Predicting employees' organizational citizenship behavior

A comparative study using artificial intelligence techniques

Bibliographic Data

ID9405322
AuthorsAkriti Gupta (0000-0003-3513-5485, corresponding author), Aman Chadha (0000-0002-7216-2637), Mayank Kumar (0000-0001-7034-3293), Vijaishri Tewari (0000-0002-1982-1402), Arup Varma (0000-0002-6530-5564)
Year2025
Volume262
Pages106032-106032
Publication date2025-12-05
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueActa Psychologica (JOURNAL)
Journal identifiersISSN: 0001-6918 • E-ISSN: 1873-6297
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.actpsy.2025.106032
PMID41351974
OpenAlexW4417044386
LanguageEN
References cited58

With the prevalent use of artificial intelligence (AI) in human resource (HR) activities, the ability to forecast employee behavior is an asset to firms and researchers. This study develops and tests a predictive model of Organizational Citizenship Behavior (OCB) using Sustainable Training Practices (STP), Psychological Contract Fulfillment (PCF), Job Satisfaction (JS), and demographic factors (i.e., age, gender, current organization experience, industry experience) as predictors. We employ a sample of 224 employees of Indian firms and conduct a comparative study of seven machine learning models: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest, XGBoost, K-Nearest Neighbors (KNN), Naïve Bayes (NB), and an Artificial Neural Network (ANN). Results indicate that AI is a good predictor of OCB, with the ANN model boasting the highest accuracy rate of 93.33 %. The study reveals that the enactment of STPs perceived by the employee as assisting personal growth is a robust predictor of OCB. This paper offers a robust, tested model that can be used by HR managers to actively induce desired employee behavior through data-driven suggestions

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