The Impact of The Financial Culture of Small and Medium-Sized Enterprises and Budgetary Bodies on Financial Planning
Bibliographic record
Abstract
Objective : Formulating my objectives: an analysis of the elements of financial culture and their impact on economic actors, based on the literature on the subject. To explore the existence and the role of financial culture in the life of domestic small and medium-sized enterprises and public sector organisations. To examine the existence and the consequences of inadequately thought-out, often ad hoc, formulated and applied corporate financial planning. Furthermore, the study explores how integrating circular economy principles can enhance financial culture and planning processes by promoting resource efficiency and sustainability. Theoretical Framework: Following the literature review, I formulated my hypotheses in close connection with the objectives. H1: The combined effect of the elements of financial culture determines the financial culture of an organisation. H2:Poorly considered financial decisions, lack of financial planning implies a lower financial culture. Method: For my thesis I used both secondary and primary research methods. During the secondary research, I tried to use studies, scientific articles, surveys of research institutions, national and international statistics, reports and data from governmental websites, surveys of universities and educational institutions, or parts of them, related to my own topic. The hypotheses were formulated through a review and in-depth study of the literature on the subject. Taking into account the results of the literature research, by comparing the findings of the different studies and on the basis of additional information obtained during the analysis of the interviews, the hypotheses formulated were answered, accepted or rejected. For my secondary research I used relevant databases (KSH, OECD,) and the Collection of Legislation in force for the cited legal sources. Among the journals I used the Financial Review published by Corvinus University of Budapest and the Economic Review, a journal of the Committee of Economic Sciences of the Hungarian Academy of Sciences. From the research data of the literature consulted and interpreted during the secondary research, I present the figures relevant to my thesis with the help of figures I have edited myself and adopted. Evaluation of results: In my thesis, I examined the topic of financial culture and the impact of the presence and nature of financial culture, including the issue of financial planning in relation to budgetary organizations and enterprises (SMEs). First of all, I have to say that the area I have chosen to study is very diverse and complex, but also very interesting. In our study of the literature and research material, I had to take care to look for answers and formulate possible correlations by focusing on the questions and hypotheses formulated in my thesis.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".