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The carrot and the stick in online reviews: determinants of un-/helpfulness voting choices

Author

Listed:
  • Filipe Sengo Furtado

    (WU Vienna University of Economics and Business)

  • Thomas Reutterer

    (WU Vienna University of Economics and Business)

  • Nadine Schröder

    (WU Vienna University of Economics and Business)

Abstract

With increasing volumes of customer reviews, ‘helpfulness’ features have been established by many online platforms as decision-aids for consumers to cope with potential information overload. In this study, we offer a differentiated perspective on the drivers of review helpfulness. Using a hurdle regression setup for both helpfulness and unhelpfulness voting behavior, we aim to disentangle the differential effects of what drives reviews to receive any votes, how many votes they receive and whether these effects differ for helpful against unhelpful review voting behavior. As potential driving factors we include reviews’ star rating deviations from the average rating (as a proxy for confirmation bias), the level of controversy among reviews and review sentiment (consistency of review content), as well as pricing information in our analysis. Albeit with opposite effect signs, we find that revealed review un-/helpfulness is consistently guided by the tonality (i.e., the sentiment of review texts) and that reviewers tend to be less critical for lower priced products. However, we find only partial support for a confirmation bias with differential effects for the level of controversy on helpfulness versus unhelpfulness review votings. We conclude that the effects of voting disagreement are more complex than previous literature suggests and discuss implications for research and management practice.

Suggested Citation

  • Filipe Sengo Furtado & Thomas Reutterer & Nadine Schröder, 2022. "The carrot and the stick in online reviews: determinants of un-/helpfulness voting choices," Journal of Business Economics, Springer, vol. 92(4), pages 565-590, May.
    • Handle: RePEc:spr:jbecon:v:92:y:2022:i:4:d:10.1007_s11573-021-01044-x
    DOI: 10.1007/s11573-021-01044-x
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    References listed on IDEAS

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    1. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    2. Marchand, André & Hennig-Thurau, Thorsten & Wiertz, Caroline, 2017. "Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 336-354.
    3. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    4. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    5. Jean-Noël Kapferer & Gilles Laurent, 1985. "Measuring consumer involvement profiles," Post-Print hal-00786781, HAL.
    6. Dezhi Yin & Sabyasachi Mitra & Han Zhang, 2016. "Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth," Information Systems Research, INFORMS, vol. 27(1), pages 131-144, March.
    7. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
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    More about this item

    Keywords

    Online review; (Un)helpfulness; Hurdle model; Confirmation bias; Price levels; Asymmetric effects;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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