Unleashing the power of salespersons’ implementation intentions through coaching
Bibliographic record
Abstract
Purpose The purpose of the paper is to validate if managers (through the use of managerial coaching) can help subordinates develop implementation intentions to address difficult problems and situations with customers. These implementation intentions take the form of new task strategies and go beyond the automated mechanisms of providing more effort, persisting longer in the pursuit of goals or adapting old strategies to solve new problems. Design/methodology We designed a cross-sectional field study with a convenience sample of 184 salespeople from different companies. Respondents provided information concerning the coaching received from their supervisors, the degree to which they were able to develop implementation intentions in future encounters with customers, and sales performance. Data was analyzed using structural equation modeling in AMOS. Findings We found that coaching can help salespeople develop better implementation intentions and, thus, be more effective in their interactions with customers, ultimately increasing their sales performance. Originality The paper explores the use of coaching to help subordinates develop new task-oriented strategies, using two theoretical frameworks: implementation intentions and goal-setting.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".