Summarizing Customer Reviews: A New Way to Optimize eWOM for Better Purchasing Experience

Abstract

The prosperity of online shopping contributes to the explosive growth of eWOM. Customer reviews are considered as one of the most important types of eWOM. While assisting customers in forming comprehensive overviews of products and services, the sheer number of reviews may cause information overload and reduce customers’ satisfaction with decision making and purchase experiences. In this study, we propose a new type of decision aid tool – a feature-based review summary to address the issue. Based on theoretical and empirical work in marketing, decision making, and support systems, we develop a set of hypotheses and tests through two experiments using manipulated e-commerce websites selling cameras. Though review summary’s effectiveness in moderating the relationship between information overload and process satisfaction is not proved directly, we find that the summary increases customers’ perceived review helpfulness, which subsequently increases customers’ process satisfaction under conditions of limited information overload. Our research is an interdisciplinary study that explores the role of feature-based review summary in assisting customers’ purchase decision making under conditions of information overload. Theoretically, it contributes to the literature by testing the efficiency of a summary as a decision facilitating tool. Practically, it demonstrates the usefulness of feature-based summary for popular search products showing a certain level of similarity.