Understanding online reviews adoption in social network communities: an extension of the information adoption model

Abstract

Purpose

Online reviews derived from peer communications have been increasingly viewed as an important approach for consumers to gather pre-purchase information. This study aims to examine factors affecting online reviews adoption in social network communities and then indicates the underlying mechanism of this process based on an extended information adoption model (IAM).

Design/methodology/approach

Using the data collected from 242 users of a social network community via an online survey, the proposed model is empirically assessed by partial least squares-based structural equation model (PLS-SEM).

Findings

The results show that both perceived diagnosticity and perceived serendipity are drivers of online reviews adoption in social network communities. Meanwhile, community identification is not only an antecedent of diagnosticity and serendipity perceived by community members, but also motivates source credibility which, in turn, positively influences argument quality. Finally, the importance of argument quality and source credibility in reviews adoption process is also presented.

Originality/value

This study extends the IAM and enriches the literature regarding online reviews adoption. It deepens the understanding of serendipitous experiences and community identification in social networking context by addressing their important roles in the authors' extended IAM.

Citation

Bao, Z. and Zhu, Y. (2023), "Understanding online reviews adoption in social network communities: an extension of the information adoption model", Information Technology & People, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ITP-03-2022-0158