Factors That Affect the Morale of Employees in the Institution of Higher Learning in South Africa
Bibliographic record
Abstract
The paper focuses on assessing and identifying the factors that affect the morale of employees in the institution of higher learning in South Africa. Employee morale is a drive to keep the organisation growing and achieve its objectives. In the worldwide ranking there are two institutions of higher learning that are featured in the top 250 of the universities which could be the result that employees are engaged in their institutions. However, it is imperative to maintain or improve more by understanding and dealing with the factors that may contribute negatively in the institutions of higher learning in SA. A quantitative approach was utilised for the paper and a questionnaire was constructed to collect data from 108 academics and support staff. A stratified approach method wad used and divide participant into groups academics and support employees. All data collected was analysed using SPSS version 22 and the findings of the paper reveals that the level of employee morale was very low and further reveals that management support and feedback, conditions of work, remuneration packages, benefits, promotion processes and recognition, communication and understanding of policies as well as treatment and workload were the crucial factors affect the morale of employees. The paper recommends that all institutions of higher learning to review they own policies and ensure that all stakeholders of their institutions understand them effectively. Similar research should be conducted in other institutions of higher learning as well in order to generalise or enrich the findings of the present paper which serve as a wake-up call.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".