A study on the global expansion localization and its influencing factors of multinational hotels
Bibliographic record
Abstract
Utilizing the related data of the global top 10 multinational hotels,this paper analyzes the spatial pattern of these multinational hotels from intercontinental and national scales respectively,and explores the factors influencing the global expansion localization of the multinational hotels by using correlation analysis and stepwise regression analysis.The results show that:1)the multinational hotels have mainly located in the developed countries in North America and Europe,but in recent years,the emerging economies in Asia and Latin America have become important destinations for the global expansion of multinational hotels;2)the distribution of the multinational hotels in the world shows the characteristic of highly concentration,with the United States as the core area of their distribution,the United States,France,Canada,China,the UK and Germany as their main distribution countries,and China,Mexico,Brazil,India,Indonesia and Turkey as the most important growing areas for the distribution of the multinational hotels;3)the number of the global multinational hotels in a country(or whether a country is taken by multinational hotels as expansion location choice)has been significantly and positively affected by the international tourist arrivals,airline passenger volume while negatively affected by the highway mileage of the country,indicating that the linear model this paper established can explains well the relationship between these three factors and the number of multinational hotels of a country.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".