Abstract
Influencer marketing has become a core component of digital advertising, yet selecting the most suitable influencer remains a complex multi-criteria problem. Despite its growing importance, research on systematic influencer selection frameworks—particularly those addressing hesitancy and inconsistency in expert judgment—remains limited. This study proposes a decision-making framework integrating Dempster–Shafer theory with Multi-Criteria Decision-Making (MCDM) techniques to address hesitant evaluations. Moreover, Murphy’s combination rule replaces the traditional Dempster–Shafer rule to manage inconsistent assessments more effectively. Objective criteria weights are derived using the CRiteria Importance Through Intercriteria Correlation (CRITIC) and the Method based on the Removal Effects of Criteria (MEREC) methods, reducing subjectivity while accounting for information variability, inter-criteria correlation, and removal effects. The Borda rule is applied to aggregate the utility functions of the Combined Compromise Solution (CoCoSo) method, ensuring fairer ranking results. A case study conducted in a beauty technology company, based on data from experienced professionals, validates the proposed model. The results confirm that the Dempster–Shafer–based CRITIC–MEREC–CoCoSo framework provides a reliable, interpretable and data-driven tool for effective influencer selection.