The effect of key user capability on supply chain digital and flexibility in improving financial performance
Bibliographic record
Abstract
Organizational competitiveness is enhanced by implementing supply chain integration. Organizations, through information technology, can integrate internal and external cross-functional. The team assigned to run the integration system in its function is designated as the key user who can implement and maintain an ongoing basis. Key user capabilities are needed to maintain a digital supply chain in information technology systems to integrate internally and externally. Data analysis using Partial least squares (PLS) on 89 hotel organizations with a one-star category or more shows that key user capability significantly affects internal cross-functional integration (β = 0.728) and external cross-functional integration (β = 0.127). Key user capability has an impact on supply chain flexibility (β = 0.370) while internal cross-functional integration influences increasing supply chain flexibility (β = 0.373) and financial performance (β = 0.421). External cross-functional integration increases supply chain flexibility (β = 0.316) and financial performance (β = 0.441). Lastly, supply chain flexibility impacts increasing financial flexibility (β = 0.192). The research contributes enrichment to the theory of digital supply chain and practical contribution to enlighten top management in information technology investment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".