MétaCan
Menu
Back to cohort
Record W2996527256 · doi:10.5539/ibr.v13n1p221

The Role of Business Intelligence in Crises Management: A Field Study on the Telecommunication Companies in Jordan

2019· article· en· W2996527256 on OpenAlexvenueno aff
Arwa Hisham Rahahleh, Majd Mohammad Omoush

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness intelligenceBusinessSample (material)Descriptive statisticsWork (physics)Crisis managementField (mathematics)Phase (matter)Knowledge managementStatistical populationMarketingComputer scienceManagementEngineeringEconomicsStatistics

Abstract

fetched live from OpenAlex

The field of business intelligence and crisis management currently became have become important issues that organization should be concerned about. The aims of the research is to identify the concepts of business intelligence (BI) and crisis management and review the importance of business intelligence in business organizations through the following independent variables (data source - data stores - specialized data - analytical processing- Data and data mining) and its impact on the crisis management stages ( pre-crisis phase, during the crisis phase and post-crisis phase) in the Jordanian telecommunications companies. The study population consisted of employees of the Jordanian telecommunications companies. A simple random sample was selected, to whom (130) questionnaires were distributed and 120 questionnaires were retrieved. The study relied on the descriptive analytical approach (SPSS as statistical analysis). The research concluded that there is a positive significant impact between business intelligence and management crisis in the Jordanian business organizations. This indicates to the interest of these organizations in the tools of business intelligence, especially with regard to the analytical processing of data based on a secure and integrated system in the work environment to manage organization crises.

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)0.002
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 score0.238
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.126
GPT teacher head0.388
Teacher spread0.263 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicBig Data and Business IntelligenceFrench-language works237,207