EXPLAINING THE AI ADVANTAGE: HOW RECOMMENDATION SYSTEMS INFLUENCE CUSTOMER ENGAGEMENT AND PURCHASE DECISIONS IN ONLINE FASHION

Abstract

The concept of Artificial Intelligence (AI) has transformed online fashion buying, and online recommendation systems have become the key elements in the formation of consumer behavior and interaction. This paper looks at how the combination of three essential AI features personalization, transparency, and content quality affect customer engagement and subsequent purchase intent. The research uses the Hayes PROCESS macro to test the direct and mediated effects using survey data on 177 online fashion shoppers in Chennai, India. The results have shown that the three AI features have a significant positive impact on customer interaction, and personalization has the greatest impact. Although engagement has a direct effect on purchase intention, mediation analysis reveals subtler mechanisms: transparency and quality content have an effect on purchase intention via engagement, and personalization effect seems more direct. These observations indicate that, though the corresponding recommendations may be able to transform consumer interest into purchases within a short period of time, trust-building transparency and aesthetically pleasing content may need more engagement to influence buying decisions. Theoretically, the research contributes to the customer engagement and AI adoption frameworks by showing the interplay between several AI features instead of a single one. In practical terms, it gives practical advice to fashion e-retailers to strike a balance between efficiency-oriented personalization and trust-oriented transparency and creativity-oriented content quality, both in making immediate conversions and long-term consumer loyalty.