Improved capital budgeting decision making: evidence from Canada
Bibliographic record
Abstract
Purpose The purpose of this article is to evaluate current techniques in capital budget decision making in Canada, including real options, and to integrate the results with similar previous studies. Design/methodology/approach A mail survey was conducted, which included 88 large firms in Canada. Findings Trends towards sophisticated techniques have continued; however, even in large firms, 17 percent did not use discounted cash flow (DCF). Of those which did, the majority favoured net present value (NPV) and internal rate of return (IRR). Overall between one in ten to one in three were not correctly applying certain aspects of DCF. Only 8 percent used real options. Research limitations/implications One limitation is that the survey does not indicate why managers continue using less advanced capital budgeting decision techniques. A second is that choice of population may bias results to large firms in Canada. Practical implications The main area for management focus is real options. Other areas for improvement are administrative procedures, using the weighted average cost of capital (WACC), adjusting the WACC for different projects or divisions, employing target or market values for weights, and not including interest expenses in project cash flows. A small proportion of managers also need to start using DCF. Originality/value The evaluation shows there still remains a theory‐practice gap in the detailed elements of DCF capital budgeting decision techniques, and in real options. Further, it is valuable to take stock of a concept that has been developed over a number of years. What this paper offers is a fine‐grained analysis of investment decision making, a synthesis and integration of several studies on DCF where new comparisons are made, advice to managers and thus opportunities to improve investment decision making.
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".