An Empirical Study of the Motivational Factors of Employees in Nigeria
Bibliographic record
Abstract
The objective of this research is to draw attention to the importance of certain factors in motivating employees in Nigeria. Specifically, the study sought to describe the ranked importance of the following seven motivating factors: (a) job security, (b) personal loyalty to employees, (c) interesting work, (d) good working conditions, (e) good wages, (f) promotions and growth in the organization, and (g) full appreciation of work done. The 15 companies selected from Oyo, Kwara, Osun and Ogun States of Nigeria are mid-sized companies that involved in Educational Consultancy, Hotel and Catering Services, Transportation services, Retail services and Manufacturing. Data were collected through a well-structured questionnaire delivered to the employees of the companies. Findings of the study suggest that good working condition, interesting work, and good pay are key factors to higher employee motivation. Purposefully designed reward systems that include job enlargement, job enrichment, promotions, internal and external stipends, monetary, and non-monetary compensation should be considered. This will help the employer identify, recruit, employ, train, and retain a productive workforce.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".