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
MSR Nagar, Bengaluru-560054, India
Article Status
Available Documents
Abstract
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.
References
- Hovy Adversarial detection of automatically or NLP/statistically generated fake reviews. 2016.
- Gandhi A, Hollenbeck B, Li Equilibrium effects of fake reviews on Amazon marketplace welfare. 2024.
- Mukherjee A, Venkataraman V, Liu B, Glance N. Comparing crowdsourced (AMT) pseudo fake reviews vs. real fake reviews. 2013.
- Friestad M, Wright The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research. 1994; 21(1):1–31.
- Alshehri Semi-supervised (PU-learning) detection of fake reviews. 2024.
- Bansode S, Pardeshi A, Okhal S, Shinde P, Birajdar An end-to-end web application to classify and remove deceptive hotel reviews. 2021.
- Elmurngi E, Gherbi Sentiment-analysis based fake review detection using classical machine learning. 2017.
- Elmurngi E, Gherbi Extended study on sentiment-based fake review detection. 2018.
- 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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- •Published on: Oct 15 2026
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