The Effects of Business Intelligence on the Effectiveness of the Organization (Case Study: Airline Companies in Iran)
Bibliographic record
Abstract
Given the increasing competition between airlines companies in the country and equipping them with modern information technologies, establishment of knowledge management system in airline industry can increase the effectiveness of business intelligence system and lead to effectiveness of this industry. The aim of this study was to identify the effects of strategy, structure, processes and organizational culture on the effectiveness of organization and mediating role of business intelligence systems in Iran’s airline companies. Statistical society includes all airlines of Iran. For sampling, non-random judgmental sampling method is used. In order to study the research hypotheses, structural equation methods have been used. Questionnaire tools were used for gathering the data. Stability of the questionnaires used in the present study was calculated higher than 0.7 in term of Cronbach alpha, confirming the validity. The results showed that there is a positive and significant effect between variables of strategy, structure, and organizational culture on the effectiveness of the organization and business intelligence systems in Iran’s airlines and that there is not a significant relationship between organizational variables and organizational effectiveness.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".