Quiet Quitting: Impact of Performance and Well-Being

This study examines the impact of Employee Well-Being on Employee Performance and Quiet Quitting in the IT sector in India, focusing on the mediating role of Employee Performance and the moderating role of gender. Data from 282 IT employees in India is analysed, using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results reveal that Employee Well-Being significantly enhances Employee Performance and reduces Quiet Quitting. Employee Performance does not mediate the Employee Well-Being and Quiet Quitting relationship, indicating that Employee WellBeing directly influences Quiet Quitting rather than through Employee Performance. Gender moderates the Employee Well-Being–Employee Performance link, with males benefiting more from Employee Well-Being improvements and being less likely to engage in Quiet Quitting. Gender does not significantly affect how Employee Well-Being or performance influences Quiet Quitting. These findings are helpful, but they mostly apply to IT employees in big cities. So, they may not fit all work settings. Future research could explore longitudinal effects, organisational culture, and technology-driven engagement strategies. Organisations should focus on mental health support, flexible work arrangements, and fostering a supportive workplace culture to sustain employee productivity. The study is grounded in JD-R Theory, which explains how job demands and resources affect well-being, performance, and Quiet Quitting. This study contributes to the literature by addressing gaps in understanding how Employee Well-Being, Employee Performance, and gender interact to influence Quiet Quitting in the IT sector in India

Authors

Publication Details

Journal
International Research Journal of Multidisciplinary Scope
Published
2025-01-01
DOI
https://doi.org/10.47857/irjms.2025.v06i02.03821
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
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article

Quiet Quitting: Impact of Performance and Well-Being

Rintu Jha, S. Priya, Narendra Singh Chaudhary
1 citations
International Research Journal of Multidisciplinary Scope
Big Data and Business Intelligence
2.28
article

Quiet Quitting: Impact of Performance and Well-Being

Rintu Jha, S. Priya, Narendra Singh Chaudhary
article en
1 citations

Abstract

This study examines the impact of Employee Well-Being on Employee Performance and Quiet Quitting in the IT sector in India, focusing on the mediating role of Employee Performance and the moderating role of gender. Data from 282 IT employees in India is analysed, using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results reveal that Employee Well-Being significantly enhances Employee Performance and reduces Quiet Quitting. Employee Performance does not mediate the Employee Well-Being and Quiet Quitting relationship, indicating that Employee WellBeing directly influences Quiet Quitting rather than through Employee Performance. Gender moderates the Employee Well-Being–Employee Performance link, with males benefiting more from Employee Well-Being improvements and being less likely to engage in Quiet Quitting. Gender does not significantly affect how Employee Well-Being or performance influences Quiet Quitting. These findings are helpful, but they mostly apply to IT employees in big cities. So, they may not fit all work settings. Future research could explore longitudinal effects, organisational culture, and technology-driven engagement strategies. Organisations should focus on mental health support, flexible work arrangements, and fostering a supportive workplace culture to sustain employee productivity. The study is grounded in JD-R Theory, which explains how job demands and resources affect well-being, performance, and Quiet Quitting. This study contributes to the literature by addressing gaps in understanding how Employee Well-Being, Employee Performance, and gender interact to influence Quiet Quitting in the IT sector in India

International Research Journal of Multidisciplinary ScopeVol. 06(02)
Openalex Percentile: Top 12%
Big Data and Business Intelligence
2.28
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