Modeling purchase intentions in community-driven e-commerce platforms using hybrid MCDM and the SOR framework

Community e-commerce platforms that operate through trust-based interactions and shared social values transform how consumers behave in the digital economy. The growing influence of these environments makes Purchase Intention (PI) modeling challenging because psychological factors interact with technological elements and social influences. This paper  presents a new hybrid Decision Support System (DSS) which unites five Multicriteria Decision-Making (MCDM) methods, including TOPSIS-COMET, Combined Compromise Solution (COCOSO), Evaluation Based on Distance from Average Solution (EDAS), Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA), and Multi-Attributive Border Approximation Area Comparison (MABAC) through the Stimulus–Organism–Response (SOR) theoretical framework. The system enables systematic product alternative evaluation through both subjective perception assessment and objective performance evaluation. The Copeland aggregation method merges different rankings into a single consensus-based final decision. The model uses ۲۰ smartphone alternatives to evaluate multiple decision criteria through its application. The study shows that community-driven elements create the most significant impact on perceived value which leads to substantial improvements in PI. The proposed framework demonstrates stability through sensitivity analysis which confirms its robustness. The research provides strategic guidance to platform designers and marketers who want to enhance product positioning and recommendation systems and consumer engagement strategies in socially-oriented e-commerce platforms.

کلیدواژه ها:

Community e-commerce ، Purchase Intention ، multicriteria decision-making ، SOR model ، hybrid DSS ، consumer trust ، Copeland aggregation