Reviewing Experts’ Restraint from Extremes and its Impact on Service Providers

Peter Nguyen, Xin (Shane) Wang, Xi Li, June Cotte

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Abstract

This research investigates reviewing experts on online review platforms. The main hypothesis is that greater expertise in generating reviews leads to greater restraint from extreme summary evaluations. The authors argue that greater experience generating reviews facilitates processing and elaboration and enhances the number of attributes implicitly considered in evaluations, which reduces the likelihood of assigning extreme summary ratings. This restraint-of-expertise hypothesis is tested across different review platforms (TripAdvisor, Qunar, and Yelp), shown for both assigned ratings and review text sentiment, and demonstrated both between (experts vs. novices) and within reviewers (expert vs. pre-expert). Two experiments replicate the main effect and provide support for the attribute-based explanation. Field studies demonstrate two major consequences of the restraint-of-expertise effect. (i) Reviewing experts (vs. novices), as a whole, have less impact on the aggregate valence metric, which is known to affect page-rank and consumer consideration. (ii) Experts systematically benefit and harm service providers with their ratings. For service providers that generally provide mediocre (excellent) experiences, reviewing experts assign significantly higher (lower) ratings than novices. This research provides important caveats to the existing marketing practice of service providers incentivizing reviewing experts and provides strategic implications for how platforms should adopt rating scales and aggregate ratings.

Original languageEnglish
Article numberucaa037
Pages (from-to)654-674
Number of pages21
JournalJournal of Consumer Research
Volume47
Issue number5
Early online date15 Jul 2020
DOIs
Publication statusPublished - 1 Feb 2021

Bibliographical note

Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press on behalf of Journal of Consumer Research, Inc. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Online word-of-mouth
  • Expertise
  • User rating average
  • Platform strategy
  • Text analysis
  • sentiment analysis

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