Leadership, Job Well-Being, and Health Effects—A Systematic Review and a Meta-Analysis
Abstract
OBJECTIVE: The aim of this systematic literature analysis was to study the association between leadership and well-being at work and work-related health. These intermediate outcomes are supposed to predict work-related loss of productivity and disability at work. METHODS: Original articles published in 1970 to 2005 were searched in MEDLINE and PsycINFO databases in a systematic manner. The main search terms were leadership, job satisfaction, well-being, sick leave, and disability pension. Out of 303 references, 93 publications were retrieved. In addition, other sources produced 69 articles. The strength of evidence was evaluated comprehensively. Altogether, 109 articles were thoroughly analyzed; our conclusions are based on 27 articles providing the best evidence. RESULTS: There was moderate evidence that leadership is associated with job well-being (risk ratio [RR] 1.40, range 1.36 to 1.57), sick leave (RR 0.73, range 0.70 to 0.89), and disability pension (RR 0.46, range 0.42 to 0.59). The evidence was weak on that leadership is associated with job satisfaction (median RR 2.23, range 1.30 to 3.51) but not with job performance (RR 1.13, range 0.55 to 1.20). CONCLUSIONS: There is a relative lack of well-founded prospective studies targeting the association between leadership and employee health, but the few available good studies suggest an important role of leadership on employee job satisfaction, job well-being, sickness absences, and disability pensions. The relationship between leadership and job performance remains unclear.
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Literature reviews and meta-analyses on leadership and well-being/health often do not consider the role of digital technology.
“or deal with the relationship between leadership and well-being or health without considering the role of digital technology (Harms et al. 2017;Inceoglu et al. 2018;Kuoppala et al. 2008;Montano et al. 2017;Skakon et al. 2010)”
From Leadership and technostress: A systematic literature review (2025) · cites this paper as Kuoppala et al. 2008
Technostress was often missed in literature reviews and meta-analyses dealing with leadership and followers' well-being.
“Hereby closing a gap in current literature, as technostress represents a topic that was up to now often missed in literature reviews and meta-analyses that deal with leadership and followers' well-being (Harms et al. 2017;Inceoglu et al. 2018;Kuoppala et al. 2008;Montano et al. 2017;Skakon et al. 2010).”
From Leadership and technostress: A systematic literature review (2025) · cites this paper as Kuoppala et al. 2008
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- Authors
- Kuoppala, J., Lamminpää, A., Liira, J., & Vainio, H.
- Affiliations
- Finnish Institute of Occupational Health (FI)
- Corresponding author
- Lamminpää, Anne
- Venue
- Journal of Occupational & Environmental Medicine
- Published
- 2008-08
- Volume
- 50
- Issue
- 8
- Pages
- 904-915
- ISSN
- 1076-2752 (ISSN-L)
- OpenAlex
- W1971253095
- Cited by
- 565
- References
- 130
- Citations by year
- 2026: 13 · 2025: 32 · 2024: 34 · 2023: 42 · 2022: 42 · 2021: 48 · 2020: 41 · 2019: 48 · 2018: 40 · 2017: 32 · 2016: 37 · 2015: 18 · 2014: 44 · 2013: 31 · 2012: 23
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Occupational and Environmental Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Workplace Health and Well-being
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Job Satisfaction and Organizational Behavior
- Social Sciences › Psychology › Social Psychology › Stress and Burnout Research
- Keywords
- leadership, job well-being, job satisfaction, sick leave, disability pension, work-related health, employee health, employee performance, productivity loss
- MeSH terms
- Humans, Job Satisfaction, Leadership, Retirement, Risk, Organizational Culture, Sick Leave
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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