Motives for Training and Management Development in the Nigerian Banking Industry
Bibliographic record
Abstract
This study focused on the motives for training and management development using the Nigerian Banking Industry as a case study .The study relied on both qualitative and quantitative analysis of data. The entire staff of the 25 commercial banks as at 2007 in Nigeria is the population of the study and the difficulty in studying the whole population makes sampling inevitable. In the selection of the banks for this study, stratified sampling technique was adopted. All the 25 banks as at 2007 were stratified into two, old and new Generation Banks. Consequently 4 banks were selected from each stratum. A total of 320 questionnaires were administered. 281 out of the 320 questionnaires were however returned and found suitable for analysis. The data obtained through questionnaire were analyzed using descriptive statistics and inferential statistics such as frequency distribution and Analysis of Variance (ANOVA), respectively. The results of the analysis showed that banks see training and management development as important factors, as well having motives for investing in Training and management development. These motives include- new technology; productivity; responding to skills deficiencies; moral duty; new hire request; and staff request. Some of the recommendations based on the findings include- training should be seen as one of the most important strategies for organizations to help employees gain proper knowledge and skills needed to meet the environmental challenges; it must also be noted that, training and development, though primarily concerned with people, is also concerned with technology, the precise way an organization does business.
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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 | 0.002 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".