Information relevance factors of argument quality for e-commerce consumer review

Abstract

With the rapid growth of E-Commerce recently, a review platform has been among the important tool in facilitating online decision making. Therefore, research scholars have put much effort in investigating various factors that may contribute to the enhancement of this tool. This study is interested in exploring the subjective characteristics of online reviews with the aim at improving the relevance filter of review content. This can be done through investigating the factors from information retrieval perspective which consider the relevance criteria for information searching in accepting text recommendation from consumer reviews. This study takes the opportunity since the problem that still rises in filtering the useful information features based on the judgment that, not every review exerts the same degree of applicability towards consumer needs. This work contributes to the literature by employing communication theory from ELM perspective to study the role of argument quality in understanding the behavior of information acceptance in E-Commerce consumer review platform. A research model is further proposed to visualize the research investigation.