The Effects of Motivation on Staff Job Performance: Evidences from the Lagos State Ministry of Environment, Nigeria
Bibliographic record
Abstract
This study examined the effects of motivation on the job performance of staff of the Lagos State Ministry of Environment, Nigeria. It identified the factors that motivate the staff to perform their jobs better and situated those preferred functional and novel strategies that enable them function more efficiently and effectively. The survey research design was adopted for the study and it obtained its data from both primary and secondary sources. The stratified random sampling technic was utilized in administering 140 copies of the questionnaire on both the project and tenured staff of the Lagos State Ministry of Environment, therefore cutting across the rank and file of the Ministry. The retrieved data were analyzed using descriptive statistics in the form of simple percentages. The study found that: regular payment of salary and provision of welfare packages (51.4%), conducive working environment (49.3%), opportunity for autonomy, creativity and innovative thinking which the job provides (48.6%) and the regular training which they are exposed to (45%) motivated them to perform their jobs better. Results of the analysis further revealed that the motivational strategies preferred by the staff and which would make them more efficient and effective include: ensuring that the project staff attain tenure (55.7%), recognizing and rewarding outstanding performance (54.3%) making the existing retirement plan more reliable (53.6%) provision of modern working facilities (52.1%), increase in salary with welfare packages and bonuses (52.9%), amongst others. The study concluded that motivation is pivotal for enhancing staff job performance and a driving force for the overall efficiency of an organization. Hence, there should be diversity in the motivation techniques adopted to meet the needs of the staff as well as the changes in the work 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 | 0.004 |
| 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) | 0.002 | 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".