Happiness at work: a multi-criteria decision-making approach
Bibliographic record
Abstract
Purpose Researchers have strived to identify the factors enhancing happiness at work (HAW), and the causal relations among the enablers of happiness remained underexplored. Therefore, this study aims to map and prioritize the causal relation structures of enablers of HAW. Design/methodology/approach Data were collected from key representatives of information technology (IT) firms located in India. A framework based on the cause and effect relationship among enablers of HAW is proposed, and to establish this causality, the decision-making trial and evaluation laboratory (DEMATEL) technique was applied. Findings The findings indicate five out of 12 enablers as causal, namely, transformational leadership, authentizotic work climate, person–organization work fit, organizational virtuousness and meaningfulness in work. Originality/value Human resource managers, organizational policymakers and scholars will gain greater understanding through this causal framework of enablers of HAW. Knowledge and facilitation of these enablers will aid in nurturing a happy workplace.
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".