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Explaining reviewing effort: Existing reviews as potential driver.

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Area of Science:

  • Human-Computer Interaction
  • Social Computing
  • Online Review Systems

Background:

  • Online review systems rely on user-generated content for success.
  • Reviewer effort in writing reviews is crucial for system helpfulness.
  • Cognitive factors may influence future reviewer engagement.

Purpose of the Study:

  • To investigate the impact of existing reviews on future reviewer effort.
  • To analyze how the quantity of reviews affects textual review engagement.
  • To determine if rating discrepancies influence reviewer effort.

Main Methods:

  • Analysis of a dataset from Google Maps (40 sights, >37,000 reviews).
  • Examination of reviewer propensity for optional textual reviews.
  • Measurement of textual review length as an indicator of effort.
  • Validation using additional data from Yelp.

Main Results:

  • Textual reviewing effort (propensity and length) is negatively correlated with the number of existing reviews.
  • Reviewers do not increase textual effort when their rating significantly differs from existing ones.
  • Findings were consistent across Google Maps and Yelp datasets.

Conclusions:

  • The volume of existing reviews can decrease individual reviewer effort.
  • Presenting review metrics impacts future textual reviewing behavior.
  • Online platforms should consider how review metrics are displayed to optimize reviewer engagement.