Bibliographic record
Abstract
Abstract Evaluating capital‐investment decisions is an important function of managerial accountants. There is anecdotal evidence, however, that managers avoid making decisions or delay decisions, which is costly in terms of time, effort, and lost opportunities. Prior research has shown that choice avoidance among nonprofessionals making personal decisions is associated with having to choose between alternatives with very different features or that require trade‐offs of very important goals (choice difficulty). It is unclear, however, whether experienced managers, using the analytical decision tools at their disposal, respond in the same way as nonprofessionals when making accounting decisions. Hence, this study examines whether increased choice difficulty increases negative affect in the capital‐investment decision‐making process and, as a result, the tendency of managers to avoid choice even when analytical decision tools are used. In an experiment with 120 executives, participants facing more difficult decisions reported they felt more worried, nervous, uneasy, and anxious and had a greater desire to postpone making the decision than participants in a control group. Participants provided with a decision aid designed to help them focus their cognitive effort reported a lower desire to postpone making the decision than participants in the choice‐difficulty conditions without the decision aid. I conclude by discussing the result's implications for managers and accountants.
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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 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".