The front end of innovation in an era of industry convergence: evidence from nutraceuticals and functional foods
Bibliographic record
Abstract
Industry convergence, defined as a ‘blurring’ of boundaries between industries, induced by converging value propositions, technologies and markets, appears to be a pervasive phenomenon leading to the emergence of inter‐industry segments. A current example of convergence can be witnessed in the nutraceuticals and functional foods sector, emerging at the boundary between the food and pharmaceutical industries. Not only technologies blur, but there is also a convergence of demand structures: consumers try to satisfy different needs in one transaction. In this context, this paper explores how actors from different industry‐specific resource backgrounds can engage in an innovative activity requiring new technological and marketing competences. Given that absorptive capacity is limited by existing competences, this paper asks how organizations with different R&D competences are able to seize opportunities for innovation emerging from convergence. Empirical findings based on primary data collected from 54 R&D projects of a nutraceutical cluster show that there are different approaches of front end decision making: while some firms follow existing processes for front end decision making, others leave existing paths and need partners to fill in gaps already identified at the front end of innovation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".