Judgment and Decision-Making Research in Auditing and Accounting: Future Research Implications of Person, Task, and Environment Perspective
Bibliographic record
Abstract
The discipline of accounting and auditing has increasingly recognized judgment and decision making (JDM) as highly important attributes in the profession because individuals such as managers, auditors, financial analysts, accountants, and standard setters make pivotal judgments and decisions. Many studies undertaken in this domain of research also substantiate the significance of JDM in accounting and auditing. This study evaluates all the papers published in 10 accounting journals among the leading ones from 1980 to 2010 that fall within the domain of JDM research. The categorization of the studies reviewed in this paper is based on Bonner's (1999) three major determinants of JDM: Person, Task, and Environment variables. The review highlights the progress in the literature over the past three decades and also identifies the methodological limitations of previous research. The identified limitations will be useful for improving the research method of future JDM studies in accounting and auditing. The review also draws inferences on how JDM research in auditing, which is well established, could usefully guide future JDM research in financial accounting.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".