Drivers and Outcomes of Perceived Authenticity of Online Reviews

Abstract

Past research has shown evidence that online reviews significantly impact e-commerce sales. With the increased growth of online retailing due to the COVID-19 pandemic, the risk of fake online reviews has increased, causing massive concern for businesses and consumers. Existing research on identifying fake versus (vs.) authentic reviews is predominantly based on automated detection algorithms that investigate the prediction power of the review (i.e., textual features) and reviewer (i.e., characteristics of the reviewer) variables. However, these methods have several limitations, which warrant further research on the human detection of fake reviews. The emerging research stream of human detection of online reviews predominantly focuses on review-related variables, while various reviewer-related variables have remained largely unexplored. Human detection of fake vs. authentic reviews is bounded by the degree of consumer perception of review authenticity, establishing a higher-level construct of perceived authenticity of online reviews. Perceived authenticity, as a construct, is distinct from perceived credibility, with scant knowledge on the influence of perceived authenticity on consumers’ attitudes and behaviours. Drawing on the leakage theory, this thesis explores the phenomenon of human detection of authentic (vs. fake) online reviews regarding conceptualising the construct (perceived authenticity), identifying its drivers focusing on the review (e.g., perceptual cues and subjectivity) and reviewer (e.g., review count and friends count) related factors. Regarding consequences, this study examines the effects of perceived authenticity on review persuasiveness, intention to revisit the review site and purchasing the target product. Moreover, based on the homophily theory, the current research explores the role of individual consumer differences by examining the moderating impact of homophily (i.e., reviewer-reader similarity). This thesis employs a mixed-methods research design consisting of two sequential studies: Study 1 and Study 2. Study 1, with a sample size of 18, uses a qualitative approach including verbal protocols and follow-up interviews. Study 2 comprises two online quasi-experimental design studies, Study 2a and Study 2b, with a sample size of 239 and 237, respectively. Study 2 investigates the effects of review-related factors (Study 2a) and reviewer-related factors (Study 2b) on perceived authenticity and their subsequent impact on review persuasiveness, revisit intention, and purchase intention.

Study 1 findings highlighted the essential elements of perceived authenticity by identifying additional indicators extending the construct measurement and offering a solid conceptual understanding before launching Study 2 (i.e., the quantitative study). The quantitative results revealed that review (verbal; i.e., perceptual cues and subjectivity) and reviewer (non-verbal; i.e., review count and friends count) variables significantly impact perceived authenticity, extending the leakage theory in the context of human detection of fake reviews. Further, evidence indicates that perceived authenticity positively influences consumers’ review persuasiveness, revisiting intention and purchase intention. Additionally, the findings have revealed the interplay effect of homophily (i.e., reviewer-reader similarity) on the impact of review and reviewer factors on perceived authenticity.

The research findings offer significant theoretical contributions in terms of extending the leakage theory to the context of online reviews, which led to a better understanding of the human aspect of deception detection in online reviews. Further, this research contributed to the human detection research stream by expanding on the conceptualisation and operationalisation of the perceived authenticity construct, providing a solid foundation for future research in this stream. Managerially, this study’s findings assist businesses in managing fake reviews from a consumer perspective, departing from the currently employed automated detection methods. As such, online review publishing platforms will be better equipped to render usability and ensure credibility.