Juxtaposing the persuasion knowledge model and privacy paradox: An experimental look at advertising personalization, public policy and public understanding

Special issue: Online Self-disclosure and Privacy

Recent studies suggest the expanding collection and use of big data by advertisers to target messages to consumers based on their location, demographics and online behaviors is escalating information privacy concerns and negatively impacting campaign outcomes. For communication scholars and practitioners, this recent attitudinal shift indicates a critical need to better understand consumer perceptions related to personalized advertising in the era of big data. It is currently assumed that U.S. self-regulatory initiatives, including the AdChoices Icon, reduce perceived risk by giving consumers a greater sense of control over the exchange of their personal information online (Castro, 2011). However, less than 37% of U.S. Internet users are familiar with the AdChoices Icon (eMarketer, 2015), and 52% incorrectly believe that privacy policies ensure the confidentiality of their personal information (Pew, 2014). To examine the complexities of the privacy paradox, the present study utilizes a 2x2x2 experiment (N = 382) to measure attitudes toward personalized advertising with and without the presence of the AdChoices Icon. A Univariate GLM analysis of the data indicate that when controlling for demographics, online trust, message credibility, and perceived risks and benefits, advertising personalization did not have a significant effect on attitude toward the ad, but inclusion of the AdChoices Icon did. Further, respondents indicating no knowledge of the AdChoices Icon reported lower attitudinal responses toward the ad compared to those who were knowledgeable of its meaning. Exploring these complex relationships offers to advance research and practice by extending Persuasion Knowledge Model to examine the effects of personalized online message delivery, as well as offering practitioners actionable insights to improve their personalized advertising outcomes.

Privacy paradox; personalized advertising; information privacy; AdChoices icon; privacy policy
Author biographies

Nancy Howell Brinson

Nancy Howell Brinson is a doctoral student at the Stan Richards School of Advertising & Public Relations, University of Texas at Austin. Building on a 25 year-career in advertising, her research interests lie in examining the effects of data mining across multiple platforms and contexts to understand how increasingly personalized advertising messages impact audience perceptions.

Matthew S. Eastin

Matthew S. Eastin is an Associate Professor at the Stan Richards School of Advertising & Public Relations, University of Texas at Austin. His research focuses on new media behavior, and from this perspective, he has investigated information processing as well as the social and psychological factors associated with new media.

Acquisti, A., & Gross, R. (2006). Imagined communities: Awareness, information sharing, and privacy on the Facebook. In Privacy enhancing technologies (pp. 36-58). Springer Berlin Heidelberg.

Ayeh, J., Au, N., & Law, R. (2013). Do we believe in TripAdvisor? Examining credibility perceptions and online travelers’ attitude toward using user-generated content. Journal of Travel Research, 52, 537-452. https://doi.org/10.1177/0047287512475217

Baek, T., & Morimoto, M. (2012). Stay away from me: Examining the determinants of consumer avoidance of personalized advertising. Journal of Advertising. 41, 59-76. https://doi.org/10.2753/JOA0091-3367410105

Castro, D. (2011). Benefits and limitations of industry self-regulation for online behavioral advertising. The Information Technology & Innovation Foundation. Retrieved from http://www.itif.org/files/2011-self-regulation-online-behavioral-advertising.pdf

Cisco (2014). VNI Global IP Traffic Forecast, 2013–2018. Retrieved from http://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-index-vni/index.html

Cleff, E. (2007). Privacy issues in mobile advertising. International Review of Law Computers and Technology, 21, 225-236. https://doi.org/10.1080/13600860701701421

Culnan, M., & Armstrong, P. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organizational Science, 10, 104-115. https://doi.org/10.1287/orsc.10.1.104

Davis, W. (2015, October 7). Lawmakers call for stronger do-not-track standards. Mediapost Policy Blog. Retrieved from http://www.mediapost.com/publications/article/259971/lawmakers-call-for-stronger-do-not-track-standards.html

Dix, S., Bellman, S., Haddad, H., & Varan, D. (2010). Using interactive program-loyalty banners to reduce TV ad avoidance: Is it possible to give viewers a reason to stay tuned during commercial breaks? Journal of Advertising Research, 50, 154-161. https://doi.org/10.2501/S0021849910091312

Ducoffe, R. (1996). Advertising value and advertising on the web. Journal of Advertising Research, 17, 21-35.

Dutta, S., & Bilbao-Osorio, B. (2014). The Global Information Technology Report 2014 – Rewards and risks of big data. In INSEAD and World Economic Forum, 35-93.

Eastin, M.S. (2001). Credibility assessments of online health information: the effects of source expertise and knowledge of content. Journal of Computer-Mediated Communication, 6(4). https://doi.org/10.1111/j.1083-6101.2001.tb00126.x

eMarketer. (2015). AdChoices: Do consumers know they can control the creepiness? Retrieved from http://www.emarketer.com/Article/AdChoices-Do-Consumers-Know-They-Control-Creepiness/1012623

Federal Trade Commission. (2010) Protecting consumer privacy in an era of rapid change. FTC report.

Friestad, M., & Wright, P. (1994). The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research, 21(1), 1-31. https://doi.org/10.1086/209380

Golembiewski, R. T., & McConkie, M. (1975). The centrality of interpersonal trust in group processes. Theories of Group Processes, 131, 185.

Greengard, S. (2015). The Internet of Things. Cambridge, MA: MIT Press.

Hastak, M., & Culnan, C. (2010). Future of privacy forum online behavioral advertising “icon” study. Retrieved from https://fpf.org/final_report.pdf

Hovland, C. I., Janis, I. L., & Kelley, H. H. (1953). Communication and persuasion. New Haven, CT: Yale University Press.

IAB. (2011). Self regulatory program for online behavioral advertising factsheet. Retrieved from https://www.iab.com/wp-content/uploads/2015/06/OBA_OneSheet_Final.pdf

IBM (2013). Big data. Retrieved from http://ibm.com/big-data/us/en/

Internet Advertising Bureau (2015). Internet advertising revenue report. Retrieved from http://www.iab.net/media/file/IAB_Internet_Advertising_Revenue_Report_HY_2015_PDF.pdf

Jensen, C., Potts, C., & Jensen, C. (2005). Privacy practices of Internet users: Self report versus observed behavior. International Journal of Human-Computer Studies, 63, 203-227. https://doi.org/10.1016/j.ijhcs.2005.04.019

John, L. (2015, October 16). We say we want privacy online, but our actions say otherwise. Harvard Business Review. Retrieved from https://hbr.org/2015/10/we-say-we-want-privacy-online-but-our-actions-say-otherwise

Kachersky, L., & Kim, H. (2011). When consumers cope with price-persuasion knowledge: The role of topic knowledge. Journal of Marketing Management, 27, 28-40. https://doi.org/10.1080/02672571003647719

Karjaluoto, H., & Alatalo, T. (2007). Consumers' attitudes towards and intention to participate in mobile marketing. International Journal of Services Technology and Management, 8(2), 155-173. https://doi.org/10.1504/IJSTM.2007.012866

Kim, P., Ferrin, D., Cooper, C., & Dirks, K. (2004). Removing the shadow of suspicion: The effects of apology versus denial for repairing competence-versus integrity-based trust violations. Journal of Applied Psychology, 89, 104-118. https://doi.org/10.1037/0021-9010.89.1.104

Lafferty, B. A., Goldsmith, R. E., & Newell, S. J. (2002). The dual credibility model: The influence of corporate and endorser credibility on attitudes and purchase intentions. Journal of Marketing Theory and Practice, 10(3), 1-12. https://doi.org/10.1080/10696679.2002.11501916

LaRose, R., & Eastin, M. (2004). A social cognitive theory of Internet uses and gratifications: Toward a new model of media attendance. Journal of Broadcasting & Electronic Media, 48, 358–377. https://doi.org/10.1207/s15506878jobem4803_2

Lerman, K. (2014). Beyond the bulls-eye: Building meaningful relationships in the age of big data. Retrieved from: https://www.cspace.com/blog/building-meaningful-relationships-in-the-age-of-big-data/

Malhotra, N., Kim, S., & Agarwal, J. (2004). Internet users' information privacy concerns (IUIPC): the construct, the scale, and a causal model. Information Systems Research, 15, 336-355. https://doi.org/10.1287/isre.1040.0032

Mayer, J. & Narayana, A. (2015) Do not track, universal web tracking opt out. Retrieved from http://donottrack.us/

Mayer-Schoenberger, V. & Cukier, K. (2013). Big data. New York, NY: Houghton Mifflin Harcourt.

McCann. (2013).Truth about privacy study. Retrieved from http://truthcentral.mccann.com/truth/

McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13, 334-359. https://doi.org/10.1287/isre.13.3.334.81

Mir, I. (2011). Consumer attitude towards m-advertising acceptance: A cross-sectional study. Journal Of Internet Banking & Commerce, 16(1), 1-22.

Morrison, M., & Peterson, T. (2015, September 14). Yes, there is a war on advertising. Now what? Advertising Age. Retrieved from http://adage.com/article/print-edition/a-war-advertising/300336/

Mozilla (2015). Lightbeam for FireFox. Retrieved from https://www.mozilla.org/en-US/lightbeam/

Nelson, M, Keum, H, & Yaros, R. (2004). Advertainment or adcreep: Game players’ attitudes toward advertising product placements in computer games. Journal of Interactive Advertising, 5, 3-21. https://doi.org/10.1080/15252019.2004.10722090

OECD. (2013). The 2013 OECD privacy guidelines. Retrieved from http://bit.ly/166TxHy

Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness and attractiveness. Journal of Advertising, 19, 39-52. https://doi.org/10.1080/00913367.1990.10673191

PageFair. (2015). The 2015 Ad blocking report. Retrieved from https://blog.pagefair.com/2015/ad-blocking-report/

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7, 101-134. https://doi.org/10.1080/10864415.2003.11044275

Peterson, T. (2015, September 4). IAB explores its options to fight ad blockers, including lawsuits. Advertising Age. Retrieved from http://adage.com/article/digital/iab-surveys-options-fight-ad-blockers-including-lawsuits/300228/

Pew Research Center. (2016). The state of privacy in America: What we learned. Retrieved from http://www.pewresearch.org/fact-tank/2016/01/20/the-state-of-privacy-in-america/

Pew Research Center. (2014). Half of online Americans don’t know what a privacy policy is. Retrieved from http://www.pewresearch.org/fact-tank/2014/12/04/half-of-americans-dont-know-what-a-privacy-policy-is/

Pornpitakpan, C. (2004). The persuasiveness of source credibility: A critical review of five decades’ evidence. Journal of Applied Psychology, 34, 243-81.

Purcell, K., Brenner, J., & Rainie, L. (2012). Search engine use 2012. Pew Internet & American Life. Retrieved from http://pewinternet.org/Reports/2012/Search-Engine-Use-2012.aspx

Richards, N. (2014). Why data privacy law is (mostly) constitutional. Intellectual Privacy. Cambridge, UK: Oxford University Press.

Rocket Fuel. (2014). Quantified self digital tools: A CPG marketing opportunity. Retrieved from http://rocketfuel.com/blog/quantified-self

Roeber, B., Rehse, O., Knorrek, R., & Thomsen, B. (2015). Personal data: How context shapes consumers’ data sharing with organizations from various sectors. Electronic Markets, 25, 95-108. https://doi.org/10.1007/s12525-015-0183-0

Schwartz, P., & Solove, D. (2011). The PII problem: Privacy and a new concept of personally identifiable information. NYU Law Review, 86, 1814.

Slefo, G. (2015, October 15). IAB to advertisers and content providers: ‘We messed up”. Advertising Age. Retrieved from http://adage.com/article/digital/iab-advertisers-content-providers-messed/300919/

Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals' concerns about organizational practices. MIS Quarterly, 20, 167-196. https://doi.org/10.2307/249477

Sunyaev, A., Dehling, T., Taylor, P., & Mandl, K. (2015). Availability and quality of mobile health app privacy policies. Journal of American Medical Information Association (JAMIA), 22, e28-e33.

Tummarello, K., (2013, September 17). Do not track effort in trouble. The Hill. Retrieved from http://thehill.com/policy/technology/322701-do-not-track-group-should-give-up-departing-online-ad-reps-say

Utz, S., & Kramer, N. (2009). The privacy paradox on social network sites revisited: The role of individual characteristics and group norms. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 3(2), article 1.

Way, H. (2014). Harnessing the power of big data: New media and advertising. Retrieved from https://www.parksassociates.com/report/advertising-big-data

Wessels, B. (2012). Identification and the practices of identity and privacy in everyday digital communication. New Media & Society, 14, 1251-1268. https://doi.org/10.1177/1461444812450679

White, T., Zarhay, D., Thorbjornsen, H. & Shavitt, S. (2008) Getting too personal: Reactance to highly personalized e-mail solicitations. Marketing Letters, 19, 39-50. https://doi.org/10.1007/s11002-007-9027-9

Zhang, J., & Wedel, M. (2009). The effectiveness of customized promotions in online and offline stores. Journal of Marketing Research, 46, 190-206. https://doi.org/10.1509/jmkr.46.2.190


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