Behavioral-driven selection framework for industrial IoT-enabled cloud platforms in SMMEs

Purpose This study tackles the critical gap where existing cloud service selection methods fail small and medium manufacturers (SMMEs). This study aims to overcome the limitations of overlooking SMMEs’ unique resource constraints, acute risk sensitivity and diverse behavioral preferences. The core purpose is to provide SMMEs with a scientifically grounded, interpretable and operationally feasible framework that genuinely captures their authentic needs and risk perceptions, enabling them to strategically select cloud services that maximize operational benefits and mitigate adoption risks. Design/methodology/approach This study developed a novel behavioral framework combining Kano-based demand classification, regret theory for dynamic weighting and a prospect–evaluation based on distance from average solution (EDAS) model. A multi-layered evaluation system (functionality, tech, cost, supplier, service) was built. Kano questionnaires refined demands and mapped regret sensitivities. Membership degrees were dynamically adjusted using a p,q-order orthogonal dual hesitant fuzzy matrix. Critically, prospect theory reference points integrated gain-loss perceptions into the final ranking. Findings Empirical testing with an SMME confirmed the framework’s effectiveness. It successfully captured authentic SMME preferences regarding tech needs, cost sensitivity and service expectations. The integrated behavioral approach offered superior interpretability and practical feasibility compared to traditional static or purely subjective methods. Originality/value This study presents a novel integration of Kano analysis, regret theory and prospect theory within an EDAS framework, specifically tailored for SMMEs. The key methodological innovations involve: i) dynamic mapping between demand attributes and regret sensitivity coefficients, and ii) incorporation of behavioral economic principles (gain-loss perception) into service ranking. The resulting behaviorally-augmented decision tool enables SMMEs to achieve risk-optimized cloud adoption through needs-based service alignment.