The Impact of Data Privacy Concerns on Customized Marketing Effectiveness: Evidence from the Jordanian Market
DOI:
https://doi.org/10.51325/dt3jr955Keywords:
Digital marketing, consumer trust, data privacy concerns, personalized advertising, personalization-privacy paradoxAbstract
Within the context of Privacy Calculus Theory, this study investigates the relationship between consumers’ data privacy concerns and the perceived effectiveness of personalized marketing. Using a quantitative survey, the study examines how consumers weigh the benefits of personalization against the risks to their privacy in the context of digital marketing. The findings show that while consumers value personalized marketing, privacy concerns influence their overall perception of its effectiveness. High privacy concerns are linked to lower perceptions of personalized marketing strategies, underscoring the conflict between utility and risk. The study applies Privacy Calculus Theory to marketing effectiveness, extending beyond disclosure intentions alone. Jordan, as a developing market, is increasingly adopting digital marketing and growing more aware of data privacy issues. The findings highlight the importance of trust-building mechanisms, privacy-sensitive personalization strategies, and organizational transparency. Companies must balance their personalization efforts with responsible data practices in order to improve customer acceptance and sustain long-term engagement.
References
Abdel Monem, H. (2021). The effectiveness of advertising personalization. Journal of Design Sciences and Applied Arts, 2, 114-121.
Beke, F. T., Eggers, F., Verhoef, P. C., & Wieringa, J. E. (2022). Consumers' privacy calculus: The PRICAL index development and validation. International Journal of Research in Marketing, 39, 20-41.
Brinson, N., Amini, B., Lemon, L., & Bawole, C. (2024). Electronic performance monitoring: The role of reactance, trust, and privacy concerns in predicting job satisfaction in the post-pandemic workplace. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 18.
Buchanan, T., Joinson, A., Paine Schofield, C., & Reips, U.-D. (2007). Development of measures of online privacy concern and protection for use on the Internet. Journal of the American Society for Information Science and Technology, 58, 157-165.
Chen, M., & Chen, M. (2025). Privacy relevance and disclosure intention in mobile apps: The mediating and moderating roles of privacy calculus and temporal distance. Behavioral Sciences, 15, 324. https://doi.org/10.3390/bs15030324
Chen, S., Wu, Y., Deng, F., & Zhi, K. (2023). How does ad relevance affect consumers' attitudes toward personalized advertisements and social media platforms? Journal of Retailing and Consumer Services, 73, 103336. https://doi.org/10.1016/j.jretconser.2023.103336
Darmody, A., & Zwick, D. (2020). Manipulate to empower: Hyper-relevance and the contradictions of marketing in the age of surveillance capitalism. Big Data & Society, 7. https://doi.org/10.1177/2053951720904112
Dienlin, T. (2023). Privacy calculus: Theory, studies, and new perspectives. In The Routledge Handbook of Privacy and Social Media (pp. 1-15). Routledge. https://doi.org/10.4324/9781003244677-8
Eg, R., Demirkol Tønnesen, Ö., & Tennfjord, M. K. (2023). A scoping review of personalized user experiences on social media. Computers in Human Behavior Reports, 9, 100253. https://doi.org/10.1016/j.chbr.2022.100253
Fernandes, T., & Pereira, N. (2021). Revisiting the privacy calculus: Why are consumers willing to disclose personal data online? Telematics and Informatics, 65, 101717. https://doi.org/10.1016/j.tele.2021.101717
Fu, J., Zhang, J., & Li, X. (2023). How do risks and benefits affect user privacy decisions? Frontiers in Psychology, 14, 1052782. https://doi.org/10.3389/fpsyg.2023.1052782
Groß, T. (2023). Toward valid and reliable privacy concern scales: The example of IUIPC-8. In N. Gerber, A. Stöver, & K. Marky (Eds.), Human Factors in Privacy Research (pp. 65-88). Springer. https://doi.org/10.1007/978-3-031-28643-8_4
IBM Corporation. (2021). IBM SPSS Statistics for Windows, Version 28.0. IBM Corp.
Irgui, A., & Qmichchou, M. (2023). Contextual marketing and information privacy concerns in m-commerce and their impact on consumer loyalty. Arab Gulf Journal of Scientific Research. https://doi.org/10.1108/AGJSR-09-2022-0198
Isaeva, N., Gruenewald, K., & Saunders, M. (2020). Trust theory and customer services research: Theoretical review and synthesis. Service Industries Journal, 40, 1-33. https://doi.org/10.1080/02642069.2020.1779225
Kawaf, F., Montgomery, A., & Thuemmler, M. (2024). Unpacking the privacy-personalisation paradox in GDPR-regulated environments. Information Technology & People, 37, 1674-1695. https://doi.org/10.1108/ITP-04-2022-0275
Lina, L. F., & Setiyanto, A. (2021). Privacy concerns in personalized advertising effectiveness on social media. Sriwijaya International Journal of Dynamic Economics and Business, 147-156. https://doi.org/10.29259/sijdeb.v1i2.147-156
Majumdar, A., & Bose, I. (2016). Privacy calculus theory and its applicability for emerging technologies. In E-Life: Web-Enabled Convergence of Commerce, Work, and Social Life (pp. 191-195). Springer. https://doi.org/10.1007/978-3-319-45408-5_20
Mini, T. (2017). Privacy calculus [Seminar thesis, University of Passau]. ResearchGate. https://www.researchgate.net/publication/322900839_Privacy_Calculus
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). McGraw-Hill.
Parra-Arnau, J., Rebollo-Monedero, D., & Forné, J. (2014). Optimal forgery and suppression of ratings for privacy enhancement in recommendation systems. Entropy, 16, 1586-1631. https://doi.org/10.3390/e16031586
Pizzey, A., Carabine, G., John, A., & Beverley, B. (2025). Consumer behavior towards personalized marketing amidst privacy concerns [Preprint]. ResearchGate. https://www.researchgate.net/publication/388491708
Plangger, K., & Montecchi, M. (2020). Thinking beyond privacy calculus: Investigating reactions to customer surveillance. Journal of Interactive Marketing, 50, 32-44. https://doi.org/10.1016/j.intmar.2019.10.004
Saura, J. R. (2024). Algorithms in Digital Marketing: Does Smart Personalization Promote a Privacy Paradox? SAGE. https://doi.org/10.1177/23197145241276898
Schöni, L., Kubicek, K., & Zimmermann, V. (2024). Block cookies, not websites: Analysing mental models and usability of privacy tools. Proceedings on Privacy Enhancing Technologies, 2024, 192-216. https://doi.org/10.56553/popets-2024-0012
Shanahan, T., Tran, T. P., & Taylor, E. C. (2019). Getting to know you: Social media personalization as a means of enhancing brand loyalty. Journal of Retailing and Consumer Services, 47, 57-65. https://doi.org/10.1016/j.jretconser.2018.10.007
Sipos, D. (2024). Harnessing artificial intelligence for hyper-personalization in digital marketing. Technium Business and Management, 9, 47-55. https://doi.org/10.47577/business.v9i.11724
Solberg, E., Kaarstad, M., Eitrheim, M. H. R., Bisio, R., Reegård, K., & Bloch, M. (2022). A conceptual model of trust, perceived risk, and reliance on AI decision aids. Group & Organization Management, 47, 187-222. https://doi.org/10.1177/10596011221081238
Stahl, B. C., Schroeder, D., & Rodrigues, R. (2023). Surveillance capitalism. In Ethics of Artificial Intelligence. Springer. https://doi.org/10.1007/978-3-031-17040-9_4
Swani, K., Milne, G. R., & Slepchuk, A. N. (2021). Revisiting trust and privacy concern in marketing information management. Journal of Interactive Marketing, 56, 137-158. https://doi.org/10.1016/j.intmar.2021.03.001
Teraiya, V., & Krishnamurthy, R. (2025, February 4). Balancing personalized marketing and data privacy in the era of AI. California Management Review. https://cmr.berkeley.edu/2025/02/balancing-personalized-marketing-and-data-privacy-in-the-era-of-ai/
Tomar, G., & Pandey, L. (2024). Influence of personalized advertising on consumer engagement and conversion rates [Preprint]. ResearchGate. https://www.researchgate.net/publication/382305674
Vecchietti, G., Liyanaarachchi, G., & Viglia, G. (2025). Managing deepfakes with artificial intelligence. Journal of Business Research, 186, 115010. https://doi.org/10.1016/j.jbusres.2024.115010
Yadav, T., Kala, K., Kolachina, R., Kanneganti, M., & Pasupuleti, S. (2024). Data privacy concerns and their impact on consumer trust. International Journal of Scientific Research in Engineering and Management, 8, 1-7. https://doi.org/10.55041/IJSREM38555
Zhu, H., Ou, C., Heuvel, W. J. A. M., & Liu, H. (2017). Privacy calculus and its utility for personalization services in e-commerce. Information & Management, 54. https://doi.org/10.1016/j.im.2016.10.001
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