Artificial Intelligence-Driven Commerce: Consumer Trust, Adoption Behaviour and Firm Performance a Mixed-Methods Investigation Across Emerging and Developed Markets

This study examines how AI-driven commerce shapes consumer trust, technology adoption and firm performance across six countries spanning emerging economies (India, Nigeria, Brazil) and developed economies (the United States, the United Kingdom, Germany). Three theoretical frameworks the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT2) and Trust-Risk Theory were combined within a single hierarchical structural equation model (SEM), with each framework contributing distinct constructs: TAM provided the adoption antecedents; UTAUT2 introduced contextual moderators; and Trust-Risk Theory supplied the mediating constructs of consumer trust and perceived risk. Survey data from 847 consumers and 214 firm managers, stratified by gender, age, income quartile and urban-rural status, were analysed alongside 32 semi-structured interviews with industry practitioners and regulators. Perceived usefulness, algorithmic transparency and data privacy assurance emerged as the strongest trust-building predictors across all markets, though their relative weight varied by context. Consumers in emerging markets placed comparatively greater emphasis on performance expectancy and peer endorsement, while those in developed markets prioritised algorithmic explainability and privacy controls differences traceable to cultural profiles and divergent regulatory regimes including the EU’s GDPR, Brazil’s LGPD and Nigeria’s NDPR. At the organisational level, AI adoption improved efficiency, customer retention and revenue, but only where implementation was sufficiently mature and complementary capabilities were in place.

AI-driven commerce; consumer trust; technology adoption; TAM; UTAUT2; firm performance; emerging markets

1. Acquisti, A., Brandimarte, L., & Loewenstein, G. (2016). Privacy and human behavior in the age of information. Science, 347(6221), 509–514. https://doi.org/10.1126/science.aaa1465 [Google Scholar] [Crossref]

2. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052 [Google Scholar] [Crossref]

3. Bauer, C., Korunovska, J., & Spiekermann, S. (2019). On the value of information: What Facebook users are willing to pay. European Journal of Information Systems, 28(1), 31–55. https://doi.org/10.1080/0960085X.2018.1424793 [Google Scholar] [Crossref]

4. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa [Google Scholar] [Crossref]

5. Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(4), 3–11. [Google Scholar] [Crossref]

6. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute. [Google Scholar] [Crossref]

7. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications. [Google Scholar] [Crossref]

8. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0 [Google Scholar] [Crossref]

9. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

10. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 [Google Scholar] [Crossref]

11. Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organisation. Harvard Business Review, 97(4), 62–73. [Google Scholar] [Crossref]

12. Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48(1), 1–8. https://doi.org/10.1007/s11747-019-00711-4 [Google Scholar] [Crossref]

13. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203 [Google Scholar] [Crossref]

14. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 [Google Scholar] [Crossref]

15. Hofstede, G. (1980). Culture’s consequences: International differences in work-related values. SAGE Publications. [Google Scholar] [Crossref]

16. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9 [Google Scholar] [Crossref]

17. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2020). Frontlines as AI: Observing the interaction between chatbots and human agents. Journal of Marketing Research, 56(1), 15–39. https://doi.org/10.1177/0022243718821215 [Google Scholar] [Crossref]

18. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organisational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.2307/258792 [Google Scholar] [Crossref]

19. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359. https://doi.org/10.1287/isre.13.3.334.81 [Google Scholar] [Crossref]

20. Morgan, D. L. (2014). Pragmatism as a paradigm for social research. Qualitative Inquiry, 20(8), 1045–1053. https://doi.org/10.1177/1077800413513733 [Google Scholar] [Crossref]

21. Pantano, E., & Pizzi, G. (2020). Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis. Journal of Retailing and Consumer Services, 55, 102096. https://doi.org/10.1016/j.jretconser.2020.102096 [Google Scholar] [Crossref]

22. Rana, N. P., Dwivedi, Y. K., & Hughes, D. L. (2020). Analysis of challenges for blockchain adoption within the Indian public sector: An interpretive structural modelling approach. Information Technology & People, 35(1), 172–198. https://doi.org/10.1108/ITP-07-2018-0327 [Google Scholar] [Crossref]

23. Srite, M., & Karahanna, E. (2006). The role of espoused national cultural values in technology acceptance. MIS Quarterly, 30(3), 679–704. https://doi.org/10.2307/25148745 [Google Scholar] [Crossref]

24. Dr. Suby Baby,(2025). Impact of Financial Literacy and Risk Tolerance on Consumer Buying Behaviour: A Cross-Sectional Study of Urban Online Shoppers. Advances in Consumer Research, 2(6), 126-132. [Google Scholar] [Crossref]

25. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 [Google Scholar] [Crossref]

26. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412 [Google Scholar] [Crossref]

• A Study on Utilisation on Vidiyal Payanam with Special Reference to Udumalpet
• Influence of Sales Promotional Strategies on Water Purifier Purchase: A Study
• Voluptatem exercitat
• Beatae voluptatum eanfjnjfnjnj
• Analysis of Marketing Strategies in Newbridge College During Covid-19 Pandemic