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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 10

Published on: October 2026

THE IMPACT OF PERCEIVED ONLINE REVIEW INAUTHENTICITY ON CONSUMER INFORMATION VIGILANCE: EVIDENCE FROM REGRESSION, CORRELATION AND PARALLEL MEDIATION ANALYSIS

Pothula Prashanth

Dr.Vimal Raj.L

Faculty of Management and Commerce,Ramaiah University of Applied Sciences, Gnanagangothri Campus,

MSR Nagar, Bengaluru-560054, India

Article Status

Plagiarism Passed Peer Reviewed Open Access

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Abstract

Online reviews are a dominant source of pre-purchase information, but fake, paid, exaggerated and AI-generated reviews have made review authenticity a central concern for consumers and platforms. Most research addresses the algorithmic detection of fake reviews, while few studies examine how a consumer’s own perception of inauthenticity shapes information-search and verification behaviour. This study examines the impact of perceived online review inauthenticity (ORI) on consumer information vigilance (CIV), directly and through two proposed parallel mediators, review skepticism (RS) and perceived information uncertainty (PIU). A quantitative, cross-sectional survey was conducted with a structured five-point Likert questionnaire (four constructs, five items each), and 270 valid responses from online shoppers who read reviews before purchase were analysed in jamovi using Cronbach’s alpha, descriptive statistics, Pearson and Spearman correlation, simple and multiple linear regression, and a parallel mediation model with 5,000 bootstrap resamples. The sample was young (72.6% aged 18–25), mostly male (68.5%) and habitual review readers (77.4% always or often). All scales were reliable (α = .704–.832) and all construct means were well above neutral (3.93–4.14). ORI significantly predicted CIV (B = 0.360; β = .394; R² = .156; F(1, 268) = 49.4; p < .001) and correlated strongly with RS (Pearson r = .507; Spearman ρ = .536; p < .001). RS (β = .251) and PIU (β = .424) jointly explained 36.9% of the variance in CIV (F(2, 267) = 78.1; p < .001). Both indirect effects were significant (via RS = 0.0908; via PIU = 0.1481) and the direct effect remained significant (0.1213; p = .018), indicating partial mediation. All four hypotheses were supported. The study extends the Persuasion Knowledge Model to online reviews and supports visible authenticity signals and incentive-free reviews as ways of reducing consumers’ verification burden. Because the design is cross-sectional and self-reported, the findings describe association and statistical pathways rather than causation.

KEYWORDS: Online review inauthenticity, Review skepticism, Perceived information uncertainty, Consumer information vigilance, Parallel mediation, Consumer behaviour.

How to Cite this Paper

Prashanth, P. (2026). The Impact of Perceived Online Review Inauthenticity on Consumer Information Vigilance: Evidence from Regression, Correlation and Parallel Mediation Analysis. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(10), 1-9. https://doi.org/10.55041/ijcope.v2i10.091

Prashanth, Pothula. "The Impact of Perceived Online Review Inauthenticity on Consumer Information Vigilance: Evidence from Regression, Correlation and Parallel Mediation Analysis." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 10, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i10.091.

Prashanth, Pothula. "The Impact of Perceived Online Review Inauthenticity on Consumer Information Vigilance: Evidence from Regression, Correlation and Parallel Mediation Analysis." International Journal of Creative and Open Research in Engineering and Management 02, no. 10 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i10.091.

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References


  1. Hovy Adversarial detection of automatically or NLP/statistically generated fake reviews. 2016.

  2. Gandhi A, Hollenbeck B, Li Equilibrium effects of fake reviews on Amazon marketplace welfare. 2024.

  3. Mukherjee A, Venkataraman V, Liu B, Glance N. Comparing crowdsourced (AMT) pseudo fake reviews vs. real fake reviews. 2013.

  4. Friestad M, Wright The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research. 1994; 21(1):1–31.

  5. Alshehri Semi-supervised (PU-learning) detection of fake reviews. 2024.

  6. Bansode S, Pardeshi A, Okhal S, Shinde P, Birajdar An end-to-end web application to classify and remove deceptive hotel reviews. 2021.

  7. Elmurngi E, Gherbi Sentiment-analysis based fake review detection using classical machine learning. 2017.

  8. Elmurngi E, Gherbi Extended study on sentiment-based fake review detection. 2018.

  9. Patel R, Kapoor R, Sonawane Fake review detection using opinion mining and tiered classification. 2018.


Huang N, Villas-Boas JM, Zhao Y. Fake reviews, rating dispersion, and consumer demand. 2023

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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Oct 15 2026
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