Distribution Differentiation and Influencing Factors of the High-Quality Development of the Hotel Industry from the Perspective of Customer Satisfaction: A Case Study of Sanya

Ma, Yong; Li, Hang; Tong, Yun

doi:10.3390/su14116476

Cited by 5 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…with previous findings [46][47][48][49][50]. Population has a negative influence, as high concentrations of tourists may lead to problems such as long wait times, noisy dining environments, and traffic congestion [49].…”

Section: Plos Onesupporting

confidence: 78%

“…It represents their food quality, dining environment and service quality to a certain extent, are closely related to the level of foodservice eWOM. (5) Transportation conditions [ 48 ], was measured as the number of public transportation stops within 1500 m of the restaurant. Good transportation conditions can improve the convenience of visit the restaurant.…”

Section: Section 5: Underlying Mechanisms In the Spatial Distribution...mentioning

confidence: 99%

See 1 more Smart Citation

Spatial differences and underlying mechanisms in electronic word of mouth in the foodservice industry: A case of Sanya, China

Yu,

Xu,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Studying the electronic word-of-mouth (eWOM) in the foodservice industry can not only provide guidance for merchants, but also spatially optimize the urban foodservice industry, restaurants’ location selection, and customers’ purchasing decisions. In this study, taking Sanya city as the research object, using big data crawling technology to collect the directory and their attribute information of 2107 restaurants with more than 100 reviews. Kernel density analysis, grid analysis and the geographically weighted regression (GWR) model were applied to reveal the distribution characteristics and influencing factors of eWOM in the foodservice industry in Sanya, China. The main results are as follows. The foodservice industry in Sanya extends along the southern coastline and is characterized by little dispersion and agglomeration at the macro level. The overall eWOM score of the foodservice industry is low. Market popularity, restaurant rating, transportation conditions, and commercial development all have a positive impact on the eWOM of the foodservice industry. Population and price have both positive and negative effects and the public services has a nonsignificant impact on the eWOM. This study not only improves the theoretical understanding of the foodservice industry, but also provides a general reference for its development in other industries and cities.

show abstract

Section: Plos Onesupporting

confidence: 78%

Section: Section 5: Underlying Mechanisms In the Spatial Distribution...mentioning

confidence: 99%

Spatial differences and underlying mechanisms in electronic word of mouth in the foodservice industry: A case of Sanya, China

Yu,

Xu,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Kernel density analysis was used to calculate the unit density of the measured values of the point and line elements in the specified neighborhood. It is widely used in spatial clustering analyses of point data, and its advantage is that it can intuitively reflect the distribution of discrete measured values in continuous areas (Ma et al, 2022). This study used kernel density analysis to explore the spatial and temporal distribution characteristics of the family farms in the MYR-UA.…”

Section: Kernel Density Analysismentioning

confidence: 99%

“…Therefore, it was necessary to use the GWR model to reflect the non-stationarity of the parameters in space. Compared with the traditional regression model, GWR allows the relationships between the dependent and independent variables to vary spatially (Ma et al, 2022). This study used the GWR model to explore the factors influencing family farms and the spatial heterogeneity of these factors.…”

Section: Geographically Weighted Regression (Gwr)mentioning

confidence: 99%

Spatiotemporal dynamic evolution and influencing factors of family farms in urban agglomerations in the middle reaches of the Yangtze River

Ren

et al. 2022

Front. Environ. Sci.

Self Cite

View full text Add to dashboard Cite

Family farms, considered the most desirable form of Chinese agriculture, play a pivotal role in promoting rural revitalization and agricultural modernization. The purpose of this study was to summarize the spatiotemporal evolution characteristics and influencing factors of family farms to better promote the development of modern agriculture. Using provincial demonstration family farms in the urban agglomeration in the middle reaches of the Yangtze River (MYR-UA) as the research object, this study applied the nearest neighbor index, kernel density analysis, multiscale spatial clustering analysis (Ripley’s K-function), and geographically weighted regression (GWR) model to reveal the spatiotemporal dynamic evolution and influencing factors of family farms. The results indicate that: 1) from 2013 to 2021, family farms exhibited annual increases, and their development stages could be divided into rapid, stable, and slow growth periods. 2) The spatial agglomeration pattern of family farms was significant, and the intercepted points at different time periods show the distribution characteristics of the entire dispersion and local concentration. The spatial evolution characteristics of different types of family farms are nearly consistent with those of the overall family farms. 3) The overall family farms and various types of family farms show a scale effect, which first strengthens and then weakens with the change in geographical distance. 4) The spatial pattern of family farms in MYR-UA is affected by both natural and social factors, of which, social factors had the greatest influence. Finally, based on the findings of the study, policy recommendations for promoting the high-quality development of family farms are proposed.

show abstract

“…These were excluded because the small islands were not conducive to research predictions. As a tourist city with a large number of hotels, Sanya is a good research sample [33].…”

Section: Data Sourcementioning

confidence: 99%

Analysis of Influencing Factors and Distribution Simulation of Budget Hotel Room Pricing Based on Big Data and Machine Learning from a Spatial Perspective

Song

2022

Sustainability

View full text Add to dashboard Cite

The goal of investors in the hotel business is to maximize profits, and the price is an important means of achieving this goal. This has attracted many scholars to study the spatiotemporal relationship between hotel room prices and their possible influencing factors from different perspectives. However, most existing studies adopt the linear assumption of the hedonic model, with limited features and a lack of feature selection procedures. Additionally, there are few forecasts of hotel pricing from a spatial perspective. To overcome these gaps, this study adopts linear and nonlinear machine learning methods based on the “big data” of Sanya City to explore the influencing factors of budget hotel pricing. Based on the spatial perspective, 81 potential factors were considered. They are further selected using a feature extraction model called recursive feature elimination. Six machine-learning algorithms were evaluated and compared: random forest, extreme gradient boosting, multi-linear regression, support vector regression, multilayer perceptron regression, and K-nearest neighbor regression. The optimal value was used to further calculate the feature importance. They disclosed 40 important impact characteristics and predicted the spatial distribution of hotel pricing.

show abstract

Distribution Differentiation and Influencing Factors of the High-Quality Development of the Hotel Industry from the Perspective of Customer Satisfaction: A Case Study of Sanya

Cited by 5 publications

References 57 publications

Spatial differences and underlying mechanisms in electronic word of mouth in the foodservice industry: A case of Sanya, China

Spatial differences and underlying mechanisms in electronic word of mouth in the foodservice industry: A case of Sanya, China

Spatiotemporal dynamic evolution and influencing factors of family farms in urban agglomerations in the middle reaches of the Yangtze River

Analysis of Influencing Factors and Distribution Simulation of Budget Hotel Room Pricing Based on Big Data and Machine Learning from a Spatial Perspective

Contact Info

Product

Resources

About