Innovation in Food and Health: Study Into Challenges and Opportunities for Dutch Small and Medium Sized Enterprises
Bibliographic record
Abstract
This article focuses on the opportunities and challenges Dutch Small and Medium sized Enterprises (SMEs) in the food and beverage industry are facing with respect to innovation in food and health. An online questionnaire was developed to give an overview on attitudes and activities of SMEs with respect to innovation; their interest in trends, among which were health and wellbeing; and their view on logos as a possible way of communication. The questionnaire was held May 2011 with a response of n = 110. Results show that innovation is important for SMEs: over 80% of the SMEs are innovating in products and over 60% in processes. The most appealing trends are: Health and wellbeing, ‘Puur en Eerlijk’ (Note 1) and sustainability. Of the SMEs 70-80% indicate to have a current, and 85% a future interest in these trends. Furthermore 43% used the health logo on part of their product portfolio. Logos that communicate health and sustainability are most popular; 23% of the SMEs indicated not to be interested in the use of logos in general. In conclusion, the identified opportunities for innovation in food and health are: a large willingness to innovate and the increasing interest in the already appealing trend of nutrition and health. There is a moderate interest in (health) logos. Challenges are time, costs, knowledge on innovation in general and lack of specific knowledge on nutrition and health, and unclarities around use and added value (informative and increasing sales) of logos. Also, taste is considered as important as health and cannot be compromised.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".