Bilateral data asset matching in digital innovation ecosystems: A regret theory approach

Abstract

This study addresses the bilateral matching of data assets with expected levels in digital innovation ecosystems, incorporating regret-avoidance behavior. First, given the potential hesitation between two parties throughout the matching process, expressing preference information using probability hesitant fuzzy sets is reasonable. Second, the Lance scoring function best captures the gap in expectation and satisfaction between the matching parties. Based on regret theory, we develop a matching strategy that considers both parties’ utilities and satisfaction levels. We construct an optimization model to determine criteria weights using a novel Lance distance metric. Subsequently, a multi-objective optimization model is formulated to maximize satisfaction while ensuring stability in the supply–demand matching process. A numerical example underscores the suggested method's effectiveness and shows its practical applicability in data asset matching scenarios. This study advances the field by integrating psychological factors and sophisticated fuzzy set theory into the decision-making process for allocating data assets in digital ecosystems.