AI-Generated Content as a Catalyst for Consumer Decision-Making: Evidence from Indian Online Shoppers

Volume 11, Issue 1, 2026

International Journal of Commerce and Management Studies, ISSN 2456-3684

Author Name and Affiliation

Dr. Abhijit R. Gajghate

Associate Professor, Department of Business Management
Madhukarrao Pandav College of Engineering, Bhandara, India

Abstract

The rapid advancement of artificial intelligence has transformed the digital marketplace, with AI-generated content increasingly influencing consumer perceptions, evaluation, and purchase decisions. This study examines how AI-driven recommendations, product descriptions, reviews, and interactive conversational agents (such as ChatGPT and similar models) affect the purchase intentions of Indian online shoppers. Using a structured questionnaire, data were collected from 500 respondents across major Indian cities representing diverse demographic groups. The study investigates key constructs such as perceived usefulness, trust in AI-generated content, content credibility, perceived personalization, and consumer engagement. Statistical analyses including correlation, regression, and mediation tests were applied to establish predictive relationships. The findings reveal that AI-generated content significantly enhances consumer decision confidence, trust, and purchase intention, with trust and perceived usefulness acting as strong mediators. The study contributes to emerging literature at the intersection of AI marketing and consumer behavior and provides practical insights for digital marketers, e-commerce platforms, and policymakers.

Keywords

AI-generated content; ChatGPT; Consumer decision-making; Purchase intention; Digital marketing; India; Personalization; Trust; Online shopping

References

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
  • Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Wamba, S. F., & Sharma, S. K. (2023). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 71, 102642.
  • Filieri, R. (2016). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 69(11), 5463–5471.
  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.
  • Huang, M., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50.
  • Lee, J. (2023). Personalized AI recommendations and their influence on consumer satisfaction and loyalty. Journal of Interactive Marketing, 62, 12–25.
  • Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.
  • Tam, K. Y., & Ho, S. Y. (2005). Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Information Systems Research, 16(3), 271–291.
  • Aydin, G. (2023). The impact of AI-generated product descriptions on consumer purchase intention. Journal of Retailing and Consumer Services, 72, 103234.
  • Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E., & Sharma, S. K. (2024). Navigating the future of generative AI: Implications for consumers, businesses, and policymakers. International Journal of Information Management, 75, 102883.
  • Kim, H., & Kim, J. (2023). Consumer trust in AI-based recommendation agents: The role of transparency and expertise. Computers in Human Behavior, 147, 107833.
  • Longoni, C., & Cian, L. (2022). Artificial intelligence in marketing: Consumer response, ethics, and future research directions. Journal of Business Research, 145, 864–878.
  • Prentice, C., & Nguyen, M. (2022). Conversational AI and consumer behavior: A systematic review and research agenda. Psychology & Marketing, 39(8), 1504–1523.
  • Zhang, T., & Lu, Y. (2023). Understanding consumer persuasion in AI-generated content: An elaboration likelihood model perspective. Information & Management, 60(5), 103730.

DOI

DOI: 10.67061/ijcams.2026.vol.11.issue.01.4014

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