Engaging with startups through corporate accelerators: the case of H‐FARM's White Label Accelerator
Bibliographic record
Abstract
Corporate accelerators have emerged in recent years as an innovation mechanism that builds bridges between corporations and startups. Through inbound Open Innovation (OI) activities, established firms open their innovation processes to startups to acquire their knowledge. Previous research has focused either on independent accelerators or on corporate accelerator programs that an established firm operates internally. The literature on how accelerators orchestrate different OI practices is sparse, yet large corporations are forging ahead with corporate accelerators. Furthermore, new corporate accelerator models have emerged, rendering the corporate accelerator phenomenon more heterogenous. In this paper, adopting an OI lens, we explore the White Label Accelerator (WLA), a recent model in which an independent accelerator manages the program on behalf of a single corporate organization. Through an in‐depth case study of Technogym Wellness Accelerator, a pioneer and most important WLA in Italy set up by H‐Farm on behalf of Technogym, a large fitness and wellness company, we investigate how such accelerators function as an OI tool. We found four key dimensions that enable successful outcomes. In particular, the importance of entrepreneurial alertness as a key driver for the effective exploitation of intellectual property represents a significant finding. Our research contributes to OI and entrepreneurial finance literature and provides insightful managerial implications to corporate accelerator stakeholders and startups' managers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".