Dimensionality and Consequences of Employee Commitment to Supervisors: A Two-Study Examination
Bibliographic record
Abstract
Research on the 3-component model of organizational commitment— affective, normative, and continuance—has suggested that continuance commitment comprises 2 subcomponents, perceived lack of alternatives and sacrifice (e.g., S. J. Jaros, 1997 Jaros, S. J. 1997. An assessment of Meyer and Allen's (1991) three-component model of organizational commitment and turnover intentions. Journal of Vocational Behavior, 51: 319–337. [Crossref], [Web of Science ®] , [Google Scholar]; G. W. McGee & R. C. Ford, 1987 McGee, G. W. and Ford, R. C. 1987. Two (or more?) dimensions of organizational commitment: Reexamination of the affective and continuance commitment scales. Journal of Applied Psychology, 72: 638–642. [Crossref], [Web of Science ®] , [Google Scholar]). The authors aimed to extend that research in the context of employees’ commitment to their immediate supervisors. Through two studies, they examined the validity and consequences of a 4-factor model of commitment to supervisors including affective, normative, continuance-alternatives, and continuance-sacrifice components. Study 1 (N = 317) revealed that the 4 components of commitment to supervisors were distinguishable from the corresponding components of organizational commitment. Study 2 (N = 240) further showed that the 4 components of commitment to supervisors differentially related to intention to leave the supervisor, supervisor-directed negative affect and emotional exhaustion. The authors discuss the implications of these findings for the management of employee commitment in organizations.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".