FINANCIAL MECHANISM IN THE SYSTEM OF ECONOMIC VECTORS OF DEVELOPMENT OF HOTEL AND RESTAURANT AND AGRICULTURAL ENTERPRISES
Bibliographic record
Abstract
The purpose of the work is to study the aspects of the financial mechanism of the hotel-restaurant and agricultural sectors of the economy that interact in the market for the provision of services and sales of products, the definition of its constituent elements, the justification of the proposal to improve the financial instruments of state influence on solving the problems of development of the hotel-restaurant sector and the agricultural sector in the conditions of the emergence of new risks and threats, as well as the need for Ukraine's integration into the global economic space. Results. Thus, it can be argued that the increase in the production of crop products contributes to the increase in the export potential of agriculture. Thanks to the introduction of a preferential regime for rural and urban products. At present, support for the main branches of production (agriculture, food industry) will provide an opportunity to ensure domestic demand and satisfy external demand. Ukraine has joined the countries of the world, which are committed to ensuring sustainable development in accordance with the global SDGs. In 2019, the directions of the country's economic audit and vectors of economic development until 2030 were developed in Ukraine. This document defines 30 development tasks and 170 basic indicators for achieving economic development, taking into account the specifics of the country. An important element in ensuring Ukraine's sustainable development is the financial component. This can be achieved by improving the functioning of certain sectors of the Ukrainian economy. The strategic importance of agricultural development is highlighted, and the interdependence of GDP growth and agricultural exports is calculated. The financial mechanism is influenced by world financial processes and globalisation, therefore it is expedient to adapt the leading experience of foreign countries. International practice shows that the reform of the financial mechanism of agricultural enterprises is carried out mainly at the expense of strengthening the direct and indirect levers of state influence. Government influence is exercised through price regulation of agricultural products and farm incomes, budgetary financing of agricultural activities, provision of affordable credit, transparent and comprehensible taxation, regulation of exports and imports of agricultural products, support for scientific and technical progress, social support for farmers, etc. The success of agriculture in the leading countries of Western Europe, the USA, Canada, China and Japan is due to the fact that the self-regulating mechanisms of the market have been limited by external levers. This is government support for agriculture through subsidies, prices, quotas, credit and tax policies, etc. Methodology. The article develops methods of financial risk management, which include identification, analysis and development of methods of neutralisation of financial risks of a company. Value/originality. The proposed model can be used to guide further GDP growth with the help of other factors. It is necessary to take into account other factors contributing to GDP growth. In general, each country chooses the optimal balance between different instruments for regulating agricultural enterprises, depending on the national characteristics of the development of the agricultural sector and the priority objectives of the agri-food policy. Prospects for further research are related to the development of optimal sources of financial support for agrarian entrepreneurship in Ukraine. Thus, summarising all the above, it can be noted that decision-making is one of the main issues in management, and in the current conditions of unpredictable challenges and changes, every manager faces the need to be creative in solving the tasks. That is why it is necessary for the management of hotel and restaurant facilities in this time of challenges and new business conditions to make certain adjustments to the management decision-making process, applying the considered managerial and organisational features. Since efficiency and consistency in making management decisions is one of the indicators of effective management of any organisation.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".