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Record W3107580776 · doi:10.5267/j.msl.2020.11.030

Investigating moderating role of emotional intelligence among counterproductive work behavior, work interference and negative emotions in development sector of Pakistan

2020· article· en· W3107580776 on OpenAlexvenueno aff
Muhammad Sarmad, Abdul Qayyum, Muhammad Qaiser Shafi, Sajjad Hussain, Sana ur Rehman

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsModerationCounterproductive work behaviorPsychologyEmotional intelligenceWork (physics)Social psychologyOrganizational commitmentOrganizational citizenship behavior

Abstract

fetched live from OpenAlex

This study reveals the worth of Emotional Intelligence (EI) to act as a moderator in undertaking the worst effects of Counterproductive Work Behavior (CWB) influenced by negative emotions and work interference. Contract based employees of the non-profit organizations in development sector of Pakistan were targeted. The responses were obtained in time lags of two weeks to overcome the issues of cross-sectional data and self-serving bias. The 258 fully responded questionnaires by the targeted employees were analyzed in SPSS. The results emphasized that negative emotions and work interference predicts CWB and EI act as a moderator in this relationship. Employees having low EI engaged more in CWB confirming the predictive relationship. It is recommended that the management needs to underline the significance of EI at all levels of the organization for desirable behaviors at workplace. Implications were deliberated to overcome CWB through high and low levels of EI.

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 score0.066
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.044
GPT teacher head0.307
Teacher spread0.263 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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