A Multifoci Person-Centered Perspective on Workplace Affective Commitment: A Latent Profile/Factor Mixture Analysis
Bibliographic record
Abstract
The current study aims to explore the usefulness of a person-centered perspective to the study of workplace affective commitment (WAC). Five distinct profiles of employees were hypothesized based on their levels of WAC directed toward seven foci (organization, workgroup, supervisor, customers, job, work, and career). This study applied latent profile analyses and factor mixture analyses to a sample of 404 Canadian workers. The construct validity of the extracted latent profiles was verified by their associations with multiple predictors (gender, age, tenure, social relationships at work, workplace satisfaction, and organizational justice perceptions) and outcomes (in-role performance, organizational citizenship behaviors, and intent to quit). The analyses confirmed that a model with five latent profiles adequately represented the data: (a) highly committed toward all foci; (b) weakly committed toward all foci; (c) committed to their supervisor and moderately committed to the other foci; and (d) committed to their career and moderately uncommitted to the other foci; (e) committed mostly to their proximal work environment. These latent profiles present theoretically coherent patterns of associations with the predictors and outcomes, which suggests their adequate construct validity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".