A structured review of start-up accelerator performance measurement: an integrated entrepreneurial program evaluation approach
Bibliographic record
Abstract
As a distinct type of early-stage entrepreneurial support organization, start-up accelerators are theoretically well positioned as a new and burgeoning phenomenon for fostering the process of new venture creation. The rapid expansion and notoriety of these intermediaries combined with a growing list of well-known high growth companies emerging from their programs hints at their potential impact. Yet, the question of whether accelerators work (or not) and to what effect is still at a formative stage. The objective of this paper is to conduct a structured review of what accelerators ‘do’ and how scholars have chosen to measure performance across various research designs, change variables and multiple levels of analysis. Drawing from program evaluation theory, an integrated entrepreneurial logic model is used to capture and sort variables associated with measuring start up accelerator performance between 2011 and 2021. We make several contributions through our analysis of research designs, linked change variables and thematic areas to provide insight into the advances, gaps, limitations and tensions arising from extant scholarly attempts at SA performance measurement. The developmental impact of SA programs is discussed with methodological, theoretical, and practical implications for documenting progress and future research pathways charted.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".