Online Review Consistency Matters: An Elaboration Likelihood Model Perspective

Navid Aghakhani, Onook Oh (), Dawn G. Gregg () and Jahangir Karimi
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Navid Aghakhani: University of Tennessee Chattanooga
Onook Oh: University of Colorado Denver
Dawn G. Gregg: University of Colorado Denver
Jahangir Karimi: University of Colorado Denver

Information Systems Frontiers, No 0, 15 pages

Abstract: Abstract To date, online review usefulness studies have explored the independent influence of central and peripheral cues on online review usefulness. Employing the Elaboration Likelihood Model (ELM), however, we argue that central and peripheral cues are jointly, not independently, processed by online users. For this exploration, we develop and measure “review consistency” variable (i.e., level of consistency between a review text and its attendant review rating), and rating inconsistency (i.e., level of inconsistency between a review rating and the average rating). We find a positive effect of review consistency on the review usefulness. Contrary to our hypothesis, however, we find a positive effect of rating inconsistency on the review usefulness. Our results also indicate that the contingency effect of rating inconsistency on the relationship between review consistency and review usefulness. Particularly, we find that rating inconsistency negatively moderates the effect of review consistency on the review usefulness. The theoretical and practical implications of the findings are discussed.

Keywords: Online reviews; Review consistency; Review helpfulness; E-word of mouth; Machine learning; Text mining (search for similar items in EconPapers)
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DOI: 10.1007/s10796-020-10030-7

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