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Antecedents and consequences of fake reviews: A marketing approach

Short title (VSI): Fake Reviews

Guest Editors:

Jean Michel SAHUT (Managing guest editor)

IDRAC Business School, France


Concordia University, Canada


INSEEC U., France

Fake reviews are inconsistent with real evaluations of products or services; thus, fake reviews are false, bogus, and deceptive reviews. Such reviews might be posted by different types of people, including consumers, online merchants, and review platforms. The deciding feature of fake reviews is whether they mislead consumers. From the valence of the reviews, fake reviews can be positive, negative, or neutral (Sudhakaran et al., 2016). The proportion of fake reviews is estimated to range from 16% (Luca, & Zervas, 2016), to 33% (Salehi-Esfahani & Ozturk 2018). Moreover, about 10% of online products were subjected to review manipulation (Hu et al., 2012). Such reviews can have a significant effect on product perception. Fake reviews decrease informativeness, information quality, and the effective use of online product reviews. Fake reviews also damage the credibility of reviews, and negatively affect review helpfulness. In addition, fake reviews seriously affect the development of online product reviews and stakeholders’ commitment to the reduction of information asymmetry between merchants and customers. Online sellers tend to publish positive fake reviews for their products or negative fake reviews against competitors’ products for financial gains. Platforms are inclined to acquiesce to review manipulations and add fake reviews to increase traffic and consumer engagement. Opportunity seeking is an example of why such individuals post fake reviews. False information about products includes fake reviews in e-commerce, hoaxes on collaborative platforms, fake news on social media (Pantano, 2020). The growing trend in the numbers of published articles per annum indicates the issue’s fast emergence in research. However, most of this research focuses on detection while antecedents and consequences remain largely unexplored.

Detection: Authentic reviews and fake reviews have significant micro-linguistic differences (Chatterjee et al., 2020). four constructs (comprehensibility, specificity, exaggeration, and negligence) (Banerjee & Chua, 2017), nine characteristics (structure and format, attributes of content, information orientation, number of words, part of speech, writing style, lexical richness, personal pronouns, and paralinguistic features), and three aspects (informativeness, subjectivity, and readability) that allow to distinguish authentic reviews from fake reviews. To significantly reduce the effects of fake reviews, review platforms should invest in their detection and methods to reduce the visibility of fake reviews (Gentina et al., 2020).

Antecedents: The essential reason for manipulating reviews would be pecuniary motivation. Studies confirm that online product reviews affect consumers’ purchase decisions (Heydari et al., 2015), product reputations (Petrescu et al., 2018), sales volumes, and merchants’ profits (Dellarocas, 2006). For instance, a 1% increase in hotel review ratings may increase sales per room by approximately 2.6% (Gössling et al., 2018). An extra half-star rating causes restaurant to sell out 19 percentage points more frequently (Anderson & Magruder, 2012). Another dominant motivation for posting fake reviews is competition (Lee et al., 2018). Fake reviews are mainly posted by firms associated with inferiors, small owners and small management companies, weak brands, low ratings, and inferior quality. Strong brands, high ratings, superior quality, and competitive advantages might also post fake reviews under fierce competition. Finally, individual consumers may post fake reviews to seek rewards (Thakur et al., 2018); this behavior is broadly rooted in psychological needs that stem from three sources: upset customers, self-appointed brand managers, and social status. To the best of our knowledge, no study has yet explored in detail why and how do individuals, review platforms, and AI agents post fake reviews. In particular, the underlying psychological mechanisms that cause individuals without external financial incentives to post fake reviews should be scrutinized. Moreover, we do not know if fake reviews affect different kinds of product or market in the same way. Contextualized behavioral research is likely to help understand the effects of situations or the environment on the decision to publish fake reviews. In addition, the motives and the rationales of platforms that post false reviews are also unknown. As a corollary, the types of platforms more prone to post fake reviews would have to be highlight. The role of AI agents in posting fake reviews remain unclear and call for analysis and studies.

Consequences: The effects of fake reviews have engendered serious concern and various theoretical models are employed to highlight the consequences of fake reviews. The existence of fake reviews constantly increases the number of online product reviews (Petrescu et al., 2018). Most fake reviews are either positive or negative, whereas few fake reviews are neutral (Luca & Zervas, 2016). As a distorted form of online product reviews, fake reviews aggravate the dispersion of review ratings. Fake reviews manipulate consumers’ purchase intentions and should directly affect product sales/revenues (Zhuang et al., 2018). However, no academic consensus emerges. In addition, brand strength, property of the targeted product, market self-exciting power could alleviate the influence of fake reviews on purchase intentions though studies are required to evaluate such moderating effects. Finally, Petrescu et al. (2018) indicate that fake reviews increase the number of online reviews and aggravate the dispersion of online ratings. However, the influence degrees of fake reviews on the development of online reviews should be quantified and further studies should be undertaken.

List of potential research topics (non-exhaustive):

Motivations for posting fake reviews: customer, or merchant platforms.What are the features of fake reviews?Fake reviews, trust, and credibility of review platforms.How do individuals, review platforms, and AI agents post fake reviews?Role of AI in creating and posting fake reviews, and perspectives for the future.What kinds of products, markets, or situations are more prone to fake reviews?Fake reviews and competition between online merchants.What factors promote the visibility of fake reviews?What are the differences of spreading features between fake reviews and fake news?Benefits, costs, and drawbacks of fake reviewsEffects of fake reviews on the development of online reviews and review platforms.Fake reviews and certified review servicesWhat can various stakeholders and regulators do to effectively respond to fake reviews?Are there cultural differences in the postings and the responses to fake reviews?

Papers should be less than 30 double-space pages, with 1” margins and 12 pt fonts, and follow the guidelines of the Journal of Business Research. Electronic submissions are required. All papers will be subject to a double blind peer review procedure. The deadline for submissions is January 1, 2022.


Anderson, M., & Magruder, J. (2012). Learning from the crowd: Regression discontinuity estimates of the effects of an online review database, The Economic Journal, 122(563), 957-989.

Banerjee, S., & Chua, A. Y. (2017). Theorizing the textual differences between authentic and fictitious reviews: Validation across positive, negative and moderate polarities. Internet Research, 27(2), 321-337.

Chatterjee, S., Goyal, D., Prakash, A., & Sharma, J. (2020). Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application. Journal of Business Research. In Press.

Choi, S., Mattila, A. S., Van Hoof, H. B., & Quadri-Felitti, D. (2017). The role of power and incentives in inducing fake reviews in the tourism industry. Journal of Travel Research, 56(8), 975-987.

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Gentina, E., Chen, R., & Yang, Z. (2020). Development of theory of mind on online social networks: Evidence from Facebook, Twitter, Instagram, and Snapchat. Journal of Business Research, 124(4), 652-666.

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Pantano, E. (2020). When a luxury brand bursts: Modelling the social media viral effects of negative stereotypes adoption leading to brand hate. Journal of Business Research, 123, 117-125.

Petrescu, M., O’Leary, K., Goldring, D., & Mrad, S.B. (2018). Incentivized reviews: Promising the moon for a few stars, Journal of Retailing and Consumer Services, 41, 288-295.

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Sudhakaran, P., Hariharan, S., & Lu, S. (2016). A framework investigating the online user reviews to measure the biasness for sentiment analysis, Asian Journal of Information Technology, 15(12), 1890-1898.

Thakur, R., Hale, D., & Summey, J.H. (2018). What motivates consumers to partake in cyber shilling? Journal of Marketing Theory and Practice, 26(1-2), 181-195.

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