Organizational Restructuring Influences Employee Quit Decision Through Employee Dissatisfaction In Commercial Banks in Kenya
Bibliographic record
Abstract
This study was aimed at investigating the antecedents and predictors of employee quit decision process during organizational restructuring. This was done by examining the nature of relationships between Organizational restructuring, employee dissatisfaction and quit decisions. The literature review revealed that a number of studies have been conducted on the predictors and antecedents of employee quit decisions. However, these studies did not examine any integration between them. The objective of this study was to explore the integrated relationship amongst organizational restructuring, employee quit decisions and dissatisfaction. A sample size of 375 was selected from a total population of 15,017 employees from commercial banks in Kenya. A structured questionnaire with Likert-type statements anchored on a five-point scale ranging from “Not at all (1)” to “To a great extent (5)” was used to collect data. The study employed Pearson's Product Moment Correlation and hierarchical Regression analysis to test the hypotheses. The findings showed a significance relationship between organizational restructuring and employee quit decisions. Further, the findings revealed that the effect of organizational restructuring on employee quit decisions is mediated by employee dissatisfaction.
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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 | 0.002 |
| 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 | 0.000 |
| 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".