Evaluating Business Model for Hotel Industry by Grey-TOPSIS
Bibliographic record
Abstract
Businesses in the past few years have paid more and more attention to brand awareness. More and more branded hotels have launched sub-brands so as to access a new market, boost brand exposure and value, and attain new market niches. The purpose of the work was to explore, on the basis of the business model, factors affecting hotel sub-brand development in Taiwan. The modified Delphi method was firstly referred to. Next, a questionnaire was designed to serve as the basis of quantitative analysis. Third, experienced professionals from the hotel business were invited to participate in a questionnaire survey. The affecting factors of hotel sub-brand development were identified, and analysis data were generated. Grey-TOPSIS was employed to evaluate, calculate, and certify weight analysis and ranking of affecting indices of hotel sub-branding. The results explained that there are nine affecting factors for developing a hotel’s sub-branding. They are channel, target customers, customer relationship, key activities, revenue model, key partners, value proposition, key resources, and cost structure. The top four are the most important ones. This finding, figured out by using soft mathematical methods, can provide a proper evaluating way for decision making by the hotel industry, which wants to establish its sub-brands.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".