Elucidation and enhancement of knowledge and technology transfer business models
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop a conceptual framework identifying and differentiating how knowledge and technology transfer organizations (KTTOs) create value from how they capture and transfer value. Design/methodology/approach The argument of the paper is developed in two steps. First, the knowledge and technology transfer process is conceptualized as a value chain. Second, the internal KTTO's value chain perspective is extended by integrating the knowledge and technology transfer value chain into a business model conceptual perspective in order to emphasize the value captured by the clients of KTTOs. Then, the authors examine how KTTO managers could describe, benchmark and improve their business models by altering or reinforcing how they are positioned with respect to the interdependent elements of their business model. Finally, the elements of the conceptual framework are used to derive emblematic types of business models and provide exemplary cases for each emblematic case. Findings Looking at KTTO management under the lenses of business models invites KTTO managers to look at knowledge and technology transfer as a whole. It suggests to managers to invest resources not only in the improvement of these elements where their organizations are strong, but also in these elements that constitute their weakest elements in the business model. Failure to improve the weakest elements of the business model might compromise the overall knowledge and technology transfer capabilities and performances of KTTOs. Originality/value The conceptual framework developed in this paper is intended as a starting point to explore how KTTO managers may be more effective in creating and capturing value from knowledge transfer.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".