The impact of supply chain practice on green hotel performance through internal, upstream, and downstream integration
Bibliographic record
Abstract
The company builds communication and collaboration with suppliers and customers to increase competitiveness in the supply chain flow. The organization's ability to involve suppliers and customers in business activities to achieve efficiency and effectiveness is one of the objectives of supply chain practice. The distribution of questionnaires directly to hotel practitioners was 25 respondents, and 65 respondents obtained the distribution of google form links. The results showed that supply chain practices with supplier relationship management and quality information sharing activities could increase internal and upstream integration and not directly impact downstream integration. Internal integration with data integration activities accurately and coordination between functions on an ongoing basis can affect upstream and downstream integration and green hotel performance. Upstream integration and downstream integration with joint decision activities and planning synchronization with external parties can directly impact green hotel performance. They were increasing the market share and image of the hotel with the implementation of caring for the environment. This research contributes to hotel practitioners adopting practical supply chains in building internal and external integration to increase competitiveness and theoretical contribution to developing supply chain theory and green performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".