A Case Study on Relationships Between Psychological Capital, Personality and Organizational Commitment
Bibliographic record
Abstract
The aim of this study is to examine the effect of psychological capital and personality on organizational commitment. Furthermore, it was also aimed to examine the relationships between these two concepts because there is a small number of studies that reveal the relationships between psychological capital and personality characteristics. In this context, a full count was performed in a manufacturing enterprise, and the questionnaire study was conducted on a total of 217 people including all white and blue-collar employees. All employees were reached by face to face interviews. The collected data were analyzed by SPSS 21 and Lisrel 8.51.The results of the study show that psychological capital positively affects affective, continuance and normative commitments. Similarly, personality characteristics also have a significant effect on organizational commitment. Extraversion, conscientiousness, agreeableness and openness to experience among personality characteristics positively affect the organizational commitment. It was observed that neuroticism had a positive effect on organizational commitment, contrary to expectations.The relationships between the relevant two concepts affecting organizational commitment were also found significant. A positive relationship was found between psychological capital and conscientiousness, agreeableness and openness to experience while a negative and significant relationship was found between psychological capital and neuroticism. Contrary to expectations, a negative and significant relationship was achieved between psychological capital and extraversion.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".