Consumers’ persuasion knowledge of algorithms in social media advertising: identifying consumer groups based on awareness, appropriateness, and coping ability
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Advertising and brand communication in social media are increasingly driven by algorithms. Theoretically rooted in the Persuasion Knowledge Model, we aim to identify and investigate the prevalence of specific consumer groups that differ in their awareness levels, critical evaluations, and abilities to cope with such algorithmic persuasion in social media, as well as to investigate the predictors of these groups. By performing an online survey among a Dutch sample (n = 450) and a two-step cluster analysis, we identified four groups: the Control Paradox (the largest group), Fatigued, Uninformed but Critical, and Skilled and Critical. The four groups differed in respect to gender, age, education level, internet skills, self-reported knowledge of algorithms, privacy concerns, and perceived personalization of branded social media messages. Theoretically, this paper contributes to our understanding of algorithmic persuasion by proposing a consumer typology and discussing implications for the Persuasion Knowledge Model. Practically, this study provides evidence-based recommendations for policymakers on how to empower different types of consumers.
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- Authors
- Voorveld, H. A. M., Meppelink, C. S., & Boerman, S. C.
- Affiliations
- University of Amsterdam (NL)
- Wageningen University & Research (NL)
- Corresponding author
- Voorveld, Hilde A. M.
- Venue
- International Journal of Advertising
- Publisher
- Informa UK Limited
- Published
- 2023-10-12
- Volume
- 43
- Issue
- 6
- Pages
- 960-986
- ISSN
- 0265-0487 (ISSN-L)
- 1759-3948
- OpenAlex
- W4387579102
- Cited by
- 62
- References
- 97
- Citations by year
- 2026: 31 · 2025: 23 · 2024: 8
- FWCI
- 26.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Advertising (publishedVersion, cc-by-nc-nd)
- Socio-Environmental Systems Modeling (submittedVersion, cc-by-nc-nd)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by-nc-nd)
- Funders
- Universiteit van Amsterdam
- Topics
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- algorithmic persuasion, social media advertising, persuasion knowledge model, consumer typology, algorithm awareness, consumer coping, personalization, privacy concerns, internet skills, consumer groups
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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