The Role of Business Intelligence in Crises Management: A Field Study on the Telecommunication Companies in Jordan
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".