R<sub>X</sub> for excessive turnover: lessons in communicating a vision (part 1)
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate causes of high staff turnover among production workers at a large‐scale retail case meat processor. Design/methodology/approach Three hypotheses were developed to investigate six independent variables as possible factors of high job turnover. The research is based on a two‐step process consisting of a literature review and field research. The literature review served to establish empirical links among the variables and construct an appropriate questionnaire for the field research. The field research consisted of 38 employees (out of 475) completing a 41‐question survey. Individual interviews were also conducted with 20 of the 38 respondents. The paper is an empirically based case analysis. Findings The results demonstrate that the employees' organizational commitment affects employee turnover. The findings also suggest that organizational commitment can be improved through increased effective communication between management and employees and ensuring that the organization's vision is shared and understood by employees. The link between job satisfaction and turnover was not supported by the research. Research limitations/implications Sample size was affected due to the limited availability of employees during production hours. Increased sample size would allow further investigation within specific departments and shifts. Additional research could also have been done on how the company's HR policy mandated from their US head office fits the needs of a Canadian based workforce. Originality/value The paper provides insight on the causes of employee turnover and low organizational commitment. The paper recommends four actions to address communication and vision sharing to improve organizational commitment and ultimately turnover.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".