Leveraging business intelligence and analytics for strategic success in the hospitality industry: Insights from hotels
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 research strives to offer valuable insights into business intelligence and analytics utilization. Besides, its expansion encompasses essential success factors for business intelligence and strategic management in the hospitality industry. The study's sample comprises 387 hotel managers from the Jordanian hospitality industry. Data collected to support the research goal was examined using multiple regression analysis through SPSS, with the assistance of PROCESS Macro v3.5. The results of this study reveal that business intelligence and analytics can serve as a bridge to connect crucial success factors for business intelligence and strategic management. These findings have implications for existing literature and stakeholders. Hotels that have integrated business intelligence and analytics acknowledge the advantages of enhancing their strategic management capabilities and decision-making processes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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