Factors Impacting Entrepreneurial Success in Accelerators: Revealed Preferences of Sophisticated Mentors
Bibliographic record
Abstract
There is a debate within the academic literature as to when during their venture’s development an entrepreneur should augment their identification of a sustainable business model with a consideration of corporate development and financing issues. Drawing upon stakeholder theory, agency theory, and signalling theory, this paper identifies that all three types of development objectives need to be pursued from the very earliest stages of the development of certain ventures. Analysing novel new detailed data, this paper finds that mentor support for a science-based very early-stage venture does not solely depend upon its characteristics upon entering the program but is impacted by interactions between the entrepreneur and potential mentors. In addition, mentors are found to advise entrepreneurs to pursue a broad range of growth objectives as opposed to solely focusing on identifying a sustainable business model. The paper also notes that entrepreneurs who are balanced in the pursuit of their strategic, organizational development, and financing goals are significantly more likely to gain mentor support. One explanation for this later finding is that mentors view an entrepreneur’s understanding of the polycentric nature of venture development as a signal of managerial competence. These findings are consistent across different types of industries and across time. A final contribution of the paper entails a discussion of potential ways to overcome the conflicts of interest that have been previously identified in the literature between angels and venture capitalists financing companies within accelerators. This paper helps resolve debates within the academic entrepreneurship literature and provides insights of interest to practitioners.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".