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Record W3216869893

THE ROLE OF CREATIVITY AND BUSINESS PERFORMANCE ON CRISIS MANAGEMENT: EVIDENCE FROM IRAQI LISTED COMPANIES

2021· article· en· W3216869893 on OpenAlexvenueno aff
Ihab Malik Raji Al-Ameedee, Haitham Obaid Abd Alzahrh

Bibliographic record

VenueInternational Journal of Economics and Finance · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityStock exchangeFinancial crisisBusinessReturn on assetsCrisis managementEquity (law)AccountingFinanceEconomicsMacroeconomicsManagement
DOInot available

Abstract

fetched live from OpenAlex

Recently, crisis management has become an international issue that affects business and country economic conditions and needs the attention of regulators and recent literature. Thus, the current study examines the role of creativity (research and development (R&D) expenditures, technology maintenance expenditures and spending on high technology) and business performance (return on equity and return on assets) on the crisis management of the listed firm in Iraq. The researchers have used the secondary data extracted from the financial statements of the listed firm in the Iraq Stock Exchange (ISX). The data has been taken from the twenty top-rated companies in ISX from 2011 to 2020. The researchers have executed the robust standard error along with the fixed-effect model (FEM) to analyze the association between the constructs. The results revealed that creativity and business performance have a positive association with the effective crisis management of the listed companies in Iraq. These outcomes provided support to the policymakers while making the policies regarding crisis management in the firm.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.222
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2021
Admission routes1
Has abstractyes

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