Achieving Human Resource Management Sustainability in Universities
Bibliographic record
Abstract
The sustainability of human resource management (HRM) is the basis for an organization's future growth and success. This study aims to investigate achieving HRM sustainability in universities. We use a quantitative research method design to investigate the factors that affect HRM sustainability at universities. The study was conducted during the spring and summer of 2020 at Iranian state universities. As the study's statistical population included 2543 employees, a sample size of 334 employees was calculated using the Cochran formula. A questionnaire with 32 statements based on a 5-point Likert scale was used to collect the data, which were analyzed using PLS3 software. The findings show that human resource practices, social factors, psychological factors, employer branding, and economic factors have positive and significant effects on HRM sustainability at universities. Findings indicate that it is essential to consider the implementation of adequate HRM practices and related socio-economic and psychological supports for HRM sustainability in universities that can lead to the competitiveness of the higher education institutions such as universities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".