IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v25y2023i6p2195-2215.html

Do Noisy Customer Reviews Discourage Platform Sellers? Empirical Analysis of an Online Solar Marketplace

Author

Listed:
  • Herbie Huang

    (Lazada, Lazada One, Singapore 189554)

  • Nur Sunar

    (Kenan-Flagler Business School, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

  • Jayashankar M. Swaminathan

    (Kenan-Flagler Business School, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

  • Rahul Roy

    (Kenan-Flagler Business School, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

Abstract

Problem definition : Customer reviews are essential to online marketplaces. However, reviews typically vary; ratings of a product or service are rarely the same. In many service marketplaces, including the ones for solar panel installations, supply-side participants are active. That is, a seller must make a proposal to serve each customer. In such marketplaces, it is not clear how (or if) the dispersion in customer reviews affects the seller activity level and number of matches in the marketplace. Our paper examines this by considering both ratings and text reviews. To our knowledge, this is the first paper that empirically studies how the review dispersion affects a seller’s activity level and the number of matches in an online marketplace with active sellers. Distinct from literature, we examine the relationship between the review dispersion and supply-side activities in an online service marketplace. Methodology/results : We collaborated with one of the largest online solar marketplaces in the United States that connects potential solar panel adopters with installers. We obtained a unique data set from the marketplace for 2013 − 2018. We complement this with public data sets. Our analysis uses traditional econometrics methods, a clustering method, and the deep-learning-based natural-language-processing model BERT developed by Google AI. We find that the dispersion in customer reviews has a significant and inverted U-shaped relationship with an installer’s marketplace activity level. Intuitively, a marketplace operator would favor having more sellers with perfect ratings. In contrast, we identify a significant and inverted U-shaped relationship between the market-level review dispersion and transactions. Managerial implications : Our paper provides key insights to marketplace operators and sellers. We find that in contrast to general belief, an operator can improve its market transactions by keeping/promoting sellers with low ratings or avoiding (negative) review filtering. Furthermore, sellers’ implementation of “rating gating” to avoid negative reviews may backfire for them by reducing their matches.

Suggested Citation

  • Herbie Huang & Nur Sunar & Jayashankar M. Swaminathan & Rahul Roy, 2023. "Do Noisy Customer Reviews Discourage Platform Sellers? Empirical Analysis of an Online Solar Marketplace," Manufacturing & Service Operations Management, INFORMS, vol. 25(6), pages 2195-2215, November.
    • Handle: RePEc:inm:ormsom:v:25:y:2023:i:6:p:2195-2215
    DOI: 10.1287/msom.2021.0104
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/msom.2021.0104
    Download Restriction: no
    File URL: https://libkey.io/10.1287/msom.2021.0104?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. Benjamin Scheibehenne & Rainer Greifeneder & Peter M. Todd, 2010. "Can There Ever Be Too Many Options? A Meta-Analytic Review of Choice Overload," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(3), pages 409-425, October.
    2. Jun Li & Serguei Netessine, 2020. "Higher Market Thickness Reduces Matching Rate in Online Platforms: Evidence from a Quasiexperiment," Management Science, INFORMS, vol. 66(1), pages 271-289, January.
    3. Jacquemin, Alexis P & Berry, Charles H, 1979. "Entropy Measure of Diversification and Corporate Growth," Journal of Industrial Economics, Wiley Blackwell, vol. 27(4), pages 359-369, June.
    4. Nur Sunar & Jayashankar M. Swaminathan, 2022. "Socially relevant and inclusive operations management," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4379-4392, December.
    5. Joel A. C. Baum & Kristina B. Dahlin, 2007. "Aspiration Performance and Railroads’ Patterns of Learning from Train Wrecks and Crashes," Organization Science, INFORMS, vol. 18(3), pages 368-385, June.
    6. Blass, Vered & Corbett, Charles J. & Delmas, Magali A. & Muthulingam, Suresh, 2014. "Top management and the adoption of energy efficiency practices: Evidence from small and medium-sized manufacturing firms in the US," Energy, Elsevier, vol. 65(C), pages 560-571.
    7. Daniel W. Elfenbein & Raymond Fisman & Brian McManus, 2015. "Market Structure, Reputation, and the Value of Quality Certification," American Economic Journal: Microeconomics, American Economic Association, vol. 7(4), pages 83-108, November.
    8. Josse Delfgaauw & Robert Dur & Joeri Sol & Willem Verbeke, 2013. "Tournament Incentives in the Field: Gender Differences in the Workplace," Journal of Labor Economics, University of Chicago Press, vol. 31(2), pages 305-326.
    9. Antonio Moreno & Christian Terwiesch, 2014. "Doing Business with Strangers: Reputation in Online Service Marketplaces," Information Systems Research, INFORMS, vol. 25(4), pages 865-886, December.
    10. Gerard Hoberg & Gordon Phillips, 2016. "Text-Based Network Industries and Endogenous Product Differentiation," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1423-1465.
    11. Smaldino, Paul E., 2013. "Measures of individual uncertainty for ecological models: Variance and entropy," Ecological Modelling, Elsevier, vol. 254(C), pages 50-53.
    12. Nur Sunar & John R. Birge, 2019. "Strategic Commitment to a Production Schedule with Uncertain Supply and Demand: Renewable Energy in Day-Ahead Electricity Markets," Management Science, INFORMS, vol. 65(2), pages 714-734, February.
    13. March, James G., 1988. "Variable risk preferences and adaptive aspirations," Journal of Economic Behavior & Organization, Elsevier, vol. 9(1), pages 5-24, January.
    14. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    15. Xitong Li, 2018. "Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold," Information Systems Research, INFORMS, vol. 29(3), pages 739-754, September.
    16. Saravanan Kesavan & Bradley R. Staats & Wendell Gilland, 2014. "Volume Flexibility in Services: The Costs and Benefits of Flexible Labor Resources," Management Science, INFORMS, vol. 60(8), pages 1884-1906, August.
    17. Hülya Karaman, 2021. "Online Review Solicitations Reduce Extremity Bias in Online Review Distributions and Increase Their Representativeness," Management Science, INFORMS, vol. 67(7), pages 4420-4445, July.
    18. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    19. Jo Thori Lind & Halvor Mehlum, 2010. "With or Without U? The Appropriate Test for a U‐Shaped Relationship," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 109-118, February.
    20. Yili Hong & Pei-yu Chen & Lorin Hitt, 2014. "Measuring Product Type with Dynamics of Online Product Review Variances: A Theoretical Model and the Empirical Applications," Working Papers 14-03, NET Institute.
    21. Demirag, Istemi & Khadaroo, Iqbal & Stapleton, Pamela & Stevenson, Caral, 2011. "Risks and the financing of PPP: Perspectives from the financiers," The British Accounting Review, Elsevier, vol. 43(4), pages 294-310.
    22. Kostas Bimpikis & Wedad J. Elmaghraby & Ken Moon & Wenchang Zhang, 2020. "Managing Market Thickness in Online Business-to-Business Markets," Management Science, INFORMS, vol. 66(12), pages 5783-5822, December.
    23. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    24. Stephen J. Mezias & Ya-Ru Chen & Patrice R. Murphy, 2002. "Aspiration-Level Adaptation in an American Financial Services Organization: A Field Study," Management Science, INFORMS, vol. 48(10), pages 1285-1300, October.
    25. Kristina Dahlin & Joel A.C Baum, 2007. "Aspiration performance and railroads' patterns of learning from train wrecks and crashes," Post-Print hal-00480399, HAL.
    26. Krishna Palepu, 1985. "Diversification strategy, profit performance and the entropy measure," Strategic Management Journal, Wiley Blackwell, vol. 6(3), pages 239-255, July.
    27. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
    28. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abbas, Antragama Ewa & Kool, Floris & Agahari, Wirawan & de Reuver, Mark, 2026. "Stimulating data marketplace adoption in manufacturing: The impact of perceived control on willingness to share data," Technovation, Elsevier, vol. 149(C).
    2. N. Bora Keskin & Yuexing Li & Nur Sunar, 2025. "Data-Driven Clustering and Feature-Based Retail Electricity Pricing with Smart Meters," Operations Research, INFORMS, vol. 73(5), pages 2636-2660, September.
    3. Xiaoshan Peng & Owen Q. Wu & Gilvan C. Souza, 2024. "Renewable, Flexible, and Storage Capacities: Friends or Foes?," Manufacturing & Service Operations Management, INFORMS, vol. 26(5), pages 1730-1749, September.

    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. Erk P. Piening & Ferdinand Thies & Michael Wessel & Alexander Benlian, 2021. "Searching for Success—Entrepreneurs’ Responses to Crowdfunding Failure," Entrepreneurship Theory and Practice, , vol. 45(3), pages 626-657, May.
    2. Scott F. Rockart & Kristin Wilson, 2019. "Learning in Cycles," Organization Science, INFORMS, vol. 30(1), pages 70-87, February.
    3. Shijie Lu & Xin (Shane) Wang & Neil Bendle, 2020. "Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry," Management Science, INFORMS, vol. 66(5), pages 2140-2162, May.
    4. Mohamed Boly, 2018. "CO 2 mitigation in developing countries: the role of foreign aid," Working Papers halshs-01740881, HAL.
    5. Bofinger, Peter & Scheuermeyer, Philipp, 2016. "Income Distribution and Aggregate Saving: A Non-Monotonic Relationship," CEPR Discussion Papers 11435, C.E.P.R. Discussion Papers.
    6. Vidya Mani & Suresh Muthulingam, 2019. "Does Learning from Inspections Affect Environmental Performance? Evidence from Unconventional Well Development in Pennsylvania," Service Science, INFORMS, vol. 21(1), pages 177-197, January.
    7. Kotiloglu, Serhan & Blettner, Daniela & Lechler, Thomas G., 2024. "Integrating national culture into the organizational performance feedback theory," European Management Journal, Elsevier, vol. 42(3), pages 327-347.
    8. Axel Zeijen & Manuel Romagnoli & Luigi Marengo, 2025. "Signposts for Problemistic Search: Reference Points and Adaptation in Rugged Landscapes," Strategy Science, INFORMS, vol. 10(3), pages 263-279, September.
    9. Yasser Alhenawi & Martha L. Stilwell, 2019. "Toward a complete definition of relatedness in merger and acquisition transactions," Review of Quantitative Finance and Accounting, Springer, vol. 53(2), pages 351-396, August.
    10. Kostiulin Maksim, 2024. "The Nonlinear Relationship Between Technological Development and Income Inequality: Evidence from Dynamic Panel Model with Threshold Effect," Journal of Economic Development, The Economic Research Institute, Chung-Ang University, vol. 49(3), pages 33-61.
    11. Aghamolla, Cyrus & Thakor, Richard T., 2022. "IPO peer effects," Journal of Financial Economics, Elsevier, vol. 144(1), pages 206-226.
    12. Pathak, Seemantini & Chiu, Shih-Chi (Sana), 2020. "Firm-advisor ties and financial performance in the context of corporate divestiture," Journal of Business Research, Elsevier, vol. 121(C), pages 315-328.
    13. Sung, Bongsuk, 2019. "Do government subsidies promote firm-level innovation? Evidence from the Korean renewable energy technology industry," Energy Policy, Elsevier, vol. 132(C), pages 1333-1344.
    14. Guilhem Bascle & Jiwook Jung, 2023. "Caught in an Expectations Trap: Risks of Giving Securities Analysts What They Expect," Organization Science, INFORMS, vol. 34(1), pages 176-196, January.
    15. Xitong Li, 2018. "Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold," Information Systems Research, INFORMS, vol. 29(3), pages 739-754, September.
    16. Alessandro Marino & Paolo Aversa & Luiz Mesquita & Jaideep Anand, 2015. "Driving Performance via Exploration in Changing Environments: Evidence from Formula One Racing," Organization Science, INFORMS, vol. 26(4), pages 1079-1100, August.
    17. Eryn Juan He & Joel Goh, 2022. "Profit or Growth? Dynamic Order Allocation in a Hybrid Workforce," Management Science, INFORMS, vol. 68(8), pages 5891-5906, August.
    18. Luca J. Uberti & Elodie Douarin, 2023. "The Feminisation U, cultural norms, and the plough," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(1), pages 5-35, January.
    19. Thomas P. Moliterno & Nikolaus Beck & Christine M. Beckman & Mark Meyer, 2014. "Knowing Your Place: Social Performance Feedback in Good Times and Bad Times," Organization Science, INFORMS, vol. 25(6), pages 1684-1702, December.
    20. Lee, Hyunmin, 2023. "Strategic similarity in the co-evolution of technological and business diversification for firm growth: Evidence from smart-manufacturing related firms," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:inm:ormsom:v:25:y:2023:i:6:p:2195-2215. 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.