Do desperdício ao sabor : como adaptar o modelo de negócios da "Loop mission" para o mercado alemão
Bibliographic record
Abstract
Twelve million tons of food are disposed in Germany annually. This presents an ecological, economic, and ethical problem. Inspired by the success of “Loop Mission” in Canada, this thesis explores the feasibility of adapting their business model for the German market, as “Loop Mission” is converting unsellable fruits and vegetables into smoothies. For this purpose, their business model was analyzed through an interview, followed by a market analysis that examined the macroeconomic and competitive landscape as well as cultural differences and concluded with a survey testing market acceptance among consumers. The findings indicate that "Loop Mission’s" smoothies would have significant potential in the German market, with the market size and consumers' willingness to pay particularly promising. From an operational perspective, it is important to find strong procurement and distribution partners or even partners to adapt to local packaging sizes. From a marketing perspective, it would be advisable to switch to a fact-based strategy, emphasizing arguments such as nutritional aspects. In contrast, the sustainability aspect can be considered an additional unique selling proposition. Notably, the identified target group of women aged 25-34 with an academic degree and higher income has proven to be lucrative early adopters and innovators, demonstrating a willingness to pay between 2,50€ and 3,00€ according to the value-based pricing method. The physical presence of the products emerged as a must. Word-of-mouth seems to be the most effective marketing strategy followed by social media ads. Considering the above, entering the German market for “Loop Mission” style products seems promising.
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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 | 0.004 | 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 | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".