Insight into budgeting practices: empirical study of the largest manufacturing companies in Lithuania
Bibliographic record
Abstract
Many research studies have identified an increasing number of disadvantages that arise from using traditional budgeting methods that must be addressed in order to achieve better performance management. The aim of this research is to investigate the current budgeting practices of the largest manufacturing in Lithuania and to find out if the budgeting practices of Lithuanian companies lead to the issues that can be observed in literature and foreign studies. The main objective of the research is to identify the prelevant budgeting trends in the largest manufacturing Lithuania companies and to compare these results with the results of the research accomplished in other countries. The design of the study is based on empirical study-questionnaire. A questionnaire for the assessment of the current budgeting practices used by the largest Lithuanian companies was created. A cross-sectional analysis of the results has been performed. The performed questionnaire-based survey indicates certain trends in budgeting practices in Lithuania. The cross-analysis results show that companies with highly rated budget have more sophisticated budgeting methods and, conversely, companies that rated their budget with the average rating, have more traditional budgeting methods. The most important aspects which affect the effectiveness of budget are indicated. The research points out the necessity of adopting more sophisticated budgeting aspects which contribute to greater satisfaction while using budgets for better performance management. This study reveals which budgeting aspects make a significant impact on the satisfaction and the effectiveness of budgets. The interpretations of results allowed to define the main trends of budgeting in companies of Lithuania. The results of previous researches from Czech Republic companies, Luxembourg companies, South African Republic companies, Spain companies, Canada companies, Malaysia companies, Australia companies and a study conducted by Quantrix, which were accomplished by other authors were presented and these results were compared with the results of the research accomplished in Lithuania. Such course of investigation allowed to identify the the following most important aspects that affect budgeting efficiency and satisfaction: strategic goals set in the company; the budgeting period; including employees in the budgeting process; the period of the budget created for the operating activities of the company; flexibility of budgets; the frequency of budget review; using and including key performance indicators. The findings of empirical analysis revealed that Lithuanian companies do not use all listed main aspects that affect budgeting efficiency and satisfaction. So it is important for these companies to include these aspects into budgeting process.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".