The Relationship Between University Student Commitment Profiles and Behavior: Exploring the Nature of Context Effects
Bibliographic record
Abstract
Theoretical concepts from the organizational behavior literature, including commitment, are rarely used to help explain university student behavior. The benefits of doing so might include the development of a synthesis of knowledge about the behavior of students in an organizational setting. Such a synthesis is important because it will help extend organizational commitment literature to student samples and will help explain student behavior as a result of their commitment. As such, the purpose of this study was twofold: (a) to test theoretical propositions advanced by Meyer and Herscovitch concerning the interactive effects of affective, normative, and continuance commitment on students’ focal and discretionary behaviors and (b) to provide an exploratory examination of the notion of a commitment profile “context effect” for normative commitment for students in a university setting. Study measures were gathered from a sample of 287 undergraduate business students. Results showed support for interactive effects of the three components of commitment for both focal and discretionary behavior. Results also showed support for commitment profile differences and for the existence of a normative commitment context effect.
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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 | 0.025 |
| 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 | 0.001 | 0.001 |
| 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".