IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v22y2022i2d10.1007_s10660-020-09419-y.html
   My bibliography  Save this article

Effect of other visible reviews’ votes and personality on review helpfulness evaluation: an event-related potentials study

Author

Listed:
  • Liyi Zhang

    (Wuhan University)

  • Daomeng Guo

    (Wuhan University
    Hubei Engineering University)

  • Xuan Wen

    (Wuhan University)

  • Yiran Li

    (Wuhan University)

Abstract

Few studies have investigated the mechanism underlying the connection between votes and review helpfulness. A within-subject experiment with a between-group factor of personality traits was adopted to measure participants’ neural response in the processing of votes regarding review helpfulness. The results showed that a larger feedback-related negativity (FRN) and smaller P300 were induced when their personal voting behavior was inconsistent with the relative majority votes. The results confirmed that the participants established a reference frame of relative majority votes via comparison of the votes associated with other visible reviews and that they applied this reference frame to evaluate their personal voting behavior. Moreover, the participants with higher openness showed lower ΔFRN values, confirming the individual differences in the influence of the reference frame of the relative majority votes on outcome evaluation.

Suggested Citation

  • Liyi Zhang & Daomeng Guo & Xuan Wen & Yiran Li, 2022. "Effect of other visible reviews’ votes and personality on review helpfulness evaluation: an event-related potentials study," Electronic Commerce Research, Springer, vol. 22(2), pages 351-375, June.
    • Handle: RePEc:spr:elcore:v:22:y:2022:i:2:d:10.1007_s10660-020-09419-y
    DOI: 10.1007/s10660-020-09419-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-020-09419-y
    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/s10660-020-09419-y?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. Gopinath, Mahesh & Nyer, Prashanth U., 2009. "The effect of public commitment on resistance to persuasion: The influence of attitude certainty, issue importance, susceptibility to normative influence, preference for consistency and source proximi," International Journal of Research in Marketing, Elsevier, vol. 26(1), pages 60-68.
    2. Ed Hopkins & Tatiana Kornienko, 2010. "Which Inequality? The Inequality of Endowments versus the Inequality of Rewards," American Economic Journal: Microeconomics, American Economic Association, vol. 2(3), pages 106-137, August.
    3. 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.
    4. Bin Zhang & Paul A. Pavlou & Ramayya Krishnan, 2018. "On Direct vs. Indirect Peer Influence in Large Social Networks," Information Systems Research, INFORMS, vol. 29(2), pages 292-314, June.
    5. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    6. Li, Kunlin & Chen, Yuhan & Zhang, Liyi, 2020. "Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    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)

    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. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    2. 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.
    3. Ana Babić Rosario & Kristine Valck & Francesca Sotgiu, 2020. "Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 422-448, May.
    4. Young Joon Park & Jaewoo Joo & Charin Polpanumas & Yeujun Yoon, 2021. "“Worse Than What I Read?” The External Effect of Review Ratings on the Online Review Generation Process: An Empirical Analysis of Multiple Product Categories Using Amazon.com Review Data," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    5. 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.
    6. Herbert Dawid & Reinhold Decker & Thomas Hermann & Hermann Jahnke & Wilhelm Klat & Rolf König & Christian Stummer, 2017. "Management science in the era of smart consumer products: challenges and research perspectives," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 203-230, March.
    7. 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.
    8. Girardin, Florent & Bezençon, Valéry & Lunardo, Renaud, 2021. "Dealing with poor online ratings in the hospitality service industry: The mitigating power of corporate social responsibility activities," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    9. Yuchi Zhang & David Godes, 2018. "Learning from Online Social Ties," Marketing Science, INFORMS, vol. 37(3), pages 425-444, May.
    10. Apostolos Filippas & John Horton & Joseph M. Golden, 2017. "Reputation in the Long-Run," CESifo Working Paper Series 6750, CESifo.
    11. 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.
    12. Guan, Chong & Lam, Shun Yin, 2019. "Product Rating Statistics as Consumer Search Aids," Journal of Interactive Marketing, Elsevier, vol. 48(C), pages 51-70.
    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. 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.
    15. 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.
    16. Moon, Sangkil & Jalali, Nima & Erevelles, Sunil, 2021. "Segmentation of both reviewers and businesses on social media," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    17. 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.
    18. Ning Zhong & David A. Schweidel, 2020. "Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model," Marketing Science, INFORMS, vol. 39(4), pages 827-846, July.
    19. Moon, Sangkil & Kim, Seung-Wook & Iacobucci, Dawn, 2024. "Dynamic relationship changes between reviewers and consumers in online product reviews," Journal of Retailing, Elsevier, vol. 100(1), pages 70-84.
    20. 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.

    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:elcore:v:22:y:2022:i:2:d:10.1007_s10660-020-09419-y. 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.