Psychological contracts and independent sales contractors: an examination of the predictors of contractor-level outcomes
Bibliographic record
Abstract
Over the past decade, the independent sales contractor (ISC) has emerged as both an important distribution channel and a management challenge. This study makes two contributions to this evolving field. First, it explores the interrelations of the psychological contract with sales performance, voluntary turnover and organisational advocacy of ISCs, which have hitherto been largely unexplored. Second, it examines differences between high- and low-performing sales contractors on these linkages, due to findings in the literature that a small number of sales contractors often achieve a majority of sales. Based on survey data as well as 7 years of contractor-level data related to sales performance and voluntary turnover (n = 189), results indicate that psychological contract fulfilment and perceived dependency are important determinants of subsequent sales performance, voluntary turnover and organisational advocacy, with significant differences reported between high- and low-performing ISCs. A notable finding pertinent for sales managers responsible for managing ISCs is that high-performing sales contractors are motivated by psychological contract fulfilment and a low perception of dependency, while low-performing sales contractors are more likely to act as advocates for the firm due to perceived dependency, but may concurrently engage in organisational advocacy as a means to leave the firm.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".