Zhang, Jincheng (2026): Consumer Behavior Theory in the Era of Generative AI.
|
PDF
MPRA_paper_129360.docx Download (34kB) |
Abstract
With the rapid development of generative AI, traditional consumer behavior theories centered on "information search-rational decision-making" are undergoing structural changes. Consumers no longer rely solely on static information platforms but engage in interactive dialogue with generative AI, completing needs identification, solution generation, and decision optimization with the support of dynamically generated content. This paper proposes a "Generative AI-Driven Interactive Consumer Behavior Model" (GAIBB) based on the integration of classic consumer behavior theories (such as the Theory of Rational Behavior, the Theory of Planned Behavior, and the Theory of Experiential Consumption). This model emphasizes three mechanisms: "co-creation decision-making," "generative recommendation," and "instant feedback loop," explaining how AI reshapes consumers' cognitive paths and purchasing behavior. The research further indicates that generative AI is driving a shift in consumer behavior from "information acquisition" to "co-creation of cognition," and reconstructing the power structure between platforms, brands, and users.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | Consumer Behavior Theory in the Era of Generative AI |
| Language: | English |
| Keywords: | Generative Artificial Intelligence (Generative AI), Consumer Behavior Theory, Human–AI Interaction, Consumer Decision-Making, Interactive Consumer Behavior Model (GAICB). |
| Subjects: | B - History of Economic Thought, Methodology, and Heterodox Approaches > B4 - Economic Methodology B - History of Economic Thought, Methodology, and Heterodox Approaches > B4 - Economic Methodology > B41 - Economic Methodology O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O35 - Social Innovation |
| Item ID: | 129360 |
| Depositing User: | Mr. Jincheng Zhang |
| Date Deposited: | 12 Jun 2026 12:41 |
| Last Modified: | 12 Jun 2026 12:41 |
| References: | [1].Maheswaran, D.; and Shavitt, S., 2014, “Issues and new directions in global consumer psychology,” Cultural Psychology, pp. 59-66, Psychology Press. [2].Tarka, P., 2017, “Managers’ beliefs about marketing research and information use in decisions in context of the bounded-rationality theory,” Management Decision, 55(5), 987-1005. [3].Faber, R. J.; Lee, M.; and Nan, X., 2004, “Advertising and the consumer information environment online,” American behavioral scientist, 48(4), 447-466. [4].Lin, X.; and Wang, X., 2023, “Towards a model of social commerce: improving the effectiveness of e-commerce through leveraging social media tools based on consumers’ dual roles,” European journal of information systems, 32(5), 782-799. [5].Peterson, R. A.; and Merino, M. C., 2003, “Consumer information search behavior and the Internet,” Psychology & Marketing, 20(2), 99-121. [6].He, R.; Cao, J.; and Tan, T., 2025, “Generative artificial intelligence: a historical perspective,” National Science Review, 12(5), nwaf050. [7].Chen, Z.; Xu, L.; Zheng, H.; Chen, L.; Tolba, A.; Zhao, L.; et al., 2024, “Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models,” Computers, Materials & Continua, 80(2). [8].Storey, V. C.; Yue, W. T.; Zhao, J. L.; and Lukyanenko, R., 2025, “Generative artificial intelligence: Evolving technology, growing societal impact, and opportunities for information systems research,” Information Systems Frontiers, 1-22. [9].Dili, G.; and Ansong, E. D., 2025, “Human–AI Collaboration: A Paradigm Shift in Decision-Making,” The Power of Agentic AI: Redefining Human Life and Decision-Making: In Industry 6.0, pp. 59-80, Springer Nature Switzerland. [10].Moschis, G. P., 2019, “Consumer behavior over the life course: Research frontiers and new directions,” Springer. [11].Park, H. S., 2000, “Relationships among attitudes and subjective norms: Testing the theory of reasoned action across cultures,” Communication studies, 51(2), 162-175. [12].Ajzen, I., 2002, “Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior 1,” Journal of applied social psychology, 32(4), 665-683. [13].Petcharat, T.; and Leelasantitham, A., 2021, “A retentive consumer behavior assessment model of the online purchase decision-making process,” Heliyon, 7(10). [14].Li, S. S.; and Karahanna, E., 2015, “Online recommendation systems in a B2C E-commerce context: a review and future directions,” Journal of the association for information systems, 16(2), 2. [15].Xu, K.; Zhou, H.; Zheng, H.; Zhu, M.; and Xin, Q., 2024, “Intelligent classification and personalized recommendation of e-commerce products based on machine learning,” arXiv preprint arXiv:2403.19345. [16].Qiu, L.; Huang, Y.; Singh, P. V.; and Srinivasan, K., 2025, “Personalization, consumer search, and algorithmic pricing,” Marketing Science, 44(6), 1278-1298. [17].Ekstrand, M. D.; Riedl, J. T.; and Konstan, J. A., 2011, “Collaborative filtering recommender systems,” Foundations and Trends® in Human–Computer Interaction, 4(2), 81-173. [18].Mishra, R.; Kumar, P.; and Bhasker, B., 2015, “A web recommendation system considering sequential information,” Decision Support Systems, 75, 1-10. [19].Mandl, M.; Felfernig, A.; Teppan, E.; and Schubert, M., 2011, “Consumer decision making in knowledge-based recommendation,” Journal of intelligent information systems, 37(1), 1-22. [20].Kılınç, H. K.; and Keçecioğlu, Ö. F., 2024, “Generative artificial intelligence: A historical and future perspective,” Academic Platform Journal of Engineering and Smart Systems, 12(2), 47-58. [21].Bagozzi, R. P.; and Dholakia, U., 1999, “Goal setting and goal striving in consumer behavior,” Journal of marketing, 63(4_suppl1), 19-32. [22].Kim, J. O.; Forsythe, S.; Gu, Q.; and Jae Moon, S., 2002, “Cross‐cultural consumer values, needs and purchase behavior,” Journal of Consumer marketing, 19(6), 481-502. [23].Zhang, Z.; Guo, C.; and Goes, P., 2013, “Product comparison networks for competitive analysis of online word-of-mouth,” ACM Transactions on Management Information Systems (TMIS), 3(4), 1-22. |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/129360 |

