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Moral Psychology at Work: Using Moral Psychology to Understand Organizational Problems

2014· article· en· W2328260927 on OpenAlexaboutno aff
David M. Mayer, Madeline Ong

Bibliographic record

VenueAcademy of Management Proceedings · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyProsocial behaviorSocial cognitive theory of moralityGeneralizability theoryIndustrial and organizational psychologyMoral disengagementOrganizational behaviorBusiness ethicsSocial psychologyContext (archaeology)Moral reasoningPublic relationsPolitical science

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.017
Scholarly communication0.0120.016
Open science0.0010.005
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.355
GPT teacher head0.452
Teacher spread0.097 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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