Herd behavior in consumers’ adoption of online reviews

Abstract

It has been demonstrated that online consumer reviews are an important source of information that affect individuals’ purchase decision making. To understand the influence of online reviews, this study extends prior research on information adoption by incorporating the perspective of herd behavior. We develop and empirically test a research model using data collected from an existing book review site. We report 2 major findings. First, argument quality and source credibility predict information usefulness, which affects the adoption of online reviews. Second, we determine that the adoption of online reviews is also influenced by 2 herd factors, namely, discounting own information and imitating others. We further identify the key determinants of these herd factors, including background homophily and attitude homophily. The theoretical and practical implications are discussed.

Do consumers always follow “useful” reviews? The interaction effect of review valence and review usefulness on consumers' purchase decisions

“…Moreover, it is worth noting that users normally evaluate product reviews and construct their judgment of a product at two different levels (i.e., product level and review level). Previous research has investigated the effects of online reviews on purchase decision either on product level (e.g., Forman, Ghose, & Wiesenfeld, 2008;Liu, 2006;Wasserman et al, 2015) or on review level (e.g., Mudambi & Schuff, 2010;Shen, Zhang, & Zhao, 2016). In this study, we analyze data at both levels by using mixed-method approach a largescale panel study (product level) followed by two experimental studies (review level).…”

“…From this angle, our findings offer a much more complete picture for policy makers, consumers, and online retailers. Third, previous research has investigated the effects of online reviews on purchase decision either on product level (e.g., Forman, Ghose, & Wiesenfeld, 2008;Liu, 2006;Wasserman et al, 2015) or on review level (e.g., Mudambi & Schuff, 2010;Shen, Zhang, & Zhao, 2016). In this study, we analyze data at both levels.…”

Exploring the social influence of multichannel access in an online health community

“…Johnson (2008) Assimilation of electronic channel Efficiency, learning orientation Gupta et al (2004) Channel-switching Risk perception, search price, evaluation effort, waiting time Ansari et al (2008) Channel migration Purchase, income, age, catalogs Our study Multichannel access Social influence, rating effects of social influence in the adoption or use of new technology and systems (Wang, Meister, & Gray, 2013). In marketing, many scholars explored the effects of social influence between customers in the decision to adopt a new product (Risselada et al, 2014;Shen, Zhang, & Zhao, 2016), demonstrating the value of investment in advertisement to enhance social influence (Terlutter & Capella, 2013). In e-commerce, neighbors or friends have been observed to purchase similar products and adopt similar sales channels (Bapna & Umyarov, 2015).…”

Successful Crowdfunding: Focusing on Social Interaction and Goal Achievement Motivations

“…In previous research on online social interactions, two types of social interactions among consumers are suggested: informationand behavior-based social interaction (Shen et al, 2016). In consumers' decision making for purchasing, information-based social interaction indicates that consumers may refer to others' opinions and information, while behaviorbased social interaction suggests that consumers may be affected by other consumers' behavior (Shen et al, 2016 (Zheng et al, 2016).…”

“…In consumers' decision making for purchasing, information-based social interaction indicates that consumers may refer to others' opinions and information, while behaviorbased social interaction suggests that consumers may be affected by other consumers' behavior (Shen et al, 2016 (Zheng et al, 2016). Some studies emphasize the importance of social ties in information-based social interaction.…”