Targeted Online Advertising: Using Reciprocity Appeals to Increase Acceptance among Users of Free Web Services
Open accessAbstract
The Internet is dominated by free web services that depend on advertising revenues and powerful marketing tools to support their business models. Targeted online advertising enables websites to increase their advertising revenues by selectively displaying advertisements according to users’ browsing behavior, sociodemographics, and interests. Yet targeting also creates negative consumer reactions, and websites confront increasing regulatory pressures to inform consumers about their practices. It is critical for such advertising-supported websites to address those challenges proactively. In one scenario experiment and two field studies, the authors show that a normative reciprocity argument is generally more effective than the current industry practice of using a utilitarian argument related to advertising relevance to increase acceptance of targeted online advertising. However, in some cases, this dominance switches depending on specific website characteristics such as website utility and level of user-generated content. Managers of free websites should remind their users of the free services they enjoy when asking permission to target them online or to use their personal information.
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Personalization works best when it meets consumer expectations without feeling intrusive.
“This approach resonates with previous findings that personalization works best when it meets consumer expectations without feeling intrusive (Schumann et al., 2014).”
From Feeling Watched: A Phenomenological Exploration of Consumer Discomfort and Data Privacy Concerns in E-Commerce Personalized Email Marketing (2025) · cites this paper as Schumann et al., 2014
Personalization is effective only when it aligns closely with consumer interests; otherwise, it can have the opposite effect, making consumers feel monitored or pressured.
“Previous studies emphasized that personalization is effective only when it aligns closely with consumer interests; otherwise, it can have the opposite effect, making consumers feel monitored or pressured (Schumann et al., 2014).”
From Feeling Watched: A Phenomenological Exploration of Consumer Discomfort and Data Privacy Concerns in E-Commerce Personalized Email Marketing (2025) · cites this paper as Schumann et al., 2014
Cites
Preacher, K. J., & Hayes, A. F. (2008) · Behavior Research Methods · doi:10.3758/brm.40.3.879 · Cited by 34,117 · see citing works
Back-translation for cross-cultural research.
Brislin, R. W. (1970) · Journal of Cross-Cultural Psychology · doi:10.1177/135910457000100301 · Cited by 14,091 · see citing works
The norm of reciprocity: A preliminary statement
Gouldner, A. W. (1960) · American Sociological Review · doi:10.2307/2092623 · Cited by 12,001 · see citing works
Customer engagement behavior: Theoretical foundations and research directions
van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010) · Journal of Service Research · doi:10.1177/1094670510375599 · Cited by 3,536 · see citing works
Normative social influence is underdetected
Nolan, J. M., Schultz, P. W., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2008) · Personality and Social Psychology Bulletin · doi:10.1177/0146167208316691 · Cited by 1,772 · see citing works
On the Use of the Mass Media for Important Things
Katz, E., Haas, H., & Gurevitch, M. (1973) · American Sociological Review · doi:10.2307/2094393 · Cited by 1,639 · cited 1× · see citing works
“According to the uses-and-gratifications approach, consumers seek specific forms of mass communication to meet their specific needs and return to a channel only if it satisfies their needs (Hausman and Siepke 2009; Katz, Gurevitch, and Haas 1973).”
BackgroundUndervalued or overvalued customers: Capturing total customer engagement value
Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010) · Journal of Service Research · doi:10.1177/1094670510375602 · Cited by 1,382 · see citing works
Effort for Payment: A Tale of Two Markets
Heyman, J., & Ariely, D. (2004) · Psychological Science · doi:10.1111/j.0956-7976.2004.00757.x · Cited by 827 · see citing works
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The role of data privacy in marketing
Martin, K. D., & Murphy, P. E. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-016-0495-4 · Cited by 902 · see citing works
Online behavioral advertising: A literature review and research agenda
Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017) · Journal of Advertising · doi:10.1080/00913367.2017.1339368 · Cited by 607 · see citing works
Advertising in social media: a review of empirical evidence
Knoll, J. (2016) · International Journal of Advertising · doi:10.1080/02650487.2015.1021898 · Cited by 362 · see citing works
Brill, T. M., Munoz, L., & Miller, R. J. (2019) · Journal of Marketing Management · doi:10.1080/0267257x.2019.1687571 · Cited by 291 · see citing works
The personalization-privacy paradox: Implications for new media
Aguirre, E., Roggeveen, A. L., Grewal, D., & Wetzels, M. (2016) · Journal of Consumer Marketing · doi:10.1108/jcm-06-2015-1458 · Cited by 251 · see citing works
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00704-3 · Cited by 204 · see citing works
“…more accurate identity graphs that allow companies to market products and services more efficiently and effectively by, for example, targeting people with the right content at the right time (McAfee & Brynjolfsson, 2012; Schumann, von Wangenheim, & Groene, 2014; Spangler, Hartzel, & Gal-Or, 2006).”
BackgroundKrishen, A. S., Raschke, R. L., Close, A. G., & Kachroo, P. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.12.002 · Cited by 64 · see citing works
Rahayu, T. A., Purnamasari, V. F. W., Rizky, M. A., Rahyadi, I., & Mani, L. (2025) · PaperASIA · doi:10.59953/paperasia.v41i1b.313 · Cited by 1 · see citing works
“Previous studies emphasized that personalization is effective only when it aligns closely with consumer interests; otherwise, it can have the opposite effect, making consumers feel monitored or pressured (Schumann et al., 2014).”
- Authors
- Schumann, J. H., von Wangenheim, F., & Groene, N.
- Affiliations
- University of Passau (DE)
- Department of Management, Technology, and Economics, ETH Zärich
- Technical University of Munich (DE)
- Corresponding author
- Schumann, Jan H.
- Groene, Nicole
- Venue
- Journal of Marketing
- Publisher
- SAGE Publications
- Published
- 2014-01
- Volume
- 78
- Issue
- 1
- Pages
- 59-75
- ISSN
- 0022-2429 (ISSN-L)
- 1547-7185
- OpenAlex
- W2014952300
- Cited by
- 287
- References
- 8
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 15 · 2023: 32 · 2022: 30 · 2021: 44 · 2020: 32 · 2019: 33 · 2018: 28 · 2017: 15 · 2016: 17 · 2015: 10 · 2014: 3
- FWCI
- 30.81
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of Marketing (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- 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
- consumer acceptance, consumer privacy, user-generated content, consumer information disclosure
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