An Investigation of Social Influence: Explaining the Effect of Group Discussion on Consensus in Auditors’ Ethical Reasoning
Bibliographic record
Abstract
Abstract: This study introduces Moscovici’s (1976, 1985) model of social influence to the accounting research domain, and uses an experiment to assess whether his theory explains how different types of discussion affects consensus in auditors’ ethical reasoning. Moscovici’s theory proposes three modalities of influence to describe how consensus is achieved following discussion: conformity, innovation, and normalization. Conformity describes the situation where individuals in the minority (e.g., auditors that do not accept the dominant view) accede to the majority (e.g., auditors that hold the dominant view) as a result of group discussion. Innovation describes the situation where individuals in the majority accede to the minority. Normalization describes the situation where there is reciprocal influence. We find that conformity occurs when auditors are asked to prescriptively discuss what ideally “should” be the resolution to an ethical dilemma. Normalization occurs when auditors are asked to deliberatively discuss what realistically would be the resolution to an ethical dilemma. The results of this study suggest that prescriptive discussion of an ethical dilemma encourages auditor groups to strive to find the best response to a moral dilemma if it is represented by the majority view. In contrast, deliberative discussion of an ethical dilemma may encourage the elimination of multiple viewpoints. The results of this study have important implications for understanding the social influence process that affects auditors’ ethical reasoning.
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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 | 0.023 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".