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The Recipe for the Perfect Review?

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  • Michael Scholz
  • Verena Dorner

Abstract

Online product reviews, originally intended to reduce consumers’ pre-purchase search and evaluation costs, have become so numerous that they are now themselves a source for information overload. To help consumers find high-quality reviews faster, review rankings based on consumers’ evaluations of their helpfulness were introduced. But many reviews are never evaluated and never ranked. Moreover, current helpfulness-based systems provide little or no advice to reviewers on how to write more helpful reviews. Average review quality and consumer search costs could be much improved if these issues were solved. This requires identifying the determinants of review helpfulness, which we carry out based on an adaption of Wang and Strong’s well-known data quality framework. Our empirical analysis shows that review helpfulness is influenced not only by single-review features but also by contextual factors expressing review value relative to all available reviews. Reviews for experiential goods differ systematically from reviews for utilitarian goods. Our findings, based on 27,104 reviews from Amazon.com across six product categories, form the basis for estimating preliminary helpfulness scores for unrated reviews and for developing interactive, personalized review writing support tools. Copyright Springer Fachmedien Wiesbaden 2013

Suggested Citation

  • Michael Scholz & Verena Dorner, 2013. "The Recipe for the Perfect Review?," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(3), pages 141-151, June.
    • Handle: RePEc:spr:binfse:v:5:y:2013:i:3:p:141-151
    DOI: 10.1007/s12599-013-0259-3
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    Cited by:

    1. Mohammadreza Mousavizadeh & Mehrdad Koohikamali & Mohammad Salehan & Dam J. Kim, 2022. "An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Elaboration Likelihood Model," Information Systems Frontiers, Springer, vol. 24(1), pages 211-231, February.
    2. Roman Tilly & Oliver Posegga & Kai Fischbach & Detlef Schoder, 2017. "Towards a Conceptualization of Data and Information Quality in Social Information Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(1), pages 3-21, February.
    3. 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.
    4. Zhen Liu & Shao-hui Lei & Yu-lang Guo & Zhi-ang Zhou, 2020. "The interaction effect of online review language style and product type on consumers’ purchase intentions," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-8, December.
    5. Scholz, Michael & Pfeiffer, Jella & Rothlauf, Franz, 2017. "Using PageRank for non-personalized default rankings in dynamic markets," European Journal of Operational Research, Elsevier, vol. 260(1), pages 388-401.

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