The innovation development process of Michelin‐starred chefs
Bibliographic record
Abstract
Purpose This paper aims to compare and contrast the innovation process described by Michelin‐starred chefs with existing theoretical innovation process models. Design/methodology/approach Semi structured interviews with Michelin‐starred chefs in Germany were conducted to better understand the underlying factors and dimensions that describe process practices. A sample of 12 Michelin‐starred chefs awarded one, two or the maximum of three stars were interviewed about how they develop new food creations in their restaurants. Findings Research results indicated that the development process of Michelin‐starred chefs has similarities and differences to traditional concepts of new product development. Michelin‐starred chefs' innovation processes do not include a business analysis stage and because of the simultaneity of production and consumption and the importance of human factors in service delivery, employees play a more important role in fine dining innovation than in other product innovation situations. Furthermore, Michelin‐starred chefs' innovation processes do not implement an all‐encompassing evaluation system. Research limitations/implications The study was conducted in only one country and on a small sample. Based on an analysis of the findings, the innovation development process of Michelin chefs can be broken down into seven main steps. Originality/value The present study expands the scope of hospitality innovation research and the findings have not only important implications for high‐end restaurant settings but also other restaurant segments, and other hospitality service endeavors.
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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 | 0.005 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".