Factors Affecting Customer Citizenship Behavior: A Model of University Students
Bibliographic record
Abstract
The purpose of this research is to measure the dimensions of student citizenship behavior and the extent of student satisfaction, loyalty, commitment, and trust as integrated factors are antecedents of student citizenship behavior in universities. A sample of 400 students in two private universities in Egypt was selected randomly and a structured questionnaire was used to collect the research data. The student citizenship behavior was found to contain two types of voluntary behavior; voluntary cooperative behavior and voluntary participation behavior. Also, despite the strong and significant interrelationships between the four antecedents of student citizenship behavior, student satisfaction and loyalty can be considered the strongest antecedents of all dimensions of student citizenship behavior in universities. The main implication of this research is that universities should consider students as valuable resources in both their formal roles and voluntary behavior that support the educational environment of a university. The research suggests satisfied and loyal students provide advantages to their universities not only through spreading positive word of mouth about their universities, attending further education in the future and supporting their universities in the community but also through their positive voluntary behavior.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".