Unpacking the performance of corporate accelerators: can management structure and strategic focus explain their success in supporting startup exits?
Bibliographic record
Abstract
Purpose Corporate accelerators have emerged as a prominent corporate venturing strategy, enabling large corporations to engage with entrepreneurial opportunities by supporting and nurturing startups. While existing research has explored various dimensions of corporate accelerators, such as location, size, and program design, scant attention has been given to the influence of management structure and strategic focus on accelerator performance. This paper aims to fill this gap by investigating how these dimensions shape the effectiveness of corporate accelerators in fostering startup success. Design/methodology/approach Using a dataset of 188 corporate accelerators registered on Crunchbase as of February 2023, we conducted econometric analyses to examine these relationships. Findings Our findings reveal that in-house accelerators, where corporations internally manage program activities, outperform powered-by accelerators in facilitating startup exits and survival. Furthermore, accelerators with a broad or no strategic focus (horizontal) are more effective in supporting startup exits than those aligned with the corporations’ core business (vertical). Conversely, vertical accelerators prove more successful at promoting startup survival. Originality/value These results enrich the sparse literature on corporate accelerator performance by demonstrating the critical role of management structure and strategic focus and provide valuable guidance for entrepreneurs in selecting acceleration programs and for corporations’ managers in designing their acceleration programs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".