Product Badges and Consumer Choice on Digital Platforms
Open accessIntegrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent empirical evidence suggests badges can meaningfully change clicks and add-to-cart probabilities, and complementary work studies badge longevity, uniformity, bias across marketplaces, and causal effects of badges in online communities.
“Recent empirical evidence suggests badges can meaningfully change clicks and add-to-cart probabilities [29]; complementary work studies badge longevity, uniformity, and bias across marketplaces, and the causal effects of badges in online communities [3, 22, 28].”
From What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce (2025) · cites this paper as [29]
The strong positive response to overall pick implies that platform endorsements are treated as credible signals, echoing the behavior of human buyers.
“the strong positive response to overall pick implies that platform endorsements are treated as credible signals-echoing the behavior of human buyers [29].”
From What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce (2025) · cites this paper as [29]
Several patterns we document mirror directionally human behavior: ranking/position materially shape choices in digital markets, and platform endorsements/badges shift demand.
“That said, several patterns we document mirror directionally human behavior-ranking/position materially shape choices in digital markets [14, 41], and platform endorsements/badges shift demand [29].”
From What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce (2025) · cites this paper as [29]
Cites
The Persuasion Knowledge Model: How People Cope with Persuasion Attempts
Friestad, M., & Wright, P. (1994) · Journal of Consumer Research · doi:10.1086/209380 · Cited by 3,811 · see citing works
When choice is demotivating: can one desire too much of a good thing?
Iyengar, S. S., & Lepper, M. R. (2000) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.79.6.995 · Cited by 2,878 · see citing works
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,219 · see citing works
Atkinson, L., & Rosenthal, S. (2014) · Journal of Advertising · doi:10.1080/00913367.2013.834803 · Cited by 620 · see citing works
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Allouah, A., Besbes, O., Figueroa, J., Kanoria, Y., & Kumar, A. (2025) · doi:10.2139/ssrn.5381574 · Cited by 8 · see citing works
Allouah, A., Besbes, O., Figueroa, J. D., Kanoria, Y., & Kumar, A. (2026) · Proceedings of the ACM Web Conference 2026 · doi:10.1145/3774904.3792943 · Cited by 7 · see citing works
Allouah, A., Besbes, O., Figueroa, J. D., Kanoria, Y., & Kumar, A. (2025) · arXiv
- Authors
- Lill, M., Gallitz, N., Stich, L., & Spann, M.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- University of Würzburg (DE)
- Corresponding author
- Spann, Martin
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2024
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4402391076
- Cited by
- 2
- References
- 4
- Citations by year
- 2026: 1 · 2025: 1
- Type
- posted content
- Language
- en
- Open access
- green
- dx.doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Keywords
- consumer choice, search behavior, online retail, search engine ranking, product ratings, click-through behavior, e-commerce platform design, seller strategies, consumer protection regulation
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.
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