IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v35y2024i3p1363-1381.html
   My bibliography  Save this article

Uncertainty Reduction vs. Reciprocity: Understanding the Effect of a Platform-Initiated Reviewer Incentive Program on Regular Ratings

Author

Listed:
  • Jingchuan Pu

    (Warrington College of Business, University of Florida, Gainesville, Florida 32611)

  • Young Kwark

    (Daniels College of Business, University of Denver, Denver, Colorado 80210)

  • Sang Pil Han

    (W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85257)

  • Qiang Ye

    (School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China)

  • Bin Gu

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

Abstract

Many e-commerce platforms have launched reviewer incentive programs, in which the platforms send free samples to experienced reviewers in exchange for feedback on the samples. This study focuses on an unexplored aspect of this practice: the impact of free samples on reviewers’ ratings of subsequently purchased products (i.e., regular ratings). We theorize that free samples can increase regular ratings through two mechanisms: uncertainty reduction and reciprocity . We collect observational data from Amazon.com and find that after beginning to receive free samples from a platform-initiated reviewer incentive program (i.e., Vine ), reviewers’ regular ratings increased by 2.25% on average (i.e., 0.093 more stars on the five-star scale). Our follow-up analyses at a granular product-category level reveal that the increase in regular ratings occurs primarily for purchased products that share common attributes with free samples that reviewer has received previously. To further delve into the mechanisms at work, we conduct a randomized controlled experiment in which participants are asked to purchase one cookie among three flavor options. Before the cookie purchase, participants either receive nothing in advance (i.e., control group) or receive one of four free samples (i.e., four treatment groups). The results indicate that uncertainty reduction (via the common attribute between the free sample and the options for purchase) significantly mediates the impact of the free sample on the ratings of the purchased cookie. We conclude that platform-initiated free samples can help consumers understand products with similar attributes, thereby reducing prepurchase uncertainty and leading to higher regular ratings. We discuss the managerial and scholarly implications of the findings.

Suggested Citation

  • Jingchuan Pu & Young Kwark & Sang Pil Han & Qiang Ye & Bin Gu, 2024. "Uncertainty Reduction vs. Reciprocity: Understanding the Effect of a Platform-Initiated Reviewer Incentive Program on Regular Ratings," Information Systems Research, INFORMS, vol. 35(3), pages 1363-1381, September.
    • Handle: RePEc:inm:orisre:v:35:y:2024:i:3:p:1363-1381
    DOI: 10.1287/isre.2019.0176
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2019.0176
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2019.0176?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kapil Bawa & Robert Shoemaker, 2004. "The Effects of Free Sample Promotions on Incremental Brand Sales," Marketing Science, INFORMS, vol. 23(3), pages 345-363, November.
    2. Bradley T. Shapiro, 2018. "Positive Spillovers and Free Riding in Advertising of Prescription Pharmaceuticals: The Case of Antidepressants," Journal of Political Economy, University of Chicago Press, vol. 126(1), pages 381-437.
    3. J. Miguel Villas-Boas, 2004. "Consumer Learning, Brand Loyalty, and Competition," Marketing Science, INFORMS, vol. 23(1), pages 134-145, December.
    4. Young Kwark & Jianqing Chen & Srinivasan Raghunathan, 2014. "Online Product Reviews: Implications for Retailers and Competing Manufacturers," Information Systems Research, INFORMS, vol. 25(1), pages 93-110, March.
    5. Hee-Woong Kim & Atreyi Kankanhalli & So-Hyun Lee, 2018. "Examining Gifting Through Social Network Services: A Social Exchange Theory Perspective," Information Systems Research, INFORMS, vol. 29(4), pages 805-828, December.
    6. Luís Cabral & Lingfang (Ivy) Li, 2015. "A Dollar for Your Thoughts: Feedback-Conditional Rebates on eBay," Management Science, INFORMS, vol. 61(9), pages 2052-2063, September.
    7. Peiyu Chen & Lorin M. Hitt & Yili Hong & Shinyi Wu, 2021. "Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application," Information Systems Research, INFORMS, vol. 32(4), pages 1470-1489, December.
    8. Bart de Langhe & Philip M. Fernbach & Donald R. Lichtenstein, 2016. "Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings," Journal of Consumer Research, Oxford University Press, vol. 42(6), pages 817-833.
    9. Yili (Kevin) Hong & Paul A. Pavlou, 2014. "Product Fit Uncertainty in Online Markets: Nature, Effects, and Antecedents," Information Systems Research, INFORMS, vol. 25(2), pages 328-344, June.
    10. Nachiketa Sahoo & Chrysanthos Dellarocas & Shuba Srinivasan, 2018. "The Impact of Online Product Reviews on Product Returns," Information Systems Research, INFORMS, vol. 29(3), pages 723-738, September.
    11. Marija Banović & Magda Aguiar Fontes & Maria Madalena Barreira & Klaus G. Grunert, 2012. "Impact of Product Familiarity on Beef Quality Perception," Agribusiness, John Wiley & Sons, Ltd., vol. 28(2), pages 157-172, March.
    12. Zhijie Lin & Ying Zhang & Yong Tan, 2019. "An Empirical Study of Free Product Sampling and Rating Bias," Service Science, INFORMS, vol. 30(1), pages 260-275, March.
    13. Anuj Kumar & Amit Mehra & Subodha Kumar, 2019. "Why Do Stores Drive Online Sales? Evidence of Underlying Mechanisms from a Multichannel Retailer," Service Science, INFORMS, vol. 30(1), pages 319-338, March.
    14. Kaiquan Xu & Jason Chan & Anindya Ghose & Sang Pil Han, 2017. "Battle of the Channels: The Impact of Tablets on Digital Commerce," Management Science, INFORMS, vol. 63(5), pages 1469-1492, May.
    15. Johnson, Eric J & Russo, J Edward, 1984. "Product Familiarity and Learning New Information," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 11(1), pages 542-550, June.
    16. Goering, Patricia A, 1985. "Effects of Product Trial on Consumer Expectations, Demand, and Prices," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(1), pages 74-82, June.
    17. Monic Sun & Feng Zhu, 2013. "Ad Revenue and Content Commercialization: Evidence from Blogs," Management Science, INFORMS, vol. 59(10), pages 2314-2331, October.
    18. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    19. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    20. Mano, Haim & Oliver, Richard L, 1993. "Assessing the Dimensionality and Structure of the Consumption Experience: Evaluation, Feeling, and Satisfaction," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(3), pages 451-466, December.
    21. Rao, Akshay R & Monroe, Kent B, 1988. "The Moderating Effect of Prior Knowledge on Cue Utilization in Product Evaluations," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(2), pages 253-264, September.
    22. Ni Huang & Tianshu Sun & Peiyu Chen & Joseph M. Golden, 2019. "Word-of-Mouth System Implementation and Customer Conversion: A Randomized Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 805-818, September.
    23. Hsing Kenneth Cheng & Yipeng Liu, 2012. "Optimal Software Free Trial Strategy: The Impact of Network Externalities and Consumer Uncertainty," Information Systems Research, INFORMS, vol. 23(2), pages 488-504, June.
    24. Bin Gu & Jaehong Park & Prabhudev Konana, 2012. "Research Note ---The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products," Information Systems Research, INFORMS, vol. 23(1), pages 182-196, March.
    25. Yi-Chun (Chad) Ho & Junjie Wu & Yong Tan, 2017. "Disconfirmation Effect on Online Rating Behavior: A Structural Model," Information Systems Research, INFORMS, vol. 28(3), pages 626-642, September.
    26. Minhi Hahn & Sehoon Park & Lakshman Krishnamurthi & Andris A. Zoltners, 1994. "Analysis of New Product Diffusion Using a Four-Segment Trial-Repeat Model," Marketing Science, INFORMS, vol. 13(3), pages 224-247.
    27. Jingchuan Pu, 2022. "Do Online Communities Improve Job Performance in the Geographically Dispersed Organization?," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 403-421, February.
    28. Ravi Bapna & Chrysanthos Dellarocas & Sarah Rice, 2010. "Vertically Differentiated Simultaneous Vickrey Auctions: Theory and Experimental Evidence," Management Science, INFORMS, vol. 56(7), pages 1074-1092, July.
    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. Christoph Schneider & Markus Weinmann & Peter N.C. Mohr & Jan vom Brocke, 2021. "When the Stars Shine Too Bright: The Influence of Multidimensional Ratings on Online Consumer Ratings," Management Science, INFORMS, vol. 67(6), pages 3871-3898, June.
    2. Hao Zhang & Mingzheng Wang, 2024. "Tapping vs. Scrolling: Effects of Different Content Acquisition Modes on Content Consumption," Information Systems Frontiers, Springer, vol. 26(5), pages 1835-1855, October.
    3. 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.
    4. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    5. Jürgen Neumann, 2021. "When Biased Ratings Benefit the Consumer - An Economic Analysis of Online Ratings in Markets with Variety-Seeking Consumers," Working Papers Dissertations 77, Paderborn University, Faculty of Business Administration and Economics.
    6. Hsing Kenneth Cheng & D. Daniel Sokol & Xinyu Zang, 2024. "The rise of empirical online platform research in the new millennium," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 416-451, March.
    7. Zhijie Lin & Ying Zhang & Yong Tan, 2019. "An Empirical Study of Free Product Sampling and Rating Bias," Service Science, INFORMS, vol. 30(1), pages 260-275, March.
    8. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    9. 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.
    10. Yue Guan & Yong Tan & Qiang Wei & Guoqing Chen, 2023. "When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics," Information Systems Research, INFORMS, vol. 34(4), pages 1641-1663, December.
    11. 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.
    12. 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.
    13. Peiyu Chen & Lorin M. Hitt & Yili Hong & Shinyi Wu, 2021. "Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application," Information Systems Research, INFORMS, vol. 32(4), pages 1470-1489, December.
    14. Ni Huang & Tianshu Sun & Peiyu Chen & Joseph M. Golden, 2019. "Word-of-Mouth System Implementation and Customer Conversion: A Randomized Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 805-818, September.
    15. Yili (Kevin) Hong & Paul A. Pavlou, 2014. "Product Fit Uncertainty in Online Markets: Nature, Effects, and Antecedents," Information Systems Research, INFORMS, vol. 25(2), pages 328-344, June.
    16. Naixin Zhu, 2023. "Dissertation on Applied Microeconomics of Freemium Pricing Strategies in Mobile App Market," Papers 2305.09479, arXiv.org.
    17. Gökçe Esenduran & James A. Hill & In Joon Noh, 2020. "Understanding the Choice of Online Resale Channel for Used Electronics," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1188-1211, May.
    18. Angela Aerry Choi & Daegon Cho & Dobin Yim & Jae Yun Moon & Wonseok Oh, 2019. "When Seeing Helps Believing: The Interactive Effects of Previews and Reviews on E-Book Purchases," Information Systems Research, INFORMS, vol. 30(4), pages 1164-1183, December.
    19. Liangfei Qiu & Arunima Chhikara & Asoo Vakharia, 2021. "Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Information Systems Research, INFORMS, vol. 32(3), pages 876-894, September.
    20. Yanni Ping & Chelsey Hill & Yun Zhu & Jorge Fresneda, 2023. "Antecedents and consequences of the key opinion leader status: an econometric and machine learning approach," Electronic Commerce Research, Springer, vol. 23(3), pages 1459-1484, September.

    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:inm:orisre:v:35:y:2024:i:3:p:1363-1381. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.