Collectives in organizations appear less morally motivated than individuals.
Bibliographic record
Abstract
on Oct 24 2019 (see record 2019-63657-001). In the article "Collectives in Organizations Appear Less Morally Motivated Than Individuals" by Arthur S. Jago, Tamar A. Kreps, and Kristin Laurin, the second affiliation of the first author was omitted from the byline and author note. The byline should appear instead as University of Southern California and University of Washington-Tacoma. The first paragraph of the author note should appear instead as Arthur S. Jago, Department of Management and Organization, University of Southern California, and Milgard School of Business, University of Washington-Tacoma. The third paragraph of the author note should appear instead as the following: Correspondence concerning this article should be addressed to Arthur S. Jago, Milgard School of Business, University of Washington - Tacoma, 1900 Commerce Street DOU 306, Tacoma, WA 98402. Email: ajago@uw.edu] Organizations often benefit from signaling moral values. Across 5 studies, we explore how people attribute moral conviction to different organizational agents. We find that people believe collectives (e.g., groups; entire organizations) have less moral conviction than individuals, even when both agents behave identically (Studies 1 and 2). We test a variety of potential mechanisms for this effect, and find evidence for two parallel pathways: first, people believe collectives have less of a capacity for emotional experience, and therefore are less likely to use emotions when making decisions; and second, people believe collectives are also more self-interested, and therefore more likely to behave out of concern for their reputations rather than morality (Study 3). In examining boundary conditions for this effect, we find that it occurs when people judge generic for-profit companies and government entities, but not family businesses or charities (Study 4). Finally, we demonstrate that, because collectives appear less morally motivated than individuals, people also assume collectives will exhibit less persistence after enacting prosocial initiatives (Study 5). We discuss theoretical, practical, and social implications of these differing attributions of moral conviction. (PsycINFO Database Record (c) 2019 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.502 | 0.263 |
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".