The Effect of AI-Driven Personalization, Chatbot Interaction, and Dynamic Pricing on Consumer Purchase Decisions through Trust in AI Systems
DOI:
https://doi.org/10.70716/emis.v4i2.554Keywords:
AI-driven personalization, Chatbot Interaction, Dynamic Pricing, Trust in AI Systems, Consumer Purchase DecisionAbstract
The rapid adoption of Artificial Intelligence (AI) in digital marketing has changed how consumers interact with brands, particularly through personalized recommendations, chatbot services, and dynamic pricing. However, although these AI-based features are widely used on digital platforms, consumers may still question their reliability, fairness, and transparency. This study aims to examine the effects of AI-driven personalization, AI-enabled chatbot interaction, and AI-based dynamic pricing on consumer purchase decisions through trust in AI systems. This research employed a quantitative approach using a cross-sectional survey of consumers who had experience using AI-based features on digital platforms in Indonesia. Data was analyzed using PLS-SEM. The results show that AI-driven personalization and chatbot interaction positively influence purchase decisions, while dynamic pricing has no direct effect. Trust in AI systems significantly mediates the relationship between AI-based marketing features and purchase decisions. This study concludes that consumer trust is essential in converting AI-driven marketing strategies into purchase behavior.
References
Aghimien, D., Ikuabe, M., Aigbavboa, C., Oke, A., & Thwala, W. (2022). PLS-SEM assessment of the impediments of robotics and automation deployment for effective construction health and safety. Journal of Facilities Management, 22(3), 458–478. https://doi.org/10.1108/JFM-04-2022-0037
Bhojwani, R., Paul, J., & Srivastava, R. (2026). Role of artificial intelligence on consumer buying behavior: The dual effects of AI-enabled features on decision-making and trust. Journal of Research in Interactive Marketing. https://doi.org/10.1108/JRIM-11-2025-0702
Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L., & Zemmel, R. (2023). The economic potential of generative AI: The next productivity frontier (pp. 1–68). McKinsey & Company. https://www.aiunplugged.io/wp-content/uploads/2023/09/The-economic-potential-of-generative-AI.pdf
Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587–595. https://doi.org/10.1016/j.jbusres.2018.10.004
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
Deloitte. (2023). The future of AI-driven personalization. Deloitte Insights.
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
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, 49(1), 1–8. https://doi.org/10.1007/s11747-019-00711-4
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications. https://doi.org/10.1007/978-3-030-80519-7
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
Huang, M.-H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 3–13. https://doi.org/10.1177/1094670520902266
Jiang, X., Wu, Z., & Yu, F. (2024). Constructing consumer trust through artificial intelligence generated content. Academic Journal of Business & Management, 6(8), 263–272. https://doi.org/10.25236/AJBM.2024.060839
Kumar, V., Dixit, A., Javalgi, R. G., & Dass, M. (2022). Digital transformation of marketing: A new perspective. Industrial Marketing Management, 142, 199–213. https://doi.org/10.1016/j.indmarman.2021.03.008
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013
Saunders, M., Lewis, P., & Thornhill, A. (2023). Research methods for business students (9th ed.). Pearson Education. https://www.pearson.com/nl/en_NL/higher-education/subject-catalogue/business-and-management/saunders-research-methods-for-business-students-9ed.html
Shin, D. (2021). The effects of explainability and transparency on trust in artificial intelligence. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Wang, S., Cheah, J.-H., Wong, C. Y., & Ramayah, T. (2024). Progress in partial least squares structural equation modeling use in logistics and supply chain management in the last decade: A structured literature review. International Journal of Physical Distribution & Logistics Management, 54(7/8), 673–704. https://doi.org/10.1108/IJPDLM-06-2023-0200
Xia, L., Monroe, K. B., & Cox, J. L. (2004). Price fairness perceptions in algorithmic pricing. Journal of Marketing, 68(4), 1–19. https://doi.org/10.1509/jmkg.68.4.1.42733
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Rizkyka Agsyana Amaretta, Alfatih Sikki Manggabarani (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
