A Biological Adaptability Approach to Innovation for Small and Medium Enterprises (SMEs): Strategic Insights from and for Health-Promoting Agri-Food Innovation
Bibliographic record
Abstract
Adaptability has emerged in management/entrepreneurship literature as a business strategy to innovate, perform, and respond in a flexible manner to ever-changing contexts. Contemporary culture blurs boundaries between physical, biological, and digital domains, accelerating what entrepreneurship in sectors such as agri-food contributes to societal-scale solutions to problems at the convergence of social and commercial activities. In this study, we build upon the adaptability of biological systems to propose an approach to innovation, anchored in a tight, dynamic alignment between the strategic DNA of small and medium enterprises (SMEs) and the contexts in which they evolve. Our model employs interviews and supporting archival research on the health-promoting innovation practices of 37 SMEs in the agri-food sector. A two-year, single firm analysis illustrates its relevance and operational feasibility. Evidence suggests that the strategic DNA of SMEs, seen through the entrepreneurs’ identity, informs behavior at various stages of the innovation process and the enterprise’s evolution. Shifting identity prioritization is a reality, and interaction between entrepreneurial organizations and the environment is best understood as an interaction between the DNA of the entrepreneur/enterprise and the environment. This is valuable and will help agri-food and other SMEs to improve their ability to make the internal and external strategic adjustments required in a rapidly changing landscapes to create viable health-promoting food products.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".