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Record W3213752936 · doi:10.4018/ijkm.291101

Effect of Emotional Exhaustion and Knowledge Sharing on Depersonalization, Work Accomplishment, and Organizational Performance

2021· article· en· W3213752936 on OpenAlexaff
Satyanarayana Parayitam, Aktharsha Syed Usman, Bradley J. Olson, Timothy Shea

Bibliographic record

VenueInternational Journal of Knowledge Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsDepersonalizationEmotional exhaustionPsychologyKnowledge sharingSocial psychologyOrganizational performancePath analysis (statistics)Work (physics)Organizational commitmentKnowledge managementBurnoutApplied psychologyClinical psychologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

The objective of the present study is to empirically investigate the relationship between emotional exhaustion and knowledge sharing of individual and organizational outcomes. Data was collected from 672 respondents from the information technology (IT) sector. The results from path analysis revealed that emotional exhaustion is (i) positively related to depersonalization, and (ii) negatively related to work accomplishment and organizational performance. The results also reveal that knowledge sharing is (i) negatively related to depersonalization, and (ii) positively related to work accomplishment and organizational performance. However, depersonalization is not negatively related to organizational performance. As predicted, work accomplishment is positively related to organizational performance. The diametrically opposite results of emotional exhaustion and knowledge sharing are particularly interesting. The implications for management and practicing mangers are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.645

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 communication0.0000.001
Open science0.0000.000
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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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