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Record W3092327394 · doi:10.5430/ijfr.v11n5p469

The Impact of Social and Positive Psychological Capital on the Efficiency: A Field Study From the Perspective of Jordanian Auditors of the Performance of Audit Offices

2020· article· en· W3092327394 on OpenAlexvenueno aff
Saqer Al-Tahat, Osama Abdel Moneim Ali, Nourdeen Mohammed Abu Nqira, Tharwat M. Alhawamdeh, Faris Soud Alqadi

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAuditPerspective (graphical)Test (biology)TeamworkDescriptive statisticsPsychologySample (material)Work (physics)Field (mathematics)Applied psychologyBusinessAccountingManagementEconomicsComputer scienceStatisticsEngineering

Abstract

fetched live from OpenAlex

The main objective of this study is to show the social positive and psychological impact on the performance efficiency of audit offices from the perspective of the employees in those offices, and to fulfill this, the researchers relied on two inputs: the inductive and descriptive analytical approach, as well as relying on spss software to analyze data of this study, and test the hypothesis, which were in descriptive statistics metrics, model fit tests, and multiple linear regression analysis, to test the study hypothesis. The study sample consisted of 325 qualified people working in these offices. The most important conclusion from this research is that the psychological capital develops the fruitful exploitation of auditors in the work, to accomplish the audit work. In addition, the directors of audit offices seek to establish social cooperative relations among office workers. The most substantial recommendations of this research are crystallized by attracting human and intellectually, psychologically, socially and practically qualified elements who have sufficient skills and experience in auditing processes, in addition, the demand to encourage teamwork, and give powers to team members, so that many of the work problems are resolved through teams.

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)0.002
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 score0.504
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.038
GPT teacher head0.371
Teacher spread0.333 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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