Motivation of User-Generated Content: Social Connectedness Moderates the Effects of Monetary Rewards
Abstract
The creation and sharing of user-generated content such as product reviews has become increasingly “social,” particularly in online communities where members are connected. While some online communities have used monetary rewards to motivate product review contributions, empirical evidence regarding the effectiveness of such rewards remains limited. We examine the possible moderating effect of social connectedness (measured as the number of friends) on publicly offered monetary rewards using field data from an online review community. This community saw an (unexpected) overall decrease in total contributions after introducing monetary rewards for posting reviews. Further examination across members finds a strong moderating effect of social connectedness. Specifically, contributions from less-connected members increased by 1,400%, while contributions from more-connected members declined by 90%. To corroborate this effect, we rule out multiple alternative explanations and conduct robustness checks. Our findings suggest that token-sized monetary rewards, when offered publicly, can undermine contribution rates among the most connected community members. Data and the online appendix are available at https://doi.org/10.1287/mksc.2016.1022
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Monetary incentives seem to increase the number of stock recommendations but reduce content contribution in online review communities.
“Monetary incentives seem to increase the number of stock recommendations (Chen et al. 2019) but reduce content contribution in online review communities (Sun et al. 2017).”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Sun et al. 2017
Cites
How Much Should We Trust Differences-in-Differences Estimates?
Bertrand, M., Duflo, E., & Mullainathan, S. (2004) · The Quarterly Journal of Economics · doi:10.1162/003355304772839588 · Cited by 10,641 · see citing works
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004) · Journal of Interactive Marketing · doi:10.1002/dir.10073 · Cited by 5,916 · 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
Receiver responses to rewarded referrals: the motive inferences framework
Verlegh, P. W. J., Ryu, G., Tuk, M. A., & Feick, L. (2013) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-013-0327-8 · Cited by 161 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ozuem, W., Ranfagni, S., Willis, M., Rovai, S., & Howell, K. (2021) · Psychology & Marketing · doi:10.1002/mar.21527 · Cited by 140 · see citing works
Hödl, T., & Myrach, T. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00808-9 · Cited by 51 · see citing works
Beyond Money: Incentive Effects of Tokenized Ownership on User Contribution in <scp>DAOs</scp>
Chen, K., Fan, Y., Fang, Y., & Luo, X. (. (2025) · Journal of Operations Management · doi:10.1002/joom.1351 · Cited by 12 · see citing works
- Authors
- Sun, Y., Dong, X., & McIntyre, S.
- Affiliations
- University of Colorado Boulder (US)
- Santa Clara University (US)
- ORCID
- Dong, Xiaojing
- Venue
- Marketing Science
- Published
- 2017-05
- Volume
- 36
- Issue
- 3
- Pages
- 329-337
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W2590104848
- Cited by
- 177
- References
- 4
- Citations by year
- 2026: 18 · 2025: 28 · 2024: 25 · 2023: 25 · 2022: 27 · 2021: 18 · 2020: 12 · 2019: 12 · 2018: 8 · 2017: 3 · 2015: 1
- FWCI
- 22.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Marketing Science (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Social Sciences › Communication › Knowledge Management and Sharing
- Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
- Keywords
- user-generated content, monetary rewards, social connectedness, product reviews, moderating effect, online communities
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 ›
Are you an author, or do you see a problem with this page? Request a review