“Meet me at the backdoor”: A multiple case study of academic entrepreneurs bypassing their technology transfer offices
Bibliographic record
Abstract
This article investigates the underexplored phenomenon of technology transfer office (TTO) bypassing in academic entrepreneurship. While TTOs are established to centralize and support intellectual property-based commercialization, a significant portion of entrepreneurial projects avoid the TTO. Relying on both economic (transaction cost theory) and ethical (Tyler's justice model) considerations, this study explores the motivations and contextual factors behind a researcher's decision to commercialize an invention using means other than the TTO. This multiple case study employs an in-depth exploratory qualitative approach to investigate five academic entrepreneurs across different disciplines in Canadian universities who chose to bypass their TTO, often in contravention of institutional policies. Our findings reveal a complex interplay among individual motivations, institutional policies, and market realities. We identified four paths of awareness and strategic intent in this process ranging from unintentional non-compliance to tactical avoidance which challenge a simplistic perception of TTO bypassing as merely unintentional rather than deliberate. The study also reveals four overlapping contexts that promote TTO bypassing: confidence in personal expertise, previous negative experience of using the TTO, peer-influenced skepticism, and external partner challenges. Furthermore, the findings show that the reasons for bypassing include both economic and ethical motivations which steer academic entrepreneurs toward alternative, privately managed commercialization paths. The article concludes with some implications for university managers and policymakers related to how to address the multifaceted motivations for TTO bypassing. • First study to explore the point of view of academic entrepreneurs who have personally bypassed their TTOs. • TTO bypassing occurs based on a series of paths representing varying levels of awareness and strategic intent. • Four overlapping bypassing contexts identified, such as expertise confidence, negative TTO experience, or peer skepticism. • The transaction cost theory and Tyler's justice model explain motivations and economic/ethical rationales for TTO bypassing.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".