Sophisticated Capital Budgeting Decisions for Financial Performance and Risk Management—A Tale of Two Business Entities
Bibliographic record
Abstract
Capital budgeting, particularly sophisticated decisions, is key to the financial performance and risk management of firms, yet academic studies have documented their relationship inconsistently. This study employs the fundamentals of resource-based view (RBV) and agency theories to investigate the impact of sophisticated capital budgeting decisions on financial performance and risk management of the firms of two different sizes, classified as small and medium enterprises (SMEs) and multinational corporations (MNCs). The empirical data of 590 Indonesian firms from between 2014 and 2023 were obtained and analyzed through the Generalized Method of Moments (GMM) technique. The results show that the usage of sophisticated capital budgeting decisions in investment appraisals of classified firms significantly improves their financial performance. Further analyses confirm that although sophisticated capital budgeting decisions are robust in resolving solvency issues, they appear less effective in reducing liquidity risks. The findings also elucidate that sampled firms may realize the financial benefits of sophisticated risk management. The mediation results highlighted that risk management has a significant and positive effect on the relationship between sophisticated capital budgeting decisions and financial performance. The present study contributes to corporate finance by validating the relevance of SCBDs in strategic financial planning and stable investments in firms of different sizes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".