Research on the Enhancing Effect of Artificial Intelligence and Machine Learning on the Productivity of Remote Workers

Authors

DOI:

https://doi.org/10.56294/hl2025658

Keywords:

Employee productivity, Remote work, Employee behavior, Productivity, Refined Random Natural Gradient Boosting (RR-NGboost)

Abstract

The rise of remote work has highlighted the need for tools and technologies that can enhance employee productivity outside of the traditional office setting. Artificial intelligence (AI) and Machine Learning (ML) have demonstrated potential for optimizing remote work environments by automating tasks, controlling workflows, and offering insights into worker performance.  Though, the unpredictability of remote work conditions across different industries and geographic regions pose some challenges affecting the applicability of the result. This research aims to examine the impact of AI and ML on remote workers' productivity.  It seeks to assess how these technologies can improve productivity by examining employee behavior and performance patterns. A novel method called Refined Random Natural Gradient Boosting (RR-NGboost) technique is implemented, to develop predictive models for analyzing productivity changes. These methods are trained to recognize patterns in workplace behavior and forecast productivity trends. Data is gathered from remote workers in various places (city, town, and village), covering factors like work hours, task completion rates, and time management.  The data is cleaned (by removing inconsistencies and missing values) and Z-score normalization is used to scale the data and develop model performance. Principal Component Analysis (PCA) is used to minimize dimensionality and highlight the most important traits. According to the results, the proposed RR-NGboost method is quite accurate in predicting production fluctuations, achieving a Mean Squared Error (MSE) of 0.3958 and a Mean Absolute Error (MAE) of 0.4234, demonstrating its strong predictive capability and minimal deviation from actual productivity scores. RR-NGboost is the best in terms of feature importance and prediction reliability. The research indicates that AI and ML approaches can significantly improve remote worker productivity by giving real-time insights and automating time management operations, which benefits workers as well as managers.

References

1. George, T.J., Atwater, L.E., Maneethai, D. and Madera, J.M., 2022. Supporting the productivity and wellbeing of remote workers: Lessons from COVID-19. Organizational Dynamics, 51(2), p.100869. https://doi.org/10.1016/j.orgdyn.2021.100869

2. Kurdy, D.M., Al-Malkawi, H.A.N. and Rizwan, S., 2023. The impact of remote working on employee productivity during COVID-19 in the UAE: the moderating role of job level. Journal of Business and Socio-economic Development, 3(4), pp.339-352. https://doi.org/10.1108/JBSED-09-2022-0104

3. Tleuken, A., Turkyilmaz, A., Sovetbek, M., Durdyev, S., Guney, M., Tokazhanov, G., Wiechetek, L., Pastuszak, Z., Draghici, A., Boatca, M.E. and Dermol, V., 2022. Effects of the residential built environment on remote work productivity and satisfaction during COVID-19 lockdowns: An analysis of workers’ perceptions. Building and Environment, 219, p.109234. https://doi.org/10.1016/j.buildenv.2022.109234

4. Howe, L.C. and Menges, J.I., 2022. Remote work mindsets predict emotions and productivity in the home office: A longitudinal study of knowledge workers during the Covid-19 pandemic. Human-Computer Interaction, 37(6), pp.481-507. https://doi.org/10.1080/07370024.2021.1987238

5. Rañeses, M.S., Bacason, E.S. and Martir, S., 2022. Investigating the Impact of Remote Working on Employee Productivity and Work-life Balance: A Study on the Business Consultancy Industry in Dubai, UAE. International Journal of Business & Administrative Studies, 8(2). https://dx.doi.org/10.20469/ijbas.8.10002-2

6. Farooq, R. and Sultana, A., 2022. The potential impact of the COVID-19 pandemic on work-from-home and employee productivity. Measuring Business Excellence, 26(3), pp.308-325. http://dx.doi.org/10.1108/MBE-12-2020-0173

7. Demerouti, E., 2023. Effective employee strategies for remote working: An online self-training intervention. Journal of Vocational Behavior, 142, p.103857. https://doi.org/10.1016/j.jvb.2023.103857

8. Kowalski, G. and Ślebarska, K., 2022. Remote working and work effectiveness: a leader perspective. International Journal of Environmental Research and Public Health, 19(22), p.15326. https://doi.org/10.3390/ijerph192215326

9. Hegde, N.P., Vikkurty, S., Kandukuri, G., Musunuru, S. and Hegde, G.P., 2022. Employee sentiment analysis towards remote work during COVID-19 using Twitter data. International Journal of Intelligent Engineering and Systems, 15(1), pp.75-84. DOI: 10.22266/ijies2022.0228.08

10. Sungheetha, A. and Sharma, R., 2020. A comparative machine learning study on IT sector edge nearer to working from home (WFH) contract category for improving productivity. Journal of Artificial Intelligence, 2(04), pp.217-225. https://doi.org/10.36548/jaicn.2020.4.004

11. Patil, D., 2024. Human-Artificial Intelligence Collaboration In The Modern Workplace: Maximizing Productivity And Transforming Job Roles. Available at SSRN 5057414. https://dx.doi.org/10.2139/ssrn.5057414

12. Shaikh, F., Afshan, G., Anwar, R.S., Abbas, Z. and Chana, K.A., 2023. Analyzing the impact of artificial intelligence on employee productivity: the mediating effect of knowledge sharing and well‐being. Asia Pacific Journal of Human Resources, 61(4), pp.794-820. https://doi.org/10.1111/1744-7941.12385

13. Bijalwan, P., Gupta, A., Mendiratta, A., Johri, A. and Asif, M., 2024. Predicting the productivity of municipality workers: A comparison of six machine learning algorithms. Economies, 12(1), p.16. https://doi.org/10.3390/economies12010016

14. Pryiatelchuk, O. and Aizenberh, T., 2024. Implementation of Ai Systems For Analysis of Productivity And Development Of Talent In Global Teams. Аctual Problems of International Relations, 1(161), pp.129-135. https://doi.org/10.17721/apmv.2024.161.1.129-135

15. Simoncelli, G., Bernardi, M.L., De Angelis, L., Anastasi, S., Bonafede, M., Artenio, E. and Pecori, R., 2024. Predictive functions of artificial intelligence for risk assessment in remote hybrid work. Artificial Intelligence and Social Computing, 122(122). https://doi.org/10.54941/ahfe1004639

16. Javaid, M., Haleem, A., Singh, R.P., Suman, R. and Gonzalez, E.S., 2022. Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability. Sustainable operations and computers, 3, pp.203-217. https://doi.org/10.1016/j.susoc.2022.01.008

17. Awada, M., Becerik-Gerber, B., Lucas, G. and Roll, S.C., 2023. Predicting office workers’ productivity: A machine learning approach integrating physiological, behavioral, and psychological indicators. Sensors, 23(21), p.8694. https://doi.org/10.3390/s23218694

18. George, A.S., 2024. Automated Futures: Examining the Promise and Peril of AI on Jobs, Productivity, and Work-Life Balance. Partners Universal Innovative Research Publication, 2(6), pp.1-17. DOI:10.5281/zenodo.14544519

19. Awan, W.N., Paasivaara, M., Gloor, P.A. and Salman, I., 2023. Creating Happier and More Productive Software Engineering Teams through AI and Machine Learning. In ICSOB Companion.

20. Razali, M.N., Ibrahim, N., Hanapi, R., Zamri, N.M. and Manaf, S.A., 2023. Exploring Employee Working Productivity: Initial Insights from Machine Learning Predictive Analytics and Visualization. Journal of Computing Research and Innovation, 8(2), pp.235-245. https://dx.doi.org/10.24191/jcrinn.v8i2.362.

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Published

2025-06-13

How to Cite

1.
Chen S. Research on the Enhancing Effect of Artificial Intelligence and Machine Learning on the Productivity of Remote Workers. Health Leadership and Quality of Life [Internet]. 2025 Jun. 13 [cited 2025 Jul. 11];4:658. Available from: https://hl.ageditor.ar/index.php/hl/article/view/658