Determinants of AI Technology Features on Post-Purchase Regret Among Generation Z in Malaysia

 




 

Kuan, Sue-Ee (2026) Determinants of AI Technology Features on Post-Purchase Regret Among Generation Z in Malaysia. Masters thesis, Tunku Abdul Rahman University of Management and Technology.

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Abstract

The rapid integration of artificial intelligence in digital commerce has raised critical concerns regarding its psychological impact on consumer well-being. This study adopted a quantitative and cross-sectional approach, collecting 429 valid responses from Generation Z consumers in Malaysia to investigate how five specific AI technology features (anthropomorphism, ease of use, interactive control, personalised configuration, and visual vividness) influence impulsive buying and post-purchase regret. The empirical results demonstrated that anthropomorphism and visual vividness are the primary catalysts for impulsive buying, while ease of use acts as a cognitive safeguard that reduces impulsive urges. Further analysis confirmed that impulsive buying serves as a robust mediator, linking all five AI affordances to increased levels of post-purchase regret. Additionally, celebrity endorsement was found to significantly moderate the relationship between impulsive buying and post-purchase regret; however, contrary to traditional marketing assumptions, it strengthened the positive association, thereby amplifying the intensity of buyer’s remorse. These findings extend Affordance-Actualization Theory and Cognitive Dissonance Theory by highlighting the dark side of AI-mediated consumption. The results suggest that while immersive AI features drive short-term retail conversions, they may simultaneously undermine long-term customer loyalty through psychological distress. While this study provides valuable insights within the Malaysian e-commerce context, future research should employ longitudinal designs and objective marketing tools to track the evolution of consumer regret across diverse cultural and industrial settings.

Item Type: Thesis / Dissertation (Masters)
Subjects: Science > Computer Science > Artificial intelligence
Social Sciences > Commerce > Electronic commerce
Faculties: Faculty of Accountancy, Finance & Business > Master of Business Administration (MBA)
Depositing User: Library Staff
Date Deposited: 04 Aug 2026 09:10
Last Modified: 04 Aug 2026 09:10
URI: https://eprints.tarc.edu.my/id/eprint/38148