Applying DEA Model to Measure the Efficiency of Hospitality Sector: The Case of Vietnam
Bibliographic record
Abstract
Tourism industry is one of the world's largest industries with a global economic contribution of over 7.6 trillion dollars in 2016 which provides an equal or even surpasses the business volume of oil exports, and ”Žfood and beverage.As the current climate of the globe, Vietnam's tourism in general, hospitality in particular has attracted investment from not only domestic enterprises but many international hospitality corporations which create a fierce competitive than ever.Identifying inefficient activities and providing improvement in whole process is crucial. The present research aims to study and evaluate the performance of Vietnam hospitality industry through 20 chosen companies that qualify criteria of Data Envelopment Analysis (DEA) model and Malmquist productivity index. It would be a useful tool in benchmarking the efficient firms and inefficient ones operating in the industry and help the former to improve their efficiency. The researcher uses 5 input variables (Cost of good sales; sales expense; operation expense; fixed assets and owner equity) and 2 output variables (Revenues and Profit after tax).DMU1 and DMU8 face with huge fluctuation in efficiency which acquires the management board to review and improve their operation process to ensure the sustainable development of the firm in current competitive market.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".