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An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Elaboration Likelihood Model

Mohammadreza Mousavizadeh, Mehrdad Koohikamali, Mohammad Salehan and Dam J. Kim ()
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Mohammadreza Mousavizadeh: Western Michigan University
Mehrdad Koohikamali: California State Polytechnic University
Mohammad Salehan: California State Polytechnic University
Dam J. Kim: University of North Texas

Information Systems Frontiers, 2022, vol. 24, issue 1, No 12, 231 pages

Abstract: Abstract Online consumer reviews (OCRs) have become an important part of online consumers’ decision-making to purchse products. Consumers use OCRs not only to get a better understanding of the characteristics of products but also to learn about other customers’ experiences with them. Drawing upon Elaboration Likelihood Model, this research investigates the predictors of popularity and helpfulness of OCRs. The results of the study show that longer reviews, as well as those with extreme star ratings, are more popular. Moreover, the amount of hedonic and utilitarian cues in a review and its sentiment significantly influence perceptions of online consumers regarding its helpfulness. The results also show how product type moderates the effect of utilitarian and hedonic cues on helpfulness. Our results can be used by online review websites to develop more efficient methods for sorting OCRs.

Keywords: Online consumer reviews; Online review performance; Text mining; Elaboration likelihood model; Sentiment mining (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10796-020-10069-6

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