On the nature, predictors, and outcomes of work passion profiles: A generalisability study across distinct types of employees
Bibliographic record
Abstract
Thus far, little research has adopted a person-centred approach to investigate the nature of work passion profiles. As a result, our understanding of the most commonly occurring combinations of harmonious passion (HP) and obsessive passion (OP) in the workplace remains limited. To achieve a more refined understanding of the nature of these work passion profiles, our first aim was thus to identify the configurations of HP and OP for work observed among five samples, including 11 subsamples, of employees (N = 7258). Then, we also considered the extent to which these profiles and their associations with theoretically-relevant predictors (work-home segmentation and organisational support) and outcomes (work engagement, work-family conflict, turnover intentions, presenteeism, and counterproductive work behaviours) generalised across all subsamples. We identified a total of five profiles with a structure that differed slightly across samples: High OP Dominant, High HP Dominant, Average HP Dominant, Low HP Dominant (i.e., low levels of passion dominated by higher levels of HP relative to OP), and Moderately Low Passion. The High OP Dominant profile was systematically the most prevalent (37.5%-54.1% of the sample), whereas the High HP Dominant was the least prevalent (2.1%-7.7%). Across all samples, work-home segmentation was related to a higher likelihood of membership into the profiles characterised by higher, relative to lower or moderate, levels of passion (HP and OP), whereas organisational support also helped employees to stay away from the High OP Dominant profile. Lastly, the least desirable outcomes were observed in the High OP Dominant profile, whereas the most desirable outcomes were observed in the High HP Dominant profile. Interestingly, work engagement levels where comparable in these two profiles. Beyond their theoretical implications for research on work passion, these results highlight how work passion has highly similar implications across contexts.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".