Bounded rationality, capital budgeting decisions and small business
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide insight into the capital budgeting decision-making of Canadian and Mexican entrepreneurs in small businesses in the food sector. The objective is to understand the capital budgeting decisions through the lens of bounded rationality and how these decisions are affected by different (national) contexts. Design/methodology/approach This is a comparative study in which the use of constructivist grounded theory allowed deep conversations about capital budgeting decisions. Data was collected from forty semi-structured interviews with entrepreneurs/managers in two regions, Mexico and Canada. Findings Insights from this study suggest that entrepreneurs’ capital budgeting decisions are not only taken under conditions of bounded rationality but also suggest a prominent role of context in how bounded rationality is applied differently towards investment decisions. Research limitations/implications While the findings cannot simply be generalized, exploring how capital budgeting decisions are made differently across two regional contexts adds to the understanding of the nexus of context, bounded rationality and capital budgeting decision-making. Practical implications Using a bounded rationality lens, this study contrasts and explains similarities and differences in the entrepreneur’s capital budgeting decision-making within small businesses. The insights add to the body of knowledge and help entrepreneurs to reflect on their approach to decision-making. Originality/value The paper uses a less commonly applied approach to understand two under-researched regional contexts. We use constructivist grounded theory to explore entrepreneurs’ capital budgeting decision-making in small businesses in two regions, Canada and Mexico. The comparative approach and the findings add to the understanding of decision-making, highlight the prominent role of context and also challenge some insights from previous research.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".