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Record W2952005783 · doi:10.5539/ibr.v12n7p34

The Impact of Retail Setting Antecedents on Organizational Citizenship Behavior through Job Satisfaction

2019· article· en· W2952005783 on OpenAlexvenueno aff
Rashad Al Saed, Sahar Moh’d Abu Bakir

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessJob satisfactionArabicFront lineOrganizational citizenship behaviorPopulationMarketingEmpowermentCapital (architecture)Business administrationPsychologyOrganizational commitmentSociologySocial psychologyPolitical scienceEconomicsGeographyEconomic growth

Abstract

fetched live from OpenAlex

This study aims to investigate the impact of retailing antecedents on Organizational Citizenship Behavior through job satisfaction of front line employees in Arabic Bank branches at Amman Capital. The study’s population included all front line employees in these branches at Amman Capital totaling (52) branches. The study sampling unit consists of individuals working at front line servicing clients in these branches totaling (235) individuals, where 235 questionnaires distributed at the surveyed employees, retrieved (232) and excluded (5) which comes to a total (227). To achieve the study objectives, the study used quantitative (descriptive analytical approach) through different statistical tools, most notably multiple regression analysis. The study showed number of results namely: The retail system antecedents (Leadership support, Empowerment and Professional Development) impacted the organizational citizenship behavior of frontline employees of Arabic Bank. The study is proposed a few recommendations for Arabic Bank branches; The managerial practices of Arabic bank management are better to link OCBs and job satisfaction, view it as a base to their strategies to manage the frontline employees.

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)0.001
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 score0.016
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.053
GPT teacher head0.366
Teacher spread0.313 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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