精化可能性模型
有用性
人气
计算机科学
产品(数学)
人工智能
机器学习
心理学
社会心理学
说服
几何学
数学
作者
Mohammadreza Mousavizadeh,Mehrdad Koohikamali,Mohammad Salehan,Dam J. Kim
标识
DOI:10.1007/s10796-020-10069-6
摘要
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.
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