Empowering Employee Wellness and Building Resilience in Demanding Work Settings Through Predictive Analytics

Authors

  • Srishti Dikshit Noida Institute of Engineering and Technology
  • Yashika Grover HSBC image/svg+xml
  • Pragati Shukla Noida Institute of Engineering and Technology
  • Akhil Mishra Noida Institute of Engineering and Technology
  • Yash Sahu Noida Institute of Engineering and Technology
  • Chandan Kumar Noida Institute of Engineering and Technology
  • Muskan Gupta Noida Institute of Engineering and Technology

DOI:

https://doi.org/10.4108/eetiot.4644

Keywords:

Employee Health, stress, prediction, predictive analysis

Abstract

In today's fast-paced and competitive corporate landscape, the well-being of employees is paramount for sustained success. This paper explores the transformative potential of predictive analytics in cultivating a healthier, more resilient workforce within high-pressure work environments. The title "Empowering Employee Wellness and Building Resilience in Demanding Work Settings Through Predictive Analytics" encapsulates our objective of harnessing data-driven insights to mitigate the negative effects of high-pressure work settings and foster an environment where employees thrive. Through an in-depth examination of predictive analytics tools and methodologies, this study offers a roadmap for organizations to proactively identify stressors, predict burnout risks, and implement targeted interventions. By collecting and analysing relevant data, employers can tailor support systems, optimize workloads, and implement mindfulness programs that enhance employee well-being. Moreover, organizations can better adapt to change, maintain workforce continuity, and drive productivity by fostering resilience through predictive insights. This research bridges the gap between data science and human resources, offering a holistic approach to employee wellness and resilience-building. By leveraging predictive analytics, companies can create a culture of care where employees feel supported, empowered, and more capable of surviving and thriving in high-pressure work environments.

Downloads

Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">

References

Leka, S., & Jain, A: Health impact of psychosocial workplace hazards: An overview. 2010; World Health Organization.

Marler, J. H., & Boudreau, J. W.: An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 2017, 28(1), 3-26. DOI: https://doi.org/10.1080/09585192.2016.1244699

Gupta, R., & Shaw, J. D.: Employee turnover intentions: An integrative analysis of their antecedents and correlates. Journal of Applied Psychology, 2014 99(3).pp. 532-548.

Wang, Y., Liu, Y., & Wang, H.:Employee wellness in the workplace: A critical review. International Journal of Environmental Research and Public Health, 2010; 16(17), 3142.

Liu, S., & Zhang, H.: Work-life balance and job performance in a Chinese enterprise: The mediating role of psychological capital. Frontiers in Psychology, 2016; 7, 311.

Shoss, M. K., Eisenbeiss, S. A., Knippenberg, D. V., & Kauffeld, S.: The impact of followers’ positive mood and performance on transformational leadership: A longitudinal study. Journal of Occupational and Organizational Psychology, 2018; 91(3), 581-606.

Smith, B. N., Montague, E., & Hartwig, R. (2020). Resilience training in the workplace from 2003 to 2019: A critical review. Industrial and Organizational Psychology, 2019; 13(1), pp. 1-56.

Miller, R. K., Potts, H. W. W., de Kadt, J., & Ladikas, M.: Predictive analytics and real-time monitoring for stress resilience in the workplace: A narrative review. Frontiers in Psychology, 2021; 12, 643224.

Rasmussen, T., & Ulrich, D.: Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 2019; 48(4), 236-242. DOI: https://doi.org/10.1016/j.orgdyn.2015.05.008

Moradi, P., & Maleki, A.: Fairness and bias in predictive policing: Lessons from criminology. 2018; arXiv preprint arXiv:1803.03342.

Kochanowski, L. A., DeFilippis, J., & Rasmussen, T.: Getting employees on board with HR analytics. 2020; Harvard Business Review.

Rosen, L. D., Whaling, K., Rab, S., Carrier, L. M., & Cheever, N. A.: Predictive analytics and AI-driven chatbots in employee support: Opportunities and ethical challenges. Computers in Human Behavior, 2022; 127, 107161.

Murray, D., Tremblay, S., Doucet, C., & Bellavance, F.: Beyond sentiment analysis: Neuroscientific data for predicting employee wellbeing. Journal of Applied Neuroscience, 2023; 1(1), 24-37.

McKinsey & Company: Organizing for the future: The new logic of talent management. 2017; McKinsey & Company.

Harvard Business Review Analytic Services. The age of analytics: Competing in a data-driven world. 2019; Harvard Business Review.

Society for Human Resource Management (SHRM): Using HR Analytics for Workforce Performance: Turning Data into Business Outcomes. 2021; SHRM Foundation.

Bersin, J.: The datafication of HR: Graduating from HR metrics to people analytics. 2015; Deloitte Consulting LLP.

Downloads

Published

19-12-2023

How to Cite

[1]
S. Dikshit, “Empowering Employee Wellness and Building Resilience in Demanding Work Settings Through Predictive Analytics”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023.

Issue

Vol. 10 (2024): EAI Endorsed Transactions on Internet of Things

Section

Research article

License

Copyright (c) 2023 EAI Endorsed Transactions on Internet of Things

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 Unported License.

This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.