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Record W2105898068 · doi:10.5267/j.msl.2014.8.009

The effect of occupational stress, psychological stress and burnout on employee performance: Evidence from banking industry

2014· article· en· W2105898068 on OpenAlexvenueno aff
Shahram Hashemnia, Somayeh Abadiyan, Behnam Ghorbani Fard

Bibliographic record

VenueManagement Science Letters · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyBurnoutApplied psychologyCronbach's alphaLikert scaleOccupational stressJob satisfactionInterpersonal communicationJob performanceSocial psychologyClinical psychologyPsychometricsDevelopmental psychology

Abstract

fetched live from OpenAlex

This paper presents an empirical investigation on the effects of occupational stress, psychological stress as well as job burnout on women's employee performance in city of Karaj, Iran. The proposed study designs a questionnaire in Likert scale and distributes it among all female employees who worked for Bank Maskan in this city. In our survey, employee performance consists of three parts of interpersonal performance, job performance as well as organizational performance. Cronbach alpha has been used to verify the overall questionnaire, all components were within acceptable levels, and the implementation of Kolmogorov-Smirnov test has indicated that the data were not normally distributed. Using Spearman correlation ratio as well as regression techniques, the study has determined that while psychological stress influenced significantly on all three components of employee performance including interpersonal performance, job performance as well as organizational performance, the effect on job performance was greater than the other components. In addition, occupational stress only influences on organizational as well as interpersonal performance. Finally, employee burnout has no impact on any components of employee performance.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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