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Do Moral Disengagers Feel Guilt and Shame? Moral Self-Condemnation Following Immoral Work Behaviors

2020· article· en· W3045728113 on OpenAlexaff
Babatunde Ogunfowora, Clara Lee, Viet Quan Nguyen, Mayowa T. Babalola, Shuang Ren

Bibliographic record

VenueAcademy of Management Proceedings · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsShameMoral disengagementWrongdoingPsychologySocial psychologyInterpersonal communicationFeelingDeviance (statistics)Moral developmentSocial cognitive theory of morality

Abstract

fetched live from OpenAlex

In the past decade, a number of studies have found a consistent, positive link between moral disengagement (Bandura, 1986) and immoral behaviors in the workplace. According to Bandura, this occurs because moral disengagement minimizes one’s anticipation of moral self- condemnation - feelings of guilt and shame that normally arise when one contemplates wrongdoing. Although research shows that moral disengagement is negatively associated with anticipatory guilt, scholars rarely consider its impact on self-condemning moral emotions after wrongdoing. In this paper, we examine the relationship between moral disengagement and experiences of guilt and shame following three types of immoral work behaviors - interpersonal deviance, (low) interpersonal citizenship behaviors (OCBI), and (low) cooperation. Across two studies using mixed methods, we find that moral disengagers experience guilt and shame following interpersonal deviance, (low) cooperation, and objectively measured cheating, but not (low) OCBI. In a third study, we show that moral disengagers exhibit greater psychological distress, turnover intentions, social loafing, and lower task performance as a result of their guilt and shame. We discuss the implications of these findings with respect to advancing moral disengagement and moral emotions theory in organizational scholarship.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.249
Teacher spread0.220 · 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 teacher head, not a consensus.

Study designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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