An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Ela
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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 1 & Mehrdad Koohikamali 2 & Mohammad Salehan 2 & Dam J. Kim 3 Accepted: 25 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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
1 Introduction Online consumer reviews (OCRs) have become an important part of online consumers’ decision making to purchase products (Huang et al. 2018; Chatterjee 2001). A recent study shows that for 90% of customers, the decision to buy is based on OCRs (Capoccia 2018). Consumers use OCRs to make better purchasing decisions, to better understand the characteristics of the product in which they are interested, and to learn about other customers’ experiences with the product. Meanwhile, OCRs provide companies with the opportunity to promote their products and services by motivating their customers to write OCRs (Korfiatis et al. 2012) and small businesses are taking advantage of OCRs the most (Capoccia 2018). * Dam J. Kim [email protected] 1
Business Information Systems, Western Michigan University, Kalamazoo, MI 49008, USA
2
Computer Information Systems, California State Polytechnic University, Pomona, CA 91768, USA
3
Department of Information Technology and Decision Sciences, G. Brint Ryan College of Business, University of North Texas, Denton, TX 76203, USA
Although online reviews contain valuable information, consumers cannot read all of them (Huang et al. 2018; Kuan et al. 2015). Thus, online platforms haven been taking actions to ensure that their customers are presented with the most helpful OCRs. For example, Yelp.com removes fake reviews (Yelp 2018) and Amazon.com sorts them based on the helpfulness of the reviews (Kuan et al. 2015).. The helpfulness of a review can be determined by calculating the proportion of people who found it to be helpful (Hong et al. 2017; Kuan et al. 2015). However, such a sorting mechanism suffers from certain limitations. Previous research show
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