How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model

Navid Aghakhani, Onook Oh (), Dawn Gregg and Hemant Jain
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Navid Aghakhani: University of Tennessee Chattanooga
Onook Oh: University of Colorado Denver
Dawn Gregg: University of Colorado Denver
Hemant Jain: University of Tennessee Chattanooga

Information Systems Frontiers, 2023, vol. 25, issue 4, No 13, 1513-1531

Abstract: Abstract This study extends our understanding of what makes an online review useful by examining the effects of review quality (i.e., as a composite variable of review comprehensiveness and review topic consistency) on review usefulness, and the moderating effects of source credibility on the relationship between review quality and review usefulness. The Elaboration Likelihood Model, convergence theory, and cueing effect literature are used to define the variables of review comprehensiveness and review topic consistency. Analyses of 27,517 restaurant reviews from Yelp show that review topic consistency has a positive effect on review usefulness, but, contrary to our hypothesis, review comprehensiveness has a negative effect on review usefulness. We also found source credibility positively moderates the effect of review comprehensiveness on review usefulness, but negatively moderates the effect of review topic consistency on review usefulness. Theoretical and practical implications are discussed.

Keywords: Text mining; Online reviews; Review usefulness; Review helpfulness; Elaboration likelihood model; Source credibility; Bias effect (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10796-022-10299-w

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