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Journals / Doğuş Üniversitesi Dergisi / 2025 / Cilt: 26 Sayı: 2

THE INFLUENCE OF PERSONALIZED MARKETING AND DATA PRIVACY CONCERNS ON CONSUMER BEHAVIOR: THE MEDIATING ROLE OF PERCEIVED VALUE

KİŞİSELLEŞTİRİLMİŞ PAZARLAMA VE VERİ GİZLİLİĞİ KAYGILARININ TÜKETİCİ DAVRANIŞI ÜZERİNDEKİ ETKİSİ: ALGILANAN DEĞERİN ARACILIK ROLÜ

Altuğ Ocak(BEYKENT ÜNİVERSİTESİ)0000-0002-8018-4158
Pages
315–337
Publication Date
2025-07-22
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results suggest that marketers need to strike a better balance between personalization and sound data privacy practices to increase PV and drive consumer behavior.

📌 3 Key Takeaways

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Objective & Hypothesis:This study examines the complex interplay between personalized marketing (PM), data privacy concerns (DPC), perceived value (PV) and purchase intention (PI) in a sample of online consumers.
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Key Findings & Data:To achieve this, all the data was collected through a constructed questionnaire involving 386 online consumers who have encountered PM.
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Conclusion & Impact:These results suggest that marketers need to strike a better balance between personalization and sound data privacy practices to increase PV and drive consumer behavior.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Kişiselleştirilmiş pazarlama · veri gizliliği endişeleri · algılanan değer

Abstract

This study examines the complex interplay between personalized marketing (PM), data privacy concerns (DPC), perceived value (PV) and purchase intention (PI) in a sample of online consumers. The main aim of the research is to investigate the dual influence of PM and DPC on PV and PI, focusing on the mediating role of PV. To achieve this, all the data was collected through a constructed questionnaire involving 386 online consumers who have encountered PM. The questionnaire used established scales to measure PM, DPC, PV and PI. Analytical tools such as Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used to validate the measurement model and test the hypotheses. The outcomes show PM positively influences both PV and PI, while DPC negatively influences PV and has a weaker effect on PI. PV was found to mediate the link between PM and PI and the relationship between DPC and PI. These results suggest that marketers need to strike a better balance between personalization and sound data privacy practices to increase PV and drive consumer behavior.

Özet

Bu çalışma, kişiselleştirilmiş pazarlama, veri gizliliği endişeleri, algılanan değer ve satın alma niyeti arasındaki karmaşık ilişkiyi çevrimiçi tüketiciler bağlamında incelemektedir. Çalışmanın temel amacı, algılanan değerin aracılık rolüne odaklanarak, kişiselleştirilmiş pazarlama ve veri gizliliği endişelerinin algılanan değer ve satın alma niyeti üzerindeki ikili etkisini araştırmaktır. Bunu başarmak için veriler, daha önce kişiselleştirilmiş pazarlama deneyimi olan 386 çevrimiçi tüketiciyi kapsayan yapılandırılm-ış bir anket aracılığıyla toplanmıştır. Ankette kişiselleştirilmiş pazarlama, veri gizliliği endişeleri, algılanan değer ve satın alma niyetini ölçmek için mevcut ölçekler kullanılmıştır. Ölçüm modelini doğrulamak ve hipotezleri test etmek için Doğrulayıcı Faktör Analizi (DFA) ve Yapısal Eşitlik Modellemesi (YEM) gibi analitik teknikler uygulanmıştır. Bulgular, kişiselleştirilmiş pazarlamanın hem algılanan değeri hem de satın alma niyetini olumlu yönde desteklediğini, veri gizliliği endişelerinin ise algılanan değeri olumsuz yönde etkilediğini ve satın alma niyeti üzerinde daha zayıf bir doğrudan etkisinin olduğunu ortaya koymaktadır. Algılanan değer, kişiselleştirilmiş pazarlama ile satın alma niyeti arasındaki ve veri gizliliği endişeleri ile satın alma niyeti arasındaki etkileşime aracılık etmektedir. Bu bulgular, pazarlamacıların algılanan değeri artırmak ve tüketici davranışını yönlendirmek için kişiselleştirme ile güçlü veri gizliliği uygulamaları arasında bir denge kurması gerektiğinin altını çizmektedir.

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Cite This Article

Ocak, A. (2025). THE INFLUENCE OF PERSONALIZED MARKETING AND DATA PRIVACY CONCERNS ON CONSUMER BEHAVIOR: THE MEDIATING ROLE OF PERCEIVED VALUE. Doğuş Üniversitesi Dergisi, Cilt: 26 Sayı: 2, 315–337. https://doi.org/10.31671/doujournal.1617271

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References (52)

  1. Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). 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
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  3. Alkadrie, S. A. (2024). Exploring the Impact of Digital Marketing Strategies on Consumer Purchase Behavior in the E-commerce Sector. The Journal of Academic Science, 1(4), 273-282. https://doi.org/10.59613/5p6gp784
  4. Alkadrie, S. A. (2024). Exploring the Impact of Digital Marketing Strategies on Consumer Purchase Behavior in the E-commerce Sector. The Journal of Academic Science, 1(4), 273-282.
  5. Arora, N., Drèze, X., Ghose, A., Hess, J. D., Iyengar, R., Jing, B., & Zhang, Y. (2008). 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
  6. Baek, T. H., & Morimoto, M. (2012). Stay away from me: Examining the determinants of consumer avoidance of personalized advertising. Journal of Advertising, 41(1), 59–76. https://doi.org/10.2753/JOA0091-3367410105
  7. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
  8. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588
  9. Bhattacharya, S., Sharma, R. P., & Gupta, A. (2023). Does e-retailer's country of origin influence consumer privacy, trust and purchase intention? Journal of Consumer Marketing, 40(2), 248–259. http://dx.doi.org/10.1108/JCM-04-2021-4611
  10. Bleier, A., & Eisenbeiss, M. (2015). Personalized online advertising effectiveness: The interplay of what, when and where. Marketing Science, 34(5), 669–688. https://doi.org/10.1287/mksc.2015.0930
  11. Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of Retailing, 91(3), 390–409. https://doi.org/10.1016/j.jretai.2015.04.001
  12. Bryman, A., & Bell, E. (2015). Business research methods (4th ed.). Oxford University Press.
  13. Chellappa, R. K., & Shivendu, S. (2005). Managing piracy: Pricing and sampling strategies for digital experience goods in vertically segmented markets. Information Systems Research, 16(4), 400–417. https://doi.org/10.1287/isre.1050.0069
  14. Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness and impersonal trust: An empirical investigation. Organization Science, 10(1), 104–115. https://doi.org/10.1287/orsc.10.1.104
  15. Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080
  16. Ding, Y., & Keh, H. T. (2016). A reexamination of service standardization versus customization from the consumer perspective. Journal of Services Marketing, 30(1), 16–28. https://doi.org/10.1108/JSM-02-2015-0088
  17. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand and store information on buyers' product evaluations. Journal of Marketing Research, 28(3), 307–319. https://doi.org/10.1177/002224379102800305
  18. 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
  19. Fan, W., Shao, B., & Dong, X. (2022). Effect of e-service quality on customer engagement behavior in community e-commerce. Frontiers in Psychology, 13, 965998. https://doi.org/10.3389/fpsyg.2022.965998
  20. 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
  21. Grewal, D., Monroe, K. B., & Krishnan, R. (1998). The effects of price-comparison advertising on buyers' perceptions of acquisition value, transaction value and behavioral intentions. Journal of Marketing, 62(2), 46–59. https://doi.org/10.1177/002224299806200204
  22. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson
  23. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares SEM (PLS-SEM) (2nd ed.). SAGE Publications.
  24. Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
  25. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based SEM. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  26. Hoang, D. P. (2019). The central role of customer dialogue and trust in gaining bank loyalty: An extended SWICS model. International Journal of Bank Marketing, 37(3), 711–729. https://doi.org/10.1108/IJBM-03-2018-0069
  27. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
  28. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk and their antecedents. Decision Support Systems, 44(2), 544–564. https://doi.org/10.1016/j.dss.2007.07.001
  29. Kim, J., Steinhoff, L., & Palmatier, R. W. (2021). An emerging theory of loyalty program dynamics. Journal of the Academy of Marketing Science, 49(1), 71–95. https://doi.org/10.1007/s11747-020-00719-1
  30. Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Publications.
  31. Liao, C., Liu, C. C., & Chen, K. (2011). Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: An integrated model. Electronic Commerce Research and Applications, 10(6), 702–715. https://doi.org/10.1016/j.elerap.2011.07.003
  32. Liao, C., Liu, C. C., & Chen, K. (2023). Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: An integrated model. Electronic Commerce Research and Applications, 10(6), 702–715. https://doi.org/10.1016/j.elerap.2023.07.003
  33. Lin, M. Y. C., Do, B. R., Nguyen, T. T., & Cheng, J. M. S. (2022). Effects of personal innovativeness and perceived value of disclosure on data privacy concerns in proximity marketing: Self-control as a moderator. Journal of Research in Interactive Marketing, 16(2), 310–327. https://doi.org/10.1108/JRIM-04-2021-0112
  34. 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
  35. Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45, 135–155. https://doi.org/10.1007/s11747-016-0495-4
  36. Muhammad, S. S., Dey, B. L., & Weerakkody, V. (2018). Analysis of factors that influence customers' willingness to leave big data digital footprints on social media: A systematic review of literature. Information Systems Frontiers, 20(3), 559–576. https://doi.org/10.1007/s10796-017-9802-y
  37. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
  38. Pappas, N. (2016). Marketing strategies, perceived risks and consumer trust in online buying behaviour. Journal of Retailing and Consumer Services, 29, 92–103. https://doi.org/10.1016/j.jretconser.2015.11.007
  39. Ponte, E. B., Carvajal-Trujillo, E., & Escobar-Rodríguez, T. (2015). Influence of trust and perceived value on the intention to purchase travel online: Integrating the effects of assurance on trust antecedents. Tourism Management, 47, 286–302. https://doi.org/10.1016/j.tourman.2014.10.009
  40. Ponte, S., Mondliwa, P., & Roberts, S. (2021). Competition and power in global value chains. Competition and Change, 25(3–4), 328–349. https://doi.org/10.1177/1024529420975154
  41. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
  42. Quach, S., Thaichon, P., Martin, K. D., Weaven, S., Palmatier, R. W., & Patterson, P. (2022). Digital technologies: Tensions in privacy and data. Journal of the Academy of Marketing Science, 50, 1299–1323. https://doi.org/10.1007/s11747-022-00845-y
  43. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25(2), 173–180. https://doi.org/10.1207/s15327906mbr2502_4
  44. Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203–220. https://doi.org/10.1016/S0022-4359(01)00041-0
  45. Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS Quarterly, 30(4), 865–890. https://doi.org/10.2307/25148757
  46. Tirtayani, I. G. A., Wardana, I., Setiawan, P. Y., Widagda, I. G. N. J. A., Kustina, K. T., & Arlita, I. G. A. D. (2024). The privacy paradox on social media: Balancing data privacy concerns, perceived value and purchase intentions with habit moderation. In B. Alareeni & A. Hamdan (Eds.), Technology and business model innovation: Challenges and opportunities (Vol. 923, pp. 429–441). Springer. https://doi.org/10.1007/978-3-031-55911-2_34
  47. Tucker, C. E. (2014). Social networks, personalized advertising and privacy controls. Journal of Marketing Research, 51(5), 546–562. https://doi.org/10.1509/jmr.10.0355
  48. Wright, K. B. (2005). Researching internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages and web survey services. Journal of Computer-Mediated Communication, 10(3), Article JCMC1034. https://doi.org/10.1111/j.1083-6101.2005.tb00259.x
  49. Wu, Y., & Huang, H. (2023). Influence of perceived value on consumers’ continuous purchase intention in live-streaming e-commerce—Mediated by consumer trust. Sustainability, 15(5), 1–19. https://doi.org/10.3390/su15054432
  50. Xu, H., Luo, X., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51(1), 42–52. https://doi.org/10.1016/j.dss.2010.11.017
  51. Zeithaml, V. A. (1988). Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1016/S0022-4359(01)00041-0
  52. Zhang, J., Agarwal, R., & Lucas Jr., H. C. (2017). The value of IT-enabled retailer learning: Personalized marketing and firm performance. MIS Quarterly, 41(2), 389–412. https://doi.org/10.2307/41409964