Attitudinal, personal, and job-related predictors of salesperson turnover
Bibliographic record
Abstract
Purpose – To contribute to the understanding of how to manage turnover, the purpose of this paper is to determine if sales managers have the ability to predict high levels of propensity to leave (PL) from variables readily available in personnel records, and on commonly used employee surveys. Design/methodology/approach – The data used for the analysis of the study variables were collected from the sales forces of a total of ten firms across a variety of consumer and industrial product categories, resulting in a sample of 604 respondents. Data were analyzed via multiple discriminant analysis. Findings – The analysis and test results demonstrate that discriminant sets of attitudinal variables, personal characteristics, and aspects of the job can be identified and used to establish meaningful classifications of a salesperson's PL. Organizational commitment, satisfaction with pay, family status, job involvement, level of education, and compensation plan were all found to be significant. Analysis fails to support the existence of several attitudinal variables generally thought to be predictors of PL. Originality/value – The overarching implication to be drawn is that any effort to address salesperson turnover must be holistic, rather than limited to a narrow set of variables. These findings hold implications for sales management researchers and human resource/personnel managers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".