录号:WOS:001509461500022)】；

基金：The authors appreciate the financial support from the National Natural Science Foundation of China (Grant Nos. 71971161, 71971186, and 72371189).

语种：英文

外文关键词：Interorganizational knowledge sharing (IKS); Influencing factors; Bayesian network (BN); Interorganizational projects (IOPs); Governance strategies

摘要：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.