Probing into commitment's nonlinear relationships to work outcomes
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the possibility of curvilinear patterns of relationships between workplace affective commitment and in‐role performance, organizational citizenship behaviors and burnout. As most commitment theories assume strictly linear relations with these outcomes, demonstrating that these positive associations do not hold above some ceiling point in the commitment continuum is potentially important for research and practice. Design/methodology/approach The possibility of nonlinear relations was examined in a sample of 273 hospital employees. Findings The results yielded strong support for the authors' hypotheses. Indeed, most of the relations observed (ten of 15) between affective commitment foci and work outcomes were curvilinear, revealing a ceiling to the positive association between commitment and outcomes. Although these results vary in strength across work outcomes and commitment targets, they reveal that affective commitment has negative associations with employee productivity and psychological health at extreme levels. Originality/value Methodologically, these results illustrate the need to systematically explore the true nature of relations among constructs, even in areas where it is assumed to be well known. Practically, these results suggest that, ultimately, moderate levels of commitment may be more beneficial than extremely high levels.
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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 | 0.005 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".