Strategic Budgeting and Budgeting Evaluation Effects on China’s Manufacturing Companies’ Performance
Bibliographic record
Abstract
This study investigates the interplay between budget planning and budgeting evaluation functions in relation to company budget management performance, specifically focusing on the phenomenon of budgetary slack among manufacturing companies in China. A total of 589 employees, including senior executives and finance managers, participated in this study to answer structured questionnaires. Structural Equation Modeling (SEM) was used to test the proposed research hypotheses; furthermore, Hayes’ mediation analysis was applied to assess the direct and indirect effects of budgetary slack in mediating the relationship between the predictor variable (e.g., budget planning and budgeting evaluation) and the outcome variable (budgeting performance). The findings reveal that both budget planning and evaluation significantly mitigate budgetary slack while enhancing the overall budget management performance. The results suggest that effective budgeting performance is positively influenced by the quality of budget planning and evaluation functions, which directly and indirectly reduce budgetary slack. Such evidence underscores the critical role of the budgeting management process in achieving optimal budgeting performance, with budgeting evaluation serving as a catalyst for constructive feedback to prevent slack. This study advocates for companies to strengthen the alignment between their budgeting processes and budgeting strategies. Furthermore, this research provides a comprehensive strategy that integrates technology, strategic budget management, and financial governance, equipping business entities to navigate and thrive in a dynamic economic landscape.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".