Data-driven strategic decisions: Leveraging business analytics and big data to improve decision-making insights in the international organizations
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
In the technological and digital revolution, the world is witnessing unprecedented environmental uncertainty as big data becomes more complex in the labor market. Hence, the study examined the relationship between business analytics, big data, and decision-making insights. The study design used a quantitative approach through a questionnaire distributed to a sample of 412 management levels from international organizations located in King Hussein Business Park in Jordan, named CISCO, Microsoft, Oracle, MBC, Samsung, Migrate, Aramex, Experia, and Ericsson. The data were managed through PROCESS Micro v3.5 software via SPSS packages to investigate the total effects of the study variables. The results confirmed the positive relationship between business analytics, big data, and decision-making insights at a statistically significant level (p < 0.01). The study presented a theoretical development of the role of management in achieving mature visions based on big data that constitute solutions to the complex interactions between technology and human orientation, facilitating the organizational complexities supported by the digital age and transforming them in favor of business decisions in the organizational environment of business companies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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