An ecosystems analysis of how sales managers develop salespeople
Bibliographic record
Abstract
Purpose The purpose of this study is to identify and explain how leadership behaviors of sales managers can enhance the development of salespeople within the context of those interpersonal connections and interactions that is the sales ecosystem. Design/methodology/approach The authors collected and analyzed qualitative data from in-depth interviews with a sample of 36 sales professionals. Over 47 hours of interviews were transcribed and analyzed via NVivo. The statements were labeled as particular leader behaviors using the Miles and Huberman (1994) coding system. Findings The study identifies coaching, customer engaging, collaborating and championing as the four key leader behaviors that are relevant to the sales ecosystem. Specifically, coaching and customer engaging enhance the individual microsystems of salespeople; and collaborating and championing enhance the corresponding mesosystems. Analysis of the interview statements further revealed that trust, confidence, optimism and resilience are four relational elements that tend to coexist with these leader behaviors in the sales ecosystem. Practical implications This study provides a structure for sales organizations to strengthen their sales ecosystem through targeted interventions and training for those that manage salespeople. Past research finds that sales organizations too often neglect this type of managerial training. Originality/value This is the first study to examine sales leadership through the lens of Bronfenbrenner’s (1979) ecological systems theory. Further, the qualitative methodology, which is relatively unique in sales research, provides rich data that is particularly useful for exploring how and why things have happened.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".