Abstract
PurposeAn appended review serves as an additional evaluation provided by the buyers after a period of product usage that complements their initial reviews. Consumers usually rely on online reviews to make their purchase decisions, with the content of these reviews playing a crucial role. However, the impact of different review content features on consumers’ perceptions in relation to initial and appended reviews remains unclear. This study aims to address this gap by categorizing review contents into surface- and deep-level features and constructing a model for analyzing the effects of these features on consumers’ trust and perceived review helpfulness while considering initial and appended review forms.Design/methodology/approachAfter collecting online reviews related to clothing products from a leading Chinese e-commerce platform (i.e. Taobao), we constructed a thematic feature corpus that includes surface- and deep-level features and then refined this corpus using theoretical sampling. Afterward, we invited consumer participants to rate the perceived trust and review helpfulness of the collected reviews. We eventually applied multiple regression models to validate our hypotheses.FindingsExperiment results indicate that surface- and deep-level review features positively affect consumers’ perceived trust toward reviews. However, the surface-level features appearing in initial reviews are perceived as more trustworthy than those appearing in appended reviews and the deep-level features in appended reviews are perceived as more trustworthy than those in initial reviews. Furthermore, consumers’ trust toward online reviews subsequently affects the perceived helpfulness of these reviews.Originality/valueThis study is among the first to uncover the joint impact of review content features (i.e. surface- vs deep-level features) and review forms (i.e. initial vs appended reviews) on consumers’ perceived review helpfulness while considering consumers’ trust as the mediating variable. The results offer viable guidance for managing online reviews on e-commerce platforms.
Published Version
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