CORPORATE ASSETS OF RESTAURANT INDUSTRY AND CATERING ENTERPRISES DURING THE PANDEMIC
Bibliographic record
Abstract
The article considers the specifics of the restaurant industry and food delivery service in Ukraine in the Covid-19 pandemic and opportunities, in accordance with existing corporate assets, the implementation of a new vector of transformation of domestic entrepreneurship - digitalization, which has long-term consequences and one is a trend of the further restaurant business. The aim of the study was to conduct an analytical review of the state and prospects of the restaurant industry and food delivery service in the country and develop recommendations for the use of internal corporate resources and assets of the restaurant sector in the Covid-19 pandemic. It is established that after the deep crisis of the second quarter of 2020, there is a gradual increase in the volume of services provided to the population by catering establishments, especially in the second and third quarters of 2021; accordingly, the dynamics of growth of the total number of institutions in 2021 was 6%. It is proposed at the initial stage of recovery of the restaurant industry, which is quite attractive to investors in the modern economic space of Ukraine and a profitable business for entrepreneurs, to emphasize the importance of increasing intra-corporate resources of food establishments. The results of our study prove the fact that in the period of reformatting the business strategies of restaurants and increasing the adaptability of food establishments during the pandemic, as well as in the future, human resources and intellectual assets will form the dominant entrepreneurial success and accelerate the capitalization of intangible assets enterprises of the restaurant industry, which is embodied in modern strategic management, expanding the creative space of restaurants and a deep knowledge of the values and preferences of customers and guests of institutions.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".