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Record W3001677183 · doi:10.5267/j.msl.2019.11.022

The effects of compassion experienced by SME employees on affective commitment: Double-mediation of authenticity and positive emotion

2019· article· en· W3001677183 on OpenAlexvenueno aff
Sung-Hoon Ko, Yongjun Choi

Bibliographic record

VenueManagement Science Letters · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMediationPsychologyCompassionOrganizational commitmentSocial psychologySelf-compassionNegative emotionApplied psychologyMindfulnessSociologyPsychotherapistPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study is to empirically examine the effects of compassion experienced by small-to-medium enterprise (SME) employees on affective commitment through authenticity and positive emotion. This study was conducted with 200 employees working at SMEs located in South Korea. The results show that the relationships between compassion and authenticity, between authenticity and positive emotion, between positive emotion and affective commit-ment, and between compassion and affective commitment were significant. In addition, the relationship between compassion and affective commitment was significantly double-mediated by authenticity and positive emotion. The results provide a meaningful implication that SME employees would build authenticity and positive emotions by experiencing compassion and that those who build authenticity and positive emotions would enhance their affective commitment.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.259
Teacher spread0.253 · 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 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

Citations17
Published2019
Admission routes1
Has abstractyes

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