Strategies for Reducing Employee Turnover in Small- and Medium-Sized Enterprises
Bibliographic record
Abstract
Employee turnover leads to increased operational costs and workloads and affects sales performance. Reducing employee turnover is essential for managers of small and medium sized enterprises to minimize costs and increase sales performance. Grounded in the job embeddedness theory, the purpose of this qualitative multiple case study was to explore strategies the managers of small and medium sized enterprises use to reduce employee turnover that negatively affects sales performance. Data were collected using semistructured, face-to-face interviews, and a review of organizational documents. The participants consisted of three managers of small and medium sized enterprises in the Bronx, New York. After conducting the interviews, the interviews were transcribed. The transcripts and organizational documents were then uploaded into NVivo v12 software to analyze the data (i.e., organize data, create codes, and identify themes). The analysis revealed that recognition and rewards, training and career advancement opportunities, effective communication, and pay, compensation, and benefits are effective in helping to reduce employee turnover. Managers of small and medium sized enterprises may use the findings to devise recognition and reward strategies to decrease employee turnover. The findings and recommendations from this inquiry may help managers of small and medium sized enterprises, business leaders or owners, and human resource personnel to reduce employee turnover and improve sales performance, profitability, and competitiveness. Keywords: employee turnover, job embeddedness, employee retention, employee retention strategies, and employee engagement
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".