Moral Psychology at Work: Using Moral Psychology to Understand Organizational Problems
Bibliographic record
Abstract
In this symposium, we draw from moral psychology to understand problems faced by business organizations and the people working in them. In the past decade, the field of moral psychology has grown exponentially. Moral psychology researchers, who typically conduct their research in a lab with undergraduate samples, investigate how moral biases and other individual differences influence individuals’ moral perceptions, decisions, and behavior. In contrast, organizational researchers investigating organizational problems related to ethics are generally more interested in the organizational environment and other factors that influence individuals’ work-related unethical (or prosocial) behavior. In contrast to the dominant approach in moral psychology research, the papers in this symposium embed moral psychology theories within an organizational context in order to better understand organizational problems. The papers do this primarily through including working adult samples, experiments embedded within an issue relevant for organizations, and/or surveys or archival data from organizations. Collectively, the five presentations in the symposium depart from much of moral psychology that has used lab experiments with undergraduates making hypothetical decisions. Although we believe lab experiments typically generalize to other contexts, we hope our symposium stimulates further research on how moral psychology theory and research can be embedded in the organizational environment. Particularly, we hope that by bringing an organizational lens to moral psychology, we can not only enhance generalizability, but can also identify novel boundary conditions and theoretical mechanisms while addressing important ethical problems in organizations. Does Working For A Socially Responsible Organization Make Employees More Or Less Prosocial? Presenter: Madeline Ong; U. of Michigan Presenter: David Mayer; U. of Michigan Presenter: Leigh Plunkett Tost; U. of Michigan, Ann Arbor Dangerous Expectations: Breaking Rules To Resolve Cognitive Dissonance Presenter: Celia Moore; London Business School Presenter: Wiley Wakeman; London Business School Why Are Do-Gooders Seen As Immoral? Formal Leadership Position And Perceptions Of Moral Rebels Presenter: Ned Wellman; Arizona State U. Presenter: David Mayer; U. of Michigan Presenter: Daniel Scott DeRue; U. of Michigan Presenter: Kathleen Grace; U. of Michigan The Contaminating Effects Of Building Instrumental Ties: How Networking Can Make Us Feel Dirty Presenter: Tiziana Casciaro; U. of Toronto Presenter: Maryam Kouchaki; Harvard U. Presenter: Francesca Gino; Harvard U. Cheating On Expenses: Evidence From The Field Presenter: Francesca Gino; Harvard U. Presenter: Lamar Pierce; Washington U. in St. Louis Presenter: Lisa L Shu; Northwestern Kellogg School of Management
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| 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".