Understanding the determinants of online review helpfulness: A meta-analytic investigation
01 Oct 2017-Vol. 102, pp 1-11
TL;DR: A meta-analysis of extant research about the determinant factors of perceived online review helpfulness finds that helpfulness measurement, online review platform, and product type are the three factors that cause mixed findings in extant research.
Abstract: Online consumer reviews can help customers reduce uncertainty and risks faced in online shopping. However, the studies examining the determinants of perceived review helpfulness produce mixed findings. We review extant research about the determinant factors of perceived online review helpfulness. All review related determinants (i.e., review depth, review readability, linear review rating, quadratic review rating, review age) and two reviewer related determinants (i.e., reviewer information disclosure and reviewer expertise) are found to have inconsistent conclusions on how they affect perceived review helpfulness. We conduct a meta-analysis to examine those determinant factors in order to reconcile the contradictory findings about their influence on perceived review helpfulness. The meta-analysis results affirm that review depth, review age, reviewer information disclosure, and reviewer expertise have positive influences on review helpfulness. Review readability and review rating are found to have no significant influence on review helpfulness. Moreover, we find that helpfulness measurement, online review platform, and product type are the three factors that cause mixed findings in extant research.
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TL;DR: Investigation of consumer perceptions about information helpfulness in electronic word-of-mouth contexts finds that popularity signals, two-sided reviews, and expert sources (but not source trustworthiness) are perceived as helpful by consumers to assess service quality and performance.
227 citations
Cites background or result from "Understanding the determinants of o..."
...Important informational influences include the content, the source, and the receiver of information [13, 14, 38]....
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...Previous studies’ findings conclude that perceived information quality (information depth, breadth, factuality, relevance, and credibility) [27], review depth, and length predict review helpfulness [4, 16, 38, 52, 54, 59]....
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...[38] found a significant and positive relationship with review helpfulness....
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...Previous studies on the relationship between source (reviewer) credibility and review helpfulness had contrasting findings as discussed in a recent meta-analysis of the literature on review helpfulness [38]....
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...addition, previous studies using large datasets have had inconsistent conclusions on the determinants of review helpfulness [38]....
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TL;DR: In this article, the authors conceptualized three distinct stages in the eWOM process: eWord creation, eWord exposure, and eWord evaluation, and adopted a dual lens to synthesize key research insights and propose a research agenda based on a multi-disciplinary systematic review of 1050 academic publications published between 1996 and 2019.
Abstract: Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multi-disciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice.
166 citations
TL;DR: A meta-analysis of 118 related empirical studies indicates that antecedents namely perceived usefulness, perceived ease of use, system quality, information quality, service quality, user interface, perceived risk, perceived security, structural assurance, ubiquity, and disposition to trust have significant relationship with trust in m-commerce.
165 citations
TL;DR: It is shown that deep learning models were more useful in predicting review helpfulness than other models, and combining review texts and user-provided photos produced the highest performance.
161 citations
TL;DR: It is suggested that product type moderates the effects of emotions on perceived review helpfulness, and fear embedded in a review is identified as an important emotional cue to positively affect the perceivedreview helpfulness with more persuasive messages.
Abstract: This paper extracted discrete emotions from online reviews based on an emotion classification approach, and examined the differential effects of three discrete emotions (anger, fear, sadness) on perceived review helpfulness. We empirically tested the hypotheses by analyzing the “verified purchase” reviews on Amazon.com. The findings of this study extend the previous research by suggesting that product type moderates the effects of emotions on perceived review helpfulness. Anger embedded in a customer review exerts a greater negative impact on perceived review helpfulness for experience goods than for search goods. Fear embedded in a review is identified as an important emotional cue to positively affect the perceived review helpfulness with more persuasive messages. As the level of sadness embedded in a review increases, perceived review helpfulness decreases. These findings contribute to a better understanding of the important role of emotions embedded in reviews on the perceived review helpfulness. This study also provides practical insights related to the presentation of online reviews and gives suggestions for consumers regarding how to select and write a helpful review.
104 citations
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