Effects of Merger and Acquisition on Employee Satisfaction in Nepalese Banking Sectors
Bibliographic record
Abstract
Background: Mergers and acquisitions (M&A) are seen as effective strategies for business growth in the corporate sector. However, there are very little study on ‘merger and acquisition’ available in the context of Nepal. Objectives: This study examines the effects of mergers and acquisitions on employees’ satisfaction in Nepalese Banking sectors. Method: The study, which adopts the Job Characteristics Theory as its theoretical foundation, was conducted among employees from Nepalese banking sector that had undergone M&A. The study seeks cause and effects relationship amongst banking employees in Kathmandu valley due to merger and acquisition and adopts explanatory research design. Data were collected from 310 respondents and Structural Equation Modeling was used to analyze the data. Results: The findings demonstrate that merger and acquisition have an influence on employees’ satisfaction, with just one out of every four employees reporting high levels of satisfaction following M&A. The results exhibit that organizational climate, recognition and nature of work remain signifi cant to employees’ satisfaction and their motivation. Likewise, pay/remuneration is also statistically significant to employees’ motivation. Again, employees’ motivation also seems significant to employees’ satisfaction. Conclusion: Therefore, this study offers practical insights to human resource managers in strengthening human resources of the organization as perceived by employees after an M&A by considering the crucial role of employees in organizational performance.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".