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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to: • Information Sciences • Optimization Sciences • Control Theory • Operational Research • Decision Sciences • Information Theory • Information Technology • Computer Networks and Communications • Mathematical Programming • Modelling and Simulation • Database Management • Applications to Engineering Sciences • Applications to Technology

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Open Access Research Article

Pathways to performance : Investigating job-related elements, HR practices, and employee wellbeing

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pp. 1931–1947Vol. 45Issue 7October 2024DOI: 10.47974/JIOS-1744XML
Received:
11 Apr 2024
Published Online:
01 Oct 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1744
Pages:
1931–1947

Abstract

Employee wellbeing is becoming increasingly important in modern organisations, serving as a vital pillar, defining overall productivity and organisational performance. The delicate interplay between numerous job-related aspects and popular human resource practises within organisational settings become critical in this context. Notably, the characteristics include work-life balance, social support, employee engagement, and workplace diversity, all of which substantially impact an employee’s wellbeing and, as a result their performance. This study builds a comprehensive framework linking these essential constructs to unravel their influence and interrelationships. This study focuses on employees working in academic and healthcare settings to understand better the dynamics of work-life balance, social support, employee engagement, and workplace diversity as they relate to employee wellbeing and consequently employee performance. The study incorporates verified procedures and approaches used for rigorous data analysis and practical implications have also been discussed.

Keywords

Subject Classifications

62 Statistics (SPSS 62-04 and Multiple Regression 62J05)

References

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