A qualitative analysis of capital budgeting in cotton ginning plants
Bibliographic record
Abstract
Purpose To analyze capital budgeting practice in a group of small cotton ginning firms in Brazil. The study aims at describing how investment decision-making in the agribusiness context may be influenced by heuristics and by the business setting. Design/methodology/approach This research adopted an exploratory and qualitative approach in gauging the practice of capital budgeting in Brazilian cotton ginning firms and discussing actual managerial decision-making. Data collection involved interviews with managers of ten different firms and a further content analysis was performed. Findings Results reveal a practical managerial approach aimed at ensuring satisfactory net operating results in the short run. Sophistication in capital budgeting is not considered as essential, as institutional and strategic environment influences directly affect impose high risks. Investment decision-making is highly influenced by managerial experience. Research limitations/implications Because of the chosen research approach, results may lack generalizability. However, in addressing a specific sector in a specific location, one can identify and craft strategies in response to managerial needs more effectively. Practical implications The paper clarifies how heuristics, managerial experience and the institutional context may influence investment decision-making in cotton ginning operations. It also suggests how actions aimed at evaluating risk and improving the screening of investment perspectives could contribute to improve investment decisions. Originality/value The paper provides an in-depth perspective in addressing the practice of capital budgeting in the context of a specific activity and describing key issues related to it.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".