Suspicion of online product reviews as fake: Cues and consequences

Harrison‐Walker, L. Jean; Jiang, Ying

doi:10.1016/j.jbusres.2023.113780

Cited by 13 publications

(6 citation statements)

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“…Our study indicates that, while FORs constitute deliberately consciously-targeted acts, such actions are far from the monotonously, anti-establishment, senseless actions of an alienated few (Kapoor et al , 2021) but rather vary considerably in tenor, tone and purpose. Contrary to the reports that the intention behind such reviews is deception and to mislead other consumers (Wu et al , 2020; Harrison-Walker and Jiang, 2023), our data demonstrates that perpetrators of FORs have varying intentions that do not always stem exclusively from a desire merely to deceive. FORs range in valence – from ringing endorsements to scathing vilifications, as well as in veracity – from tendencies to hyperbole to fantasies of inventive fabrications.…”

Section: Discussioncontrasting

confidence: 99%

“…Previous literature offering insights on FORs has focused on reviews instigated by companies covering a range of activities including but not limited to paying individuals to post reviews (Thakur et al , 2018) and posting reviews with fake accounts (Dellarocas, 2006). We aim to expand these prior studies (Baker and Kim, 2019; Harrison-Walker and Jiang, 2023) by studying the perspectives of the reviewers themselves, when such activities are not initiated by the company but introduced by the individuals who posted the review. Specifically, our study generates data that contributes novel insights into the nature, dimensionality and, therefore, key forms of FORs that are shared on and spread through the use of social media.…”

Section: Discussionmentioning

confidence: 99%

“…Although face-to-face illegitimate consumer behaviours including complaining and opportunistic behaviours have been investigated previously (Reynolds and Harris, 2005; Baker et al , 2012), consumers’ publishing of FORs is an area that has only recently captured the attention of the academic community. Academic studies to date have investigated the reasons for FORs (Thakur et al , 2018; Wu et al , 2020), differentiating between truthful reviews and falsified ones (Banerjee and Chua, 2017; Moon et al , 2021) and the consequences of FORs (Baker and Kim, 2019; Harrison-Walker and Jiang, 2023). While these studies suggest that FORs significantly impact online and offline marketplaces, no theory has been forwarded that focuses on the dimensions of FORs from the perspective of the reviewers.…”

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confidence: 99%

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Forms of falsified online reviews: the good, the bad, and the downright ugly

Istanbulluoglu,

Harris

2023

EJM

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Purpose Falsified online reviews (FORs) are the published/viewable consumer-generated online content regarding a firm (or its representatives) or its services and goods that is, to some degree, untruthful or falsified. The purpose of this study is first to explore the nature of FORs, focusing on reviewers' interpretations and refections on falsity, intent, anonymity and the target of their FOR. Secondly, the authors examine the valence and veracity dimensions of FORs and introduce a typology to differentiate their variations. Design/methodology/approach using an exploratory research design, 48 interviews were conducted with participants who post online reviews on social media about their experiences with food and beverage serving outlets. Findings The results show four common forms of FORs on social media. These are reviews focused on equity equalizing, friendly flattery, opinionated opportunism and malicious profiteering. Research limitations/implications The authors provide exploratory and in-depth information via interviews, but do not analyse the content of FORs. Practical implications Firms should be aware of varieties of FORs and that these may not be limited to malicious content. This is important in terms of showing that in dealing with FORs, a one-size-fits-all approach will not work. FORs are not always entirely fabricated, and instead various levels of falseness are observed, ranging from slight alterations to complete fabrications. Originality/value Previous research explored how to identify and differentiate FORs from truthful ones, focusing on the reviews or how they are perceived by readers. However, comparatively little is known of the reviewers of FORs. Hence, this study focuses on reviewers and offers new insights into the nature of FORs by identifying and examining the main forms of FORs on social media.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Forms of falsified online reviews: the good, the bad, and the downright ugly

Istanbulluoglu,

Harris

2023

EJM

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“…The study [4] focuses on the most serious issue faced by the customers on ecommerce websites i.e. Fake product reviews which affect their purchasing decisions as well as harm the businesses.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Machine Learning and Deep Learning Techniques for Fake Review Detection: A Comprehensive Literature Review

Shagufta Khalif,

Kishor Mane

2024

Int Res J Adv Engg Hub

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Over the last few years, the impact of reviews on ecommerce industries and the folk who rely on the online reviews have increased. Most of the online shoppers depends on the reviews to make their purchasing decisions. Moreover, genuine reviews can assist the businesses acquire higher sales. Implementing effective systems can ensure the reliability of reviews and create trustworthiness on online platforms. Deceptive reviews can deteriorate the integrity of online feedback system. Numerous studies have explored this domain employing Machine Learning, Deep Learning, NLP methodologies etc., in the last decade. In this paper, through the survey we address the challenges which the current systems encounter where they lack adequate capabilities in detection and removal of false reviews. Diverse methods are applied on datasets like Amazon, Yelp etc., to obtain organized information and to improve the performance of the employed systems in classifying reviews as fake or genuine. Furthermore, this paper provides details of each method, their accuracies and also the future directions in this area. There is a pressing need of systems that can effectively address the issue of fake reviews as the existence of these can mislead customers leading to the decline in their preference for ecommerce.

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“…Consumers use visual cues when reading online reviews because they often provide richer information beyond marketers' generated photos (Filieri, Galati, et al, 2021; Filieri, Lin, et al, 2021; Michaelidou et al, 2013). Furthermore, consumers use those photos as a purchase validation cue indicating that the review is credible and not fake (Harrison‐Walker & Jiang, 2023). Therefore, a photo showing the real product from a reviewer boosts consumers' trust in the review and purchase intention regardless of the written text (Zinko et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

The effects of size referents in user‐generated photos on online review helpfulness

Dang,

Nichols

2023

J of Consumer Behaviour

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Product size misperceptions are a common problem for consumers when shopping online. To help their peers estimate a product's size, customers often include a size referent next to the focal product when adding photos to their online reviews. The purpose of this article is to investigate the effects of user‐provided review photos (with and without a size referent) on online review helpfulness. From two online experiments and one eye‐tracking lab experiment, this article finds that photo‐enhanced reviews that include a size referent object can improve review helpfulness. In study 1, eye‐tracking data shows that consumers pay greater attention to the photo and less to the text when the size referent is included. Study 2 shows that size referents are more helpful when consumers have limited experience with the product and the size referent is familiar. The role of perceived reviewer effort is investigated as a mediator to explain this result. Study 3 shows that the effects of a familiar size referent are robust when the seller provides detailed size information but vary depending on review valence.

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Suspicion of online product reviews as fake: Cues and consequences

Cited by 13 publications

References 100 publications

Forms of falsified online reviews: the good, the bad, and the downright ugly

Forms of falsified online reviews: the good, the bad, and the downright ugly

Exploring Machine Learning and Deep Learning Techniques for Fake Review Detection: A Comprehensive Literature Review

The effects of size referents in user‐generated photos on online review helpfulness

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