Normative Social Influence and Online Review Helpfulness: Polynomial Modeling and Response Surface Analysis

(ProQuest: . denotes formulae omitted.)1. IntroductionConsumers encounter difficulties in making online purchase decisions because product information is limited in the online market [Dimoka et al. 2012]. Online reviews, defined as "product information created by users based on personal usage experience" [Chen & Xie 2008], are important sources of information that can help consumers make purchase decisions [Basuroy et al. 2003; Chen et al. 2008; Chevalier & Mayzlin 2006; Dellarocas et al. 2007; Duan et al. 2008]. Therefore, consumers consult online reviews, written by other consumers who have bought the product, to reduce the uncertainty of their purchase decision.However, online reviews are not always valuable. Online markets that use online reviews to increase their sales volume must identify the helpful reviews among a huge number of online reviews. Many online markets, such as Amazon.com, have attempted to measure the helpfulness of online reviews to provide their consumers with the most informative ones, thereby reducing the information overload caused by rapid increase of reviews.Previous studies on the factors that determine the helpfulness of online reviews have considered several factors; such as review rating [Cao et al. 2011; Ghose & Ipeirotis 2007; Mudambi & Schuff 2010], reviewer credibility [Baek et al. 2012; Forman et al. 2008], word count [Baek et al. 2012; Kim et al. 2006; Mudambi & Schuff 2010], subjectivity [Ghose & Ipeirotis 2011], and readability [Ghose & Ipeirotis 2011; Zhu et al. 2014]. Many studies have been conducted to determine the effect of review ratings (i.e., review extremity) on review helpfulness [Cao et al. 2011; Ghose & Ipeirotis 2007]. However, these studies have provided inconclusive findings regarding the relationship between the helpfulness of online reviews and rating extremity. Several studies have proposed that reviews that offer extreme ratings are more helpful compared with reviews that provide moderate ratings [Cao et al. 2011; Ghose & Ipeirotis 2007]. Mudambi and Schuff [2010] determined that review ratings influence review helpfulness, but the relationship between review helpfulness and review rating is not consistent. To explain this result, they suggested that the type of product (either an experience good or a search good) has a moderating effect on the relationship between rating extremity and review helpfulness.Owing to their limited opportunities to experience e-commerce products, people tend to form their viewpoints on these products based on the opinions of others. Online reviews usually provide a large number of opinions, thereby establishing attitudes toward a product of the majority easily. The present research suggests that these attitude-creating mechanisms can be explained through normative social influence, which is "the influence to conform to the positive expectations of another" [Deutsch & Gerard 1955]. This study uses the normative social influence concept and posits that online reviews that provide a rating similar to the average product rating, which is an intensive form of customer opinion, are helpful. Average rating, one of the important variants of group decision making (such as voting, consensus, averaging, and prediction market) and a type of collective intelligence [Malone et al. 2010] , is defined as "the capacity of human collectives to engage in intellectual cooperation in order to create, innovate, and invent" [Levy 2010]. This study demonstrates through the perspective of normative social influence that previous studies have provided inconsistent findings because these have failed to consider that consumer attitudes to product quality can be affected by product average rating.To this end, this study collects 15,059 online review data on various goods sold on Amazon.com, examines the relationship between review rating and review helpfulness, and investigates how this relationship changes depending on the average product rating. …