Relevance judgment of argument quality and online review adoption during information search in e-commerce review platform

• Main Authors: Che Lah, Nur Syadhila, Che Hussin, Ab. Razak, A. Jalil, Norazira, Subri, Nor Fatiha
• Format: Conference or Workshop Item
• Published: 2022
• Subjects: HB Economic Theory
• Online Access: http://eprints.utm.my/id/eprint/99824/; http://dx.doi.org/10.1007/978-3-030-98741-1_50

The landscape of e-Commerce review platforms can be assumed to be in a state of constant growth due to the viral nature of web content. Furthermore, the leading features of these platform has been acclaimed to be among the influential factors in shaping the behavior of online consumer. Even so, in this regard, if the platform presents too many reviews in non-relevant manner, this may be time-consuming and cumbersome to be understand. Hence, awareness on identifying valuable content of online reviews during information searching process has become important part for online businesses. This study purposely aims to develop a model to understand consumer adoption of online reviews based on dynamics relevance judgment of argument quality in e-Commerce review platform. Elaboration Likelihood Model (ELM) is used in developing the research model to find the potential effects of consumer relevance judgment from information retrieval perspective, which include perceived informative and affective relevance. A quantitative research method has been applied to test and validate the proposed research model. Total of 238 valid respondents has been analyzed using the Partial Least Square Structural Modelling (PLS-SEM) technique. From the research findings, the study found that, content novelty, content topicality, content similarity, content tangibility and content sentimentality could positively influence perception of argument quality which led to information adoption behavior. To be concluded, the importance of information relevancy was also highlighted in this study, which reveals some appropriate features that can be utilized by e-Commerce practitioners to better refine their information search criteria in online review platforms.