Understanding the role of interpersonal identification in online review evaluation: An information processing perspective. Issue 1 (February 2018)

• Record Type: Journal Article
• Authors: Davis, Joshua M.; Agrawal, Deepti
• Is Part Of: International journal of information management. Volume 38:Issue 1(2018)
• Page Start: 140
• Page End: 149
• Publication Date: 2018-02
• Subjects: Online reviews -- eWOM -- Identity theory -- Elaboration likelihood model -- Dyadic social influence; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2017.08.001
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: We examine online review evaluation through a distinctly social lens, emphasizing the role of dyadic social information processing. Combining identity theory and ELM, we develop a robust theoretical model of dyadic social influence through online reviews. We introduce three types of identification—value-, consumption-, and website-driven—which drive online review adoption. Abstract: While the proliferation of online reviews has increased consumers' access to resources for informing the purchase decision, it has also substantially increased the cognitive effort required for finding personally relevant information through this channel. In the face of this challenge, an increasingly valuable capability of online review platforms relates to delivering the right reviews to the right consumer at the right time. Many platforms have sought to develop this capability by leveraging generic review characteristics like recency and valence, or crowd-level performance metrics like helpfulness score. While useful, these approaches may be overlooking important individual-to-individual (dyadic) social mechanisms that underpin review evaluation and selection. In an effort to inform the development of more robust information management capabilities of online review platforms, we introduce and test a model that highlights the influence of dyadic social information processing in online review evaluation. Results from model testing support most of the hypotheses and reveal important social selection mechanisms consumers employ in this context, which could be leveraged to add additional value through online review platforms.