Cyberloafing Analytics: Predicting Causes Using Machine Learning Models
DOI:
https://doi.org/10.30595/jrst.v10i1.25997Keywords:
Cyberloafing, machine learning, orange data mining, boredom, fatigueAbstract
Cyberloafing refers to the practice of employees utilizing internet access for non-job-related activities during work hours. Cyberloafing poses a dilemma for organizations, as it is deemed aberrant conduct that might impact overall performance. Consequently, organizations must ascertain the determinants of cyberloafing. This study seeks to identify a suitable predictive model for the determinants of cyberloafing behavior in the workplace using a machine learning methodology. The employed methodology utilizes the conventional data mining cycle, namely the Cross-Industry Standard Process for Data Mining (CRISP-DM), with Orange Data Mining as the application tool. The findings indicate that Logistic Regression is the most effective model for forecasting cyberloafing. Logistic Regression yields performance scores of 90.5% Precision and 88.9% Recall. Conversely, the Naïve Bayes model had the lowest metrics, with a Precision of 64.8% and a Recall of 51.9%. This study serves as a reference demonstrating that Logistic Regression effectively predicts cyberloafing. This study enables firms to examine the factors contributing to cyberloafing, facilitating the development of policies aimed at mitigating its adverse effects.
References
Abapihi, B., Mukhsar, Adhi Wibawa, G. N., Baharuddin, Lumbanraja, F. R., Faisal, M. R., & Sani, A. (2021). Parameter estimation for high dimensional classification model on colon cancer microarray dataset. Journal of Physics: Conference Series, 1899(1), 012113. https://doi.org/10.1088/1742-6596/1899/1/012113
Alabadee, S., & Thanon, K. (2021). Evaluation and Implementation of Malware Classification Using Random Forest Machine Learning Algorithm. 2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM), 112–117. https://doi.org/10.1109/ICCITM53167.2021.9677693
Chidambaram, S., & Srinivasagan, K. G. (2016). Optimization Approach for Feature Selection and Classification with Support Vector Machine (Vol. 410, pp. 103–111). Springer Verlag. https://doi.org/10.1007/978-81-322-2734-2_11
Chung, D., Yun, J., Lee, J., & Jeon, Y. (2023). Predictive model of employee attrition based on stacking ensemble learning. Expert Systems with Applications, 215, 119364. https://doi.org/10.1016/j.eswa.2022.119364
Demirboga, S., Baykara, M., & Kılınç, A. (2025). Detection and Analysis of Cyberloafing Behaviors in The Workplace: A Multi-Method Literature Review. 2025 9th International Symposium on Innovative Approaches in Smart Technologies (ISAS), 1–7. https://doi.org/10.1109/ISAS66241.2025.11101964
Divya, & Narwal, M. (2023). Digital Distractions in the Workplace: Exploring Cyberloafing Impact on Employee Behaviour and Innovation. Virtual Economics, 6(4). https://doi.org/10.34021/ve.2023.06.04(1)
Henle, C. A., & Kedharnath, U. (2012). Cyberloafing in the Workplace. In Encyclopedia of Cyber Behavior (Vol. 1, pp. 560–573). IGI Global. https://doi.org/10.4018/978-1-4666-0315-8.ch048
Hoque, N., Bhattacharyya, D. K., & Kalita, J. K. (2021). KNN-DK: A Modified K-NN Classifier with Dynamic k Nearest Neighbors (Vol. 1319, pp. 21–34). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-33-6919-1_2
Hu, Z. (2020). Economic Forecast of Coastal Regions Based on Support Vector Machine Model. Journal of Coastal Research, 110(sp1). https://doi.org/10.2112/JCR-SI110-050.1
Jamaluddin, H., Ahmad, Z., & Wei, L. T. (2023). Exploring cyberloafing as a coping mechanism in relation to job-related strain and outcomes: A study using the mediational model of stress. Cogent Social Sciences, 9(2). https://doi.org/10.1080/23311886.2023.2278209
Kalra, V., Kashyap, I., & Kaur, H. (2022). Effect of Distance Measures on K-Nearest Neighbour Classifier. 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), 1–7. https://doi.org/10.1109/ICCSEA54677.2022.9936314
Karabadji, N. E. I., Amara Korba, A., Assi, A., Seridi, H., Aridhi, S., & Dhifli, W. (2023). Accuracy and diversity-aware multi-objective approach for random forest construction. Expert Systems with Applications, 225, 120138. https://doi.org/10.1016/j.eswa.2023.120138
Krishna, S. M., & Agrawal, S. (2023). Cyberloafing: Exploring the Role of Psychological Wellbeing and Social Media Learning. Behavioral Sciences, 13(8). https://doi.org/10.3390/bs13080649
kumar, S., & Kumar, Mr. N. (2019). Diagnosis of Diabetes by using Data Mining Techniques. International Journal of Innovative Technology and Exploring Engineering, 9(1), 2368–2372. https://doi.org/10.35940/ijitee.L3174.119119
Li, C., Yang, L., Lu, P., & Wang, Z. (2021). Application of decision tree model to surgical action analysis. 2021 2nd International Conference on Artificial Intelligence and Education (ICAIE), 163–166. https://doi.org/10.1109/ICAIE53562.2021.00041
Li, X., Xu, K., Men, X., Lin, Z., Zhao, Y., & Wei, D. (2023). Application Research of Neural Network-based Data Assessment and Prediction Method in Statistical Analysis. 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP), 1185–1189. https://doi.org/10.1109/ICSP58490.2023.10248747
Lizarte Simón, E. J., Khaled Gijón, M., Galván Malagón, M. C., & Gijón Puerta, J. (2024). Challenge-obstacle stressors and cyberloafing among higher vocational education students: the moderating role of smartphone addiction and Maladaptive. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1358634
Maindola, M., Al-Fatlawy, R. R., Kumar, R., Boob, N. S., Sreeja, S. P., Sirisha, N., & Srivastava, A. (2024). Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics. 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), 1679–1685. https://doi.org/10.1109/IC3I61595.2024.10828609
Mara, M. N., Hidayat, S. S., Putri, F. T., Rahmawati, D., Wahyuni, S., Prabowo, M. C. A., Kabir, N. N. S., & Indra, R. T. (2025). Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification. IAES International Journal of Artificial Intelligence (IJ-AI), 14(1), 62. https://doi.org/10.11591/ijai.v14.i1.pp62-72
Metin-Orta, I., & Demirtepe-Saygılı, D. (2023). Cyberloafing behaviors among university students: Their relationships with positive and negative affect. Current Psychology, 42(13), 11101–11114. https://doi.org/10.1007/s12144-021-02374-3
Mojsilovic, A. (2005). A Logistic Regression Model for Small Sample Classification Problems with Hidden Variables and Non-Linear Relationships: An Application in Business Analytics. Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 329–332. https://doi.org/10.1109/ICASSP.2005.1416307
Muntean, M., & Militaru, F.-D. (2023). Metrics for Evaluating Classification Algorithms (Vol. 321, pp. 307–317). https://doi.org/10.1007/978-981-19-6755-9_24
Novia, A., & Yuadi, I. (2023). Forecasting Employee Potential through Probationary Assessment. JBMP (Jurnal Bisnis, Manajemen Dan Perbankan), 9(2), 131–149. https://doi.org/10.21070/jbmp.v9i2.1718
O’Neill, T. A., Hambley, L. A., & Chatellier, G. S. (2014). Cyberslacking, engagement, and personality in distributed work environments. Computers in Human Behavior, 40, 152–160. https://doi.org/10.1016/j.chb.2014.08.005
Pajila, P. J. B., Sheena, B. G., Gayathri, A., Aswini, J., Nalini, M., & R, S. S. (2023). A Comprehensive Survey on Naive Bayes Algorithm: Advantages, Limitations and Applications. 2023 4th International Conference on Smart Electronics and Communication (ICOSEC), 1228–1234. https://doi.org/10.1109/ICOSEC58147.2023.10276274
Pindek, S., Krajcevska, A., & Spector, P. E. (2018). Cyberloafing as a coping mechanism: Dealing with workplace boredom. Computers in Human Behavior, 86, 147–152. https://doi.org/10.1016/j.chb.2018.04.040
Pious, I. K., Rajalakshmi, A., R, P. K., C M, V., Nalini, M., & R, S. S. (2024). Enhancing Prediction Accuracy Through Random Forest in Classification and Regression. 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG), 1–6. https://doi.org/10.1109/ICSTSDG61998.2024.11026358
Raposo, L. M., Rosa, P. T. C. R., & Nobre, F. F. (2020). Random Forest Algorithm for Prediction of HIV Drug Resistance (pp. 109–127). https://doi.org/10.1007/978-3-030-38021-2_6
Riyanto, S., Sitanggang, I. S., Djatna, T., & Atikah, T. D. (2023). Comparative Analysis using Various Performance Metrics in Imbalanced Data for Multi-class Text Classification. International Journal of Advanced Computer Science and Applications, 14(6). https://doi.org/10.14569/IJACSA.2023.01406116
Schmidhuber, J. (2015). Deep Learning in neural networks: An overview. In Neural Networks (Vol. 61, pp. 85–117). Elsevier Ltd. https://doi.org/10.1016/j.neunet.2014.09.003
Shobika, G. V., Siddharth, S. P., Vishwa, K., Hemachandran, K., & Rege, M. (2023). ANN Model for Analytics. In Artificial Intelligence and Knowledge Processing (1st ed., pp. 195–207). CRC Press. https://doi.org/10.1201/9781003328414-17
Song, M., Ugrin, J., Li, M., Wu, J., Guo, S., & Zhang, W. (2021). Do Deterrence Mechanisms Reduce Cyberloafing When It Is an Observed Workplace Norm? A Moderated Mediation Model. International Journal of Environmental Research and Public Health, 18(13), 6751. https://doi.org/10.3390/ijerph18136751
Sun, Y., Zhang, Z., Yang, Z., & Li, D. (2019). Application of Logistic Regression with Fixed Memory Step Gradient Descent Method in Multi-Class Classification Problem. 2019 6th International Conference on Systems and Informatics (ICSAI), 516–521. https://doi.org/10.1109/ICSAI48974.2019.9010220
Tay, J. K., Narasimhan, B., & Hastie, T. (2023). Elastic Net Regularization Paths for All Generalized Linear Models. Journal of Statistical Software, 106(1). https://doi.org/10.18637/jss.v106.i01
Trabelsi, A., Elouedi, Z., & Lefevre, E. (2019). Decision tree classifiers for evidential attribute values and class labels. Fuzzy Sets and Systems, 366, 46–62. https://doi.org/10.1016/j.fss.2018.11.006
Wong, T.-T. (2022). Linear Approximation of F-Measure for the Performance Evaluation of Classification Algorithms on Imbalanced Data Sets. IEEE Transactions on Knowledge and Data Engineering, 34(2), 753–763. https://doi.org/10.1109/TKDE.2020.2986749
Wu, J., Pan, S., Zhu, X., Cai, Z., Zhang, P., & Zhang, C. (2015). Self-adaptive attribute weighting for Naive Bayes classification. Expert Systems with Applications, 42(3), 1487–1502. https://doi.org/10.1016/j.eswa.2014.09.019
Zhong, J., Chen, Y., Yan, J., & Luo, J. (2022). The mixed blessing of cyberloafing on innovation performance during the COVID-19 pandemic. Computers in Human Behavior, 126, 106982. https://doi.org/10.1016/j.chb.2021.106982
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