The Influence of Affect on Managers' Capital-Budgeting Decisions
Bibliographic record
Abstract
In this paper, we propose that affective reactions are integral to accounting decision contexts like capital budgeting, and that researchers must jointly consider affect and cognition to better understand accounting decision makers' behavior. We argue that interpersonal relationships are characteristic of many capital-budgeting contexts, and that these relationships can lead to emotional affective reactions. For example, reactions such as frustration and anger may result if a manager is treated unfairly by another individual involved in a capital project. Drawing on relevant work in neurobiology and psychology, we then predict that these affective reactions can influence managers' capital-budgeting decisions. We report on four experimental scenarios that demonstrate the impact of affective reactions on capital-budgeting decisions. Consistent with our predictions, the results indicate that managers consider both financial data and affective reactions when evaluating the utility of an investment alternative. Our results suggest that researchers should consider both affect and cognition to more fully understand decision making in accounting contexts.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".