Ethical champions, emotions, framing, and team ethical decision making.
Bibliographic record
Abstract
Research has offered a pessimistic (although limited) view regarding the effectiveness of ethical champions in teams and the social consequences they are likely to experience. To challenge this view, we conducted two multimethod (quantitative/qualitative) experimental studies in the context of entrepreneurial team decision-making to examine whether and how an ethical champion can shape team decision ethicality and whether ethical champions experience interpersonal costs. In Study 1, we found that confederate ethical champions influenced team decisions to be more ethical by increasing team ethical awareness. Focusing on the emotional expressions of ethical champions, we found that sympathetic and angry ethical champions both increased team decision ethicality but that angry ethical champions were more disliked. Analysis of team interaction videos further revealed moral disengagement in team discussions and the emergence of nonconfederate ethical champions who used business frames to argue for the ethical decision. Those emergent phenomena shifted our focus, in Study 2, to how ethical champions framed the issues and the mediating processes involved. We found that ethical champions using ethical frames not only increased team ethical awareness but also consequently reduced team moral disengagement, resulting in more ethical team decisions. Ethical champions using business frames also improved team decision ethicality, but by increasing the perceived business utility of the ethical decision. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".