The impact of business intelligence system (BIS) on quality of strategic decision-making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to investigate the impact of Business Intelligence Systems (BIS) on the quality of strategic decision-making in top-level management. The independent variables in this study are Data Quality, Data Visualization, and BI Management, while the dependent variable is the Quality of Strategic Decision-Making. Additionally, the study explores the moderator variable, BI Scope, to further understand the relationship between BIS and the quality of strategic decision-making. By providing valuable insights into the relationship between BIS and the quality of strategic decision-making, this study contributes to the existing body of knowledge on business intelligence and strategic decision-making. The findings show that BI Management, BI Scope, Data Quality, and Data Visualization have substantial and favorable correlations with the quality of strategic decision-making. Effective BI Management techniques contribute to higher decision-making quality, emphasizing the necessity of BI resource management. The study also underlines the importance of BI Scope as a moderator variable, demonstrating its impact on the connection between BI and quality of decision-making. In addition, the research shows that Data Quality and Data Visualization have a considerable influence on strategic decision-making quality. Using effective visualization tools and ensuring high-quality data improves the results of decision-making processes. The interaction impact between BI Scope and Data Quality, on the other hand, was determined to be non-significant.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it