Personalization - Privacy Paradoxes: The Personalized Advertising Privacy Calculus and Behavioral Intentions of Generation Z Consumers in Online Stores

Document Type : Original Article

Authors

1 MSc in Business Administration, Faculty of Economics and Management, Urmia University, Iran

2 Associate Professor of Business Administration, Faculty of Economics and Management, Urmia University, Iran

3 Assistant Professor of Business Administration, Faculty of Economics and Management, Urmia University, Iran

Abstract

Purpose: This study investigates the personalization–privacy paradox and its influence on the behavioral intentions of Generation Z consumers. Specifically, it examines how personalized marketing affects consumers’ perceptions of the benefits and threats of sharing personal information, as well as the role of privacy concerns in shaping their behavioral responses.
Method: The study population consisted of students at Urmia University aged between 18 and 27 years. Based on Cochran’s formula, a sample of 384 participants was selected. Data were collected using a standardized questionnaire. The research model and hypotheses were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach with SPSS and SmartPLS software.
Findings: The results revealed that personalization has a significant positive effect on consumers’ behavioral intentions, including purchase intention and viral behavioral intention. Personalization was also found to significantly influence consumers’ perceptions of both the benefits and threats associated with sharing personal information. Perceived benefits positively affected the perceived value of information disclosure, whereas perceived threats had a negative effect on this value. Furthermore, the perceived value of information disclosure significantly enhanced consumers’ behavioral intentions. The findings also indicated that privacy concerns negatively moderate the relationship between the perceived value of information disclosure and behavioral intentions.
Conclusion: The findings suggest that while personalized marketing can effectively enhance Generation Z consumers’ behavioral intentions, its success depends on managing the privacy concerns associated with personal data disclosure. Therefore, marketers should develop personalization strategies that maximize perceived benefits while minimizing privacy-related concerns to foster positive consumer responses.

Keywords

References

Ackermann, K. A., Burkhalter, L., Mildenberger, T., Frey, M., & Bearth, A. (2022). Willingness to share data: Contextual determinants of consumers' decisions to share private data with companies. Journal of Consumer Behaviour, 21(2), 375-386. https://doi.org/10.1002/cb.2012
Alhabash, S., McAlister, A. R., Hagerstrom, A., Quilliam, E. T., Rifon, N. J., & Richards, J. I. (2013). Between likes and shares: Effects of emotional appeal and virality on the persuasiveness of anticyberbullying messages on Facebook. Cyberpsychology, Behavior, and Social Networking, 16(3), 175-182. https://doi.org/10.1089/cyber.2012.0265
Alhabash, S., Almutairi, N., Lou, C., & Kim, W. (2019). Pathways to virality: Psychophysiological responses preceding likes, shares, comments, and status updates on Facebook. Media Psychology, 22(2), 196-216. https://doi.org/10.1080/15213269.2017.1416296
Ameen, N., Hosany, S., & Paul, J. (2022). The personalisation-privacy paradox: Consumer interaction with smart technologies and shopping mall loyalty. Computers in Human Behavior, 126, 106976. https://doi.org/10.1016/j.chb.2021.106976
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, 13-28. https://doi.org/10.2307/25148715
Baek, T. H., & Morimoto, M. (2012). Stay away from me. Journal of advertising,41(1),59-76.https://doi.org/10.2753/JOA0091-3367410105  
Balapour, A., Nikkhah, H. R., & Sabherwal, R. (2020). Mobile application security: Role of perceived privacy as the predictor of security perceptions. International Journal of Information Management, 52, 102063. https://doi.org/10.1016/j.ijinfomgt.2019.102063
Barth, S., & De Jong, M. D. (2017). The privacy paradox–Investigating discrepancies between expressed privacy concerns and actual online behavior–A systematic literature review. Telematics and informatics, 34(7), 1038-1058. https://doi.org/10.1016/j.tele.2017.04.013
Boehmer, J., LaRose, R., Rifon, N., Alhabash, S., & Cotten, S. (2015). Determinants of online safety behaviour: Towards an intervention strategy for college students. Behaviour & Information Technology, 34(10), 1022-1035. https://doi.org/10.1080/0144929X.2015.1028448
Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online behavioral advertising: A literature review and research agenda. Journal of advertising, 46(3), 363-376.
https://doi.org/10.1080/00913367.2017.1339368
Boniface, B., Gyau, A., & Stringer, R. (2012). Linking price satisfaction and business performance in Malaysia's dairy industry. Asia Pacific Journal of Marketing and Logistics, 24(2), 288-304. https://doi.org/10.1108/13555851211218066
Brinson, N. H., Eastin, M. S., & Cicchirillo, V. J. (2018). Reactance to personalization: Understanding the drivers behind the growth of ad blocking. Journal of Interactive Advertising, 18(2), 136-147. https://doi.org/10.1080/15252019.2018.1491350
Burgoon, J. K., Parrott, R., Le Poire, B. A., Kelley, D. L., Walther, J. B., & Perry, D. (1989). Maintaining and restoring privacy through communication in different types of relationships. Journal of social and personalrelationships,6(2),131-158.    https://doi.org/10.1177/026540758900600201?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle
Chandra, S., Verma, S., Lim, W. M., Kumar, S., & Donthu, N. (2022). Personalization in personalized marketing: Trends and ways forward. Psychology&Marketing,39(8),1529-1562. https://psycnet.apa.org/doi/10.1002/mar.21670
Chellappa, R. K., & Sin, R. G. (2005). Personalization versus privacy: An empirical examination of the online consumer’s dilemma. Information technologyandmanagement,6(2),181-202. https://doi.org/10.1007/s10799-005-5879-y
Childers, C., & Boatwright, B. (2021). Do digital natives recognize digital influence? Generational differences and understanding of social media influencers. Journal of Current Issues & Research in Advertising, 42(4), 425-442. https://doi.org/10.1080/10641734.2020.1830893
Chu, S. C., Deng, T., & Mundel, J. (2024). The impact of personalization on viral behavior intentions on TikTok: The role of perceived creativity, authenticity, and need for uniqueness. Journal of Marketing Communications,30(1),1-20. https://doi.org/10.1080/13527266.2022.2098364
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
Davari, A., & Rezazadeh, A. (2013). Structural equation modeling with PLS. Tehran: Jahad University, 215(2), 224.
Davenport, T. H., Mule, L. D., & Lucker, J. (2011). Know what your customers want before they do. harvard Business review, 89(12), 84-92.
Dawn, S. K. (2014). Personalised Marketing: concepts and framework. Productivity, 54(4), 370.
Dimock, M. (2019). Defining generations: Where Millennials end and Generation Z begins. Pew Research Center, 17(1), 1-7. https://www.pewresearch.org/fact-tank/2019/01/17/where-millennials-end-and-generation-z-begins
Esmaeilzadeh, P. (2020). The effect of the privacy policy of Health Information Exchange (HIE) on patients’ information disclosure intention.Computers&Security,95,101819. https://doi.org/10.1016/j.cose.2020.101819
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketingresearch,18(1),39-50. https://doi.org/10.1177/002224378101800104
Franke, N., Keinz, P., & Steger, C. J. (2009). Testing the value of customization: when do customers really prefer products tailored to their preferences?. Journal of marketing, 73(5), 103-121. https://doi.org/10.1509/jmkg.73.5.103
Gana, M. A., & Koce, H. D. (2016). Mobile marketing: The influence of trust and privacy concerns on consumers’ purchase intention. International Journal of Marketing Studies, 8(2), 121. https://doi.org/10.5539/ijms.v8n2p121?urlappend=%3Futm_source%3Dresearchgate.net%26utm_medium%3Darticle 
Gefen, D., & Straub, D. W. (2004). Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services.Omega,32(6),407-424. https://doi.org/10.1016/j.omega.2004.01.006
Goldfarb, A., & Tucker, C. E. (2011). Privacy regulation and online advertising. Management science, 57(1), 57-71.
Hayes, J. L., Brinson, N. H., Bott, G. J., & Moeller, C. M. (2021). The influence of consumer–brand relationship on the personalized advertising privacy calculus in social media. Journal of Interactive Marketing, 55(1), 16-30. https://doi.org/10.1016/j.intmar.2021.01.001
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2009)0000020014
Ho, S. Y. (2012). The effects of location personalization on individuals' intention to use mobile services. Decision Support Systems, 53(4), 802-812. https://doi.org/10.1016/j.dss.2012.05.012
Jahari, S. A., Hass, A., Hass, D., & Joseph, M. (2022). Navigating privacy concerns through societal benefits: A case of digital contact tracing applications. Journal of Consumer Behaviour, 21(3), 625-638. https://doi.org/10.1002/cb.2029
Jang, C., & Sung, W. (2021). Beyond the privacy paradox: the moderating effect of online privacy concerns on online service use behavior. Telematics and Informatics, 65, 101715. https://doi.org/10.1016/j.tele.2021.101715
Johnson, O., & Chattaraman, V. (2019). Conceptualization and measurement of millennial's social signaling and self‐signaling for socially responsible consumption. Journal of Consumer Behaviour, 18(1), 32-42. https://doi.org/10.1002/cb.1742
Jung, A. R. (2017). The influence of perceived ad relevance on social media advertising: An empirical examination of a mediating role of privacy concern. Computers in human behavior, 70, 303-309. https://doi.org/10.1016/j.chb.2017.01.008
Kamalul Ariffin, S., Mohan, T., & Goh, Y. N. (2018). Influence of consumers’ perceived risk on consumers’ online purchase intention. Journal of research in Interactive Marketing, 12(3), 309-327.
Kang, J. W., & Namkung, Y. (2019). The role of personalization on continuance intention in food service mobile apps: A privacy calculus perspective. International Journal of Contemporary Hospitality Management, 31(2), 734-752.
Keith, M. J., Babb, J., Furner, C., Abdullat, A., & Lowry, P. B. (2016). Limited information and quick decisions: consumer privacy calculus for mobile applications. AIS Transactions on Human-Computer Interaction (THCI), 8(3), 88-130.
Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision support systems, 43(1), 111-126. https://doi.org/10.1016/j.dss.2005.05.009
Kim, Y. J., & Han, J. (2014). Why smartphone advertising attracts customers: A model of Web advertising, flow, and personalization. Computers in human behavior, 33, 256-269.
Kotler, P. dan Keller, K. L. (2016) Marketing Management. 15th ed. Boston: Pearson.
Kulkarni, K. K., Kalro, A. D., & Sharma, D. (2020). The interaction effect of ad appeal and need for cognition on consumers' intentions to share viral advertisements. Journal of Consumer Behaviour, 19(4), 327-338. https://doi.org/10.1002/cb.1809
Krishnaraju, V., & Mathew, S. K. (2013). Web personalization research: an information systems perspective. Journal of Systems and Information Technology, 15(3), 254-268. https://doi.org/10.1108/JSIT-11-2012-0065
Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of social Issues, 33(3), 22-42. https://doi.org/10.1111/j.1540-4560.1977.tb01880.x
Lee, J. M., & Rha, J. Y. (2016). Personalization–privacy paradox and consumer conflict with the use of location-based mobile commerce. Computers in Human Behavior, 63, 453-462.
Lee, Y., & Larsen, K. R. (2009). Threat or coping appraisal: determinants of SMB executives’ decision to adopt anti-malware software. European Journal of Information Systems, 18(2), 177-187.
Li, C. (2016). When does web-based personalization really work? The distinction between actual personalization and perceived personalization. Computers in human behavior, 54, 25-33.
Lim, J. S., & Zhang, J. (2022). Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency. Technology in Society, 69, 101965. https://doi.org/10.1016/j.techsoc.2022.101965
McKee, K. M., Dahl, A. J., & Peltier, J. W. (2024). Gen Z's personalization paradoxes: A privacy calculus examination of digital personalization and brand behaviors. Journal of Consumer Behaviour, 23(2), 405-422. https://doi.org/10.1002/cb.2199
Milne, G. R., & Gordon, M. E. (1993). Direct mail privacy-efficiency trade-offs within an implied social contract framework. Journal of Public Policy & Marketing, 12(2), 206-215. https://doi.org/10.1177/074391569101200206
 
Milne, G. R., Rohm, A. J., & Bahl, S. (2004). Consumers’ protection of online privacy and identity. Journal of Consumer Affairs, 38(2), 217-232. https://doi.org/10.1111/j.1745-6606.2004.tb00865.x
Norberg, P. A., Horne, D. R., & Horne, D. A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of consumer affairs, 41(1), 100-126. https://doi.org/10.1111/j.1745-6606.2006.00070.x
Nyheim, P., Xu, S., Zhang, L., & Mattila, A. S. (2015). Predictors of avoidance towards personalization of restaurant smartphone advertising: A study from the Millennials’ perspective. Journal of Hospitality and Tourism Technology, 6(2), 145-159. https://doi.org/10.1108/JHTT-07-2014-0026
Özpolat, K., & Jank, W. (2015). Getting the most out of third party trust seals: An empirical analysis. Decision Support Systems, 73, 47-56. http://dx.doi.org/10.1016/j.dss.2015.02.016
Pal, D., Arpnikanondt, C., & Razzaque, M. A. Personal information disclosure via voice assistants: the personalization–privacy paradox. SN Comput. Sci. 1 (5), 1–17 (2020). https://doi.org/10.1007/s42979-020-00287-9
Pavlou, P. A., & Stewart, D. W. (2000). Measuring the effects and effectiveness of interactive advertising: A research agenda. Journal of InteractiveAdvertising,1(1),61-77. https://doi.org/10.1080/15252019.2000.10722044
Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6). https://doi.org/10.1016/j.heliyon.2020.e04284
Pfiffelmann, J., Dens, N., & Soulez, S. (2020). Personalized advertisements with integration of names and photographs: An eye-tracking experiment. Journal of Business Research, 111, 196-207. https://doi.org/10.1016/j.jbusres.2019.08.017
Piccoli, G., Lui, T. W., & Grün, B. (2017). The impact of IT-enabled customer service systems on service personalization, customer service perceptions, and hotel performance. Tourism Management, 59, 349-362. https://doi.org/10.1016/j.tourman.2016.08.015
Polk, J., Tassin, C., & McNellis, J. (2020). Magic Quadrant for Personalization Engines. Gartner Report Reprint, 13.
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
Reena, M., & Udita, K. (2020). Impact of Personalized Social Media Advertisements on Consumer Purchase Intention. Annals of the University Dunarea de Jos of Galati: Fascicle: I, Economics & Applied Informatics, 26(2). https://doi.org/10.35219/eai15840409101
Scarpi, D., Pizzi, G., & Matta, S. (2022). Digital technologies and privacy: State of the art and research directions. Psychology & Marketing, 39(9), 1687-1697. https://doi.org/10.1002/mar.21692
Shanahan, T., Tran, T. P., & Taylor, E. C. (2019). Getting to know you: Social media personalization as a means of enhancing brand loyalty and perceived quality. Journal of Retailing and Consumer Services, 47, 57-65. https://doi.org/10.1016/j.jretconser.2018.10.007
Sharma, S., & Crossler, R. E. (2014). Disclosing too much? Situational factors affecting information disclosure in social commerce environment. Electronic Commerce Research and Applications, 13(5), 305-319. https://doi.org/10.1016/j.elerap.2014.06.007
Smith, A. (2014). Half of online Americans don’t know what a privacy policy is.
Sultan, F., Rohm, A. J., & Gao, T. (2009). Factors influencing consumer acceptance of mobile marketing: a two-country study of youth markets. Journal of interactive marketing, 23(4), 308-320.  
Stewart, K. A., & Segars, A. H. (2002). An empirical examination of the concern for information privacy instrument. Information systems research, 13(1), 36-49. https://doi.org/10.1287/isre.13.1.36.97
Tabachnik, B. G., & Fidell, L. S. (2007). Using multivariate statistics Fifth Edition.
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
Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. Handbook of partial least squares: Concepts, methods and applications, 47-82.
Wessel, M., & Thies, F. (2015). The effects of personalization on purchase intentions for online news: An experimental study of different personalization increments.
Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly, 177-195. https://doi.org/10.2307/20650284
White, T. B., Zahay, D. L., Thorbjørnsen, H., & Shavitt, S. (2008). Getting too personal: Reactance to highly personalized email solicitations. Marketing Letters, 19, 39-50. https://doi.org/10.1007/s11002-007-9027-9
Wynne, C. W. (1998). Issues and opinion on structural equation modelling. Management Information Systems quarterly, 22(1), 1-8. https://doi.org/10.2307/249674  https://www.researchgate.net/publication/220260360_Issues_and_Opinion_on_Structural_Equation_Modeling
Xu, H., Luo, X. R., 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
Zhao, Z., & Renard, D. (2018). Viral promotional advergames: How intrinsic playfulness and the extrinsic value of prizes elicit behavioral responses. Journal of Interactive Marketing, 41(1), 94-103. https://doi.org/10.1016/j.intmar.2017.09.004
Zhou, T. (2011). The impact of privacy concern on user adoption of location‐based services. Industrial Management & Data Systems, 111(2), 212-226. https://doi.org/10.1108/02635571111115146

Files

History

  • Receive Date: 05 May 2025
  • Revise Date: 13 December 2025
  • Accept Date: 23 February 2026

Share

How to cite

Statistics