Invention to innovation: A framework and the roles of uncertainty and open innovation in an emerging personalized medicine ecosystem
Bibliographic record
Abstract
In this dissertation, I explore the antecedents, processes and outcomes of the translation of scientific invention to innovation, which is of particular interest to scholars and policy makers who wish to understand how publicly funded university research can be harnessed to increase national productivity. While there has been an increasing amount of research on the roles that innovation intermediaries and mechanisms play in science innovation, there have been few attempts at characterizing the inputs, mediators and outputs of this process. Innovation management and policy research is far more developed around technology innovation than science innovation. Few empirical studies have been conducted at the ecosystem level in emerging science-based contexts. Firms in such contexts exhibit markedly different characteristics compared to large software incumbents, including higher technical and market uncertainty. In three essays I explore the following research questions: “what factors influence the translation of scientific invention to innovation?”, “do uncertainty, partnerships and patenting have an influence on innovation performance outcomes?” and “why and under what conditions do the Open Innovation mechanisms of selective revealing, strategic timing and strategic partnering affect value capture by personalized medicine firms?”. The investigation of these questions aims to inform scientist-entrepreneurs, scholars, policy makers and university leadership on how to more effectively translate breakthrough invention to improved economic, health and social outcomes. This dissertation contributes to the Technology and Innovation Management literature in three ways. In the first essay, the results of a bibliographic review are synthesized into a novel theoretical framework on science innovation. In the second essay, the relationship between uncertainty, patenting and innovation performance outcomes is explored, using a novel and custom-built dataset on British Columbia personalized medicine firms. The results show that uncertainty, partnerships and patenting play a role in innovation performance outcomes, which have implications for both practitioners and policy makers. In the third essay, I heed the call from the Open Innovation research community to more deeply explore the boundary conditions of Open Innovation. A new model on Open Innovation is presented, which is empirically supported by two key findings: the Open Innovation mechanisms of Strategic Partnerships, Selective Revealing and Strategic Timing appear to play important roles in value capture, but this relationship is moderated by technical uncertainty.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".