The viral nature the content of the Web has transformed the landscape of e-Commerce review platforms to be in a state of constant growth. Similarly, the prominent features of these platforms have been recognized to be among the dominant factors in shaping online consumer behavior. Nonetheless, in this regard, if the review platform returns too many reviews, and the reviews are presented in non-relevant manner, in which this may be cumbersome and time-consuming for consumers. Therefore, identifying credible reviews that contain valuable information has becomes increasingly important for online businesses. The main research question to be addressed in this study is to determine on how can a model be developed to improve the argument quality perceptions in the adoption of online reviews across e-Commerce review platform. Subsequently, the main objective to be achieved is to develop a model of argument quality for review‘s adoption in the e-Commerce review platform. The potential effects of consumer relevance judgment from information retrieval perspective have been considered, which include perceived informative and affective relevance in developing the research model by using Elaboration Likelihood Model (ELM). A quantitative research method has been applied to test and validate the propose research model. The response data from 238 valid respondents was analyzed using the Partial Least Square Structural Modelling (PLS-SEM) technique. The findings from the results indicate that content novelty, content topicality, content similarity, content tangibility and content sentimentality could positively influence the perception of argument quality which lead to information adoption behavior. Finally, 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 the online review platforms.
Volume 12 | 04-Special Issue