Bibliographic record
Abstract
Employee retention of an organization's most talented and skilled employees is vital to success. A lack of managerial strategies for motivating teams and a lack of understanding employees' needs adds to an increased rate of employee turnover in banking organizations. Some bank managers do not possess the abilities and strategies required to reduce employee turnover. Grounded by the motivation-hygiene theory; the purpose of this qualitative case study was to explore successful strategies some bank managers used to reduce employee turnover. The population consisted of 5 banking managers in 3 banking organizations located in Toronto GTA, Ontario Canada in which successful retention strategies have been implemented in the last 5 years. Data were collected from semistructured face-to-face interviews and employee handbooks. Member checking aided to assure the credibility of the analysis and interpretations. Data were analyzed by using coding techniques to identify keywords, phrases, and concepts. The process led to the following 4 themes: (a) the motivational effect to retain bank employees, (b) management traits to retain bank employees, (c) effective strategies to retain bank employees, and (d) trends shaping future retention of bank employees. The implications for social change include the potential to reduce turnover by improving the employee work experience and retaining talent by building a positive work environment and a positive customer experience.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".