Research on Factors Influencing Hotel Revenue Management Decision-making and Performance: An Empirical Study Based on High Star-rated Hotels in China
Bibliographic record
Abstract
As an important technology and strategy for revenue and profitability optimization,revenue management(RM) has caught wide attention in the international hotel industry,especially in developed countries,such as the United States,Canada,United Kingdom,France,Germany,and Australia.It has been an important subject for research in the world since it was introduced to the American hotel industry by Marriott about 30 years ago.However,it has been only a few years since Chinese domestic hoteliers began to pay attention to RM.Even now,only a small percentage of hotels in China have applied RM strategies and tactics in their daily pricing and distribution practices.Most of Chinese hotels owned or/and operated by domestic hotel companies without carrying an international brand name have just started to hear about the word of RM when they compete with other hotels run by international hotel chains,most of which have implemented RM.Therefore,the understanding and application of RM in Chinese hotel industry is still in its infancy.This paper is the first empirical study to learn what the factors are that affect hotel RM decision-making and its performance in the unique context of China.Our study is based on in-depth interviews with hotel industry leaders in China and an online survey of 174 valid questionnaires answered by the hoteliers from different high star-rated hotels throughout the country.We used quantitative approaches to analyze the data we have collected.We also compared our findings with those of international researchers.We found that:(1) Who owns or/and runs the hotel affects significantly the hotel's RM decision-making and performance.For example,if a Chinese hotel owner hires an international hotel management contractor to operate its hotel(s),it tends to accept RM and support the management contractor to practice it.If an international hotel chain owns or/and runs a hotel in China,it is highly possible that it will use RM approaches in daily operation to yield the hotel's products and services as it does in other hotels in other countries.(2) The size or the number of guest rooms of a hotel greatly affects its RM decision-making;but it is not related to the hotel's RM performance.On the other hand,a hotel's star rating has strong impact on its RM performance;but it does not affect RM decision-making.(3) Unlike what some international researchers have found,the location,average room rate,occupancy rate and market segmentation of a hotel do not affect its RM decision-making and its RM performance.In conclusion,we suggest that given the current situation in China,the understanding of RM among Chinese hotel owners and managers' and their attitude towards RM play the most important role in RM decision-making and performance.If they understand RM and believe it is helpful,managers will welcome it and invest time and other resources on it.If it is the case,RM decision making will be much easier and effective and it will lead to better RM performance.To make that happen,Chinese hotels,hotel associations,hotel schools,tourism and hotel government authorities need to work together to invest in and improve RM training and education.We believe our study will enrich the findings in RM research and it will be beneficial to the Chinese hotel industry.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".