An Integrated S-O-R and UTAUT2 Framework for Examining the influence of AI-Powered Shopping Experiences on Emotional Engagement, Trust, and Purchase Intention in Online Fashion Retailing

AI Powered Shopping, Experiences, Emotional Engagement, Trust, Purchase Intention, On, line Fashion Retailing, UTAUT2 and S-O-R Theory

1Dr. Asiya Anas,
Assistant Professor, Department of
Commerce, SAFI Institute of Advanced Study
(Autonomous), Vazhayur East, Malappuram,
Kerala, India.
E-mail ID: dr.asiya@siasindia.org

2*Mr. Abdul Majeed K,
Assistant Professor, Department of
Commerce, SAFI Institute of Advanced Study
(Autonomous), Vazhayur East, Malappuram,
Kerala, India.
E-mail ID: majeed@siasindia.org

3Ms. Safa Hanan C K,
Assistant Professor,
Department of Commerce, SAFI Institute of
Advanced Study (Autonomous), Vazhayur
East, Malappuram, Kerala, India.
E-mail ID: safa@siasindia.org

4Dr. Jasmin J,
Assistant Professor , PG
department of Commerce, Mannaniya
College of Arts & Science, Pangode,
Thiruvananthapuram, Kerala, India.
Email ID: drjasminnazeer@gmail.com

5Dr. Ranjith P,
NVT Commerce (Sr.) (HG),
VHS wing, General Education Deptartment,
Govt. of Kerala, India.
E-mail ID: rpnair100@gmail.com

6Prof.(Dr.) Kumari V K Shyni,
Professor, Department of Commerce, Iqbal College,
Peringammala, Thiruvananthapuram, Kerala,
India.
E-mail ID: kvkshyni@gmail.com

Received:21-06-2026
Revised:28-07-2026
Accepted:05-08-2026

European Journal of Prosthodontics and Restorative Dentistry (2026) 34 (7s), 751–761

An Integrated S-O-R and UTAUT2 Framework for Examining the influence of AI-Powered Shopping Experiences on Emotional Engagement, Trust, and Purchase Intention in Online Fashion Retailing

The increasing use of artificial intelligence (AI) in online fashion retailing is transforming consumers’ shopping experiences, yet the psychological mechanisms through which AI-enabled features influence purchase intention remain insufficiently understood. This study integrates the Stimulus– Organism–Response (S-O-R) paradigm with specific UTAUT2 components to investigate the effects of Performance Expectancy, Effort Expectancy, Hedonic Motivation, and Consumer Trust on Emotional Engagement and Purchase Intention. Customers in Kozhikode District, Kerala, India, who had used online platforms with AI-enabled features like chatbots, visual search, virtual try-ons, personalised offers, and personalised recommendations to buy fashionable clothing participated in a quantitative, cross-sectional study. A structured seven-point Likert-scale questionnaire was used to gather data, and SmartPLS's Partial Least Squares Structural Equation Modelling (PLSSEM) was used for analysis. The results demonstrate that consumer trust, hedonic motivation, performance expectancy, and effort expectancy all have a major impact on emotional engagement. Purchase intention is strongly predicted by emotional engagement. Effort Expectancy and Hedonic Motivation have an indirect impact on Purchase Intention thru Emotional Engagement, but Performance Expectancy and Consumer Trust have substantial direct and indirect impacts. By showing that emotional engagement serves as a crucial mechanism connecting AI-enabled retail perceptions with purchase intention, the study expands on the S-O-R and UTAUT2 views. The results indicate that in order to develop emotionally compelling AI-powered buying experiences, online fashion businesses need combine practical utility, ease of engagement, enjoyment, and trust.

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