Determinants of Employee Job Satisfaction in a Public Organisation in the Province of KwaZulu-Natal
Bibliographic record
Abstract
This study assesses factors that determined employee job satisfaction at the South African Social Security Agency (SASSA) in the Pietermaritzburg office of the KwaZulu-Natal Province. The intention of this study is to recommend workable strategies and mechanisms that can be considered by SASSA as they enhance their organisational development and employee standards. The two-factor theory of Herzberg was applied to comprehend the motivating issues that might determine the fulfilment and discontentment of workers at work. The case of SASSA is used to fill a gap in the literature regarding work values and to provide lessons that can be learnt by other organisations that aspire to improve employee job satisfaction. In order to respond to the aim of this study, the data was collected and analysed using a mixed research methodology. A purposive sample was drawn from the employees who had interest and understanding of job satisfaction using mainly interviews and questionnaires. The findings that the political and socio-economic factors have a potential to limit SASSA from offering a suitable and viable healthy working environment, hence job satisfaction might not be realised. Even though it was revealed that some employees were dissatisfied with their working conditions, none of them were planning to leave their jobs. The findings of this study contribute towards the understanding of job satisfaction aspects of human resources management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".