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Article

Spatial Pattern Evolution and Influencing Factors of Foreign Star-Rated Hotels in Chinese Cities

by
Xiang Zhang
1,2,3,
Dongxiao Han
1,2,3,
Chunfeng Zhang
1,4,
Wenyi Feng
1,2,3,
Jinsong Wu
1,2,3,
Yan Xie
1,2,3 and
Yating He
1,2,3,*
1
Wuhan Branch of China Tourism Academy, Central China Normal University, Wuhan 430079, China
2
Hubei Tourism Research Institute, Central China Normal University, Wuhan 430079, China
3
College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China
4
Department of Human Dimensions of Natural Resources, Colorado State University, Fort Collins, CO 80523, USA
*
Author to whom correspondence should be addressed.
Reg. Sci. Environ. Econ. 2025, 2(1), 1; https://doi.org/10.3390/rsee2010001
Submission received: 23 September 2024 / Revised: 24 November 2024 / Accepted: 9 December 2024 / Published: 24 December 2024
Browse Figures
Figure 1
<p>Local spatial autocorrelation results in 2000.</p> "> Figure 2
<p>Local spatial autocorrelation results from 2005 to 2010.</p> "> Figure 3
<p>Local spatial autocorrelation results in 2015.</p> ">
Versions Notes

Abstract

:
Spatial distribution is a critical factor influencing the success or failure of hotel management. This study examines the spatial distribution patterns of foreign star-rated hotels in China from 2000 to 2015 based on 27 typical city cases, using global and local spatial autocorrelation methods within GIS spatial analysis. The research explores the evolution of these patterns, analyzes key characteristics, and combines these insights with a stepwise regression method. Pearson correlation analysis is used to identify factors that influence the evolution of the spatial pattern. This study reveals that, first, the Z-value of global spatial autocorrelation of foreign star-rated hotels in China decreases from 2.38 to 1.63, indicating that the spatial distribution of foreign star-rated hotels in China has shifted from imbalanced to balanced, transitioning from economically developed regions such as areas with overseas Chinese populations, provincial capitals, and municipalities directly under central government control, toward tourist cities. Second, star-rated hotels hold a critical position within the spatial pattern, highlighting their central role in shaping the hospitality landscape. Third, the spatial distribution of foreign star-rated hotels is primarily influenced by the number of inbound tourists, followed by the presence of scenic spots rated 4A and above. The influence of other factors is found to be less significant. Fourth, the correlation coefficient between tourism demand and foreign star-rated hotels increased by 0.004, whereas the correlation coefficient between tourism supply and foreign star-rated hotels decreased by 0.036, indicating that market factors are playing an increasingly important role in shaping the evolution of foreign star-rated hotels in China, reflecting broader market dynamics. This study provides practical guidance for local Chinese hotels facing competition from foreign-funded establishments and offers theoretical insight into the strategic implementation of transnational operations. It points out the expansion direction of local Chinese hotels across different developmental stages.

1. Introduction

Historically, foreign hotels began entering the Chinese market in the 1980s. From an economic and policy perspective, China’s introduction of the reform and opening-up policy in 1978 catalyzed rapid economic growth and improved living standards, leading to a significant increase in demand for high-quality accommodation services, which in turn presented market opportunities for foreign hotels. On the social front, the augmentation of international exchanges and a substantial surge in the number of foreign visitors have propelled the growth in demand for international brand hotels. For foreign hotels themselves, internationalization will increase their corporate value [1]. Over the course of three decades, these establishments have cemented their presence within the luxury segment of China’s hotel industry [2]. Concurrently, China has emerged as a leading destination for international hotel chains. During the 1990s, many hotel groups commenced large-scale multinational expansions, with China ranking among their most sought-after targets [3]. Foreign hotels possess extensive service experience and strong brand images, which allow them to dominate the high-end hotel market in China. At the same time, they are also expanding into the mid-range and economy hotel markets. The knowledge spillover of foreign hotels will promote the formation of an independent management knowledge system in the Chinese hotel market and help the growth of local Chinese hotel enterprises. Local Chinese hotels have competitive advantages in the mid-market and economy markets, but they need to consolidate these advantages and develop the high-end market. Foreign hotels and local hotels compete in the same city. Foreign hotels have intensified competition in the Chinese hotel market, squeezing the development space of local Chinese hotels. They are strengthening their own advantages while trying to enter each other’s dominant areas [4]. Therefore, in order to occupy the market, Chinese local hotel enterprises must understand the spatial distribution of foreign hotels, occupy the blind area of foreign hotel layouts, and improve their market share. So what is the spatial distribution pattern of foreign hotels in China? What characteristics does it embody? What factors affect this spatial layout? The exploration of these scientific questions will help to clearly understand the expansion of foreign hotels in China. This research also provides a theoretical foundation for Chinese hotels looking to expand internationally, offering insights into the transnational operations of foreign chains. Chinese hotel enterprises are an important force in the global hotel market. This study will help Chinese hotel enterprises rationally plan their layout in their home countries and provide high-quality accommodation services for Chinese tourists. At the same time, it will also help Chinese hotel enterprises scientifically decide the direction of cross-border expansion, which will alter the supply map of the global hotel market. Chinese hotel companies will play an increasingly important role in the global hotel market.

2. Literature Review

In the 1980s, foreign hotels in China were mainly located in the central cities along the coast. In the 1990s, foreign hotels expanded into second-tier coastal cities and central China. In the 21st century, foreign hotels continued to expand into western China [2]. According to the relevant literature, the current research on the spatial pattern of foreign-funded hotels in China is mainly conducted by domestic scholars and supplemented by foreign scholars. Moreover, foreign hotels are mostly located in the eastern region, the Pearl River Delta, the Yangtze River Delta, and the Beijing–Tianjin–Hebei region [5]. In terms of provincial scope, they are mostly distributed in Jiangsu, Zhejiang, Shandong, Liaoning, and Shanghai. In addition, Beijing and Guangdong are also gathering areas of foreign hotels [6]. The investment location of foreign-funded hotels at the provincial level in China has significant characteristics of coastal type, central collapse, and low transition [7], but the regional difference shows an overall trend of reduction [8]. For the foreign hotels that landed in China earlier, their development reflects the characteristics of expansion from the first-tier cities to the second-tier tourist cities and the large and medium-sized cities in the economically developed eastern region [2]. From the perspective of influencing factors, the existing research results mainly hold that the layout of foreign-funded hotels in China is affected by the following factors, such as the following: overseas customer flow, external contact intensity, transportation and communication facilities, agglomeration effect [5], market demand, production cost [9], ownership advantage [10], popularity, crime rate [11], economic development level, geographic and cultural affinity [12], productivity spillover effect [13], etc. The final determination of these factors is mainly based on the construction of the theoretical model, empirical model, and operation model [14], as well as GIS spatial analysis [15] the and Poisson model [12].
Numerous experts and scholars have conducted extensive research on the spatial distribution of foreign-funded hotels in China. However, it is evident that most of these studies focus primarily on the three major economic zones and the provincial level, with relatively few addressing the urban scale. Given that the spatial distribution of foreign hotels in China is ultimately determined by urban geography, if the spatial pattern of foreign-invested hotels is solely studied from the macro perspective of economic zones and provincial levels, it may lead to an artificial averaging of the development levels and spatial distributions of foreign-invested hotels within the same jurisdiction, thereby obscuring the actual regional imbalances. Therefore, conducting research from the micro perspective at the city level appears to be more precise and objective. Existing studies predominantly describe the characteristics of spatial patterns, yet they often overlook the underlying drivers behind the evolution of these patterns. Moreover, critical factors relevant to the modern hotel industry, such as the influence of business exhibitions and tourism resources, have not been adequately considered in explaining the changes in spatial patterns over time.
To address these gaps, this paper aims to examine the evolution of the spatial distribution of foreign-funded hotels in China through the lens of urban development. It seeks to identify and summarize the key characteristics of this evolution and elucidate the reasons behind these changes. Furthermore, on the basis of describing the evolution process of its spatial pattern, this paper makes a quantitative analysis of the factors influencing the evolution and changes of the spatial pattern of foreign-funded hotels in China from the aspects of the number of tourists, the number of exhibitions, tourism resources and so on, with the aim of figuring out the main influencing factors.

3. Research Design and Data Sources

3.1. Research Object

In terms of research targets, this paper mainly involves foreign-funded hotels, including wholly foreign-owned hotels (including Hong Kong, Macao, and Taiwan), sino-foreign joint ventures and cooperative hotels, joint-stock hotels with foreign investment, and hotels managed by foreign-funded hotel groups. Among the various categories of foreign-funded hotels in China, star-rated hotels represent the most significant segment, as reflected in the statistics maintained by the Ministry of Culture and Tourism. Due to the accessibility of reliable data, the research object of this paper refers to foreign star-rated hotels.

3.2. Sample City Selection

Currently, the systematic tracking of foreign star-rated hotels in Chinese cities by various government departments remains limited. In this regard, only the annual “China Tourism Statistics Yearbook” issued by the Ministry of Culture and Tourism has tracked and monitored foreign star-rated hotels in 27 cities throughout the country (Table 1). In view of this, the paper chooses these 27 cities as research subjects mainly for the following 3 reasons. First, foreign star-rated hotels distributed in these 27 cities account for approximately 60% of the total number of foreign star-rated hotels in China, which is representative in terms of quantity. Second, these 27 cities are widely distributed in the most economically developed provinces in China, such as Beijing, Shanghai, Guangdong, Jiangsu, Zhejiang, Fujian, and Shandong, and they are representative in terms of region. Third, the “China Tourism Statistics Yearbook” is one of the most authoritative official documents in China. To a certain extent, it represents the official recognition of the influence of foreign star-rated hotels in these 27 cities. Moreover, the accuracy and accessibility of official data can ensure the feasibility and scientific nature of this study.
The 27 cities are categorized based on their characteristics into five groups: municipalities directly under the Central Government and provincial capitals, cities with a high concentration of foreign enterprises, special economic zones (SEZs), tourist cities, and overseas Chinese towns (Table 2). Given that China’s tourism industry is government-driven, foreign-funded star-rated hotels tend to cluster in regional administrative centers, which provides access to preferential policies and fosters strong relationships with government authorities. This proximity facilitates smoother transnational business operations. Moreover, these administrative hubs often double as economic centers, which makes them attractive to foreign-funded star-rated hotels.
In cities with high concentrations of foreign enterprises, international business exchanges are frequent. This kind of foreign enterprise business market is the traditional market of foreign star-rated hotels. Similarly, SEZs feature concentrated foreign investment and dynamic economies, along with strong international connectivity, making them ideal locations for such hotels. Nowadays, there are many tourists in tourist cities, especially in Huangshan and Guilin, which are the traditional tourist hotspot cities that have developed and become famous earlier. The scale of the inbound tourism flow is large, and there is an obvious coupling interaction effect between the inbound tourism flow and the hotel industry [16]. These inbound tourists are a primary source of clientele for foreign star-rated hotels. Inbound tourists are one of the most important source markets for foreign star-rated hotels. As the home town of overseas Chinese has a large number of overseas Chinese and is closely connected with overseas Chinese, overseas Chinese will also bring overseas investment and personnel exchanges, so it provides a corresponding source market for foreign-funded star-rated hotels.
It is important to note that some cities may belong to multiple categories. As this classification does not involve a hierarchical ranking, overlapping city classifications do not affect the overall categorization.

3.3. Research Method

3.3.1. Spatial Autocorrelation Analysis

The notable advantage of spatial autocorrelation analysis lies in its ability to comprehensively capture and quantify the implicit geographical location dependency and spatial pattern characteristics within spatial data, thereby enabling a more precise revelation of the spatial distribution patterns of variables and their mutual influence mechanisms [17]. This complements the potential oversight of spatial effects in traditional statistical methods when dealing with data possessing spatial attributes. Global spatial autocorrelation can detect whether aggregation occurs in a given region, while local spatial autocorrelation can detect where specific aggregation regions occur. However, it cannot detect the factors that influence spatial patterns. In academic research, spatial autocorrelation analysis typically involves two key steps.
Firstly, the global spatial autocorrelation is used to determine whether the overall distribution of foreign-funded star-rated hotels in China has congeries characteristics. On the other hand, taking Moran’s I [18] as the calculation and evaluation index, the general situation of the spatial distribution pattern of foreign-invested star-rated hotels from 2000 to 2015 was obtained.
Secondly, with reference to the results of global spatial autocorrelation, and with a view to the city, local spatial autocorrelation analysis is conducted to evaluate the clustering degree of foreign-funded star-rated hotels in small areas. This helps to reveal the evolutionary relationships between these clusters.
Both global and local spatial autocorrelation analyses were conducted within the ArcGIS 10.2 environment. In the global analysis, Moran’s I index, Z score, and significance level P were used to evaluate the overall distribution patterns of foreign-funded star-rated hotels. In contrast, local spatial autocorrelation results visually illustrate the clustering and dispersion of hotels within different regions, providing intuitive insights into the degree of aggregation.
The formula for Moran’s I index is as follows:
I = n ∑ i = 1 n   ∑ j = 1 n   W i j x i − x x j − x ∑ i = 1 n   ∑ j = 1 n   W i j · ∑ i = 1 n   ( x i − x ) 2
In the formula, n refers to the sample size; xi and xj are the attribute values of spatial units i and j; x is the average value of the variable; Wij is the spatial weight matrix, the value of the Moran index is between [−1, 1]. If the Moran index is greater than 0, it indicates a positive spatial autocorrelation, that is, a high value is correlated with a high value. If the Moran index is less than 0, it means that there is a negative spatial autocorrelation; that is, there is a spatial correlation between the high-value region and the low-value region. If the Moran index is 0, it indicates no spatial correlation.

3.3.2. Stepwise Regression Analysis

In this paper, the stepwise regression method of multiple linear regression analysis is used to measure the influencing factors of the evolution of the spatial pattern of foreign-invested star-rated hotels in China. Stepwise regression is particularly useful in determining which independent variables have a significant impact on the dependent variable. It effectively avoids the issues of multicollinearity and overfitting while reducing computational burden, thereby enhancing the predictive accuracy and interpretability of the model. We use SPSS 22.0 and its stepwise regression module. The stepwise regression method compares the influence of independent variables on dependent variables, and the f-test calculates according to the criteria of “probability entry ≤ 0.050” and “probability deletion ≥ 0.100” [19]. Obviously, it can be seen that the independent variables that finally enter the model all have a significant impact on the dependent variables, while the independent variables that do not enter the model do not have a significant impact on the dependent variables.
The results showed that Pearson correlation analysis of SPSS22.0 had a significant influence on the correlation of factors. Pearson correlation coefficient and two-sided test were selected and a significant correlation was marked. Mean and standard deviation were selected for the statistics, and it was concluded that the significance of the p value was not significant until the p value was less than 0.05, and the smaller p value indicates a stronger correlation. The advantage of the stepwise regression method is that it can accurately measure the degree of correlation between various factors and the degree of regression fitting and enhance the predictive performance of the equation. Its limitation is that the selection of factors to be included in the model is somewhat speculative, which can affect the results of regression analysis. This paper selects factors that have been shown to be effective in the published literature to enter the model to compensate for the limitations.

3.4. Data Sources

The data of foreign star-rated hotels in sample cities are from the “China Tourism Statistics Yearbook” (2001, 2006, 2011, 2016). The data on GDP in 2015 are from the “China Urban Statistics Yearbook” (2016). The data on inbound tourists are from the “China Tourism Statistics Yearbook” (2006, 2016). Data on the number of domestic tourists and the size of the population are derived from “China’s Regional Economic Statistics Yearbook” (2006, 2016). The data of scenic spots above 4A (including) level are from the website of the Ministry of Culture and Tourism and the statistical bulletin of national economic and social development of sample cities (2006, 2016). The exhibition data are derived from the Statistical Analysis Report of China Conference (2015, 2016), the website of the Ministry of Commerce, the website of China Conference and Exhibition Economic Research Conference, and the statistical bulletin of national economic and social development of sample cities (2001, 2002, 2015, 2016).

4. Evolution of Spatial Pattern of Foreign Star-Rated Hotels in China

4.1. Patterns Evolution from the Perspective of Global Spatial Autocorrelation

In the background of ArcGIS 10.2, the distribution data of foreign star-rated hotels in 2000, 2005, 2010, and 2015 are analyzed by autocorrelation. The results are as follows (Table 3).
In spatial autocorrelation analysis, the p-value represents the probability of an event occurring, and if the p-value is less than 0.1, spatial autocorrelation is generally considered to exist. The smaller the p-value, the more significant the spatial autocorrelation. The Z-value is used to determine whether a space is clustered. It is generally believed that a Z-value greater than 1.65 indicates a concentrated distribution. A Z-value less than −1.65 indicates a discrete distribution. A Z-value between −1.65 and 1.65 indicates a random distribution. Moran’s I represents the direction of the correlation. A Moran’s I greater than 0 indicates a positive correlation, while a Moran’s I less than 0 indicates a negative correlation.
The global spatial autocorrelation analysis shows that the p-value in 2000 is 0.017, which is less than 0.1. This indicates that the spatial distribution of foreign star-rated hotels in China exhibited spatial autocorrelation. Moreover, Moran’s I is greater than 0, indicating positive spatial autocorrelation. The Z-value is 2.38, exceeding 1.65, which indicates an agglomerated distribution. From 2005 to 2015, all the P-values were greater than 0.1, suggesting no significant agglomeration. Similarly, all the Z-values were less than 1.65, reflecting a random distribution. In 2015, however, the Z-value was 1.63, approaching 1.65, suggesting a near-agglomeration distribution pattern. These changes suggest that, after exhibiting spatial autocorrelation in 2000, the distribution of foreign-funded star-rated hotels in China became random. In recent years, however, there appears to be a trend toward re-aggregation.

4.2. Patterns Evolution from the Perspective of Local Spatial Autocorrelation

Building on the findings from the global spatial autocorrelation analysis, this paper delves deeper into regions with significant concentrations of foreign star-rated hotels in 2000, 2005, 2010, and 2015, exploring the changes over time and summarizing the spatial layout characteristics of these areas.
Using ArcGIS 10.2 for local spatial autocorrelation analysis, the data were categorized into three groups: the year 2000, characterized by clustering; the years 2005 and 2010, marked by random distribution; and the year 2015, again showing clustering. Then, the local spatial autocorrelation analysis was carried out.
The results indicate that in 2000, the high-density clusters of foreign star-rated hotels were primarily found in the Bohai Rim, Pearl River Delta City Cluster, and Kunming. The hotels were concentrated in special economic zones, cities with significant foreign investment, municipalities directly under central government control, and provincial capitals of key tourism provinces (Figure 1). This was mainly because at that time, China’s economic development level was generally low, and the accommodation prices of foreign star-rated hotels were relatively high, making them affordable only to residents of these economically developed cities. As China’s high-grade hotels started relatively late, during this period, foreign-funded star-rated hotels held advantages in special economic zones, cities with more foreign investment, municipalities directly under the central government, and capital cities of major tourist provinces, providing a model for high-end hotels in China. In order to build their image and attract outside investment, China’s second- and third-tier cities began to increase their efforts to build high-end hotels, whose management models benefit from the knowledge spillover of foreign star-rated hotels.
In 2005 and 2010, the distribution pattern of foreign star-rated hotels shifted (Figure 2). The concentrated areas of foreign hotels transitioned from cities in the Bohai Sea and Pearl River Delta regions to the Yangtze River Delta. The high-value agglomeration of foreign star-rated hotels within the Yangtze River Delta expanded, reflecting a broader spread of these establishments. Notably, by 2010, Beijing, despite being a major first-tier city with substantial economic power, was no longer a high-value area in terms of foreign star-rated hotels. This shift suggests a more dispersed trend, with foreign star-rated hotels no longer confined to specific areas but expanding into other regions. This is mainly because, after entering the 21st century, China’s connection with the world economy continued to deepen, especially after it joined the World Trade Organization in 2001. This led to increased foreign investment in China, stimulating the economic vitality of the Yangtze River Delta and other regions and attracting foreign star-rated hotels. The expansion of foreign-funded star-rated hotels has promoted the development of local Chinese hotels through the knowledge spillover effect, and local Chinese hotels dominate the middle and low-end markets in central and western cities. Chinese brand budget hotels in Wuhan, Xi’an, and Chengdu are expanding rapidly.
Since 2015, the layout of foreign star-rated hotels in China has presented a new situation (Figure 3). In this case, the cities around the Bohai Sea, the Yangtze River Delta, and the Pearl River Delta have become stable high-density concentration areas of foreign star-rated hotels in China, while radiating the distribution of foreign star-rated hotels in the surrounding areas, which is particularly evident in the Yangtze River Delta region. Additionally, cities like Chongqing, Kunming, and Xiamen—municipalities directly under central government control or provincial capitals—emerged as high-value areas despite being located in regions with lower overall densities of foreign hotels. These cities became focal points for economic and tourism development in their respective areas. Moreover, the tourist city of Huangshan, for the first time, became a high-value cluster for foreign star-rated hotels. This suggests that foreign hotel chains were increasingly drawn to regions with strong tourism sectors, particularly those with developed infrastructure and significant tourist traffic. This analysis highlights the evolving spatial patterns of foreign star-rated hotels in China, demonstrating a shift from concentration in traditional economic centers to a more diversified spread across tourism-driven and economically vibrant regions. This is mainly because China has entered a new stage of development, China’s GDP rose to second place in the world in 2010, and the effects of regional coordinated development gradually emerged. Regional core cities began driving benefits to the hinterland [20]. At the same time, tourism became a strategic pillar industry in China. These factors have promoted the spread of foreign star-rated hotels to more second-tier and tourist cities. This phenomenon increases the accommodation supply of tourist cities, improves the service quality of tourist cities, and promotes the high-quality development of China’s tourism industry. For example, the increase in foreign-funded star-rated hotels in Guilin, Qingdao, and Hangzhou has helped these cities grow into tourism centers.

4.3. Evolution Characteristics of Spatial Pattern of Foreign Star-Rated Hotels in China

Based on the results of global and local spatial autocorrelation, it can be concluded that the evolution characteristics of the spatial distribution of foreign star-rated hotels in China are as follows.
Firstly, the overall spatial pattern of foreign star-rated hotels in China has evolved from being uneven to more balanced. Based on the analysis of global spatial autocorrelation, it is concluded that there is a positive spatial correlation in the layout of foreign star-rated hotels in China in 2000, which reflects the convergence of the spatial layout of foreign star-rated hotels and shows a clustering pattern. For instance, the four first-tier cities—Beijing, Shanghai, Guangzhou, and Shenzhen—accounted for 53.06% of all foreign star-rated hotels, highlighting the imbalance in distribution. However, from 2005 to 2015, spatial autocorrelation analysis showed that the distribution became more random. The local spatial autocorrelation analysis further demonstrated that high-density hotel areas were more evenly spread across various types of cities. During this period, the number of foreign star-rated hotels in cities like Shenyang, Dalian, Changchun, Harbin, Nanjing, Wuxi, Suzhou, Hangzhou, Ningbo, Fuzhou, Xiamen, Qingdao, Wuhan, Chengdu, Kunming, Xi’an, Tianjin, and Chongqing increased to 47.15%, signifying a more balanced spatial distribution.
Secondly, it can be said that from the perspective of distribution region, urban agglomeration plays a very important role in the spatial pattern of foreign-funded star-rated hotels. In 2000, the number of foreign star-rated hotels in the sample cities of the Yangtze River Delta accounted for 13.94% of the total sample cities, which was 12.63%, 12.5%, and 19.39% in 2005, 2010, and 2015, respectively. In 2000, the number of foreign star-rated hotels in the Pearl River Delta sample cities accounted for 30.32% of the total sample cities, which was 21.89%, 23.72%, and 20.74% in 2005, 2010, and 2015, respectively. In 2000, 29.83% of the total sample cities were located around the Bohai Sea, which was 34.53%, 33.65%, and 32.11% in 2005, 2010, and 2015, respectively. In 2000, 2005, 2010, and 2015, 74.09%, 69.05%, 69.87%, and 72.24% of the total sample cities were foreign-owned star-rated hotels in the three metropolitan agglomerations around the Bohai Sea, the Pearl River Delta, and the Yangtze River Delta, respectively. It can be seen that the sample cities of the three major urban agglomerations are 51.82%, and about 70% of foreign-funded star-rated hotels are concentrated within, which reflects the importance that foreign-funded star-rated hotels attach to the urban agglomeration.
Thirdly, from the perspective of city type, the spatial layout of foreign star-rated hotels in China shifted over time. Initially concentrated in economically developed cities such as provincial capitals, municipalities, and overseas Chinese towns, the distribution expanded to foreign investment hubs and tourist cities. From 2000 to 2015, the layout of foreign-funded star-rated hotels in China initially exhibited agglomeration but later transitioned to diffusion. The spatial distribution concentration index of foreign star-rated hotels in China was 77.78 in 2000, 84.7 in 2005, 84.22 in 2010, and 74.07 in 2015. In 2000, the majority of high-density clusters were located in cities like Beijing, Tianjin, Guangzhou, and Zhongshan, representing 69.83% of the total. In 2005 and 2010, the direction of agglomeration was mainly first-tier cities and cities with concentrated foreign investment. It can be seen from the local spatial autocorrelation analysis that Suzhou, Ningbo, and other foreign-capital-concentrated cities appeared in the high-density distribution area of hotels, and all of them were of high value, indicating that the distribution concentrated area of foreign-capital star-rated hotels turned to the above-mentioned foreign-capital-concentrated city during this period. Notably, Huangshan emerged as a high-density cluster for foreign star-rated hotels in 2015, highlighting the increasing influence of tourist cities on hotel distribution.

5. Influencing Factors of Spatial Pattern Evolution of Foreign Star-Rated Hotels in China

5.1. Spatial Pattern of Key Tourist Cities and Foreign Star-Rated Hotels

The primary customer base for foreign-funded star-rated hotels consists of international tourists, business travelers, and professionals. Therefore, it is essential to analyze the relationship between the number of inbound tourists and the distribution of foreign star-rated hotels. Cities that receive over 1 million inbound tourists are classified as key tourist cities. The number and proportion of key tourist cities in 2005 and 2015 are shown in Table 4. It can be seen that most of the inbound tourists from 27 cities are concentrated in key tourist cities.
By matching the number of inbound tourists in key tourist cities with the number of foreign star-rated hotels, it is found that the overall matching is good, and most of the key tourist cities are also cities with more foreign star-rated hotels (Table 5). However, in different years, the matching situation is different. In 2005, there was a good match between them. Foreign star-rated hotels were mainly distributed in key tourist cities. In 2015, Chongqing, Huangshan, Zhuhai, and Hangzhou attracted fewer than five foreign star-rated hotels, reflecting the deep expansion of foreign star-rated hotels. The factors affecting the evolution of its spatial pattern are becoming more and more complex, and the number of inbound tourists is not enough to have a decisive impact on the number of foreign star-rated hotels in cities.

5.2. Measurement of Influencing Factors of Spatial Pattern Evolution of Foreign Star-Rated Hotels in China

In reality, foreign star-rated hotels mainly provide accommodation, catering, exhibition, and other services for tourists and business people, which are also related to the level of urban economic development. With reference to the authenticity, availability, and integrity of data, this paper suggests that the number of urban tourists, population size, business activities, tourism resources, urban GDP, and other factors closely related to tourists and business people have an important impact on the spatial pattern of foreign-funded star-rated hotels in China [21,22,23].
The number of city tourists is represented by the number of inbound tourists and domestic tourists. In some business activities, the hotel is close to the exhibition. There is no doubt that the demand for the exhibition will promote the construction of the hotel, because the participants’ accommodation needs to rely on the hotel, and even part of the exhibition will rely on the hotel. At the same time, the construction of the hotel will also promote the growth of the exhibition industry [24]. In particular, the exhibitions with great influence and the exhibitions held periodically have an important impact on the spatial distribution pattern of the hotel in various cities [25]. Therefore, the frequency of business activities is represented by the number of exhibitions. Generally speaking, some important, well-known, and long-standing exhibitions are included in the Statistical Analysis Report of China’s Conference and on the website of the Ministry of Commerce, the website of China Conference and Exhibition Economic Research Association, and that of the Statistical Bulletin of National Economic and Social Development. The types of exhibitions studied in this paper are also those listed on the official website reports and bulletins above. The reason why tourism resources are represented by the number of scenic spots above the 4A (including) level is that the scenic spots above the 4A (including) level are assessed by the National Tourism Administration, which is relatively uniform and normative. Other A-level scenic spots are evaluated by local tourism authorities, which have certain subjectivity and regional differences, and thus exert an impact on comparability.
The data of all the above indicators were investigated in 2005 and 2015. Only part of the exhibition data in 2005 and 2015 are missing. Therefore, 2006 and 2016 are selected as the year of investigation in this paper. All data sources are described in paragraph 2 of Section 5.2, and some cities lack individual data, which are fitted and supplemented by regression analysis. A total of 16 data were added, accounting for 4.94% of the total data. Therefore, the processing of missing data does not affect the evaluation of the overall authenticity of the final results.
Taking the number of foreign star-rated hotels as a dependent variable (y), and the number of inbound tourists (x1), domestic tourists (x2), exhibitions (x3), and population size (x4), and the number of scenic spots (x5) and urban GDP (x6) as independent variables. Independent and dependent variables are included in the linear regression model of SPSS22.0, and the stepwise regression method is used to calculate (standard: the probability of using F is less than 0.050, and the probability of using F is less than 0.100).
In 2005, the independent variables of the final entry model were only the number of inbound tourists and the number of scenic spots above the 4A (including) level. The number of domestic tourists, the number of exhibitions, the size of the population, and the GDP of cities were excluded because they did not reach a significant level. The specific model is as follows:
y = 4.516 + 0.003 x 1 + 1.927 x 5
Adjusted R2 = 0.716, F = 20.965, Sig. = 0.000.
In 2015, the independent variables of the final entry model were also the number of inbound tourists and the number of scenic spots above the 4A (including) level. The number of domestic tourists, the number of exhibitions, the size of the population, and the GDP of cities are excluded because they do not reach a significant level. The specific model is as follows:
y = 1.355 + 0.003 x 1 + 0.406 x 5
Adjusted R2 = 0.815, F = 21.742, Sig. = 0.000.
Based on the above analysis, it can be seen that the number of inbound tourists and 4A (including) scenic spots are positively correlated with the number of foreign star-rated hotels in the six variables of urban population size, number of inbound tourists, number of domestic tourists, number of exhibitions, number of scenic spots above 4A level, and urban GDP. Therefore, the scale of inbound tourism and tourism resources endowment have a significant impact on the evolution of the spatial pattern of foreign star-rated hotels. The correlation between other indicators and the number of foreign star-rated hotels is not significant. On the one hand, these indicators have a certain impact on the number of foreign star-rated hotels, but not significant. On the other hand, the small number of samples will have a certain impact on the measurement results.

5.3. The Change of Influencing Factors of Spatial Pattern Evolution of Foreign Star-Rated Hotels in China

In this paper, the correlation between the number of inbound tourists, the number of scenic spots above the 4A level, and the number of foreign star-rated hotels is analyzed (Table 6). It can be found that the correlation between foreign star-rated hotels and inbound tourists is stronger than that between foreign star-rated hotels and scenic spots above the 4A level. This result reflects that the number of inbound tourists is the main factor affecting the spatial pattern of foreign star-rated hotels, while the other four factors are the secondary factors. From 2005 to 2015, the impact of inbound tourists on the spatial pattern of foreign star-rated hotels grew, while the impact of scenic spots above the 4A level on the spatial pattern of foreign star-rated hotels became smaller and smaller. The regression equation also shows a similar trend.
It can be seen through comparing the regression Equations (1) and (2) that, compared with 2005, the influence of the 4A (including) and above scenic spots on the spatial pattern of foreign-funded star-rated hotels is gradually weakened. In 2005, for every additional 4A (including) and above scenic spots, the number of foreign-funded star-rated hotels will increase by about 2. By 2015, the number of foreign-invested star-rated hotels will increase by less than 0.5 for every additional 4A (including) scenic spot. It reflects that the layout of foreign-funded star-rated hotels values the market demand most rather than the market supply, and the market demand has an increasing impact on its spatial pattern.

6. Discussion and Conclusions

6.1. Discussion

The expansion of foreign star-rated hotels in China follows a three-stage model. The first stage is the risk-aversion-driven layout model, concentrated in the hometowns of overseas Chinese, provincial capitals, and cities directly under the jurisdiction of the government. These cities are closely connected with foreign countries, are more familiar with and sensitive to transnational investment policies, and have a large economic scale and higher demand for foreign star-rated hotels, and the cross-border investment risk of foreign star-rated hotels is relatively small. The second stage is the market-driven layout model, with its spatial layout concentrated in first-tier cities and cities with concentrated foreign capital. These cities have a huge international capital flow, and foreign star-rated hotels’ transnational investment returns are relatively high. At the same time, these cities have a more open system and a higher acceptance of foreign culture, and these factors will attract foreign tourists, thereby indirectly attracting foreign-funded star-rated hotels. The third stage is a comprehensive market- and resource-driven model. The scale of inbound tourism dominates the expansion direction of foreign-funded star-rated hotels, but tourism resources also have a certain degree of influence. Different cities have different tourism resource endowments, and rich tourism resources can form more tourism products and provide more types of experiences for foreign tourists. Foreign star-rated hotels in China want to be more closely integrated with tourism resources in order to attract more passenger flow.
The three-stage model of foreign star-rated hotel expansion in China can guide the local operation and transnational expansion of Chinese hotel enterprises. Firstly, cities with high concentrations of foreign capital and strong tourism potential must continue to create favorable conditions—both in terms of infrastructure and policy—to attract foreign investment and tourists. These cities should leverage foreign star-rated hotels to boost their global image and enhance their city functions. At the same time, local hotels should capitalize on the knowledge transfer and brand-building strategies of foreign star-rated hotels to improve their own management capabilities [26]. Chinese hotel enterprises, in particular, must move away from extensive management models and instead focus on improving service quality, brand reputation, and guest experience in response to increasing consumer demands. It is necessary to strengthen the research and development of the local market so as to form its own ability to compete with foreign hotels. At this stage, local governments mainly attract foreign star-rated hotels by providing a good investment environment and preferential fiscal and tax policies, thus affecting the layout of foreign star-rated hotels. At the same time, environmental quality and digital development should not be ignored [27,28], a beautiful environment will enhance the attractiveness of foreign tourists, and advanced digital facilities will improve hotel management efficiency and reduce hotel costs [29], both of which are conducive to attracting foreign hotels.
Secondly, Chinese hotel groups should assess market saturation levels. In cases of heightened competition and declining profit margins, expansion into urban agglomerations and tourist cities should be pursued to realize business growth. When expanding into mature urban agglomerations like the Yangtze River Delta, Beijing–Tianjin–Hebei, and the Pearl River Delta, enterprises must carefully study these markets to ensure accurate positioning and steady, differentiated growth. In emerging agglomerations, the focus should be on renovating local hotels and intensifying efforts to capture market share. For these areas, a high-quality product strategy should be implemented, setting brand benchmarks and cultivating a strong consumer base. At this stage, the local government should standardize the market order, avoid disorderly competition, and provide a good institutional environment, ecological environment, and digital facilities for the development of hotel enterprises. When local hotel enterprises expand outward, it provides them with policy and environmental information about the target city, promotes the expansion of local hotel enterprises through active policies, and thus affects the layout of hotel enterprises.
Thirdly, once Chinese hotel groups are ready to operate internationally, they should focus on regions with high numbers of Chinese tourists, a superior ecological environment and digital network, and strong trade ties with China. Key markets with similar economic environments can help reduce operational risk during early international expansion [30]. With experience and skills gained from transnational management, these enterprises can later expand into broader urban agglomerations in developed countries. It is critical to adopt differentiated management strategies that embrace advanced concepts, build brand image, and cultivate corporate reputation. After establishing world-renowned brands, Chinese hotel groups should continue to expand into both developed and emerging markets to broaden their global presence. This balanced approach will help them secure their position in the international hotel industry. At this stage, the government should provide local hotel enterprises with streamlined procedures for transnational operation and information on the business environment and environmental quality of the host country, especially to help local hotel enterprises understand the quality of infrastructure such as the legal and ecological environments and network in key countries, developed countries, and emerging markets. Helping local hotel enterprises avoid transnational business risks at different stages helps hotel enterprises expand internationally in a more strategic way.

6.2. Conclusions

There are four main findings in this paper. Firstly, the spatial distribution of foreign star-rated hotels in China has gradually transitioned from an unbalanced to a more balanced state. Initially concentrated in overseas Chinese towns, provincial capitals, and municipalities with developed economies, the distribution is now shifting toward foreign-capital-concentrated cities and tourist destinations. This shift is driven primarily by the dispersal of inbound tourists, who are increasingly visiting coastal second-tier cities and inland regional hubs. Foreign-funded star-rated hotels expand in order to occupy the inbound tourist market and follow the flow path of inbound tourists. Additionally, the rising domestic demand for high-quality accommodations, where brand reputation, service quality, and guest experience play key roles has further fueled the expansion of foreign star-rated hotels.
Secondly, urban agglomerations also play a significant role in this spatial pattern evolution. The main reason is that the scale of business activities market between urban agglomerations has brought a lot of demand for hotels and the strong agglomeration effect of the world cities themselves in urban agglomerations [31,32]. For example, Beijing and Shanghai, located in key metropolitan areas like the Bohai Sea and Yangtze River Delta, respectively, act as financial and political centers, while Guangzhou and Shenzhen in the Pearl River Delta benefit from quick access to policy updates and government support, thanks to China’s reform and opening-up initiatives.
Thirdly, the evolution of foreign star-rated hotels is primarily influenced by the number of inbound tourists. Secondary factors include the presence of 4A-level (and above) scenic spots, domestic tourism, exhibitions, and population size, though these factors play a less significant role. It should be noted that the number of domestic tourists has no significant impact on the evolution of the spatial pattern of foreign star-rated hotels, mainly because the main body of domestic tourists is mass tourists, whose consumption level is limited, and it is not the most important source market of foreign star-rated hotels. Moreover, the number of exhibitions has no significant influence on the evolution of the spatial pattern of foreign-funded star-level hotels, mainly because a large part of the current exhibitions are dominated by the government with strong administrative color, which does not necessarily indicate the prosperity of international business. Population size also does not significantly influence hotel distribution, mainly because the large population size does not mean a large scale of inbound tourism flow and business flow. For instance, Chongqing is China’s most populous city, yet it ranks eighth in inbound tourist numbers, and several other populous cities do not rank among the top ten for inbound tourism.
Fourthly, as foreign star-rated hotels continue to expand in China, market demand is becoming the most critical factor in determining their spatial distribution. At the same time, the impact of the supply of scenic spots is less and less, and the motivation to occupy the market is more obvious. In this context, inbound tourists become one of the most important source markets for foreign star-rated hotels. The evolution of the space pattern of foreign-funded star-rated hotels is largely influenced by the market demand, that is, the number of inbound tourists. The scale and growth rate of inbound tourists determines the speed of the entry of foreign-funded star-rated hotels. Moreover, the occupation of the inbound tourism market is the main impetus for the expansion of foreign star-rated hotels. Inbound tourism is affected by the tourism resource endowment, and inbound tourists favor high-grade tourism resources with international influence [33]. Therefore, the spatial pattern evolution of foreign-invested star-rated hotels in China is also indirectly affected by the number of high-level scenic spots. With the increasingly abundant supply of tourism resources, the increasingly diverse demand for tourism, and the continuous renewal of tourism modes and concepts, the impact of traditional scenic spots on inbound tourists is gradually reduced, and its impact on the evolution of the spatial pattern of foreign star-rated hotels is becoming increasingly weaker.
Hakanson proposed the global expansion model of enterprises in 1979, arguing that market occupation is the primary driving force behind the spatial expansion of enterprises [34]. Building on this perspective, the scale of inbound tourism serves as a key indicator of market demand, and occupying the inbound tourism market is the main driving force for the expansion of foreign-funded star-rated hotels. This paper agrees with this view. In addition, this paper holds that the utilization of host country resources is also one of the main driving forces for the spatial expansion of enterprises. A significant number of tourist attractions and tourism resources are concentrated in tourist cities. The spatial expansion of foreign-funded star-rated hotels in China is significantly affected by the number of high-level scenic spots. However, this influence is gradually decreasing.
The conclusion confirms and complements Hakanson’s global expansion model, highlighting the theoretical significance of this study. The practical significance lies in providing suggestions for Chinese local hotel enterprises on which regions to expand in both their home and host countries.

6.3. Research Limitations and Future Prospects

While this study delves into the spatial pattern evolution of foreign-funded star-rated hotels in China at the urban scale, it still has certain limitations. The research object of this paper is limited to foreign-funded star-rated hotels, which makes it difficult to fully reflect the spatial pattern of foreign-funded hotels in China. Additionally, although the consideration of influencing factors has been as comprehensive as possible, some significant variables may still be omitted, such as ownership status, average price, policy changes, cultural factors, and pandemics [35,36]. In the future, we will use big data to further expand the sample size and time and space span, supplement the latest data, include more potential influencing factors, and deepen the understanding of the evolution of the spatial pattern of foreign-funded star-rated hotels in China from a longer time span and a more comprehensive set of influencing factors.

Author Contributions

Conceptualization, X.Z.; methodology, X.Z. and D.H.; software, C.Z.; formal analysis, X.Z., C.Z. and Y.H.; investigation, Y.H.; resources, J.W. and Y.X.; data curation, C.Z. and Y.H.; writing—original draft preparation, X.Z., D.H. and Y.H.; writing—review and editing, X.Z., D.H., W.F., J.W., Y.X. and Y.H.; visualization, W.F.; supervision, X.Z. and D.H.; project administration, D.H. and Y.H.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (Project number: 41601123), the Science Foundation of the Ministry of Education of China (Project number: 21YJCZH225), Wuhan Association for Science and Technology Innovation Think Tank Project (Project number: WHKX202409).

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Local spatial autocorrelation results in 2000.
Figure 1. Local spatial autocorrelation results in 2000.
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Figure 2. Local spatial autocorrelation results from 2005 to 2010.
Figure 2. Local spatial autocorrelation results from 2005 to 2010.
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Figure 3. Local spatial autocorrelation results in 2015.
Figure 3. Local spatial autocorrelation results in 2015.
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Table 1. Regional distribution of the sample cities.
Table 1. Regional distribution of the sample cities.
RegionCities
Northeast ChinaShenyang, Changchun, Harbin, Dalian
North ChinaBeijing, Tianjin
Northwest ChinaXi’an
East ChinaShanghai, Nanjing, Hangzhou, Wuxi, Suzhou, Qingdao, Ningbo
Central ChinaWuhan, Huangshan
Southeast ChinaFuzhou, Xiamen
South ChinaGuangzhou, Haikou, Shenzhen, Zhuhai, Zhongshan
Southwest ChinaChengdu, Chongqing, Kunming, Guilin
Table 2. Foreign star-rated hotels distribution of sample cities.
Table 2. Foreign star-rated hotels distribution of sample cities.
TypeCities
Municipalities and provincial capitalsBeijing, Shanghai, Tianjin, Shenyang, Changchun, Harbin, Nanjing, Hangzhou,
Fuzhou, Guangzhou, Haikou, Wuhan, Chengdu, Xi’an, Chongqing, Kunming
Cities where foreign-funded enterprises are concentratedWuxi, Suzhou, Qingdao, Dalian, Ningbo
Sez cityShenzhen, Zhuhai, Xiamen
Tourist cityHuangshan, Guilin
Home town of overseas ChineseZhongshan
Table 3. Global spatial autocorrelation analysis results.
Table 3. Global spatial autocorrelation analysis results.
The Year 2000The Year 2005The Year 2010The Year 2015
Moran’s I0.0291440.0134040.0094040.018645
Z score2.3767570.9694970.9352111.630026
Significance level p0.0174660.2322970.3496800.103096
Table 4. The list of key tourism cities.
Table 4. The list of key tourism cities.
Key Tourism CitiesIt Accounts for the Proportion of
Total Inbound Tourists in 27 Cities
the year 2005Guangzhou, Shenzhen, Beijing, Shanghai (4 cities)58.65%
the year 2015Guangzhou, Shenzhen, Beijing, Shanghai, Suzhou, Hangzhou, Zhuhai, Guilin, Dalian, Nanjing, Huangshan, Xiamen,
Qingdao, Tianjin, Chongqing (15 cities)		86.84%
Table 5. The number grade of foreign star-rated hotels and inbound tourists of sample cities.
Table 5. The number grade of foreign star-rated hotels and inbound tourists of sample cities.
Number of Foreign Star-Rated HotelsThe Year 2005The Year 2015
>20Beijing *, Guangzhou *, Dalian, Shenzhen *, Tianjin, ShenyangBeijing *, Shanghai *, Shenzhen *, Guangzhou *, Dalian *
11~20Shanghai *, Kunming, Changchun, Fuzhou, Xiamen, Guilin, Zhuhai, Hangzhou, Qingdao, Wuxi, ChengduTianjin *, Fuzhou
6~10Chongqing, Wuhan, Xi’an, Zhongshan,
Nanjing, Ningbo, Haikou		Qingdao *, Suzhou *, Xiamen *, Xi’an, Ningbo, Shenyang, Haikou, Guilin *, Kunming, Nanjing *, Chengdu
0~5Suzhou, Huangshan, HarbinWuhan, Harbin, Chongqing *, Zhongshan, Huangshan *, Changchun, Wuxi, Zhuhai *, Hangzhou *
Number of inbound tourists/10,000the year 2005the year 2015
>200Guangzhou *, Shenzhen *, Beijing *Shenzhen *, Guangzhou *, Shanghai *, Beijing *, Zhuhai *, Hangzhou *, Suzhou *
100~200Shanghai *Tianjin *, Xiamen *, Guilin *, Chongqing *, Nanjing *, Dalian *, Qingdao *, Huangshan *
50~100Zhuhai, Guilin, Hangzhou, Xi’an, Zhongshan, Suzhou, KunmingNingbo, Wuhan, Kunming, Xi’an, Wuxi, Chengdu, Fuzhou, Shenyang
<50Xiamen, Nanjing, Tianjin, Dalian, Fuzhou, Wuxi, Chongqing, Qingdao, Chengdu, Wuhan, Huangshan, Shenyang, Harbin, Haikou, Ningbo, ChangchunZhongshan, Harbin, Changchun, Haikou
Note: * represents key tourist cities.
Table 6. The Pearson correlation coefficient between foreign star-rated hotels, inbound tourists, and scenic spots in AAAA-class (and above) tourism areas.
Table 6. The Pearson correlation coefficient between foreign star-rated hotels, inbound tourists, and scenic spots in AAAA-class (and above) tourism areas.
Number of Inbound TouristsNumber of Scenic Spots Above Grade 4A
The Year 2005The Year 2015The Year 2005The Year 2015
Pearson Correlation0.695 **0.699 **0.576 **0.540 **
Sig. (2-tailed)0.0000.0000.0020.004
N27272727
Note: ** represents a significant correlation at the level of 0.01 (bilateral).
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Zhang, X.; Han, D.; Zhang, C.; Feng, W.; Wu, J.; Xie, Y.; He, Y. Spatial Pattern Evolution and Influencing Factors of Foreign Star-Rated Hotels in Chinese Cities. Reg. Sci. Environ. Econ. 2025, 2, 1. https://doi.org/10.3390/rsee2010001

AMA Style

Zhang X, Han D, Zhang C, Feng W, Wu J, Xie Y, He Y. Spatial Pattern Evolution and Influencing Factors of Foreign Star-Rated Hotels in Chinese Cities. Regional Science and Environmental Economics. 2025; 2(1):1. https://doi.org/10.3390/rsee2010001

Chicago/Turabian Style

Zhang, Xiang, Dongxiao Han, Chunfeng Zhang, Wenyi Feng, Jinsong Wu, Yan Xie, and Yating He. 2025. "Spatial Pattern Evolution and Influencing Factors of Foreign Star-Rated Hotels in Chinese Cities" Regional Science and Environmental Economics 2, no. 1: 1. https://doi.org/10.3390/rsee2010001

APA Style

Zhang, X., Han, D., Zhang, C., Feng, W., Wu, J., Xie, Y., & He, Y. (2025). Spatial Pattern Evolution and Influencing Factors of Foreign Star-Rated Hotels in Chinese Cities. Regional Science and Environmental Economics, 2(1), 1. https://doi.org/10.3390/rsee2010001

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