The Effects of Personal Attributes of Managing Directors on Startup Accelerator Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most research on accelerators to date focuses on the startups themselves. There has been limited research on the accelerators and their performance as the unit of analysis. Using the upper echelon theory, this article hypothesises the effects of individual attributes of managing directors on the startup accelerator’s performance and tests these by analysing data from 154 Techstars accelerator cohorts comprising more than 1,500 startups. Two personal attributes of managing directors, education and management tenure, influenced the accelerator performance. The education level of the managing director affects the proportion of the graduating cohort that is acquired, the speed of these acquisitions and the survival prospects of graduates that are not acquired. The tenure of the managing director affects the proportion of the graduating cohort that is acquired. These results suggest that certain attributes of an investor play a role in the future success of startups in their portfolio, extending the upper echelon theory from senior management to outside investors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it