AI Recommendations: Driving Gen Z Consumer Behaviour in E-Commerce Fashion

Ms. N. Vaishnavi Gupta, Fashion Management Scholar, Department of Fashion Management Studies,

National Institute of Fashion Technology, Ministry of Textiles, Govt of India, Daman Campus

Abstract:

This study explores AI recommendation systems’ influence on Generation Z (18-27) consumer behaviour in Indian e-commerce fashion platforms like Myntra, Amazon etc. It examines personalization, trust, flow, and privacy’s role in driving purchase intent and impulsivity. Findings show strong positive effects from tailored suggestions, moderated by privacy concerns, with implications for ethical AI strategies in retail

Keywords: AI recommendations, Gen Z, e-commerce, purchase intent, personalization

Introduction:

The rapid expansion of the Indian e-commerce industry, backed by the widespread availability of smartphones and cheap data, has made AI-driven recommendation systems one’s first choice for consumer interactions. Such systems comprising algorithmically driven product recommendations, virtual try-ons, intelligent chatbots, and predictive purchase analytics have become responsible for a major share of online sales, especially in fast-moving categories like fashion and apparel. Companies like Myntra, Flipkart, and Ajio are using AI to help in customer data analysis which includes browsing history, preferences, and real-time behavior creating hyper-personalized user experiences that are both intuitive and engaging. For the Gen Z (ages 18-27), who are online natives spending on average 4+ hours daily just on social media and e-commerce applications, AI recommendations are not merely tools but rather invisible assistants that convert passive surfing into precise discovery; they even lead to impulse buys during flash sales and indulging in trend-based shopping.

This transition greatly affects the consumer behaviour of the younger generation in several highly dependent aspects. Among these factors, Personalization is recognized as the most important cause of change; the AI recommends the fashion that goes with each individual’s style, body type and the purpose, hence the relevance and satisfaction born of it are heightened—this is crucial because in the case of the Indian market 60% of the Gen Z 

Objective:

• Examine AI recommendations’ impact on Gen Z purchase intent in e-commerce fashion
• Roles of AI personalization, trust, and flow in driving youth consumer behaviour.

Research Methodology:

The study employs a qualitative systematic literature review approach to analyse the impact of AI recommendations on the youth (Gen Z) consumer behavior in e-commerce. The peer-reviewed journals and scholarly articles published between 2024 and 2026 were sourced from Google Scholar and Scopus databases, with the focus being on the contexts of fashion retail. Thematic analysis brought together the important themes—personalization, trust, flow, privacy, and purchase intent—discovering patterns, gaps (e.g., Indian markets), and implications. The criteria for inclusion guaranteed the connection to AI-powered platforms such as Myntra, and the results created a conceptual framework for textile and fashion management.

Literature Review:

Guerra-Tamez et al. explore how Artificial Intelligence affects Generation Z’s consumer behaviour in fashion, technology, beauty, and education. They gathered data from 224 respondents. The study shows that exposure to AI, positive feelings about AI, and trust in AI accuracy increase brand trust. This trust, in turn, affects purchasing decisions. The research also points out that flow experience plays a key role between brand trust and purchase intention, highlighting the importance of engaging AI-driven interactions. These findings show that AI is valuable for building trust and impacting buying behaviour among Generation Z in online settings.

Alex Benny et al. conducted research on the online fashion industry's consumer purchasing intentions and the role of Artificial Intelligence, particularly focusing on Generation Z. The researchers enlarged the Technology Acceptance Model (TAM) by adding AI-powered personalization and trust in AI as major drivers of consumer behaviour. The quantitative cross-sectional design and the PLS-SEM analysis led to the conclusion that AI-driven personalization, perceived ease of use, perceived usefulness, and trust in AI not only influence attitudes toward AI but also affect purchasing intentions, whereby trust in AI is recognized as the most potent predictor. The study points out the mediation of consumer attitude and underscores the necessity of having reliable and user-friendly AI systems in the process of making online fashion purchase decisions.

Nguyen Thi Hoi et al. in their study "Gen Z's Purchase Intentions in Using Artificial Intelligence Applications on E-Commerce Platforms," analyses the literature but also proposes the UTAUT model from Venkatesh et al. as a foundation for grounding the review, thus extending it to the case of e-commerce by adding factors like perceived usefulness, ease of use, social influence, and facilitating conditions from previous research on technology acceptance in online shopping to the model. The review brings to the forefront the AI-specific applications for the Gen Z segment as a gap and cites recent studies wherein AI tools, viz., chatbots, have been able to increase personalization, engagement, and purchase intent on platforms such as Shopee.vn, while also tackling the technological trends and ease of use as new amplifiers to pull the digital-native consumers in the Vietnamese market.

Guanghong Xie, in "The Impact of Generative AI Shopping Assistants on E-commerce Consumer Motivation and Behaviour: Consumer-AI Interaction Design" (International Journal of Information Management, 2026), builds upon existing literature mainly from three disciplines: human-computer interaction (HCI), cognitive psychology, and marketing, in order to pinpoint the limitations of earlier Gen AI studies that were confined to the developed markets only. In doing so, he proposes a new model called the Motivation–Expectation Management Model (MEMM) that associates the motivation point of view to personalization and user experience (UX), through the mediators of expectation confirmation, satisfaction, and repurchase intent, the context of China's Taobao being the focus here. A thorough mixed-methods analysis of Taobao Wenwen users bares the fact that extrinsic motivation juiced up the effect more than intrinsic motivation did and also confirmed the chain of mediation to be significant between the constructs, thus providing a model for the e-commerce industry in developing countries to maximize the consumer-AI interactions.

In the paper "Influence of AI on the Impulsive Buying Behaviour of Gen-Z" (SSRN, 2025), Pragathi Prakash, Dr. Truptha Shankar et al utilize the Stimulus-Organism-Response (SOR) model to explore the impact of AI features on impulsive purchases of online shoppers from Gen-Z. The literature review reviews SOR frameworks in similar consumer behaviour studies and identifies the lack of research on AI-specific drivers, such as accuracy, interactivity, and insights for digitally native Gen-Z. The SEM analysis affirms that these AI traits encourage impulsive buying directly, thus connecting the limited study on technology-induced spontaneity in younger groups.

In "AI Recommendation Systems and Generation Z Consumer Behaviour: Insights from Vietnam's E-commerce Landscape," researchers H.K. Chau et al. take the Expectation-Confirmation Model (ECM) a step further by incorporating Technology Acceptance Model (TAM), Self-Determination Theory (SDT), Trust Theory, and Social Exchange Theory (SET) to scrutinize post-adoption satisfaction among 223 Vietnamese Gen Z (aged 18-34) users of AI-driven platforms such as Shopee and Lazada. The literature review evaluates the traditional ECM's incapability to cope with AI-related factors like perceived enjoyment, privacy protection, interactivity, and autonomy, relying on studies like Davis (1989) for perceived usefulness, Nguyen & Ha (2021) for expectation confirmation's role in loyalty, and Alnaim et al. (2022) for privacy-trust links, while highlighting Gen Z's emotional-rational decision-making in the face of personalization and data concerns. PLS-SEM analysis uncovers the important connections: expectation confirmation → perceived usefulness → AI satisfaction, privacy → AI trust → satisfaction, and autonomy/enjoyment fostering engagement—alluding to the predominance of psychological aspects among the digital natives and providing advice on how to culturally sensitive AI-driven customer relations in emerging regions.

In their paper, “Influence of AI on Shopping Experience of Generation Z Customers” (Mercy College, Palakkad, Kerala, India, 2024), authors Ramya John and Deepthi S conduct a survey of 61 Gen Z respondents residing in the Palakkad district to judge the role of AI in improving retail experiences through personalized targeting, the use of computer vision for seamless checkouts, and offering post-purchase support, while at the same time dealing with issues of trust regarding privacy of personal data. The literature review presents AI as a cross-industries force driving change, which retailers could rely on for easily connecting with the digital-native Gen Z customers, but at the same time, it brings forward the problem of consumer ignorance and privacy issues that retailers need to clear through the provision of education and active engagement to be able to win over the trust and loyalty of customers. The results indicate that AI has a positive effect on shopping in terms of time-saving and convenience, and among the retailer's advices is to make the AI capabilities clear to consumers on websites. 

Insights From Literature Review:

The latest research indicates that AI has a significant impact on the purchasing behavior of the youngest generation especially in the online fashion segment. According to Guerra-Tamez et al. research with 224 respondents AI exposure, positive attitudes, and trustall contribute to the building of brand trust, which is then mediated by flow to puchase. A. B. et al. also pave the way by personalizing and trusting as key drivers of attitudes and intentions through PLS-SEM who consequently the TAM extension. N. T. H. et al. change the UTAUT, focussing on-shopee.vn chatbots' contribution to customer personalization. Xie (2026) comes up with MEMM for Taobao, associating external motivation to consumer delight/repeat purchase. Prakash et al. (2025) adopt SOR linking the degree of interaction of AI/its precision to one’s impulsivity. Chau et al. suggest combining and adopting the ECM/TAM/SDT theory in the case of satisfaction on Shopee/Lazada, pointing out the importance of privacy/trust. John and Deepthi S (2024, India) validate the convenience of AI but also advocate for privacy training. In conclusion, all of them mark the following: trust, personalization, and model extensions, while there are still issues related to privacy and longitudinal research in developing markets that need to be addressed.

Findings:

AI exposure, positive attitudes, and trust in accuracy are the factors that have the potential to enhance trust in brands and are the barriers that lead through the flow experiences to the purchase intentions of the online fashion consumers of the Gen Z demographic. The extensions of the TAM and UTAUT models consist of personalization, ease of use, usefulness, and social influence, with trust being the most important predictor of attitudes and PLS-SEM-based buying behaviour. Chatbots are the ones that increase personalization, engagement, and intent on e-commerce sites such as Shopee.vn. MEMM establishes a link between extrinsic motivators and expectation confirmation, satisfaction, and repurchase through mixed methods on Taobao. The SOR model affirms the direct impact of AI accuracy, interactivity, and insights on spontaneous buying through the use of SEM. The integrated ECM/TAM/SDT frameworks reveal paths such as privacy-trust, autonomy, and enjoyment that result in post-adoption satisfaction as shown in PLS-SEM, 223 Gen Z on Shopee/Lazada. The AI personalization, computer vision, and support have made convenience and time-saving better, but privacy concerns require consumer education for trust-building.

Conclusion:

AI's transformative role in shaping Gen Z's online fashion purchasing through trust-building mechanisms like exposure, personalization, and flow experiences, as evidenced by extended models (TAM, UTAUT, SOR, MEMM, ECM/TAM/SDT). Chatbots, recommendation systems, and interactive features drive intentions, impulsivity, satisfaction, and repurchase, particularly on platforms like Shopee and Taobao, with trust and extrinsic motivation as dominant predictors. While AI enhances convenience and engagement, persistent privacy concerns highlight the need for education and culturally sensitive designs. These insights reveal opportunities for future research in developing markets, emphasizing reliable, user-centric AI to bridge gaps in longitudinal and ethical studies.

References:

Guerra-Tamez, C. R., Flores, K. K., Serna-Mendiburu, G. M., Robles, D. C., & Cortés, J. I. (2024). Decoding Gen Z: AI’s influence on brand trust and purchasing behavior. Frontiers in Artificial Intelligence, 7, 1323512. https://doi.org/10.3389/frai.2024.1323512

Xavior, A. B. J. S. T. S. E. a. D. K. P. M. M. T. R. (2025, November 25). THE IMPACT OF ARTIFICIAL INTELLIGENCE ON GENERATION Z’S ONLINE FASHION PURCHASE INTENTIONS. TPM – Testing, Psychometrics, Methodology in Applied Psychology. https://tpmap.org/submission/index.php/tpm/article/view/3235

Rolando, B. (2025). THE IMPACT OF ARTIFICIAL INTELLIGENCE-BASED RECOMMENDATION SYSTEMS ON CONSUMER PURCHASE DECISIONS IN E-COMMERCE. journal.dinamikapublika.id. https://doi.org/10.1234/aira.v4i2.47

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Ingriana, A., & Rolando, B. (2025). REVOLUTIONING E-COMMERCE: INVESTIGATING THE EFFECTIVENESS OF AI-DRIVEN PERSONALIZATION IN INFLUENCING CONSUMER PURCHASING BEHAVIOR. Jurnal Ilmiah Manajemen Dan Kewirausahaan (JUMANAGE), 4(1), 549–565. https://doi.org/10.33998/jumanage.2025.4.1.2040

Dey, C. (2026). Understanding the influence of AI-driven personalized recommendations on consumer buying behavior in halal marketing. Journal of Islamic Marketing, 1–21. https://doi.org/10.1108/jima-05-2025-0311