Quantification and Persuasion in Managerial Judgement*
Bibliographic record
Abstract
Abstract Accounting involves assigning numbers to events — quantifying them. Conventional wisdom holds that putting numbers to an argument enhances its persuasive power. There is, however, little scholarly evidence to support or refute this claim, in accounting or elsewhere. In this paper, we develop an original process‐based model of how quantification influences persuasion. We posit that including a high‐quality quantified analysis in a proposal enhances its persuasive power by increasing both the perceived competence of the proposal preparer and the perceived plausibility that a favorable outcome could occur. Under some conditions, however, quantification also encourages criticism of the details of the proposal, which potentially offsets these effects. We experimentally test implications of our model in a managerial decision setting, investigating conditions in which quantification is more and less likely to result in criticism of the quantified proposal and, thus, less and more likely to be persuasive. We also test the model itself using structural equations methods. Results largely support the model, which should prove of value to researchers interested in the effects of quantification on judgements and to those interested in persuasion.
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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 | 0.050 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".