When normative commitment leads to lower well-being and reduced performance
Bibliographic record
Abstract
Normative commitment, or employees’ loyalty to their organization based on a sense of obligation, has received less attention than affective and continuance commitment. Building on recent work suggesting that normative commitment’s meaning is influenced by the within-person context provided by the other components of commitment, we theorized that normative commitment would be experienced as externally driven, hence detrimental to well-being and performance, when few alternatives commitment, a sub-component of continuance commitment, is high. Based on two independent samples ( Ns = 366 and 100), Study 1 found normative commitment to be more positively related to emotional exhaustion and psychological distress at high levels of few alternatives commitment. Study 2 ( N = 187) found normative commitment to be less positively related to job performance when few alternatives commitment was high. Implications of these findings for our understanding of normative commitment’s workings are highlighted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".