Big data analytics capabilities and supply chain sustainability: Evidence from the hospitality industry
Bibliographic record
Abstract
Big data analytics capabilities have piqued the curiosity of academics and practitioners in recent years. However, there has been little research conducted on the effects of these capabilities on supply chain sustainability, particularly in emerging economies. To address this gap, this article attempted to investigate the impact of big data analytics capabilities on the supply chain sustainability of Jordan's hospitality industry using quantitative data derived from 512 managers in senior and middle levels of hotels listed in Jordan Hotels Association (JHA). Structural Equation Modelling (SEM) was conducted for hypothesis evaluation. The research findings proved that the dimensions of big data analytics capabilities, which were infrastructure flexibility, management capabilities, and personnel capabilities, had a significant positive role in enhancing supply chain sustainability. Therefore, the research provided a series of recommendations for managers in these hotels, the most important of which was allocating significant investments in modern data-collecting technologies to record key changes across the supply chain, including manufacturing, transportation, and distribution.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".