Cross-Organizational knowledge sharing partner selection based on Fogg Behavioral Model in probabilistic hesitant fuzzy environment

In the knowledge economy era, knowledge is a vital asset for organizations. Knowledge sharing, as a means of knowledge exchange, effectively facilitates the flow of knowledge among organizations, thereby promotes organizational development. However, existing research on knowledge sharing primarily focuses on intra-organizational contexts, with relatively limited attention given to inter-organizational knowledge sharing. Furthermore, research on the selection of inter-organizational knowledge sharing partners is even scarcer. To address this gap, this paper proposes a multi-criteria decision-making method for the selection of inter-organizational knowledge sharing partners based on the Fogg Behavior Model. In this method, we use probabilistic hesitant fuzzy sets as the information representation tool. Then, we improve the determination of criterion weights by introducing gray relational coefficients and distance correlation coefficients to CRITIC. In addition, we systematically propose two types of probabilistic hesitant fuzzy Aczél-Alsina operators to aggregating the decision information. Furthermore, we propose Fogg Behavior Model operators for aggregating the final decision results. To validate this method, we applied it to a real-world case of inter-organizational knowledge sharing and conducted a thorough discussion and analysis of the results. The findings indicate that the Fogg Behavior Model, specialized in describing behavioral occurrences, is feasible when applied to inter-organizational knowledge-sharing behavior. Moreover, the proposed probabilistic hesitant fuzzy operators can leverage their tunable parameters, demonstrating consistency in aggregating inter-organizational knowledge-sharing decision information compared to existing operators.