The Impact of Financial Manager Decisions on the Business Results of Micro and Small Companies in the Republic of Croatia in the Area of the City of Split
Bibliographic record
Abstract
Doing business in today’s turbulent and, above all, changing environment represents a great challenge for financial managers in achieving quality management of the organization. Financial data and financial basis are key sources of information and a basis for the financial manager to make decisions and manage well. Financial data are the basis for the effective management of a company, they enable the monitoring of business performance and making operational and strategic decisions, and they are key for future planning and communication with stakeholders. The aim of this paper is an in-depth analysis of the business operations of selected micro and small companies from the city of Split, which are registered for the preparation and serving of food and beverages, to determine whether financial data, their proper use, and management have an impact on business performance. The resource-based view (RBV) is the basis for the development of this research framework. The RBV theory enables and explains the importance of resources, and through this study, the connection with how to interpret and use financial resources (financial data, i.e., information) and what potential effects they have on the financial performance of micro and small companies in the sector registered for the activity of preparing and serving food and beverages in the tourism sector. According to the Accounting Act in the Republic of Croatia, micro companies meet two of three criteria: total assets up to EUR 350,000, revenues up to 700,000, and an average number of employees during the business year of up to 10, while small companies meet two of the following three criteria: total assets up to EUR 4,000,000, revenues up to EUR 8,000,000, and an average number of employees during the business year of up to 50. The results of the research showed the importance of financial information as a resource necessary for business management and competitive position, but also the necessity of continuous investment in the education of financial managers in order to be able to implement the prescribed acts in a way that maximizes the evaluation of the companies’ business performance.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 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".