Trustworthiness evaluation of reviews for products with their traits in e-commercial communities

Purpose This study aims to evaluate the trustworthiness of online product reviews in e-commerce platforms by analyzing the traits disclosed on commercial websites, thereby enabling users to efficiently access credible review information. Design/methodology/approach The research classifies review-related information into three categories: review content, reviewer behavior and response. A hierarchical evaluation framework is constructed, and the Delphi Hierarchical Process is applied to assess the relative importance of each trait through expert-based pairwise comparisons and weight normalization. Findings Traits in the content category are found to exert the greatest influence on perceived trustworthiness, followed by reviewer and response traits. The proposed method demonstrates strong alignment with expert evaluations, enabling more accurate and credible ranking of reviews. Research limitations/implications This research lies in its reliance on disclosed information regarding reviews, reviewers, responses and responders to evaluate the trustworthiness of reviews. The results may be limited by the contextual constraints of the reviews, potentially affecting the generalizability of the findings to all review scenarios. Practical implications This research assists e-commerce platforms to thick of reordering reviews based on trustworthiness in addition to temporal posting order. Such an approach can provide customers with more reliable reviews for the products. Social implications Enhancing the credibility of online reviews contributes to maintaining reliance and transparency in online transactions and strengthens consumer trust in e-commerce. Originality/value This study introduces a novel, trait-based trustworthiness evaluation model that integrates review content, reviewer characteristics and response dynamics within a hierarchical structure. It offers a practical, explainable and scalable solution for review credibility assessment in real-world e-commerce settings.