The Path to Sales Center Leadership: Key Differences Between Academic and Practitioner Leaders
Bibliographic record
Abstract
More universities are teaching sales to meet growing employer demand, thereby increasing the prominence of university sales centers. Sales center directors tend to be a PhD or a non-PhD faculty member. While there are advantages to both backgrounds, we know little about how sales center directors view their roles and what behaviors they enact to satisfy demands. The purpose of this research is to investigate the activities of sales center directors and gain deeper insights into their thought worlds. Leveraging job demands–resources theory and a work-based identity perspective, we posit that sales center directors with versus without a PhD will emphasize different job demands. Using a web survey to examine sales center director behaviors and in-depth interviews to explore their thought worlds, we find twice as many sales center directors with a PhD spend time on research activities than their non-PhD counterparts. Sales center directors with a PhD spend twice as much time on research activities than their non-PhD counterparts. Sales center directors without a PhD spend a quarter of their time coaching individual students while those with a PhD express strong desire to impact the sales profession, suggesting that their attention is broader than coaching students.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".