Human-Centered Vibe Working Transformation: A Spherical Fuzzy Multi-Criteria Decision-Making Model

The rapid diffusion of artificial intelligence within knowledge-intensive work has given rise to vibe working, a transformative paradigm that reshapes organizational structures and career pathways. The fundamental problem associated with this transformation lies in balancing efficiency gains with the preservation of human roles, skill development, and long-term workforce sustainability. While numerous studies address AI-driven automation, limited attention has been paid to the systematic prioritization of transformation strategies and evaluation criteria in vibe working contexts. This gap creates uncertainty for organizations seeking effective and human-centered adaptation pathways. The primary aim of this study is to identify the most critical criteria and the most appropriate alternative strategies for minimizing vibe working-related risks. To address this gap, a novel fuzzy multi-criteria decision-making model is proposed, integrating distance-based expert weighting, the IDOCRIW method for criterion weighting, the RAM technique for alternative ranking, and spherical fuzzy sets for improved uncertainty modeling. The results indicate that potential for human-centered transformation is the most important criterion, followed by reduction in error rates and quality standardization. In terms of strategic alternatives, university–company–AI platform integration and the AI–human hybrid career ladder emerges as the most appropriate solutions. The study contributes to the literature by offering a structured prioritization framework and a robust methodological approach that accounts for expert heterogeneity and uncertainty. The findings suggest that integrated, human-centered, and collaborative strategies should be prioritized to ensure sustainable vibe working transformations.