The role of chef competency in driving process innovation, product innovation, knowledge communication, and restaurant performance
Bibliographic record
Abstract
Restaurants currently receive numerous government incentives for opening their businesses, as they can absorb labor and contribute to generating substantial taxes. The success of a restaurant business is achieved through product innovation and service processes provided to customers. The study aims to investigate the relationship between chef competence and restaurant performance, focusing on knowledge communication, restaurant menus, and process innovation. The results of data collection in the provinces of the Special Region of Yogyakarta, Central Java, and East Java amounted to 115 restaurant businesses. Researchers collected data by direct distribution and using Google Forms. Data processing was conducted using SmartPLS 4 to address all research hypotheses. The results showed that chef competency has a significant impact on process innovation, product innovation, and communication of knowledge. Restaurants have implemented process innovations that have a significant impact on increasing product innovation by 0.357, knowledge communication by 0.316, and restaurant performance by 0.218. Restaurant innovation of product occurs, which cannot have a significant impact on communication of knowledge, but has a significant impact on restaurant performance by 0.322. The chef's ability to effectively communicate knowledge can have a significant impact on restaurant performance. The research results provide practical contributions for restaurant managers to maintain an environment that facilitates knowledge sharing between senior and junior chefs, thereby promoting menu and process innovation that meets restaurant standards.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".