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Record W4328025136 · doi:10.5267/j.uscm.2022.12.008

Impact of strategic agility on the financial performance of commercial banks in Jordan

2023· article· en· W4328025136 on OpenAlexvenueno aff
Naim Salameh Al-Qadi

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsStructural equation modelingBusinessOrder (exchange)PopulationWork (physics)Resource (disambiguation)AccountingEmpirical researchStrategic managementSoundnessFinanceMarketingComputer scienceEngineering

Abstract

fetched live from OpenAlex

The Jordanian banking sector has been able to achieve remarkable growth rates at various levels. This sector is based on a robust infrastructure and is subject to the supervision of the Central Bank, which guarantees the likes of banks operating to financial reporting standards in order to maintain good financial soundness indicators. Therefore, this study aimed to examine the impact of strategic agility on the financial performance of commercial banks in Jordan. The study population was represented by senior managers. The purposeful sampling method was used to collect primary data from (188) respondents who constituted (81.74%) of the sent questionnaires. Structural Equation Modeling (SEM) was applied to test the study's hypotheses. The results showed that strategic agility had a positive impact on financial performance, as the greatest impact was strategic sensitivity, followed by resource fluidity, and finally leadership unity. This study contributed to the development of a logical framework supported by empirical evidence about the possibility of developing financial performance in dynamic environments. Hence, senior managers recommended focusing on making rational decisions derived from studying the reality of the work environment and adjusting organizational structures to become more flexible in response to the volatile business environment.

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 score0.462
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.033
GPT teacher head0.264
Teacher spread0.231 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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