The Role of Cultural Capital in Organizational Citizenship Behavior (Case Study: Islamic Azad University of Mahmudabad)
Bibliographic record
Abstract
Background: Developing and strengthening Organizational and Citizenship Behavior (OCB) needs various capitals such as Cultural Capital. The aim of this research was studying about the relation between cultural capital and organizational citizenship behavior and also predicting organizational and citizenship behavior.Materials and Methods: The method of this study was description. The Statistical population under study was composed of 380 MA students in Mahmud-Abad Islamic Azad University. Finally on the basis of Kook ran formula a sample of 191 people was chosen randomly stratified for this study. Standard questionnaire was used in this study. Bourdieu standard cultural capital questionnaire with kronbakh alpha factor .857 and Organ and Kanoski organizational and citizenship behavior questionnaire with the stability of .76 were used in this study for statistical analysis data with Pierson and Regression correlation factor was used to check the hypothesis. All data analysis was done with Spss 18.Results: Achievements of this study were shown that there is a meaningful correlation between cultural capital and OBC with the factor of 0.428. Also cultural capital with determination factor of .183 plays an important role in predicting OBC.Conclusion: Therefore universities must improve their culture index regarding their cultural mission in order to improve OBC which is a valuable and useful behavior. They achieve sustainable development through their evolutionary process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".