IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2201.11051.html

Toward a More Populous Online Platform: The Economic Impacts of Compensated Reviews

Author

Listed:
  • Peng Li
  • Arim Park
  • Soohyun Cho
  • Yao Zhao

Abstract

Many companies nowadays offer compensation to online reviews (called compensated reviews), expecting to increase the volume of their non-compensated reviews and overall rating. Does this strategy work? On what subjects or topics does this strategy work the best? These questions have still not been answered in the literature but draw substantial interest from the industry. In this paper, we study the effect of compensated reviews on non-compensated reviews by utilizing online reviews on 1,240 auto shipping companies over a ten-year period from a transportation website. Because some online reviews have missing information on their compensation status, we first develop a classification algorithm to differentiate compensated reviews from non-compensated reviews by leveraging a machine learning-based identification process, drawing upon the unique features of the compensated reviews. From the classification results, we empirically investigate the effects of compensated reviews on non-compensated. Our results indicate that the number of compensated reviews does indeed increase the number of non-compensated reviews. In addition, the ratings of compensated reviews positively affect the ratings of non-compensated reviews. Moreover, if the compensated reviews feature the topic or subject of a car shipping function, the positive effect of compensated reviews on non-compensated ones is the strongest. Besides methodological contributions in text classification and empirical modeling, our study provides empirical evidence on how to prove the effectiveness of compensated online reviews in terms of improving the platform's overall online reviews and ratings. Also, it suggests a guideline for utilizing compensated reviews to their full strength, that is, with regard to featuring certain topics or subjects in these reviews to achieve the best outcome.

Suggested Citation

  • Peng Li & Arim Park & Soohyun Cho & Yao Zhao, 2022. "Toward a More Populous Online Platform: The Economic Impacts of Compensated Reviews," Papers 2201.11051, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:2201.11051
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2201.11051
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Warut Khern-am-nuai & Karthik Kannan & Hossein Ghasemkhani, 2018. "Extrinsic versus Intrinsic Rewards for Contributing Reviews in an Online Platform," Information Systems Research, INFORMS, vol. 29(4), pages 871-892, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    2. Pengkun Wu & Eric W. T. Ngai & Yuanyuan Wu, 2023. "Impact of praise cashback strategy: Implications for consumers and e‐businesses," Production and Operations Management, Production and Operations Management Society, vol. 32(9), pages 2825-2845, September.
    3. Liu, Xianwei & Han, Meini & Liu, Jianwei & Zhang, Ziqiong, 2024. "Smart users: Effort management in earning rewards," Annals of Tourism Research, Elsevier, vol. 108(C).
    4. Lena Abou El-Komboz & Anna Kerkhof & Johannes Loh, 2023. "Platform Partnership Programs and Content Supply: Evidence from the YouTube “Adpocalypse”," CESifo Working Paper Series 10363, CESifo.
    5. Yang Li & Junrui Zhang & Miao Feng & Haoran Si, 2026. "Dynamic interaction effects between online reviews and spoilers: a PVAR approach," Electronic Commerce Research, Springer, vol. 26(1), pages 297-317, February.
    6. Yuewen Liu & Juan Feng, 2021. "Does Money Talk? The Impact of Monetary Incentives on User-Generated Content Contributions," Information Systems Research, INFORMS, vol. 32(2), pages 394-409, June.
    7. Li, Peng & Park, Arim & Cho, Soohyun & Zhao, Yao, 2025. "Toward a more populous online platform: The economic impacts of compensated reviews," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 202(C).
    8. Victoria Fast & Daniel Schnurr, 2026. "Data Donations for Digital Contact Tracing: Short- and Long-Term Effects of Monetary Incentives," Information Systems Research, INFORMS, vol. 37(1), pages 627-642, March.
    9. Qiu-Hong Wang & Ruibin Geng & Seung Hyun Kim, 2024. "Chilling Effect of the Enforcement of Computer Misuse Act: Evidence from Publicly Accessible Hack Forums," Information Systems Research, INFORMS, vol. 35(3), pages 1195-1215, September.
    10. Xinyu Liu & Yezheng Liu & Yang Qian & Yuanchun Jiang & Haifeng Ling, 2025. "Learning consumer preferences through textual and visual data: a multi-modal approach," Electronic Commerce Research, Springer, vol. 25(4), pages 2955-2984, August.
    11. Hülya Karaman, 2026. "The Asymmetric Effects of Posting an Online Review on Future Spending and the Dark Side of Solicitations," Management Science, INFORMS, vol. 72(2), pages 1579-1594, February.
    12. Niam Yaraghi & Ramaswamy Ramesh & Giri Kumar Tayi, 2025. "Pay, Pat, and Clawback: Incentivizing Service Providers’ Participation in On-Demand Digital Platforms," Management Science, INFORMS, vol. 71(9), pages 7579-7599, September.
    13. Cheng Zhao & Chong Alex Wang, 2023. "A cross-site comparison of online review manipulation using Benford’s law," Electronic Commerce Research, Springer, vol. 23(1), pages 365-406, March.
    14. Jinghui (Jove) Hou & Xiao Ma, 2022. "Space Norms for Constructing Quality Reviews on Online Consumer Review Sites," Information Systems Research, INFORMS, vol. 33(3), pages 1093-1112, September.
    15. Marios Kokkodis & Theodoros Lappas, 2020. "Your Hometown Matters: Popularity-Difference Bias in Online Reputation Platforms," Information Systems Research, INFORMS, vol. 31(2), pages 412-430, June.
    16. Tianyi Li & Xiaoquan (Michael) Zhang, 2024. "Development Trajectory of Blockchain Platforms: The Role of Multirole," Information Systems Research, INFORMS, vol. 35(3), pages 1296-1323, September.
    17. Johannes Wachs & Leonore Roseler & Tobias Gesche & Elliott Ash & Anik'o Hann'ak, 2026. "Good Question! The Effect of Positive Feedback on Contributions to Online Public Goods," Papers 2604.10360, arXiv.org.
    18. Jason Chan & Zihong Huang & De Liu & Zhigang Cai, 2024. "Better to Give Than to Receive: Impact of Adding a Donation Scheme to Reward-Based Crowdfunding Campaigns," Information Systems Research, INFORMS, vol. 35(1), pages 272-293, March.
    19. Young Kwark & Gene Moo Lee & Paul A. Pavlou & Liangfei Qiu, 2021. "On the Spillover Effects of Online Product Reviews on Purchases: Evidence from Clickstream Data," Information Systems Research, INFORMS, vol. 32(3), pages 895-913, September.
    20. Dimitrios Tsekouras & Dominik Gutt & Irina Heimbach, 2024. "The robo bias in conversational reviews: How the solicitation medium anthropomorphism affects product rating valence and review helpfulness," Journal of the Academy of Marketing Science, Springer, vol. 52(6), pages 1651-1672, November.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2201.11051. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.