Capital Budgeting Practices: A Survey of Two Industries
Bibliographic record
Abstract
This research examines the capital budgeting practices used by small and medium-sized firms (SMEs) in two Portuguese industries, footwear and metalworking, aiming at answering the following research questions: How much knowledge do managers have about capital budgeting practices? What are the most used practices? How much importance do they attribute to applying them? The research was conducted through an online survey with a response rate of 14.9%. The results document that most companies in both industries are familiar with capital budgeting practices, despite differences between the two. The footwear industry recognizes the importance of these indicators but makes little use of them, and many companies prefer using payback period (PBP). The metalworking industry, on the other hand, makes greater use of capital budgeting practices, with net present value being the favored indicator and PBP being used as supplementary. This study contributes to the capital budgeting literature in two ways: first, by focusing on SMEs instead of only large firms, and second, by exploring data from two industries rather than multiple, heterogeneous industries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".