The Influence of Conversational AI on Consumer Behavior and Counterfactual Thinking: A Systematic Review
Keywords:
Conversational AI, consumer behavior, personalization, counterfactual thinking, e-commerce
Abstract
This document evaluates the development and importance of conversational AI, chatbots, and virtual assistants in human behavior and the process of making decisions. Designed to investigate the impact of AI on the personalization of user experiences, the study maps how the automation of decision-making and the alteration of cognitive processes like thinking in the counterfactual mode and regret are affected. The main idea is that while conversational AI service might improve the user experience and be more efficient, problems with transparency, personalization overdone and emotive manipulation might emerge. The conclusion of the research is that it is necessary to operate in a balanced way in order for the trust to be established, to be transparent, and to be authentic, thus reinforcing the transformative impact of conversational AI in e-commerce and consumer engagement in the future.
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References
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2. Bagozzi, R. P., Belanche, D., Casaló, L. V., & Flavián, C. (2016). The Role of Anticipated Emotions in Decision Making. Paper Presented at a Conference on the Role of Anticipation and Regret in Decision Making, La Jolla, CA.
3. Bai, L. (2022). Analysis of the change of artificial intelligence to online consumption patterns and consumption concepts. Soft Computing, 26(16), 7559–7569.
4. Baron, A. S., Zaltman, G., & Olson, J. (2017). Barriers to advancing the science and practice of marketing. Journal of Marketing Management, 33(11–12), 893–908.
5. Celuch, K., Saxby, C., & Oeding, J. (2015). The Influence of Counterfactual Thinking and Regret on Ethical Decision Making. Journal of Education for Business, 90(4), 175–181.
6. Cui, H., Xiao, L., & Zhang, X. (2021). Application of Mobile Big Data and Artificial Intelligence in the Efficiency of E-Commerce Industry. Mobile Information Systems, 2021.
7. Dawetas, V., & Diamantopoulos, A. (2018). “Should have i bought the other one?” Experiencing regret in global versus local brand purchase decisions. Journal of International Marketing, 26(2), 1–21.
8. Fung, T. K. F. (2019). The Role of Counterfactual Thinking in Narrative Persuasion: Its Impact on Patients’ Adherence to Treatment Regimen. Health Communication,1482–1493.
9. Galiano-Coronil, A., Blanco-Moreno, S., Tobar-Pesantez, L. B., & Gutiérrez-Montoya, G. A. (2023). Social media impact of tourism managers: a decision tree approach in happiness, social marketing and sustainability. Journal of Management Development, 42(6), 436–457.
10. Gerlich, M., Elsayed, W., & Sokolovskiy, K. (2023). Artificial intelligence as toolset for analysis of public opinion and social interaction in marketing: identification of micro and nano influencers. Frontiers in Communication, 8.
11. Guha, R. (2021). Improving the Performance of an Artificial Intelligence Recommendation Engine with Deep Learning Neural Nets. 2021 6th International Conference for Convergence in Technology, I2CT 2021, 1–7.
12. Herath Pathirannehelage, S., Shrestha, Y. R., & von Krogh, G. (2024). Design principles for artificial intelligence-augmented decision making: An action design research study. European Journal of Information Systems, 00(00), 1–23.
13. Hu, B., Mao, Y., & Kim, K. J. (2023). How social anxiety leads to problematic use of conversational AI: The roles of loneliness, rumination, and mind perception. Computers in Human Behavior, 145(September 2022).
14. Huang, M. H., & Rust, R. T. (2022). A Framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98(2), 209–223.
15. Huang, M., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing.
16. Iskef, G. (2022). The adaption of AI for marketing management.
17. Jain, V., Wadhwani, K., & Eastman, J. K. (2024). Artificial intelligence consumer behavior: A hybrid review and research agenda. Journal of Consumer Behaviour, 23(2), 676–697.
18. Jia, J., Li, J., & Liu, W. (2023). Expectation-based consumer purchase decisions: behavioral modeling and observations. Marketing Letters, 34(3), 397–413.
19. Kim, J. E., & Johnson, K. K. P. (2013). The Impact of Moral Emotions on Cause-Related Marketing Campaigns: A Cross-Cultural Examination. Journal of Business Ethics, 79–90.
20. Li, Y., Wu, B., Huang, Y., & Luan, S. (2024). Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust. Frontiers in Psychology, 15(April), 1–13.
21. Luo, X. (2002). Uses and Gratifications Theory and E-Consumer Behaviors. Journal of Interactive Advertising, 2(2), 34–41.
22. Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504.
23. Meier, M., Maier, C., Thatcher, J. B., & Weitzel, T. (2024). Chatbot interactions: How consumption values and disruptive situations influence customers’ willingness to interact. Information Systems Journal, 34(5), 1579–1625.
24. Misra, R. R. (2024). The Impact of Personalisation Algorithms on Consumer Engagement and Purchase Behaviour in AI-Enhanced Virtual Shopping Assistants.
25. Mustafa Ayobami Raji, Hameedat Bukola Olodo, Timothy Tolulope Oke, Wilhelmina Afua Addy, Onyeka Chrisanctus Ofodile, & Adedoyin Tolulope Oyewole. (2024). E-commerce and consumer behavior: A review of AI-powered personalization and market trends. GSC Advanced Research and Reviews, 18(3), 066–077.
26. Ngwenyama, O., & Rowe, F. (2024). Should We Collaborate with AI to Conduct Literature Reviews? Changing Epistemic Values in a Flattening World. Journal of the Association for Information Systems, 25(1 Special Issue), 122–136.
27. Oulasvirta, A., & Hornbæk, K. (2022). Counterfactual Thinking: What Theories Do in Design. International Journal of Human-Computer Interaction, 38(1), 78–92.
28. Pappalardo, L., Ferragina, E., Citraro, S., Cornacchia, G., Nanni, M., Rossetti, G., Gezici, G., Giannotti, F., Lalli, M., Gambetta, D., Mauro, G., Morini, V., Pansanella, V., & Pedreschi, D. (2024). A survey on the impact of AI-based recommenders on human behaviours: methodologies, outcomes and future directions. 1(1), 1–41.
29. Rajkumar, A., & Agarwal, S. (2014). Online decision-making in general combinatorial spaces. Advances in Neural Information Processing Systems, 4(January), 3482–3490.
30. Reinhard, P. (2024). Augmentation through Generative AI : Exploring the Effects of Human-AI Interaction and Explainable AI on Service Performance. 2018–2023.
31. Seva, R. R., Duh, H. B. L., & Helander, M. G. (2007). The marketing implications of affective product design. Applied Ergonomics, 38(6), 723–731.
32. Sharma, A., & Shafiq, M. O. (2022). A Comprehensive Artificial Intelligence Based User Intention Assessment Model from Online Reviews and Social Media. Applied Artificial Intelligence, 36(1).
33. Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021). A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence. IEEE Access, 9, 11974–12001.
34. Tien, D. H., Amaya Rivas, A. A., & Liao, Y. K. (2019). Examining the influence of customer-to-customer electronic word-of-mouth on purchase intention in social networking sites. Asia Pacific Management Review, 24(3), 238–249.
35. Xu, A., Li, Y., & Donta, P. K. (2024). Marketing Decision Model and Consumer Behavior Prediction With Deep Learning. Journal of Organizational and End User Computing, 36(1), 1–25.
36. Zhang, K. Z. K., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95–108.
Published
2025-01-20
How to Cite
Bouhlal, H., & Belahcen, N. (2025). The Influence of Conversational AI on Consumer Behavior and Counterfactual Thinking: A Systematic Review. European Scientific Journal, ESJ, 37, 221. Retrieved from https://eujournal.org/index.php/esj/article/view/19016
Section
ESI Preprints
Copyright (c) 2025 Hind Bouhlal, Noureddine Belahcen
This work is licensed under a Creative Commons Attribution 4.0 International License.
