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Record W4213014063 · doi:10.5539/ibr.v15n3p31

Enhancing Performance of Mwasalat Misr Company in Greater Cairo-Egypt during COVID-19 Pandemic

2022· article· en· W4213014063 on OpenAlexvenueno aff
Ashraf Elsafty, Mohamed Shaarawy

Bibliographic record

VenueInternational Business Research · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsStakeholderCoronavirus disease 2019 (COVID-19)BusinessHuman resource managementOrganizational performanceSurvey researchPandemicHuman resourcesMarketingKnowledge managementPublic relationsManagementPolitical scienceComputer scienceEconomicsBusiness administrationMedicine

Abstract

fetched live from OpenAlex

This research study explores human resource management decisions, management roles, and how they can have a significant and unique impact on the performance of the Mwasalat Misr organization. The survey adopted a qualitative survey design using a sequential narrative approach using unstructured interviews and a stakeholder analysis approach using open-question questionnaires. The research focuses on how to advance the public transport industry by advancing the relationship between HRM and   organizational performance. Some of the key unresolved issues that require further research and help industry management investigate these issues and build a more cumulative body of knowledge that has important implications for theory and practice. Suggestions. The study concludes that four factors influence performance: personnel decisions, management roles, employee satisfaction, and cultural and organizational behavior. The COVID 19 pandemic has been found to serve as a parameter between the completed variable and the company's 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 score0.007
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.074
GPT teacher head0.344
Teacher spread0.270 · 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
Published2022
Admission routes1
Has abstractyes

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