IDEAS home Printed from https://ideas.repec.org/a/spr/orspec/v43y2021i4d10.1007_s00291-021-00639-8.html
   My bibliography  Save this article

Learning judgment benchmarks of customers from online reviews

Author

Listed:
  • Xingli Wu

    (Sichuan University)

  • Huchang Liao

    (Sichuan University)

Abstract

Online reviews play an important role for the purchasing decision of customers. One challenge is that different reviewers have different judgment benchmarks when making online reviews, which can mislead purchasing decisions. Specifically, the same star rating may correspond to different levels of sentiment for different reviewers because of the explicit preference differences in individuals. This study explores the personal judgment benchmarks through a preference learning process. Considering the nonlinear cognition of reviewers, we propose a marginal value function with smooth shapes and clear parameters to model the scores of online reviews. A mathematical programming model is established to predict the specific marginal value function for each reviewer. Two kinds of performance accurateness are defined to measure the performance of the learning model. We evaluate two empirical data sets extracted from TripAdvisor.com to deepen the understanding of personal judgment benchmarks. A simulation study is conducted to validate the proposed model. The results have important theoretical and practical implications for purchasing decisions based on online reviews.

Suggested Citation

  • Xingli Wu & Huchang Liao, 2021. "Learning judgment benchmarks of customers from online reviews," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(4), pages 1125-1157, December.
    • Handle: RePEc:spr:orspec:v:43:y:2021:i:4:d:10.1007_s00291-021-00639-8
    DOI: 10.1007/s00291-021-00639-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00291-021-00639-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00291-021-00639-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chunhua Wu & Hai Che & Tat Y. Chan & Xianghua Lu, 2015. "The Economic Value of Online Reviews," Marketing Science, INFORMS, vol. 34(5), pages 739-754, September.
    2. Decui Liang & Zhuoyin Dai & Mingwei Wang & Jinjun Li, 2020. "Web celebrity shop assessment and improvement based on online review with probabilistic linguistic term sets by using sentiment analysis and fuzzy cognitive map," Fuzzy Optimization and Decision Making, Springer, vol. 19(4), pages 561-586, December.
    3. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    4. 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.
    5. Sobrie, Olivier & Gillis, Nicolas & Mousseau, Vincent & Pirlot, Marc, 2018. "UTA-poly and UTA-splines: Additive value functions with polynomial marginals," European Journal of Operational Research, Elsevier, vol. 264(2), pages 405-418.
    6. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    7. Huchang Liao & Xiaomei Mi & Zeshui Xu, 2020. "A survey of decision-making methods with probabilistic linguistic information: bibliometrics, preliminaries, methodologies, applications and future directions," Fuzzy Optimization and Decision Making, Springer, vol. 19(1), pages 81-134, March.
    8. Branke, Juergen & Corrente, Salvatore & Greco, Salvatore & Słowiński, Roman & Zielniewicz, Piotr, 2016. "Using Choquet integral as preference model in interactive evolutionary multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 250(3), pages 884-901.
    9. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.
    10. Zhou, Wei & Xu, Zeshui, 2016. "Generalized asymmetric linguistic term set and its application to qualitative decision making involving risk appetites," European Journal of Operational Research, Elsevier, vol. 254(2), pages 610-621.
    11. Ghaderi, Mohammad & Ruiz, Francisco & Agell, Núria, 2017. "A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1073-1084.
    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. Xiaohong Chen & Hui Wang & Xihua Li, 2024. "Doctor recommendation under probabilistic linguistic environment considering patient’s risk preference," Annals of Operations Research, Springer, vol. 341(1), pages 555-581, October.
    2. Wu, Xingli & Liao, Huchang, 2023. "Value-driven preference disaggregation analysis for uncertain preference information," Omega, Elsevier, vol. 115(C).
    3. Liu, Fan & Liao, Huchang & Al-Barakati, Abdullah, 2023. "Physician selection based on user-generated content considering interactive criteria and risk preferences of patients," Omega, Elsevier, vol. 115(C).
    4. Zhenfang Zhang & Nan Zhang & Jiguang Wang, 2022. "The Influencing Factors on Impulse Buying Behavior of Consumers under the Mode of Hunger Marketing in Live Commerce," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
    5. Zhang, Zhiying & Liao, Huchang & Tang, Anbin, 2022. "Renewable energy portfolio optimization with public participation under uncertainty: A hybrid multi-attribute multi-objective decision-making method," Applied Energy, Elsevier, vol. 307(C).

    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. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    2. Guo, Mengzhuo & Zhang, Qingpeng & Liao, Xiuwu & Chen, Frank Youhua & Zeng, Daniel Dajun, 2021. "A hybrid machine learning framework for analyzing human decision-making through learning preferences," Omega, Elsevier, vol. 101(C).
    3. 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.
    4. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    5. Xingli Wu & Huchang Liao, 2022. "A gained and lost dominance score method with conflict analysis for green economy development evaluation," Annals of Operations Research, Springer, vol. 316(1), pages 623-655, September.
    6. Tao Lu & May Yuan & Chong (Alex) Wang & Xiaoquan (Michael) Zhang, 2022. "Histogram Distortion Bias in Consumer Choices," Management Science, INFORMS, vol. 68(12), pages 8963-8978, December.
    7. Steffen Zimmermann & Philipp Herrmann & Dennis Kundisch & Barrie R. Nault, 2018. "Decomposing the Variance of Consumer Ratings and the Impact on Price and Demand," Information Systems Research, INFORMS, vol. 29(4), pages 984-1002, December.
    8. Zhen Li & Aoi Shimizu, 2018. "Impact of Online Customer Reviews on Sales Outcomes: An Empirical Study Based on Prospect Theory," The Review of Socionetwork Strategies, Springer, vol. 12(2), pages 135-151, December.
    9. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.
    10. Kadziński, Miłosz & Wójcik, Michał & Ciomek, Krzysztof, 2022. "Review and experimental comparison of ranking and choice procedures for constructing a univocal recommendation in a preference disaggregation setting," Omega, Elsevier, vol. 113(C).
    11. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    12. Frota Neto, João Quariguasi & Bloemhof, Jacqueline & Corbett, Charles, 2016. "Market prices of remanufactured, used and new items: Evidence from eBay," International Journal of Production Economics, Elsevier, vol. 171(P3), pages 371-380.
    13. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    14. Zhenzhen Ma & Jianjun Zhu & Shitao Zhang, 2021. "Probabilistic-based expressions in behavioral multi-attribute decision making considering pre-evaluation," Fuzzy Optimization and Decision Making, Springer, vol. 20(1), pages 145-173, March.
    15. Zhuolan Bao & Wenwen Li & Pengzhen Yin & Michael Chau, 2021. "Examining the impact of review tag function on product evaluation and information perception of popular products," Information Systems and e-Business Management, Springer, vol. 19(2), pages 517-539, June.
    16. Liu, Jiapeng & Liao, Xiuwu & Kadziński, Miłosz & Słowiński, Roman, 2019. "Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1071-1089.
    17. Tunç, Murat & Cavusoglu, Huseyin & Raghunathan, Srinivasan, 2021. "Online product reviews : Is a finer-grained rating scheme superior to a coarser one?," Other publications TiSEM ec57cbf3-7415-4427-aafc-6, Tilburg University, School of Economics and Management.
    18. Dominik Gutt, 2018. "In the Eye of the Beholder? Empirically Decomposing Different Economic Implications of the Online Rating Variance," Working Papers Dissertations 40, Paderborn University, Faculty of Business Administration and Economics.
    19. Wu, Xingli & Liao, Huchang, 2023. "A compensatory value function for modeling risk tolerance and criteria interactions in preference disaggregation," Omega, Elsevier, vol. 117(C).
    20. Zunqiang Zhang & Guoqing Chen & Jin Zhang & Xunhua Guo & Qiang Wei, 2016. "Providing Consistent Opinions from Online Reviews: A Heuristic Stepwise Optimization Approach," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 236-250, May.

    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:spr:orspec:v:43:y:2021:i:4:d:10.1007_s00291-021-00639-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.