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Does the dispersion of online review ratings affect review helpfulness?

机译:在线评论评级的分散是否会影响审核助人?

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The impact of online consumer reviews on online purchase decisions has increased with the growth of e-commerce. This paper tries to explain how rating dispersion impacts the process of review consumption based on the heuristic systematic model. For this research, 10,198 online consumer reviews for 516 DVD products were collected from Amazon.com using a web data crawler. Our results show that when trusting average ratings (i.e., when rating dispersion is low), the incentive to read individual reviews decreases because of the principle of least effort. In this case, consumers consider average ratings to be representative of collective intelligence, so rating inconsistency negatively impacts review helpfulness. On the other hand, when average ratings are not trusted (i. e., when rating dispersion is high), the incentive to read individual reviews increases because of the principle of sufficiency. When it happens, extreme ratings affects review helpfulness more because extreme opinions are not ambiguous. Our findings provide new perspectives to address the inconsistent findings of the previous studies on rating and review helpfulness, and the practical implications for e-commerce platforms.
机译:随着电子商务的增长,在线购买决策的在线消费者评论的影响增加。本文试图解释评级分散对基于启发式系统模型的审查消耗过程。对于本研究,从Amazon.com使用Web Data Crawler收集了516个DVD产品的10,198个在线消费者评论。我们的研究结果表明,当相信平均评分(即,当额定分散低)时,由于最少努力的原则,读取个人评论的激励减少了。在这种情况下,消费者考虑普通评级是代表集体智能的评级,因此评级不一致产生对审查助人的影响。另一方面,当平均评分不信任时(即,当额定等级,额定分散高)时,读取个人评论的激励因充足的原则而增加。当它发生时,极端评分会影响助人的助人更多,因为极端意见不是暧昧的。我们的调查结果提供了新的观点,以解决以往的评级和审查助人的研究的不一致结果,以及电子商务平台的实际影响。

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