Authenticity and well‐being in the workplace: a mediation model
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the link between authenticity at work and well‐being. First, the relationship between authenticity at work and hedonic and eudemonic well‐being indexes is assessed. Second, the mediating role of meaning of work in the relationship between authenticity at work and subjective well‐being at work is investigated. Design/methodology/approach In total, 360 managers from public organizations completed self‐reported questionnaires. Multiple hierarchical regressions were used to assess the hypotheses. Findings Cognitive and behavioral components of authenticity at work explained a significant proportion of variance in each hedonic and eudemonic well‐being indexes. Authenticity is positively associated with well‐being at work. Moreover, meaning of work is a partial mediator of the relationship between authenticity and subjective well‐being at work. Practical implications The results suggest that meaning of work is a mechanism in the relationship between authenticity and subjective well‐being at work. The study highlighted a growing need to promote authenticity within organizations since it has been associated with public managers' well‐being. Originality/value To the authors' knowledge, this is the first study showing the positive relationship between authenticity and well‐being in the workplace amongst public organizations managers. It sheds a very new light on the importance of authenticity in work settings and on how it could be linked to meaningfulness in managerial roles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".