Why Am I Seeing This Ad? The Effect of Ad Transparency on Ad Effectiveness
Open accessAbstract
Given the increasingly specific ways marketers can target ads, consumers and regulators are demanding ad transparency: disclosure of how consumers’ personal information was used to generate ads. We investigate how and why ad transparency impacts ad effectiveness. Drawing on literature about offline norms of information sharing, we posit that ad transparency backfires when it exposes marketing practices that violate norms about “information flows”—consumers’ beliefs about how their information should move between parties. Study 1 inductively shows that consumers deem information flows acceptable (or not) based on whether their personal information was: 1) obtained within versus outside of the website on which the ad appears, and 2) stated by the consumer versus inferred by the firm (the latter of each pair being less acceptable). Studies 2 and 3 show that revealing unacceptable information flows reduces ad effectiveness, which is driven by increasing consumers’ relative concern for their privacy over desire for the personalization that such targeting affords. Study 4 shows the moderating role of platform trust: when consumers trust a platform, revealing acceptable information flows increases ad effectiveness. Studies 5a and 5b, conducted in the field with a loyalty program website (i.e., a trusted platform), demonstrate this benefit of transparency.
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Cites
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001) · Review of General Psychology · doi:10.1037/1089-2680.5.4.323 · Cited by 7,678 · see citing works
Higgins, E. T. (1997) · American Psychologist · doi:10.1037/0003-066x.52.12.1280 · Cited by 6,775 · see citing works
Altruistic punishment in humans
Fehr, E., & Gächter, S. (2002) · Nature · doi:10.1038/415137a · Cited by 5,616 · see citing works
Negativity Bias, Negativity Dominance, and Contagion
Rozin, P., & Royzman, E. B. (2001) · Personality and Social Psychology Review · doi:10.1207/s15327957pspr0504_2 · Cited by 4,342 · see citing works
Influence of extraversion and neuroticism on subjective well-being: Happy and unhappy people
Costa, P. T., & McCrae, R. R. (1980) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.38.4.668 · Cited by 2,652 · see citing works
The Effect of Corporate Social Responsibility (CSR) Activities on Companies With Bad Reputations
Yoon, Y., Gürhan‐Canli, Z., & Schwarz, N. (2006) · Journal of Consumer Psychology · doi:10.1207/s15327663jcp1604_9 · Cited by 1,368 · see citing works
When Is Honesty the Best Policy? The Effect of Stated Company Intent on Consumer Skepticism
Foreh, M. R., & Grier, S. (2003) · Journal of Consumer Psychology · doi:10.1207/s15327663jcp1303_15 · Cited by 1,003 · see citing works
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015) · Journal of Retailing · doi:10.1016/j.jretai.2014.09.005 · Cited by 868 · cited 3× · see citing works
“Previous research suggests that consumers assess the attractiveness of an advertised product by looking to its fit with, or personalization to, their needs and wants (Aguirre et al. 2015; Montgomery and Smith 2009; Tam and Ho 2006).”
BackgroundIntimate Exchanges: Using Computers to Elicit Self-Disclosure from Consumers
Moon, Y. (2000) · Journal of Consumer Research · doi:10.1086/209566 · Cited by 842 · see citing works
Social Networks, Personalized Advertising, and Privacy Controls
Tucker, C. E. (2014) · Journal of Marketing Research · doi:10.1509/jmr.10.0355 · Cited by 829 · see citing works
Getting Too Personal: Reactance to Highly Personalized Email Solicitations
White, T. B., Zahay, D. L., Thorbjørnsen, H., & Shavitt, S. (2008) · Marketing Letters · doi:10.1007/s11002-007-9027-9 · Cited by 365 · cited 3× · see citing works
“Such concerns can overshadow interest in purchasing the product itself, and more immediately, dampen the likelihood of engaging with advertisements for it—even for a product that personalized targeting would predict to be a good person-product fit (White et al. 2008).”
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Large Language Model in Creative Work: The Role of Collaboration Modality and User Expertise
Chen, Z., & Chan, J. (2024) · Management Science · doi:10.1287/mnsc.2023.03014 · Cited by 204 · see citing works
Hayes, J. L., Brinson, N. H., Bott, G. J., & Moeller, C. M. (2021) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2021.01.001 · Cited by 180 · see citing works
The persuasive effects of political microtargeting in the age of generative artificial intelligence
Simchon, A., Edwards, M., & Lewandowsky, S. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae035 · Cited by 143 · see citing works
Geng, S., Yang, P., Gao, Y., Tan, Y., & Yang, C. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106834 · Cited by 102 · see citing works
McKee, K. M., Dahl, A. J., & Peltier, J. W. (2024) · Journal of Consumer Behaviour · doi:10.1002/cb.2199 · Cited by 75 · see citing works
Wang, Y., Zhu, J., Liu, R., & Jiang, Y. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102755 · Cited by 43 · see citing works
Epistemic fragmentation poses a threat to the governance of online targeting
Milano, S., Mittelstadt, B., Wachter, S., & Russell, C. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00358-3 · Cited by 29 · see citing works
Dogruel, L. (2019) · Communication Research Reports · doi:10.1080/08824096.2019.1684253 · Cited by 27 · see citing works
Portes, A., N’Goala, G., & Cases, A.-S. (2020) · Recherche et Applications en Marketing (English Edition) · doi:10.1177/2051570720973548 · Cited by 26 · see citing works
Shin, W., Kim, E., & Huh, J. (2025) · Young Consumers · doi:10.1108/yc-06-2024-2105 · Cited by 5 · see citing works
Dark Patterns in Data-Consent Disclosures and Consumer Reactance to Online Behavioral Advertising
Ju, I., Ham, C.-D., & Yel, E. (2026) · Journal of Advertising · doi:10.1080/00913367.2025.2593666 · Cited by 5 · see citing works
“…ads after manipulative consent interfaces, targeting cues reactivate prior consent memories and make manipulative intent more cognitively accessible, intensifying persuasion knowledge and reactance (Acquisti, Brandimarte, and Loewenstein 2015; Kim, Barasz, and John 2019; White et al. 2008).”
- Authors
- Kim, T., Barasz, K., & John, L. K.
- Affiliations
- University of Virginia (US)
- IESE Business School (ES)
- Harvard University (US)
- Corresponding author
- Kim, Tami
- Venue
- Journal of Consumer Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-07
- Volume
- 45
- Issue
- 5
- Pages
- 906-932
- ISSN
- 0093-5301 (ISSN-L)
- 1537-5277
- OpenAlex
- W2801318856
- Cited by
- 274
- References
- 11
- Citations by year
- 2026: 36 · 2025: 57 · 2024: 42 · 2023: 41 · 2022: 35 · 2021: 24 · 2020: 25 · 2019: 11 · 2018: 3
- FWCI
- 33.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Journal of Consumer Research (publishedVersion, public-domain)
- Deposito Adademico Digital Universidad De Navarra (University of Navarra) (submittedVersion, public-domain)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- advertising effectiveness, targeted advertising, personal information disclosure, information flow, privacy concerns, personalization, platform trust, loyalty programs, consumer privacy
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