Protective behavior against personalized ads: Motivation to turn personalization off

Special Issue: Digital Advertising and Consumer Empowerment


Data collection and processing for personalized advertising has become a common practice in the industry. For this reason, regulators have been aiming to empower consumers to exercise more control over their data. Companies that collect and process data for personalized advertising are required to be transparent and among others, provide consumers with technical knowledge about the personalization process. At the same time, they have started offering settings to withdraw consent for processing data for personalization purposes by opting out from personalized advertising. However, such opt-out functions remain mostly unused. Thus, this study investigates first, if technical knowledge about personalization empowers consumers to use such opt-out functions and second, what mechanisms can explain the empowering impact of knowledge. Drawing on Rogers’ (1975) protection motivation theory (PMT) and applying an innovative combination of traditional (online experiment, N = 425, Mage = 48) and computational (online behavior tracking, N = 80, Mage = 48) research methods, the study shows that technical knowledge has no empowering effect on consumers by indirectly lowering opt-out motivation and behavior. The results also demonstrate that perceived severity and response efficacy increase motivation to opt-out, while positive attitude towards personalization and perceived self-efficacy lower it. Being one of the first studies to apply PMT to personalization context and computational methods to measure opt-out, it offers several important societal and theoretical implications regarding consumer empowerment and personalized advertising online.

Personalized digital advertising, consumer empowerment, consumer knowledge, protection motivation theory, computational research

Acquisti, A., & Grossklags, J. (2005). Privacy and rationality in individual decision making. IEEE Security and Privacy Magazine, 3(1), 26-33.

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, 13-28.

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.

Bang, H., & Wojdynski, B. W. (2016). Tracking users' visual attention and responses to personalized advertising based on task cognitive demand. Computers in Human Behavior, 55, 867-876.

Baruh, L., & Popescu, M. (2017). Big data analytics and the limits of privacy self-management. New Media & Society, 19, 579–596.

Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44, 175-184.

Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of Retailing, 91, 390-409.

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, 1022-1035.

Boerman, S., Kruikemeier, S., & Zuiderveen Borgesius, F. (2017). Online behavioral advertising: A literature review and research agenda. Journal of Advertising, 46, 363-376.

Boerman, S., Kruikemeier, S., & Zuiderveen Borgesius, F. (2018). Exploring motivations for online privacy protection behavior: Insights from panel data. Communication Research. Advanced online publication.

Bol, N., Dienlin, T., Kruikemeier, S., Sax, M., Boerman, S. C., Strycharz, J., . . .de Vreese, C. H. (2018). Understanding the effects of personalization as a privacy calculus: Analyzing self-disclosure across health, news, and commerce contexts. Journal of Computer-Mediated Communication, 23, 370-388.

Brandimarte, L., Acquisti, A., & Loewenstein, G. (2013). Misplaced confidences: Privacy and the control paradox. Social Psychological and Personality Science, 4, 340-347.

Centraal Bureau voor de Statistiek. (2015). Bevolking; Kerncijfers. [Country Population; Core Statistics]. Retrieved from:,10,20,30,40,50,60,%28l-1%29,l&HD=130605-0924&HDR=G1&STB=T

Cranor, L. F. (2012). Can users control online behavioral advertising effectively? IEEE Security & Privacy, 10(2), 93-96.

Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17, 61-80.

Ermakova, T., Fabian, B., Kelkel, S., Wolff, T., & Zarnekow, R. (2015). Antecedents of health information privacy concerns. Procedia Computer Science, 63, 376-383.

GDPR. (2018). General data protection regulation (GDPR). Retrieved from:

Gerber, N., Gerber, P., & Volkamer, M. (2018). Explaining the privacy paradox: A systematic review of literature investigating privacy attitude and behavior. Computers & Security, 77, 226-261.

Gironda, J. T., & Korgaonkar, P. K. (2018). iSpy? Tailored versus invasive ads and consumers’ perceptions of personalized advertising. Electronic Commerce Research and Applications, 29, 64-77.

Goldfarb, A., & Tucker, C. (2011). Online display advertising: Targeting and obtrusiveness. Marketing Science, 30, 389-404.

Ham, C.-D. (2017). Exploring how consumers cope with online behavioral advertising. International Journal of Advertising, 36, 632-658.

Hayes, A. F. (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling. Retrieved from

Katz, M. L., Heaner, S., Reiter, P., Van Putten, J., Murray, L., McDougle, L., . . . Paskett, E. D. (2009). Development of an educational video to improve patient knowledge and communication with their healthcare providers about colorectal cancer screening. American Journal of Health Education, 40, 220-228.

Kim, L. (2012, November 2). How many ads does Google serve in a day? Business 2 Community. Retreived fom

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.

Kim, H., & Huh, J. (2017). Perceived relevance and privacy concern regarding online behavioral advertising (OBA) and their role in consumer responses. Journal of Current Issues & Research in Advertising, 38, 92-105.

Lee, C. H., & Cranage, D. A. (2011). Personalisation-privacy paradox: The effects of personalisation and privacy assurance on customer responses to travel Web sites. Tourism Management, 32, 987-994.

Lee, D., Larose, R., & Rifon, N. (2008). Keeping our network safe: A model of online protection behaviour. Behaviour and Information Technology, 27, 445-454.

Maddux, J. E., & Rogers, R. W. (1983). Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change. Journal of Experimental Social Psychology, 19, 469-479.

Meppelink, C. S., Van Weert, J., Haven, C. J., & Smit, E. G. (2015). The effectiveness of health animations in audiences with different health literacy levels: An experimental study. Journal of Medical Internet Research, 17(1), e11.

Milne, G. R., & Culnan, M. J. (2004). Strategies for reducing online privacy risks: Why consumers read (or don’t read) online privacy notices. Journal of Interactive Marketing, 18(3), 15-29.

Milne, G. R., Labrecque, L. I., & Cromer, C. (2009). Toward an understanding of the online consumer's risky behavior and protection practices. Journal of Consumer Affairs, 43, 449-473.

Milne, S., Sheeran, P., & Orbell, S. (2000). Prediction and intervention in health-related behavior: A meta-analytic review of protection motivation theory. Journal of Applied Social Psychology, 30, 106-143.

Morman, M. T. (2000). The influence of fear appeals, message design, and masculinity on men’s motivation to perform the testicular self-exam. Journal of Applied Communication Research, 28, 91-116.

Norberg, P. A., Horne, D. R., & Horne, D. A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of Consumer Affairs, 4, 100-126.

Pan, B., Hembrooke, H., Joachims, T., Lorigo, L., Gay, G., & Granka, L. (2007). In Google we trust: Users’ decisions on rank, position, and relevance. Journal of Computer-Mediated Communication, 12, 801-823.

Cerulus, L., & Scott, M. (2018, June 25), Europe’s new privacy rules: 1 month in, 7 takeaways. Politico. Retrieved from:

Robles, P. (2018, January 26). In a blow to marketers, Google will let users opt-out of remarketing ads. Econsultancy. Retrieved from:

Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. The Journal of Psychology, 91, 93-114.

Smit, E. G., Van Noort, G., & Voorveld, H. (2014). Understanding online behavioural advertising: User knowledge, privacy concerns and online coping behaviour in Europe. Computers in Human Behavior, 32, 15-22.

Strycharz, J., Van Noort, G., Helberger, N., & Smit, E. (2019). Contrasting perspectives–practitioner’s viewpoint on personalised marketing communication. European Journal of Marketing, 53, 635-660.

Strycharz, J., Van Noort, G., Smit, E., & Helberger, N. (2018). Consumer view on personalized advertising: Overview of self-reported benefits and concerns. In Proceedings of ICORIA 2018. 148.

Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research, 50, 546-562.

Tugend, A. (2015, December 20). Key to opting out of personalized ads, hidden in plain view. The New York Times. Retrieved from

Turow, J., Hennessy, M., & Draper, N. A. (2015). The tradeoff fallacy: How marketers are misrepresenting American consumers and opening them up to exploitation. SSRN Electronic Journal. Advanced online publication.

Ur, B., Leon, P. G., Cranor, L. F., Shay, R., & Wang, Y. (2012). Smart, useful, scary, creepy: Perceptions of online behavioral advertising. In Proceedings of the Eighth Symposium on Usable Privacy and Security (SOUPS) (article 4). Washington, DC, US: ACM.

Van Noort, G., Kerkhof, P., & Fennis, B. M. (2008). The persuasiveness of online safety cues: The impact of prevention focus compatibility of Web content on consumers’ risk perceptions, attitudes, and intentions. Journal of Interactive Marketing, 22(4), 58-72.

Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process model. Communication Monographs, 59, 329-349.

Wottrich, V. M., Van Reijmersdal, E. A., & Smit, E. G. (2018). App users unwittingly in the spotlight: A model of privacy protection in mobile apps. Journal of Consumer Affairs. Advanced online publication.

Xiao, H., Li, S., Chen, X., Yu, B., Gao, M., Yan, H., & Okafor, C. N. (2014). Protection motivation theory in predicting intention to engage in protective behaviors against schistosomiasis among middle school students in rural China. PLoS Neglected Tropical Diseases, 8(10), e3246.

Xu, H., Dinev, T., Smith, H. J., & Hart, P. (2008). Examining the formation of individual’ s privacy concerns: Toward an integrative view. In Proceedings of the Twenty Ninth International Conference on Information Systems, ICIS 2008. 6. Retrieved from