Heavy Work Investment, Workaholism, Servant Leadership, and Organizational Outcomes: A Study among Italian Workers
Bibliographic record
Abstract
Heavy Work Investment (HWI) is a construct that comprises both workaholism and work engagement. We tested a path analysis model on 364 Italian workers, with servant leadership as a predictor of HWI and HWI as a predictor of Organizational Citizenship Behaviors (OCB) and Counterproductive Work Behaviors (CWB). We also performed ANOVAs and MANOVAs. Among the main findings, servant leadership is a positive predictor of both workaholism and work engagement. Work engagement is a positive predictor of OCB and a negative predictor of CWB. Conversely, workaholism, is a positive predictor of CWB, but it does not predict OCB. Hence, we encourage implementing soft-skills interventions aimed at making leaders aware of the different worker types in their organization to develop tailored measures to foster work engagement rather than workaholism. Also, we recommend controlling for work engagement when analyzing workaholism, given the different findings that arose when controlling or not controlling for work engagement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".