The impact of context clues on online review helpfulness

Abstract

PurposeThis paper seeks to propose and empirically validate a conceptual model on the antecedents of review helpfulness comprising three constructs, namely, valence dissimilarity, lexical dissimilarity and review order.Design/methodology/approachA panel dataset of customer reviews was collected from Amazon. Using deep learning and text processing techniques, 650,995 reviews on 13,612 products from 570,870 reviewers were analyzed. Using negative binomial regression, four hypotheses were tested.FindingsThe results indicate that new reviews with high valence dissimilarity and lexical dissimilarity compared to existing reviews are less helpful. However, over the sequence of reviews, the negative effect of review dissimilarity on review helpfulness can be moderated. This moderation differs for valence and lexical dissimilarity.Research limitations/implicationsThis study explains review dissimilarity in the context of online review helpfulness. It draws on the elaboration likelihood model and explains how the impacts of peripheral and central cues are moderated over the sequence of reviews.Practical implicationsThe findings of this study provide benefits to online retailers planning to implement online reviews to improve user experience.Originality/valueThis paper highlights the importance of review dissimilarity in identifying user perception of online review helpfulness and understanding the dynamics of this perception over the sequence of reviews, which can lead to improved marketing strategies.

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“…Results shows that a neutral rating and the presence of the sentiment were found to be more useful. The significant results from model confirm the effects of rating and sentiment on the helpfulness, these are consistent with prior studies (Mudambi & Schuff, 2010;Namvar & Chua, 2022;Siering et al, 2018).…”

“…They contain rich information about the desired product. They also establish credibility and expertise of the reviewer due to their cognitive involvement when publishing the review (Namvar & Chua, 2022;Wu et al, 2021). For potential buyers, a longer review will make it easier to fulfil their information search about the product, evaluate the product features, confirm the appropriateness of the use scenario, and compare the various products available in that product category (Choi & Leon, 2020;Chua & Banerjee, 2016).…”

“…Review data is readily available from Amazon.com or through several repositories of datasets that outline data based on product categories, price, and user information. Amazon reviews are regularly studied and analyzed to study online consumer patterns and behavior online as they are robust and detailed (Mudambi & Schuff, 2010;Namvar & Chua, 2022). The dataset used in this research consists of users with verified purchases.…”

“…Although consumers rely on online reviews for information about the products, the reviews often sway consumers away from their purchasing intention (Chen, 2016). The accessibility and ease of writing online reviews mean that consumers are often overloaded with information as they try to form a holistic impression of the product (Namvar & Chua, 2022). The higher cognitive effort required to analyze the overpopulated reviews means consumers might not even act on their prior purchasing intentions (Gholizadeh et al, 2021).…”