The Multifaceted Nature of Job Satisfaction among Academic Staff in Public and Private Universities in Tanzania: A Critical Perspective of Counterproductive Behaviours
Bibliographic record
Abstract
This study investigated the multifaceted nature of job satisfaction among academic staff in the selected public and private universities in Tanzania. A cross-sectional survey design with mixed research approaches was employed. Probability and non-probability sampling techniques were used to get a total sample of 128 respondents; 84 from public and 44 from private universities. Data were collected by using four scale Likert-type questionnaires, semi-structured interviews, focus group discussion and documentary review techniques. The study findings indicated varying job satisfaction levels among the academic staff in public and private universities in Tanzania. Despite the same regulatory authority of universities in Tanzania (Tanzania Commission of Universities –TCU), experiences of the academic staff working in universities were different on their work benefits, relationship with their leaders and communication feedbacks. On the other hand, the study revealed existing counterproductive behaviours which are detrimental to the attainment of universities’ core functions of teaching, research and consultancy. In spite their severity, counterproductive behaviours ranging from conflicts, absenteeism, revenge, emotional cruelty, divided loyalty and intention to quit (job exit) were reported as among the main threats to public and private universities in Tanzania. This study suggests that university leadership needs to consider factors such as fairness in promotion, improving work benefits and effective communications among others, to create a friendly organizational culture. It is also recommended that there should be dialogue, through regular academic staff meetings, effective communication, and enough academic freedom to foster a culture of curiosity, autonomy, and trust in public and private universities in Tanzania.
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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 | 0.004 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".