Does Money Talk? The Impact of Monetary Incentives on User-Generated Content Contributions
Open accessAbstract
Many platforms use monetary incentives to encourage user-generated content (UGC) contributions. The empirical evidence, however, is contradictory: monetary incentives are shown to either increase or decrease contribution. We make the first attempt to build a unified theoretical model to understand the complex nature of the impact of monetary incentives. We consider contributors differentiated not only by their attitudes toward monetary incentives but also by their effectiveness to attract audience. We identify two scenarios where contributors can be crowded out when monetary incentives are present: (1) when a small amount of monetary incentive is introduced, the non–money-driven contributors reduce or even stop contributing (motivation crowding out); or (2) when the monetary incentive is relatively large, the high-effectiveness contributors crowd out the low-effectiveness ones (competition crowding out). As a result, an increase in the monetary incentive can either increase or decrease contributors’ participation and the total content volume contributed. Our results offer guidelines for different UGC platforms to design monetary incentive mechanisms.
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Previous studies have not consistently shown that the quality of content changes as a result of revenue sharing.
“previous studies have not consistently shown that the quality of content changes as a result of revenue sharing”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Liu and Feng 2021
Incentivisation does not primarily affect the quality of content but its supply.
“Our data demonstrate that incentivisation does not primarily affect the quality of content but its supply (cf. Chen et al. 2019; Liu and Feng 2021).”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Liu and Feng 2021
Cites
Lepper, M. R., Greene, D., & Nisbett, R. E. (1973) · Journal of Personality and Social Psychology · doi:10.1037/h0035519 · Cited by 2,471 · see citing works
Pay Enough or Don't Pay at All*
Gneezy, U., & Rustichini, A. (2000) · Quarterly Journal of Economics · doi:10.1162/003355300554917 · Cited by 2,175 · 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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Hödl, T., & Myrach, T. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00808-9 · Cited by 51 · see citing works
- Authors
- Liu, Y., & Feng, J.
- Affiliations
- Xi'an Jiaotong University (CN)
- Tsinghua Shenzhen International Graduate School (CN)
- Venue
- Information Systems Research
- Published
- 2021-06
- Volume
- 32
- Issue
- 2
- Pages
- 394-409
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2392595142
- Cited by
- 103
- References
- 3
- Citations by year
- 2026: 28 · 2025: 20 · 2024: 18 · 2023: 18 · 2022: 13 · 2021: 5 · 2017: 1
- FWCI
- 11.07
- Type
- journal article
- Language
- en
- Open access
- green
- papers.ssrn.com
- Locations
- Information Systems Research (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
- Keywords
- financial incentives, user-generated content, content contribution, incentive design, digital platforms
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