Effects of Reengineering in Banks on Employees Perception of Job Security
Bibliographic record
Abstract
Banks in Nigeria are passing through a process of reorganizing how works are done in their organization with the aim of becoming more efficient and effective. This paper examines employees' perception of job security in response to re-engineering or technological changes in Banks. A job insecurity scale was use to measure employee perception to job insecurity. The scale was adapted from Ashford, Lee and Bobkos (1989) job insecurity scale and standardized by the researcher for the purpose of this research. A total of 150 participants (86 males and 64 females) were randomly drawn from two head office branches of Spring and Wema banks (Plc) respectively. Four hypotheses were tested. Findings revealed that there was no significant effect of re-engineering on perception of job insecurity among banks employee. Significant relationship was, however, observed between age and job insecurity of employee, there was also significant effect of job status on job insecurity. The result also revealed a significant effect of gender on perception of job insecurity among bankers. The implication of these findings is that when worker have a good understanding of the process of reengineering through proper education that it does not connote job loss. This study has shown that some negative attitude display by worker in an organization were largely due to lack of proper education by 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".