Computational Modeling of AI-Driven Personalization in Iranian E-Commerce: A Case Study of Digikala

Abstract

The escalating integration of Artificial Intelligence (AI) in e-commerce has fundamentally reshaped consumer interactions, with machine learning-driven personalization enhancing user experiences through recommendation systems, dynamic pricing, and targeted engagement. However, the impact of such personalization on consumer trust, engagement, and long-term behavioral loyalty remains contentious, particularly amid concerns over data privacy, algorithmic bias, and ethical implications. This study investigates the role of AI-driven personalization in influencing consumer behavior, using Digikala—Iran’s leading e-commerce platform—as a case study. A mixed-methods approach was employed, combining semi-structured interviews with ten domain experts, thematic analysis, Interpretive Structural Modeling (ISM), and SWOT analysis. Findings reveal that AI-enabled personalization significantly enhances consumer engagement and purchase intention while simultaneously amplifying challenges related to data security, algorithmic transparency, and bias. Ethical data governance emerges as a critical determinant of sustained brand loyalty in personalized recommendation systems. The study advances the discourse on AI ethics in e-commerce by underscoring the necessity for robust regulatory frameworks and transparent algorithmic decision-making to foster consumer confidence. These insights provide actionable guidance for Digikala and comparable platforms seeking to optimize AI personalization strategies while mitigating ethical risks.