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Record W4380537492 · doi:10.5267/j.ijdns.2023.3.020

An investigation into the effect of social support on job performance and job satisfaction in the Jordanian insurance industry

2023· article· en· W4380537492 on OpenAlexvenueno aff
Asaad Alsakarneh, Bilal Eneizan, Baha Aldeen Mohammad Fraihat, Hebah Zaki Makhamreh, Shehadeh Mofleh Al-Gharaibeh, Khaled M.K. Alhyasat

Bibliographic record

VenueInternational Journal of Data and Network Science · 2023
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsJob satisfactionJob attitudeJob performanceSocial supportStructural equation modelingPersonnel psychologyJob designBusinessPsychologyJob analysisApplied psychologyMarketingSocial psychologyComputer science

Abstract

fetched live from OpenAlex

An increased sense of job performance and better job satisfaction can be achieved by receiving social support. In order to fulfil this purpose, this study aimed to determine the relationship between social support on the one hand and job performance and job satisfaction on the other hand for the Jordanian insurance industry employees. Survey data were gathered from 269 employees from the Jordanian insurance industry. The PLS 3.0 software was used to process data using the structural equation modelling method. The study’s findings revealed that all social support factors were positively and significantly related to job performance and job satisfaction, including manager support, peer support, friends support, and others’ support. Hence, job performance and job satisfaction in the Jordanian insurance industry can be predicted by studying the existence of social support. The study’s findings concluded that the higher the social support, the higher the job performance and job satisfaction.

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)0.003
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 score0.047
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.000
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 head0.293
Teacher spread0.274 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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