Presenting a Model of Online Consumer Behavior Based on Artificial Intelligence Tools in the Retail Industry
Keywords:
Artificial Intelligence, Online Consumer Behavior, Online Retail, Smart Shopping Experience, Grounded TheoryAbstract
This study aims to develop and explain a comprehensive model of online consumer behavior based on artificial intelligence tools in the retail industry. This qualitative study employed a grounded theory approach. The research population consisted of experts in e-commerce and artificial intelligence, selected through purposive sampling. Data were collected via semi-structured interviews with 15 specialists and analyzed through open, axial, and selective coding procedures. To ensure validity, techniques such as member checking and data consensus were applied, and reliability was confirmed using Cohen’s kappa coefficient (0.78). The findings identified “AI-driven smart shopping experience” as the core category shaping online consumer behavior. Causal conditions including intelligent personalization, smart customer interaction, and online shopping motivations significantly influenced consumer behavior formation. Contextual factors such as IT infrastructure, trust and security, and technology acceptance culture strengthened these relationships, while intervening conditions including privacy concerns, technical limitations, and economic factors significantly moderated them. Strategic actions such as AI development, user experience enhancement, and intelligent interaction led to outcomes including increased customer satisfaction and loyalty, improved decision-making, and enhanced business performance. The proposed model demonstrates that integrating artificial intelligence into online retail can significantly enhance consumer behavior through smart experiences and data-driven interactions, ultimately creating sustainable competitive advantages.
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Alizadeh, H., Nazarpour Kashani, H., Jalali Filshour, M., & Pourjabbari Khameneh, A. (2023). Evaluation of consumer behavior prediction based on artificial intelligence-based marketing. National Conference on Management and Humanities Research in Iran,
Behera, R. K., Bala, P. K., & Rana, N. P. (2023). Creation of sustainable growth with explainable artificial intelligence: An empirical insight from consumer packaged goods retailers. Journal of Cleaner Production, 399, 136605. https://doi.org/10.1016/j.jclepro.2023.136605
Chand, T., Nair, V. S., Srinivasu, P. N., & Kumar, V. J. (2025). Prospects and challenges in decoding consumer behavior using neurotechnology. In Artificial Intelligence Applications for Brain-Computer Interfaces (pp. 161-182). Elsevier. https://doi.org/10.1016/B978-0-12-824410-4.00009-5
Chen, D., Liu, Y., Guo, Y., & Zhang, Y. (2024). The revolution of generative artificial intelligence in psychology: The interweaving of behavior, consciousness, and ethics. Acta Psychologica, 251, 104593. https://doi.org/10.1016/j.actpsy.2024.104593
Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard business review, 96(1), 108-116.
Ghorbani Ghader, A., Hashemi Nasab, M., & Hedayati, M. S. (2024). Designing a customer behavior prediction model using artificial intelligence algorithms and neural networks. Journal of Smart Strategic Management, 3(2).
Hermann, E., & Puntoni, S. (2024). Artificial Intelligence and Consumer Behavior: From Predictive to Generative AI. Journal of Business Research, 180, 114720. https://doi.org/10.1016/j.jbusres.2024.114720
Huang, M. H., & Rust, R. T. (2021). Artificial Intelligence in Service. Journal of Service Research, 24(1), 6-23.
Khan, S., Tomar, S., Fatima, M., & Khan, M. Z. (2022). Impact of Artificial Intelligence and Industry 4.0 Based Products on Consumer Behaviour Characteristics: A Meta-Analysis-Based Review. Sustainable Operations and Computers, 3, 218-225. https://doi.org/10.1016/j.susoc.2022.01.001
Kim, M. J., Hall, C. M., Kwon, O., & Sohn, K. (2024). Space Tourism: Value-Attitude-Behavior Theory, Artificial Intelligence, and Sustainability. Journal of Retailing and Consumer Services, 77, 103654. https://doi.org/10.1016/j.jretconser.2024.103654
Kotler, P., & Keller, K. L. (2016). Marketing Management (15th ed.). Pearson.
Li, T. G., Zhang, C. B., Chang, Y., & Zheng, W. (2024). The impact of AI identity disclosure on consumer unethical behavior: A social judgment perspective. Journal of Retailing and Consumer Services, 76, 103606. https://doi.org/10.1016/j.jretconser.2023.103606
Londaran Esfahani, S., & Jaberzadeh Ansari, A. (2024). The impact of artificial intelligence on brand, customer behavior analysis, and marketing management. 10th International Conference on Management Sciences and Accounting,
Mahdavipour, M., Farajipour, M., & Movahedi Sefat, M. (2024). Application of artificial intelligence in user and entity behavior analysis. 15th National and 2nd International Conference on Command and Control in Iran,
Murugan, T. S., & Kumar, C. M. (2024). Unveiling the evolution: Impact of artificial intelligence on consumer buying behaviors in online retail purchase. Educational Administration: Theory and Practice, 30(5), 1072-1078. https://kuey.net/
Nozawa, C., Togawa, T., Velasco, C., & Motoki, K. (2022). Consumer responses to the use of artificial intelligence in luxury and non-luxury restaurants. Food Quality and Preference, 96, 104436. https://doi.org/10.1016/j.foodqual.2021.104436
Rather, R. A. (2025). Does Consumers' Reveal Engagement Behaviours in Artificial Intelligence (AI)-Based Technologies? The Dynamics of Perceived Value and Self-Congruence. International Journal of Hospitality Management, 126, 103989. https://doi.org/10.1016/j.ijhm.2025.103989
Zhang, Y., & Wang, S. (2023). The Influence of Anthropomorphic Appearance of Artificial Intelligence Products on Consumer Behavior and Brand Evaluation Under Different Product Types. Journal of Retailing and Consumer Services, 74, 103432. https://doi.org/10.1016/j.jretconser.2023.103432
