The innovation development process of Michelin‐starred chefs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it