IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2201.11051.html
   My bibliography  Save this paper

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.
  • Handle: RePEc:arx:papers:2201.11051
    as

    Download full text from publisher

    File URL: http://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. Zibo Liu & Zhijie Lin & Ying Zhang & Yong Tan, 2022. "The Signaling Effect of Sampling Size in Physical Goods Sampling Via Online Channels," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 529-546, February.
    3. Martin Poniatowski & Hedda Lüttenberg & Daniel Beverungen & Dennis Kundisch, 2022. "Three layers of abstraction: a conceptual framework for theorizing digital multi-sided platforms," Information Systems and e-Business Management, Springer, vol. 20(2), pages 257-283, June.
    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. Gordon Burtch & Qinglai He & Yili Hong & Dokyun Lee, 2022. "How Do Peer Awards Motivate Creative Content? Experimental Evidence from Reddit," Management Science, INFORMS, vol. 68(5), pages 3488-3506, May.
    6. Yipu Deng & Jinyang Zheng & Warut Khern-am-nuai & Karthik Kannan, 2022. "More than the Quantity: The Value of Editorial Reviews for a User-Generated Content Platform," Management Science, INFORMS, vol. 68(9), pages 6865-6888, September.
    7. 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.
    8. Dandan Qiao & Shun-Yang Lee & Andrew B. Whinston & Qiang Wei, 2020. "Financial Incentives Dampen Altruism in Online Prosocial Contributions: A Study of Online Reviews," Information Systems Research, INFORMS, vol. 31(4), pages 1361-1375, December.
    9. Chen, Ruolan & Yuan, Ruizhi & Huang, Bo & Liu, Martin J., 2023. "Feeling warm or skeptical? An investigation into the effects of incentivized eWOM programs on customers’ eWOM sharing intentions," Journal of Business Research, Elsevier, vol. 167(C).
    10. Hui Zhao & Xiaoyuan Wang & Debing Ni & Kevin W. Li, 2023. "The Quality-Signaling Role of Manipulated Consumer Reviews," Group Decision and Negotiation, Springer, vol. 32(3), pages 503-536, June.
    11. 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.
    12. 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.
    13. 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.
    14. Ina Garnefeld & Tabea Krah & Eva Böhm & Dwayne D. Gremler, 2021. "Online reviews generated through product testing: can more favorable reviews be enticed with free products?," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 703-722, July.
    15. Maria Marchenko & Hendrik Sonnabend, 2022. "The Never Ending Book: The role of external stimuli and peer feedback in user-generated content production," Department of Economics Working Papers wuwp320, Vienna University of Economics and Business, Department of Economics.
    16. Yue Jin & Yong Tan & Jinghua Huang, 2022. "Managing contributor performance in knowledge‐sharing communities: A dynamic perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 3945-3962, November.
    17. Anna Ressi, 2020. "Discussion of “The Market for Reviews: Strategic Behavior of Online Product Reviewers with Monetary Incentives”," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 72(3), pages 437-445, July.
    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. Kaiwei Zhang & Xi Weng & Xienan Cheng, 2022. "Optimal Pricing Schemes in the Presence of Social Learning and Costly Reporting," Papers 2211.07362, arXiv.org, revised Dec 2023.

    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: http://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.