Employees' affective commitment to multiple work-related targets: A longitudinal person-centered investigation
Bibliographic record
Abstract
This study uses a person-centered approach to investigate the structure, stability, antecedents, and outcomes of employees' affective commitment to multiple work-related targets. Following Perreira et al.'s (2018) hierarchical representation of commitment, profiles of affective commitment were estimated by considering both global levels of commitment to the work life and specific levels of commitment to organization, supervisor, coworkers, occupation, work, and career. To this end, a sample of 468 individuals working in firefighting stations located in France was surveyed twice over a four-month period. Our results revealed six commitment profiles: (1) Globally Moderately Committed with a Hierarchical-Organizational Orientation , (2) Globally Weakly Committed with a Balanced Orientation , (3) Globally Strongly Committed with an Occupational Orientation , (4) Globally Moderately Committed with a Hierarchical-Supervisor Orientation , (5) Globally Strongly Committed with a Career Orientation , and (6) Globally Strongly Committed with a Social Orientation . Over time, these profiles displayed a high level of within-sample and within-person stability. Global levels of authentic leadership were related to a higher likelihood of membership into profiles displaying higher global levels of commitment (especially those with a social or occupational orientation) than into the other profiles. Levels of perceived health, work efficiency, improvement-oriented behaviors, and job satisfaction also differed across profiles, with some of the worst outcomes found in the Globally Moderately Committed with a Hierarchical-Organizational Orientation profile.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".