Understanding the business model design for complex technology systems: The case of the bioeconomy
Bibliographic record
Abstract
• Analyses of technology and actor system. • Framework for business model design. • Application to the bioeconomy. The commercialization of bio-based technologies can foster the transition from a fossil-based to a bio-based industry. However, the development of bio-based technologies often implies a co-evolution of process and product technologies (compare for example a biorefinery producing biobased specialty chemicals) as well as the design of a larger systems architecture involving different actors. Additionally, technologies are increasingly developed in a modular design, where different components of the final technology are managed by several actors that only together engage in value-creation. Complexity of the system complicates the design of a business model due to different interdependencies and the need for co-evolution of different components of a larger technology system. To alleviate these challenges, we aim to build a framework that disentangles the interdependencies of technologies and actors as well as the inherent complexity of technology systems and guides the design of a business model for a complex technology system. By drawing upon five well-established concepts, namely (i) the technology-product-market linkages, (ii) the technology system, (iii) the system-of-systems perspective, (iv) the technology innovation system and (v) the ecosystem pie model, the proposed framework supports the design of a business model for scientist entrepreneurs of a focal technology, which aligns well with the overall (eco)system. To illustrate its use, we apply our proposed framework to the bioeconomic case of microbial palm oil produced in a biorefinery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".