Impact of psychological capital on occupational burnout and performance of faculty members
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact and interrelation between positive psychological capital and occupational burnout among faculty associates of technical and professional training institutions. Design/methodology/approach In total, 282 faculty members from 17 technical institutions were selected from the province of Punjab, Pakistan. Results were drawn using bivariate correlation and linear regression on the data that were collected through adopted questionnaire. Findings Results of the study revealed that occupational burnout is significantly related to the performance of the faculty of technical institutes and that psychological capital moderates this relationship. Practical implications Conclusions from this research suggested that the personality trait of a psychological capital is an essential determinant of job burnout and performance among faculty members; therefore, it must be considered as an essential part of the selection criteria and job-assessment process. This is important, since psychological capital reduces the detrimental impact of occupational burnout on performance outcomes. Further the faculty members with higher psychological capital should be retained to create positive work environment which can be an inspiration for others. Originality/value The research provides important information about the impact of personality traits upon the performance of a faculty member who joins an educational institution coming from an industrial environment. Although having similar technological skills required for the position, still the effects of a new environment can cause performance issues, the research insisted that positive personality traits like psychological capital can help in gaining required confidence to perform in a changed environment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".