Significant and hierarchy of variables affecting online knowledge-sharing using an integrated logit-ISM analysis

This study aims to explore the significant variables affecting online knowledge-sharing and the hierarchical structure, from the perspective of online learners. To comprehensively discuss the relationship between these variables, binary logit regression and interpretative structural model (ISM) was used. Based on literature analysis, the data of 29 candidates were obtained, and 670 valid data was acquired through an electronic questionnaire. A total of 13 significant variables were also obtained using the Logit model of SPSS 22, with an 8-layer ISM program established by MATLAB 2017A software. The results showed that six of the 13 variables had positive effects on online knowledge-sharing behavior, with the remaining seven having a negative impact. The ISM model also proved that trust and delete/block, reward, and the remaining elements were shallow, deep, and intermediate variables, respectively. Combining the Logit and ISM advantages, these results strengthened the reports on online knowledge-sharing behavior, subsequently obtaining five suggestions for its development. This study is expected to help teachers and online course developers design better digital programs, as well as ensure the accurate decision-making of students in knowledge sharing activities.