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Detecting Workplace Hubris

A Machine Learning Approach to Narcissism Identification. The Case of the Healthcare Industry in the Emerging Markets

Bibliographic Data

ID13133758
AuthorsRachid Alami (0000-0002-9558-8163, Abu Dhabi University, corresponding author), Sugandha Agarwal (0000-0002-5126-8651, Canadian University of Dubai), Belal Shneikat (0000-0002-5349-3529, Skyline University College), Turki Al Masaeid (0000-0002-7901-5656, Abu Dhabi University), Stachowicz Stanusch (Canadian University of Dubai)
Year2025
Volume5
Issue5
Pages2677-2692
Publication date2025-05-10
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Posthumanism (JOURNAL)
Journal identifiersISSN: 2634-3576 • E-ISSN: 2634-3584
PublisherNobel Press Resources (PUBLISHER)
DOI10.63332/joph.v5i5.1662
OpenAlexW4410249899
LanguageEN

Despite the extensive research on narcissism and its origin, the world of health practice, risk factors, as well as the case in a developing country like Morocco, is a new untapped area. This work explores uncharted territory as it attempts to replace the existing social behavior prediction tools with different machine learning models that promise the best approach to narcissist behavior prediction by identifying psychological features characteristic of narcissist personalities. Among different machine learning models used in this study, Support Vector Machine (SVM) shows the highest metrics with an accuracy of 0.910, precision of 0.890, and recall of 0.880. SVM reveals that vanity, self-sufficiency, authority, and exhibitionism are the best predictors of narcissism in organizational settings

Art · Business · Economics · Health care · Healthcare industry · Hubris · Identification (biology · Industrial organization · Knowledge management · Narcissism · Computer Science · History, Medicine, and Leadership · Psychology · Social Psychology · Artificial Intelligence

Citation velocityhistorical
Highly citedNo

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Open DOIOpen Access
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