Feelgood Management in German SMEs and its Impacts on Employees’ Health, Satisfaction and Performance
Bibliographic record
Abstract
Feelgood Management is an emerging concept first applied in the German start-up scene in 2012. The approach is gaining popularity, even though the measurement is difficult and academic research is scarce. Accordingly, this study aims to close this research gap by answering the research question about the impact of Feelgood Management in German SMEs, especially on the employees’ heath, satisfaction and performance willingness. Our findings show that Feelgood Management is just emerging and faces several challenges, related to the ambiguous term that implies ridicule, the lack of standardization that is allowing various interpretations and opposition towards novelty. Despite being limited, due to the risk of bias and subjectivity that is natural for qualitative data collection along with the uni-dimensional perspective of solely Feelgood Managers, this study produces a valuable model of the influences on Feelgood Management and its impact on employee health, satisfaction and performance willingness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".