The Impact Of Mentoring On Career Plateau And Turnover Intentions Of Management Accountants
Bibliographic record
Abstract
The presence of frustrated employees in an organization is likely to have a significant adverse effect on the organization’s operations. Employees faced with a career plateau are likely to exhibit feelings of frustration. Such employees may have a higher tendency to leave the company, increasing employee turnover. Using Canadian Certified Management Accountants (CMAs), as subjects, this study examined the effect of mentoring on employee career plateau tendencies and turnover intentions. Survey questionnaires were mailed and responses obtained from 235 CMAs. Subjects’ responses were factor analyzed to develop composite scales about CMAs’ perceptions for mentoring (MENTOR), career plateau (PLAT), turnover intentions (EXIT), positive job attributes (PJA), and job satisfaction rate (JSR). For hypotheses testing, the means of the scaled values were used in statistical tests of relationships between the measures. Tests indicated that mentoring reduces plateau tendency significantly and significantly lowers turnover intentions even after controlling for career plateau, job satisfaction, and positive job attributes. The results imply that fostering a mentoring environment can reduce career plateau attainment and turnover intentions. Reducing career plateau in turn is likely to have positive impact on organization’s operations. For example, CMAs are often involved in, among other matters, the operational information and financial reporting process. Therefore, reducing CMAs’ career plateau tendencies and turnover intentions could improve the quality of an organization’s financial reporting process.
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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 | 0.011 |
| 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) | 0.004 | 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".