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Soothing the Ego: Self-Compassion Improves Performance via Humility

2017· article· en· W2766882433 on OpenAlexaff
Michael Daniels, Adam Kay, Daniel P. Skarlicki

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHumilityPsychologyOrganizational citizenship behaviorCompassionId, ego and super-egoSocial psychologyInterpersonal communicationSelf-compassionProsocial behaviorMindfulnessClinical psychologyOrganizational commitmentPolitical science

Abstract

fetched live from OpenAlex

An emerging body of research indicates that expressed humility is an adaptive quality that is positively related to organizationally-relevant outcomes. However, less is known about the antecedents to expressed humility. Across three mixed-methods studies, we draw from research on self-enhancement and ego threat to explore the relationship between self-compassion, expressed humility, and interpersonal organizational citizenship behaviors (OCBI). In Study 1, we show through cross-sectional self-report data that self-compassion is positively associated with OCBI, and that this relationship is mediated by expressed humility. In Study 2, we replicate this finding with peer-report data from a sample of managers attending an EMBA program. In Study 3, we use experimental methods to examine the conditions under which self-compassion is especially helpful for bolstering humility and show that it is more likely to do so in the presence of negative performance feedback (i.e., ego threatening information). These findings contribute to the nascent literature on humility in organizational settings and answer the call to better understand the antecedents to expressed humility as well as its relation with performance outcomes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.313
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2017
Admission routes1
Has abstractyes

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