Bridging the knowledge gap of memorable dining experience at Michelin-starred restaurants: insights from a new two-dimensional strategic matrix
Bibliographic record
Abstract
Purpose This study integrated the service gap of the PZB model, hidden importance and Taguchi’s quality engineering to develop a new two-dimensional strategic matrix, the Hidden Importance and Relative Quality Performance (HI-RQP) model. The HI-RQP model was then used to determine the managerial implications and improvement direction of memorable dining experience (MDE) attributes of Michelin-starred restaurants (MSRs). Design/methodology/approach This study collected 619 MDE questionnaires from customers of seven MSRs. Findings The results reveal that the HI-RQP model comprises four quadrants. Professional and high-quality service, carefully prepared dishes, restaurant style and customer-oriented service attitude are competitive advantages that should be maintained. Conversely, taste and freshness that exceed customers’ expectations and provide extra services to customers are MDE attributes that require urgent improvement. Originality/value Besides contributing practically to the enhancement of MDE in MSRs, the findings facilitate a more plausible identification of MDE attributes by integrating various theories into the HI-RQP model.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".