IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v109y2018icp26-30.html
   My bibliography  Save this article

The dynamics of online ratings with heterogeneous preferences in online review platform

Author

Listed:
  • Zhang, Yuhan
  • Feng, Xin
  • Wu, Ye
  • Xiao, Jinghua

Abstract

Nowadays online consumer reviews (OCR) has increasingly received scholars' attention as an important form of word-of-mouth. Recent study shows that online reviews of a product, such as a book or a restaurant, have effect on long-term consuming behavior and the future rating of the product, it mainly reflects that the early high rating of a product will lead the decrease trend of rating over time. To confirm the existence of the effect and explore how it works, over 180,000 reviews on Dianping.com were collected to investigate the behavior patterns and intrinsic dynamics. In this paper, four temporal evolution patterns were observed via evaluating the cumulative average rating series for each restaurant. Moreover, a conceptual model considering the influence of heterogeneous preferences and the self-selection mechanism was introduced, and the numerical results coincided with the empirical analysis well enough to support the hypotheses. We find special preferences result in tendentious consumption and unrepresentative reviews, these reviews lead the potential consumers to over- or under-estimate the products and directly affect the subsequent ratings. The conclusions of this paper can contribute to the specific policies to adjust the initial rating effect for the specific marketing strategies.

Suggested Citation

  • Zhang, Yuhan & Feng, Xin & Wu, Ye & Xiao, Jinghua, 2018. "The dynamics of online ratings with heterogeneous preferences in online review platform," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 26-30.
    • Handle: RePEc:eee:chsofr:v:109:y:2018:i:c:p:26-30
    DOI: 10.1016/j.chaos.2018.02.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077918300481
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2018.02.003?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. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    2. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    3. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    4. Guo, Qiang & Ji, Lei & Liu, Jian-Guo & Han, Jingti, 2017. "Evolution properties of online user preference diversity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 698-713.
    5. Paulo B. Goes & Mingfeng Lin & Ching-man Au Yeung, 2014. "“Popularity Effect” in User-Generated Content: Evidence from Online Product Reviews," Information Systems Research, INFORMS, vol. 25(2), pages 222-238, June.
    6. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    7. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
    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. Nilashi, Mehrbakhsh & Ahmadi, Hossein & Arji, Goli & Alsalem, Khalaf Okab & Samad, Sarminah & Ghabban, Fahad & Alzahrani, Ahmed Omar & Ahani, Ali & Alarood, Ala Abdulsalam, 2021. "Big social data and customer decision making in vegetarian restaurants: A combined machine learning method," Journal of Retailing and Consumer Services, Elsevier, vol. 62(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. 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.
    2. 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.
    3. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    4. 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.
    5. Linyi Li & Shyam Gopinath & Stephen J. Carson, 2022. "History Matters: The Impact of Online Customer Reviews Across Product Generations," Management Science, INFORMS, vol. 68(5), pages 3878-3903, May.
    6. 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.
    7. Zhang, Ziqiong & Zhang, Zili & Yang, Yang, 2016. "The power of expert identity: How website-recognized expert reviews influence travelers' online rating behavior," Tourism Management, Elsevier, vol. 55(C), pages 15-24.
    8. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    9. Engler, Tobias H. & Winter, Patrick & Schulz, Michael, 2015. "Understanding online product ratings: A customer satisfaction model," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 113-120.
    10. Dipankar Das, 2022. "Measurement of Trustworthiness of the Online Reviews," Papers 2210.00815, arXiv.org, revised Nov 2023.
    11. Yuchi Zhang & David Godes, 2018. "Learning from Online Social Ties," Marketing Science, INFORMS, vol. 37(3), pages 425-444, May.
    12. Apostolos Filippas & John Horton & Joseph M. Golden, 2017. "Reputation in the Long-Run," CESifo Working Paper Series 6750, CESifo.
    13. Liye Ma & Baohong Sun & Sunder Kekre, 2015. "The Squeaky Wheel Gets the Grease—An Empirical Analysis of Customer Voice and Firm Intervention on Twitter," Marketing Science, INFORMS, vol. 34(5), pages 627-645, September.
    14. 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.
    15. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    16. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    17. 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.
    18. Schindler, Diana & Decker, Reinhold, 2013. "Some remarks on the internal consistency of online consumer reviews," Australasian marketing journal, Elsevier, vol. 21(4), pages 221-227.
    19. Lu, Shuya & Wu, Jianan & Tseng, Shih-Lun (Allen), 2018. "How Online Reviews Become Helpful: A Dynamic Perspective," Journal of Interactive Marketing, Elsevier, vol. 44(C), pages 17-28.
    20. Foster, Joshua, 2022. "How rating mechanisms shape user search, quality inference and engagement in online platforms: Experimental evidence," Journal of Business Research, Elsevier, vol. 142(C), pages 791-807.

    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:eee:chsofr:v:109:y:2018:i:c:p:26-30. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.