Salesforce output control and customer-oriented selling behaviours
Bibliographic record
Abstract
Purpose This study sets out to empirically investigate the effect of salesforce output control on perceived job autonomy, customer-oriented selling behaviours and sales performance. Design/methodology/approach Data are gathered from 704 salespeople and their visiting customers in Ghana. The hypotheses are tested using the structural equations modelling technique (SEM). Findings According to the findings of the study, output control has a significant and positive impact on perceived job autonomy. It also discovers that perceived job autonomy improves both customer-directed problem solving and adaptive selling behaviours. Furthermore, the study finds that customer-directed problem solving and adaptive selling behaviours both improve sales performance. Moreover, the study uncovers that perceived job autonomy mediates the relationship between output control and customer-oriented selling behaviours, whereas both customer-oriented selling behaviours mediate the relationship between perceived job autonomy and sales performance. Practical implications The current study provides both practical and theoretical insights into salesforce control dynamics, job autonomy, adaptive selling behaviour, customer-directed problem-solving behaviour and sales performance. The findings have important implications for sales organisations because they can assist sales managers in determining the best type of salesforce control systems to deploy and highlight the strategic role job autonomy plays in enhancing sales performance. Originality/value The current study shows how output control can influence salespeople's perceived job autonomy, adaptive selling and customer-directed problem-solving behaviours, and how these can improve sales performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".