Explaining Sales Pay Strategy Using Agency, Transaction Cost and Resource Dependence Theories*
Bibliographic record
Abstract
ABSTRACT The purpose of this study is to investigate, using data gathered from 325 French‐Canadian organizations, the influence of key constructs related to agency, transaction cost and resource dependence theories on the proportion of salary in sales compensation. Usefulness analysis showed that performance information (9 per cent), uncertainty (8 per cent) and dependence resource (5 per cent) constructs have a significant incremental contribution to sales compensation. Regarding specific hypothesis tests, results of full model show that the capacity to observe behaviour, team sales and financial resources offered are associated with an increased use of salary pay. In contrast, adaptability of product/service‐related resources, salesforce experience and high marginal salesforce productivity are associated with decreased use of the salary component. However, the results of full model have failed to support the direction or influence of relationships between programmability, span of control, market and selling uncertainty, the predominance of salespeople and TCA measures on proportion of salary. The results support the argument that integration of multiple theoretical perspectives better explains pay policy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".