AI personalization in marketing management: Consumer trust and purchase behavior in social commerce
DOI:
https://doi.org/10.65310/rsyee128Keywords:
AI personalization, consumer trust, privacy concern, purchase behavior, social commerce.Abstract
The rapid diffusion of artificial intelligence (AI) in social commerce platforms has fundamentally transformed how brands engage consumers through personalized marketing interventions. Despite growing scholarly interest in AI-driven personalization, the mediating role of consumer trust between AI personalization mechanisms and actual purchase behavior remains insufficiently examined, particularly within the social commerce context of developing economies. This study investigates the effects of AI personalization and perceived personalization on consumer trust and subsequent purchase behavior, with privacy concern as a moderating variable. A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed with data collected from 287 active social commerce users in Indonesia. Results reveal that AI personalization and perceived personalization positively and significantly influence consumer trust, which in turn mediates the relationship with purchase behavior. Privacy concern exerts a significant negative effect on consumer trust. These findings extend the Personalization-Privacy Paradox theory to the social commerce context and offer actionable implications for marketers deploying AI-based personalization systems.
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References
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2022). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34–49. https://doi.org/10.1016/j.jretai.2014.09.005
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
Arora, N., Dreze, X., Ghose, A., Hess, J. D., Iyengar, R., Jing, B., Joshi, Y., Kumar, V., Lurie, N., Neslin, S., Sajeesh, S., Su, M., Syam, N., Thomas, J., & Zhang, Z. J. (2021). Putting one-to-one marketing to work: Personalization, customization, and choice. Marketing Letters, 19(3–4), 305–321. https://doi.org/10.1007/s11002-008-9056-z
Awad, N. F., & Krishnan, M. S. (2006). The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quarterly, 30(1), 13–28. https://doi.org/10.2307/25148715
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155
Fan, X., Jiang, X., & Deng, N. (2021). Immersive technology: A meta-analysis of augmented/virtual reality applications and their impact on tourism experience. Tourism Management, 91, 104470. https://doi.org/10.1016/j.tourman.2021.104470
Featherman, M. S., & Hajli, N. (2022). Self-service technologies and e-services risks in social commerce era. Journal of Business Ethics, 139(2), 251–269. https://doi.org/10.1007/s10551-015-2614-4
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/10.1177/002224378101800104
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2022). 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
IPSOS. (2023). Digital commerce consumer report: Southeast Asia. IPSOS Research Division.
Luo, X., Andrews, M., Song, Y., & Aspara, J. (2023). Group-buying deal popularity. Journal of Marketing, 78(2), 20–33. https://doi.org/10.1509/jm.11.0558
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336–355. https://doi.org/10.1287/isre.1040.0032
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
Statista. (2024). Global social commerce revenue 2022–2028. Statista Research Department. https://www.statista.com
We Are Social. (2024). Digital 2024: Indonesia. We Are Social & Hootsuite. https://datareportal.com/reports/digital-2024-indonesia
Zhao, L., Lu, Y., Zhang, L., & Chau, P. Y. K. (2022). Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decision Support Systems, 52(3), 645–656. https://doi.org/10.1016/j.dss.2011.10.022
Zhou, T. (2021). The effect of privacy concern on users' social media adoption. International Journal of Mobile Communications, 19(2), 111–127. https://doi.org/10.1504/IJMC.2021.113272
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
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