Explaining an Online Consumer Behavior Model in the Retail Industry: A Mixed-Methods Approach Based on Artificial Intelligence Tools

Abstract

This study aimed to explain a comprehensive model of online consumer behavior in Iran’s retail industry, with an emphasis on the role of artificial intelligence tools and local contextual considerations. The study was conducted using an exploratory mixed-methods design. In the qualitative phase, the main categories were extracted through grounded theory and semi-structured interviews with 15 experts in the retail industry. Subsequently, in the quantitative phase, a researcher-developed questionnaire was designed based on the qualitative findings and administered to 384 customers selected through convenience-based random sampling. The conceptual model was then tested. Quantitative data were analyzed using path analysis and structural equation modeling. The qualitative findings resulted in the identification of a paradigmatic model comprising causal conditions, including intelligent personalization and data-driven interaction; contextual conditions, including technological infrastructure, digital trust, and a culture of technology acceptance; and intervening conditions, including privacy concerns and economic constraints. In addition to confirming the significance of the proposed relationships, the quantitative findings demonstrated that “environmental readiness for e-commerce” played a key mediating role in transforming intelligent capabilities into an “intelligent purchasing process” and “perceived value.” The analyses indicated that artificial intelligence exerted direct and significant effects on customer satisfaction and loyalty by improving the quality of the customer experience and simplifying the decision-making journey. The proposed model demonstrates that artificial intelligence in Iran’s retail industry is not merely a technological tool but rather a strategic capability whose success depends on a dual approach integrating technology and ethics. Explaining consumer behavior in this complex environment requires strengthening trust-building infrastructure and algorithmic transparency alongside the development of data analytics capabilities. The findings provide retail managers with a roadmap for transitioning toward “knowledge-based intelligent retailing” to achieve sustainable competitive advantage.