On passion and heavy work investment: personal and organizational outcomes
Bibliographic record
Abstract
Purpose – The present research aimed to conceptually position passion for work as a predictor of HWI, as well as to assess the short and long-term influence of passion for work on workers' satisfaction, depression and turnover intentions. In addition, the paper tests whether the effects of passion for work were independent from those of work motivation. Design/methodology/approach – Hypotheses were tested in two field studies in work settings. The first study ( n =2,393) was cross-sectional while the second study ( n =335) used a prospective design. Findings – Harmonious passion was positively related to positive individual outcomes – higher work satisfaction, lower depression – and organizational outcomes – lower turnover intentions. Negative consequences – depression and turnover intentions – were positively related to obsessive passion. Furthermore, passion for work was found to be a distinct concept from work motivation as the above findings held even when controlling for work motivation. Research limitations/implications – Applications are limited to teachers. Only self-reported measures were used. Originality/value – The present research contributes significantly to the organizational and passion literature by showing that HWI may lead to either positive or negative outcomes depending on HWI's underlying motivational force, namely harmonious or obsessive passion. In addition, the present findings yield the first empirical evidence that passion and motivation are distinct but related concepts. In sum, findings from both studies provide valuable insights into the dynamics of passionate workers who are heavily invested in their work.
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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 | 0.005 |
| 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) | 0.004 | 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".