Functional mushroom industry analysis : consumer-centric innovation
Bibliographic record
Abstract
The functional mushroom industry emerges at a time when health is a global issue and there is a reorientation towards health products. This research analyzes emerging mushroom markets, determining, and exploring the impact of specific market factors on consumption to improve market penetration and expansion of the mushroom industry. Empirical research was 3 conducted using in-depth interviews and a survey for a sample of mushroom consumers in emerging markets (Portugal and Canada). Several key findings emerged that can benefit mushroom industry stakeholders. By leveraging product information, the industry can provide consumers with tools to make good assessments and decisions about mushroom consumption. Price was not considered highly impactful for the sample of consumers; however, making mushrooms and mushroom products accessible pricewise using comparative pricing strategies can benefit the industry. Also, product-formats and solutions can be explored to improve limited shelf-life of current products. Researchers can perform consumer-focused data collection with an increased sample size to yield more applicable results and expand the scope to other countries with emerging mushroom markets in North America and Europe. Integrating stage-specific consumer-behaviour research into development processes can benefit both businesses and consumers alike. By introducing this consumer-centric research, the aim is to increase overall knowledge in the field, augment innovation strategies for functional food product development (FFPD), and ultimately improve market penetration and expansion for the functional mushroom industry.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".