Research on Correlations between Academicians’ Levels of Organisational Commitment and Their Intention to Quit Their Job: A Comparison of State and Foundation Universities
Bibliographic record
Abstract
This study aims to demonstrate the correlations between academicians’ organisational commitment and their intention to resign from their job. For this purpose, first the concepts of organisational commitment and quitting the job were considered within the framework of relevant literature, and then hypotheses for the correlations were developed.276 lecturers in total 198 of whom were teaching in faculties or departments of sport sciences and 78 of whom were teaching in foundation universities were included in this study.A personal information form in addition to Turnover Intention Scale developed by Rosin and Korabick (1995) and adapted into Turkish by Tanrıöver (2005), and Organisational Commitment Scale developed by Meyer and Allen (1997) and adapted into Turkish by Varol (2010) were used for our purposes.In conclusion, it was found that academicians’ intention to quit their job was low but their organisational commitment levels were high and that they had emotional commitment most—which was followed by normative commitment and continuance commitment. Lecturers employed in foundation universities had higher levels of intention to quit their job than those employed in state universities. Lecturers working in state universities had higher levels of emotional commitment than those working in foundation universities. It was also found that lecturers’ rate of quitting the job was reduced as their levels of emotional and normative commitment increased in both state and foundation universities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".