Toward a Theoretical Model of Authentic and Fake User-Generated Online Reviews

Toward a Theoretical Model of Authentic and Fake User-Generated Online Reviews

Snehasish Banerjee (University of York, UK) and Alton Y. K. Chua (Nanyang Technological University, Singapore)
DOI: 10.4018/978-1-5225-8535-0.ch007

Abstract

As consumers increasingly rely on user-generated online reviews to make purchase decisions, the prevalence of fake entries camouflaged among authentic ones has become a growing concern. On the scholarly front, this has given rise to two disparate research strands. The first focuses on ways to distinguish between authentic and fake reviews but ignores consumers' perceptions. The second deals with consumers' perceptions of reviews without delving into their ability to discern review authenticity in the first place. As a result of the fragmented literature, what has eluded scholarly attention is the extent to which consumers are able to perceive actual differences between authentic and fake reviews. To this end, the chapter highlights the theoretical value of weaving the two research strands together. With the aim to contribute to the theoretical discussion surrounding the problem, it specifically develops what is referred as the Theoretical model of Authentic and Fake reviews (the TAF). New research directions are identified based on the TAF.
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Introduction

Background

User-generated online reviews are heavily read by consumers to make purchase decisions. Thought to be unbiased accounts of actual post-purchase experiences, reviews are often seen as being highly authentic and trustworthy (Ott et al., 2011). However, the authenticity of reviews cannot be taken for granted. Their disembodied nature allows anyone with Internet access to post fake reviews easily without entering into any sales transactions (Singh et al., 2018). In fact, fake reviews have recently been shown to outnumber authentic ones. Specifically for electronics, as much as 61% of all reviews could be fake (Sterling, 2018).

Increasingly, websites such as Amazon and Expedia only allow individuals who have completed an actual transaction to post reviews. However, in the absence of user authentication on websites such as TripAdvisor, the authenticity of reviews remains a nagging question. Moreover, given that fake reviews are deliberately written to appear authentic, it is tough for consumers to distinguish between the two. When consumers overlook authentic reviews and follow those that are fake, they make sub-par purchase decisions that in turn unfairly affect the fate of businesses. This not only makes the relationship between consumers and businesses untenable but also diminishes the worth of user-generated content in general (Pal & Chua, 2016; Pal et al., 2017; Singh et al., 2018; Thomas & Elias, 2017).

The growing popularity of reviews—some authentic, others fake—coupled with consumers’ increasing proclivity to trust them have given rise to two disparate research strands. The first focuses on ways to distinguish between authentic and fake reviews (e.g., Harris, 2012). With a technical orientation, studies related to this strand are generally conducted by computer scientists using techniques such as machine learning (e.g., Ott et al., 2011). However, the consumer behavior perspective remains largely ignored.

The second research strand deals with consumers’ perceptions of reviews (e.g., Gupta & Harris, 2010). Usually undertaken by management scholars with a socio-psychological orientation, works related to this strand are characterized by user studies (e.g., Casaló et al., 2011). However, reviews shown to participants of the user studies were often implicitly assumed to be authentic.

Interestingly, little scholarly efforts have been invested hitherto in uniting the two research strands. As a result, the literature on authentic and fake reviews remains fragmented. The piecemeal scholarship begs the question: What theoretical lenses can be used to holistically study differences—encompassing both actual and perceived—between authentic and fake reviews? It appears that the question can be tackled effectively only by dovetailing the two research strands.

Key Terms in this Chapter

Authenticity: The extent to which something is genuine or real.

Social media: A subset of new media that allows people to exchange user-generated content.

User-Generated Content: Anything that people create online using social media applications, and can be accessed by others in the online community.

Deception: The practice of misrepresenting the reality.

Opinion Spam: Fictitious views about something intended to deceive recipients.

Online Review: Post-purchase experience of products or services conveyed by customers using platforms such as Amazon or TripAdvisor.

Deception Detection: Identification of somebody’s attempt to misrepresent the reality.

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