China–ASEAN Tourism Economic Relationship Network: A Geopolitical Risk Perspective

Abstract

Despite extensive research on the network structure of tourism destinations, literature on the spatial network structure of cross-border tourism–economic connections is relatively limited, specifically the complex geopolitical relationship between China and the Association of Southeast Asian Nations (ASEAN). Thus, in this study we explored the relevant characteristics and influencing factors of the spatial structure of tourism economic networks. The results first indicate that the China–ASEAN tourism economic network exhibits distinct stage characteristics, with an ‘N’-shaped fluctuating growth trend, evolving from a point-like network to a multipolar development in spatial structure. In addition, China demonstrates a distinctive personality in the network and occupies a central leadership position. Secondly, an analysis of influencing factors shows that institutional distance and geographic distance have a significant impact on the network. Geopolitical risk plays a notable indirect moderating role in the network through its association with tourism policies and regulations, tourism promotion, and cooperation. This further reveals the diverse pathways through which geopolitical risk affects the network, providing a novel perspective for research on the tourism economic network.

1. Introduction

The spatial network structure of the cross-border tourism economy, functioning as a complex system, has the potential to promote the flow of various tourism economic factors such as capital, technology, talent, and information within tourism destinations through the mechanisms of radiation and spillover effects [1]. In addition, the flow and changes in its elements can affect the tourism economy network [2]. Geopolitical challenges, including economic agreements [3,4], territorial disputes [5], health crises [6], and terrorist attacks [7], have led to noteworthy shifts in global tourism and economic transactions. For example, the war between Russia and Ukraine has posed significant challenges to the tourism industry in the affected regions. Although it has not completely halted tourism development, it has greatly altered the direction of tourist flows [8,9]. Mostafanezhad et al. [10] indicate that sudden political events, such as the COVID-19 pandemic, have a disruptive impact on the tourism industry. These challenges have heightened caution among tourism firms regarding investments in tourist destinations [11]. Simultaneously, this has prompted tourists to deliberate more thoughtfully on their choice of destination [12]. Geopolitics is currently exerting a significant influence on regional economies and spatial patterns through the characteristics of ‘daily tourism’ [13,14,15]. Therefore, tourism scholars must explore the spatial network structure of the tourism economy. They should closely examine the impact of geopolitical, economic, and social factors on forming interregional tourism economic networks.

The literature includes a certain degree of research on geopolitics and tourism. Studies explain how geopolitics affects the tourism industry from both the supply and demand perspectives. From the supply perspective, scholars have concentrated on various aspects, including tourism project development [16] and tourism investment [17]. From the demand perspective, scholars have delved into various facets, such as tourism services [18], the number of tourists and revenues [19], and tourism stock returns [11], to scrutinise the correlation between geopolitical risk and tourism. The scope of research has expanded from the impact of unilateral geopolitics on the tourism in a single country [19,20] to the effects between two countries [14,21], and even across an entire continent [22]. For example, Gozgor et al. [17] found that geopolitical risk negatively impacts tourism capital investment by studying its effects on 18 developing economies. Lee et al. [23] used a multi-panel model to examine the long-term negative impact of geopolitical risk on tourism demand in 16 countries. At the same time, research indicates that geopolitics is currently exerting a significant influence on regional economies and spatial patterns through its “everyday” characteristics [13,14,15]. In addition, a critical exploration of the spatial and temporal impacts of geopolitics on the tourism economy from the perspective of mobility can enhance our understanding of the characteristics and patterns of these impacts, especially in the cross-border context. However, current research lacks an examination and discussion of the geopolitical influences on the cross-border tourism economic network from a spatial perspective.

There are advantages in the natural geographical proximity and cultural similarities between China and ASEAN countries. Regional economic integration not only facilitates tourism flows but also lays a foundation for tourism cooperation. As they are strategic partners in regional development, tourism cooperation can accelerate the economic collaboration between them. Therefore, they increasingly rely on the development of the tourism economy to drive their economic growth [24]. However, due to the particularities of China–ASEAN geopolitics, the geopolitics of countries in the region can also act as a barrier to tourism exchange. For instance, the dynamic geopolitical differences between China and Myanmar can hinder the flow of tourists to some extent [21]. Especially in recent years, global geopolitical uncertainty has increased, which undoubtedly impacts the geopolitical situation of China–ASEAN relations, thus posing a challenge to tourism interactions [25]. Therefore, more contemporary research is needed to address the geopolitical issues in the context of friendly exchanges and cooperation between China and the ASEAN. Such research should elucidate the relationship between geopolitics and the tourism economy to better promote the tourism development of the China–ASEAN Free Trade Area.

Therefore, the purpose of this study is to explore the China–ASEAN tourism economic network and its influencing factors from the perspective of geopolitical risk, with a focus on the driving role of these risk factors. Using the period from 2010 to 2019 as a case study, we construct a matrix and apply a modified gravity model to assess the strength of tourism economic ties. Next, employing social network analysis, we investigate the spatial structure of the China–ASEAN regional tourism economic network and discuss the evolving “power” status of each participating entity. Finally, using QAP analysis, we examine the factors that influence the formation of the tourism economic network.

The research aims to address the following questions: (1) What are the characteristics of the tourism economic network between China and the ASEAN? (2) What factors influence the formation of this tourism economic network? (3) Particularly, do geopolitical factors affect the formation of the tourism economic network? This study contributes in three main aspects. Firstly, we construct the China–ASEAN tourism economic network and analyze its network characteristics, filling gaps in the existing literature regarding cross-border tourism economic networks. Secondly, we focus on discussing the driving factors behind the formation of cross-border tourism economic networks, especially distance factors, thereby enriching the understanding within the tourism literature. Lastly, this study responds to previous calls in the literature by expanding empirical research on the formation of tourism economic networks from a geopolitical perspective. It finds that geopolitical risks indirectly moderate tourism economic networks, expanding the research perspective in this field and carrying important policy implications for enhancing regional cooperation among China and ASEAN countries.

The remainder of this paper is structured as follows: Section 2 presents the literature review. Section 3 details the research methodology and data sources. Section 4 analyses the strength of China–ASEAN tourism economic linkages, spatial structural characteristics, and influencing factors. Section 5 offers conclusions and discusses the findings.

2. Literature Review and Hypotheses

2.1. Geopolitics and Tourism

Given that cross-border tourism fundamentally involves visitors crossing borders, it is intricately connected with geopolitics [26]. Indeed, consumer needs, such as cross-border shopping and sightseeing, are exposed to geopolitical influences [17,19]. Furthermore, the sensitivity of the tourism industry makes it highly susceptible to disruptions [23]. Any unfavourable event can have profound and far-reaching impacts, potentially affecting the flow of tourism [27]. Research has indicated that natural disasters, health crises, and geopolitical turmoil can potentially impact the development of the tourism sector [10]. Current empirical findings indicate that tourism and geopolitics are mutually constitutive [28,29]. Existing research has explored these dynamics from both macro and micro perspectives (

Table 1. Dimensions of geopolitical exploration of tourism.

Perspective	Dimension	Reference
micro level	a space for anti-normalisation	[30,31]
	territorial socialisation	[32]
	post-imperial periods	[33]
	tourist encounters	[34]
macroeconomic level	tourism investment	[17]
	stock returns	[11]
	the number of tourist arrivals and revenue	[19]

). At the micro level, these studies investigate how tourism mirrors the geopolitics of daily life, predominantly employing methods such as questionnaires, interviews, and field research. They have explored various dimensions, including a space for anti-normalisation [30,31], territorial socialisation [32], post-imperial periods [33], and tourist encounters [34]. These perspectives have shed light on the influence of everyday geopolitics on tourist experiences and perceptions. The findings show that geopolitics intersects with tourists’ embodied encounters in an everyday and widespread manner from multiscale research perspectives. At the macroeconomic level, studies have explored geopolitics’ influence on the economic aspects of cross-border tourism. This includes diverse aspects such as tourism investment [17] and stock returns [11]. It also encompasses metrics such as the number of tourist arrivals and revenue [19].

In recent years, complex changes in the international landscape have led to geopolitical risks such as the US–Iran conflict, the US–China trade war, and nuclear threats from North Korea [35]. Geopolitical risks, as a challenge to global governance, have attracted international attention [36]. These risks pose substantial challenges to the sustainable development of the tourism industry. Previous empirical studies have separately examined various aspects of geopolitical risks on the tourism economy, such as the impacts of terrorism, political violence, sanctions, and conflicts. Lanouar et al. [7] used Markov-switching models to investigate the effects of terrorism and political violence on tourist numbers. They found that terrorism and political violence reduce inbound tourism, thereby hindering the tourism economy growth of affected countries. Furthermore, Pratt et al. [37] demonstrated that sanctions lead to economic hardships, and with the lifting of UN sanctions on Iran, tourism numbers in Iran increased, fostering the development of its tourism economy.

Geopolitical risks themselves bring uncertainty that disrupts the stability of tourism market supply and demand dynamics, affecting capital investments and thus influencing the development of the tourism economy [38]. Research by Gozgor et al. [17] indicates that geopolitical risks threaten tourism capital investment. They argue that geopolitical risks undermine market stability, leading to shifts in tourism investment directions, which in turn affect capital inflows to destinations and hinder the development of their tourism economies. Tiwari et al. [19], using wavelet analysis, studied the impact of geopolitical risks on tourist numbers in developing countries. Their findings suggest that geopolitical risks have long-term effects on tourist arrivals and a decline in tourism demand can impede economic growth.

The previous literature has primarily focused on examining the impact of geopolitical risks on the tourism economies of individual or a few countries. Empirical studies involving more countries include Balli et al. [39], who employed the wavelet squared coherence approach to measure the effect of geopolitical risks on international tourism demand in emerging economies. Their findings indicate that the impact of geopolitical risks varies across countries. They observed that geopolitical risks have relatively smaller effects on the tourism economy of countries with strong tourism attractiveness. Papagianni et al. [40] utilized a novel Bayesian heterogeneous panel vector autoregressive model (B-HP-VAR) to discuss the impact of geopolitical risks on the tourism industry of 14 emerging market and developing economies (EMDEs). Their study demonstrates that tourism demand in most EMDEs is affected by geopolitical risks. Doğan et al. [22] employed a multivariate wavelet causality framework to study the effects of global fluctuations, uncertainty, and geopolitical risk factors on international tourist arrivals in Asia. They indicated that geopolitical risks impact the arrival of international tourists. Overall, geopolitical risks primarily affect destination countries’ tourism economies through their influence on both tourism demand and supply. High geopolitical risks may prompt travellers to choose their destinations cautiously and consider postponing or cancelling trips, leading to a reduction in inbound tourism [19,22]. Moreover, elevated geopolitical risks may compel decision-makers to focus on diplomatic policies during the investment process, resulting in decreased investments and negative impacts on tourism economic development [11,27]. All of the above research findings indicate that geopolitical risk factors negatively impact the economy of tourist destinations. Geopolitical risks have a significant influence on global economic operations and are increasingly becoming a crucial consideration for the development and cooperation of the tourism economy.

Despite ongoing tourism cooperation between China and the ASEAN, the tourism relationship between China and ASEAN countries has not been fully studied due to its unique background and the distinct geographical, geopolitical, and economic characteristics of the region. In particular, geopolitical issues between these countries have persisted and escalated in response to changes in the international structure. For example, the territorial dispute in the South China Sea has consistently posed an obstacle to cooperation and development between China and ASEAN countries. Some countries have also used geopolitics as a tool to limit tourist flows and to assert their positions regarding the South China Sea [41]. Therefore, the complex geopolitical issues surrounding the South China Sea dispute are likely to alter the geopolitical risks between China and the ASEAN, subsequently impacting tourism economic exchanges between the regions. Based on the above discussion, we proposed the following hypothesis:

H1. 

Geopolitical risks have a negative impact on the formation of the China–ASEAN tourism economic network.

2.2. Cross-Border Tourism

Cross-border tourism constitutes a crucial facet of economic development in destination countries [42]. Thus, assessing the state of regional economic cooperation in cross-border tourism is imperative for fostering national tourism collaboration. Research has substantiated the crucial role of cross-border tourism in advancing national and regional peace and development [43]. Cross-border tourism fulfils this role in two aspects. One is at the micro level, involving tourists and locals. While travelling, tourists inevitably engage with locals and exchange their respective countries’ cultures, values, and lifestyles [44]. In this process, tourism serves as a platform for communication and helps dispel potential stereotypes about each other’s countries. The other aspect operates at the macro level of the country and its economy. Nations can establish regional cooperation with their neighbouring countries to stimulate tourism activities and foster economic prosperity. Such collaborative tourism endeavours could benefit nations economically [45].

When studying how regional cooperation affects the regional tourism economy, most studies focus on the impact of trade agreements on tax rates and trade conditions [46]. Importantly, scholars have pointed out that trade agreements are essential for promoting regional integration [47] and sustainable development in the tourism industry [48]. Existing literature has explored the role of different types of regional cooperation agreements in international tourism. Regional cooperation agreements such as the North American Free Trade Agreement and the European Union have positively impacted cross-border tourism [49], enhancing the competitiveness of regional countries in the global market [50]. These cooperation agreements promote the influx of international tourists and drive the development of the tourism industry among member countries by reducing travel costs, eliminating barriers, and strengthening infrastructure connectivity [47]. This indicates that formal and informal agreements and cooperation between countries in regional alliances play promotional roles in economic development and regional tourism cooperation.

In addition, tourism policy is recognized as an effective tool for promoting regional tourism development [51]. However, research on the impact of policies and regulations on cross-border tourism remains limited. Existing literature primarily examines the role of tourism policy in influencing the cross-border tourism economy. For example, McKay and Tekleselassie used instrumental variable estimation methods to analyse the impact of visa policy on cross-border tourism, concluding that a flexible visa policy can not only directly stimulate bilateral tourism but also enhance trade flow and foreign direct investment [52]. Similarly, Goel and Budak, using transnational data from over 100 countries, explored the impact of various tourism policies and measures on economic growth, finding that positive tourism policies and regulations support economic growth [51]. Furthermore, border tourism policy, as a critical factor in advancing cross-border tourism, facilitates two-way tourist flow, drives economic growth, and creates conditions for citizens on both sides of the border to engage in tourism activities [53].

The above studies demonstrate that regional cooperation agreements have effectively promoted the development of cross-border tourism among member countries. However, existing research primarily focuses on Europe and North America, where developed countries predominate. Additionally, research on tourism policies and regulations tends to examine their impact within bilateral country contexts. Whether these previous findings apply to the tourism economic network of developing countries requires further investigation. Therefore, we proposed the following hypotheses:

H2. 

Tourism policies and regulations have a significant positive impact on promoting the formation of the China–ASEAN tourism economic network.

H3. 

Tourism promotion and cooperation have a significant positive impact on the formation of the China–ASEAN tourism economic network.

2.3. Distance Decay Theory and Multidimensional Distance Framework

Distance decay is a fundamental principle in geography [54] which states that the spatial interaction between geographic elements decreases as the spatial distance between them increases [55]. In tourism studies, distance decay is typically applied to the spatial distribution of tourist source markets, where literature suggests that tourist arrivals at tourism destinations decrease as distance increases [56,57]. In human geography, distance often operates through transportation costs or intervening opportunities, weakening the communication between two locations [58]. Therefore, distance becomes a crucial impediment to tourist activities, particularly in cross-border tourism, where institutional, economic, cultural, and geographic distances exert an influence, encompassing tangible and perceptual barriers. Existing research has demonstrated that tangible barriers include war, crime, terrorism, and stringent cross-border policies that intensify diplomatic tensions [17,32]. Perceptual barriers involve differences in social and cultural norms and individual safety perceptions [59], predominantly in the psychological realm of tourists. Even without overt tangible impediments between nations, perceptual barriers may hinder cross-border tourism. The fundamental origin of these barriers lies in the geopolitical disparities between nations in political, economic, and cultural dimensions, hindering the establishment of economic cooperation. While multidimensional distances can adequately characterise tangible and perceptual barriers, current research focuses on singular dimensions, such as culture [60] and geographic distance [61], lacking comprehensive depth. The economic, geographical, cultural, and institutional distance (CAGE) framework has emerged as a pivotal multidimensional theoretical framework [62], comprehensively revealing the impact of distance factors on international tourism [63]. Previous studies have introduced the CAGE distance framework from the perspective of cross-border tourism consumption, highlighting the heterogeneity in the effects of the different dimensions of distance within the CAGE framework [64]. Even within the same dimension, variations in research scale, background, and other factors may lead to disparate conclusions [65]. Therefore, the findings of previous studies suggest that multidimensional distance is a crucial tool for analysing cross-border tourism interactions. Introducing a multidimensional distance framework based on distance decay theory is essential to comprehensively examine the influence of distance on tourism economic networks. Based on these considerations, we proposed the following hypothesis:

H4. 

Multidimensional distance has a significant impact on the formation of China–ASEAN tourism and economic networks.

2.4. Spatial Network of Tourism Economy and Its Influencing Factors

As a socioeconomic phenomenon, tourist activities depend on the spatial influences on their occurrence and development. The spatial structure of tourism reflects the movement of tourism elements and signifies the spatial attributes and relationships inherent in tourist activities [66]. Researchers have extensively explored network structures and interactions across various dimensions, including collaborative networks among tourist attractions [67], networks involving stakeholders [68], and networks facilitating cross-border tourism cooperation [69]. While researchers have shown interest in cross-border tourism economic networks (especially cross-border regional alliances) in the context of globalisation, further in-depth research is required regarding case studies and content [69,70].

Furthermore, exploring the factors influencing the tourism economic network is crucial. Current research has confirmed that economic, social, and resource factors profoundly impact the formation of the tourism economic network [2,71,72]. For example, Ruan and Zhang [2] examined the importance of transportation, tourism reception capacity, and the endowment of tourism resources in influencing tourism–economic connections. Tourism resources form the foundation for developing the tourism industry and propel the integrated development of regional tourism economies [73]. The strong interdependence of tourism economies in regional tourism networks is closely related to the endowment of tourism resources [74]. Scholars have confirmed that the core competitiveness of destinations lies in tourism resources; however, there remains a need for additional verification regarding the impact of tourism resources on destination economies [75], particularly at the economic network level. Notably, studies have confirmed that the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Global Geoparks (UGGps) [76], world heritage sites [70,77,78], and UNESCO Man and Biosphere Reserves [70] are essential tourism resources that attract visitors and their quantity and distribution have a critical impact on regional economic balance and sustainable development. These studies demonstrate that tourism resources, as a primary factor in enhancing tourism appeal and fostering tourism economy development, profoundly impact the balanced growth of cross-border tourism economies. Therefore, within the cross-border tourism economic network, it is essential to examine the role of tourism resource endowment in promoting economic exchanges among countries in the network. Based on these perspectives, we tested the following hypothesis:

H5. 

Tourism resource endowments (e.g., UGGps, World Heritage Sites, UNESCO Man, and Biosphere Reserves) have a significant impact on tourism economic networks. Figure 1 presents the hypothetical model.

2.5. Filling in the Gaps

In summary, the existing literature has provided valuable insights into geopolitics and tourism, cross-border tourism, tourism economic spatial networks, and their influencing factors. However, this study identifies several shortcomings in current research on the cross-border tourism economic network level by comparing the differences between geopolitical risks and tourism economic network studies: (1) The existing literature predominantly focuses on stakeholder networks or destination networks, lacking research specifically on tourism economic networks, particularly in the context of cross-border tourism. (2) Studies on the factors influencing tourism economic network formation are limited to typical economic and social factors, overlooking the driving role of distance factors. (3) The impact of geopolitical risk factors on tourism is primarily concentrated on the supply–demand relationship in tourism, with limited application within tourism economic networks. In light of these gaps in the current literature, this study focuses on China–ASEAN countries to examine the characteristics of cross-border tourism economic networks. Additionally, it seeks to explore the relationship between geopolitical risk and the formation of tourism economic networks from a geopolitical perspective, thereby addressing theoretical and case study gaps in the existing research.

3. Methods and Data

3.1. Research Method

3.1.1. The Modified Gravity Model

The gravity model has been used as a prominent tool for investigating economic connections among regions since its inception by Reilly in 1931. Establishing a spatial linkage matrix using the tourism-specific economic gravity model serves as the basis for analysing the spatial network structure of the tourism economy [79]. The classical tourism economic gravity model is represented as follows [2]:

$$F_{ij}=k_{ij}\times {\displaystyle \frac{\sqrt{T_i{I}_i}\sqrt{T_j{I}_j}}{D_{ij}^2}}$$

(1)

In Formula (1), F_ij represents the intensity of tourism economic ties, T_i and T_j represent the number of inbound tourists in countries i and j, I_i and I_j represent the inbound tourism income of countries i and j, D_ij represents the geographical distance between countries i and j, and k_ij denotes an empirical constant.

Previous studies indicate that the traditional tourism economic gravity model cannot entirely reflect regional economic relations [80] because tourism economic linkages exhibit strong economic characteristics, such as the economic distance between nations influencing their tourism exchanges [81]. Simultaneously, within the tourism environment, economic interactions and cooperation are also influenced by the local industrial development context [82,83]. Therefore, integrating geographic distance, economic distance, and the industrial development context comprehensively impacts these interactions. Based on previous studies [71,79,80,84], this study modified the gravity model by incorporating economic distance and the industrial development context as relatively mature variables, aiming to assess the strength of tourism economic relationships between countries. Compared to traditional gravity models, the modified model better reflects the tourism economic relationships across international regions. The formula of the modified gravity model is

$$F_{ij}=K_{ij}\times \sqrt{T_i{I}_i}\times {\displaystyle \frac{\sqrt{T_j{I}_j}}{G{D}_{ij}E{D}_{ij}}}$$

(2)

$$K_{ij}={\displaystyle \frac{S{I}_i}{S{I}_i+S{I}_j}}$$

(3)

$$E{D}_{ij}={\displaystyle \frac{{\left(GDPP{C}_i-GDPP{C}_j\right)}^2}{GD{P}_i\times GD{P}_j}}$$

(4)

where K_ij indicates the attraction coefficient of tourism cooperation between countries i and j, SI_i and SI_j indicate the proportion of service employees in the total number of employees in countries i and i, GD_ij indicates the geographical distance between countries i and j, ED_ij indicates the economic distance between countries i and j, GDPC_i and GDPPC_j indicate the per capita GDP of countries i and j, and GDP_i and GDP_j indicate the GDP of countries i and j. The formula for calculating tourism economic contact quantity (C_ij) is as follows:

$$C_{ij}=\sum F_{ij}$$

(5)

3.1.2. Social Network Analysis (SNA)

This study chose indicators such as network density, degree centrality, closeness centrality, betweenness centrality, and cooperative status index to assess the overall network structure and nodes.

The descriptions of the selected metrics and formulas [84,85,86] are shown in

Table 2. Formulas of indexes regarding spatial network structure characteristics of the China–ASEAN tourism economy.

Indicator	Formula	Description
Network density (D)	$D=2l/n\left(n-1\right)$ (6)	D is the network density, with the range of [0,1]; l represents the actual number of effective connections; and n is the size of the regional network, that is, the number of node countries. Network density is usually used to measure the closeness of the relevant countries in the research area. The greater the network density, the closer the connection.
Degree centrality (RD)	$C_{RD}\left(c_i\right)={\displaystyle \frac{{{\displaystyle \sum }}_i{a}_{ij}}{\left(n-1\right)}}$ (7)	$C_{RD}\left(c_i\right)$$\mathrm{is} \mathrm{the} \mathrm{degree} \mathrm{centrality} \mathrm{and} a_{ij}$$$ and other countries. In-centralization is used to express the attraction of a country. Out-centralization reflects a country’s enthusiasm for communication in the network.
Closeness centrality (RP)	$C_{RP}\left(c_i\right)={\displaystyle \frac{\left(n-1\right)}{{{\displaystyle \sum }}_{j\ne 1}^n{d}_{ij}}}$ (8)	$C_{RP}\left(c_i\right)$$\mathrm{represents} \mathrm{the} \mathrm{closeness} \mathrm{centrality} \mathrm{to} \mathrm{the} \mathrm{center} \mathrm{and} d_{ij}$$$$$. In-closeness centrality is used to measure the ease with which tourists from other countries can reach this country. Out-closeness centrality refers to the ease with which tourists from a country can reach other countries.
Betweenness centrality (RB)	$C_{RB}(c_i)=\left[{\displaystyle \sum }_{j<k}{\displaystyle \frac{{\mathrm{g}}_{jk}\left(c_i\right)}{{\mathrm{g}}_{jk}}}\right]/\left(n^2-3n+2\right)$ (9)	$C_{RB}\left(c_i\right)$$\mathrm{is} \mathrm{the} \mathrm{betweenness} \mathrm{centrality}, {\mathrm{g}}_{jk}\left(c_i\right)$$\mathrm{is} \mathrm{the} \mathrm{number} \mathrm{of} \mathrm{shortest} \mathrm{distances} \mathrm{between} \mathrm{countries}, \mathrm{and} {\mathrm{g}}_{jk}$$$$$. Betweenness centrality can be used to express the ability of the node to control other nodes.
Cooperative status index	$C{S}_i=a_{1}D{C}_i+a_{2}B{C}_i+a_{3}C{C}_i$ (10)	$\mathrm{The} \mathrm{cooperative} \mathrm{status} \mathrm{index} \mathrm{is} \mathrm{used} \mathrm{to} \mathrm{measure} \mathrm{the} \mathrm{status} \mathrm{of} \mathrm{an} \mathrm{individual} \mathrm{in} \mathrm{the} \mathrm{whole} \mathrm{network}, \mathrm{which} \mathrm{is} \mathrm{mainly} \mathrm{evaluated} \mathrm{by} \mathrm{degree}, \mathrm{betweenness}, \mathrm{and} \mathrm{closeness} \mathrm{centrality}. C{S}_i$$\mathrm{represents} \mathrm{the} \mathrm{cooperative} \mathrm{position} \mathrm{index}, D{C}_i$$\mathrm{represents} \mathrm{the} \mathrm{degree} \mathrm{centrality}, B{C}_i$$\mathrm{represents} \mathrm{the} \mathrm{betweenness} \mathrm{centrality}, C{C}_i$$\mathrm{represents} \mathrm{the} \mathrm{closeness} \mathrm{centrality}, \mathrm{and} a_1$$, a_{2,}$$\mathrm{and} a_3$ represent the weights of degree centrality, betweenness centrality, and closeness centrality, respectively. We assign the weights of all three to 1/3.

.

3.1.3. The Quadratic Assignment Procedure (QAP)

The quadratic assignment procedure (QAP) was used to analyse the relationships between the matrices. The correlation coefficients between the two matrices were derived by comparing the lattice values within the matrices, and these coefficients were subjected to non-parametric testing [87]. The factors influencing the evolutionary structure of the China–ASEAN tourism economic relationship network are associated with the spatial correlation of the tourism economy among countries. The spatial correlation matrix of the tourism economy represents relational network data. Employing QAP to examine its influencing factors helps overcome the potential issues of multiple collinearities that might arise in traditional regression methods [2,88].

By selecting independent variables, this study introduced one of the most influential theoretical frameworks in international business and trade based on the distance attenuation theory: the CAGE distance framework [62]. The CAGE framework emphasises the bilateral attributes between countries and captures the ‘differences’ in bilateral distance measurements across different dimensions. In geographical research, many geographical phenomena and economic laws rely on the factor of ‘distance’ for explanation [89]. Therefore, multidimensional distance is a crucial indicator of bilateral attributes between countries and a crucial factor influencing the formation of the cross-border tourism economy [63,65]. Cultural distance was not considered because of the lack of cultural distance data in several ASEAN countries.

Second, some recent studies have pointed out that formal trade agreements and informal economic cooperation interact with the institutional environment to stimulate economic activities [47,50]. For example, the China–ASEAN Free Trade Agreement has fostered bilateral economic cooperation and supported the growth of tourism by creating tourism-related investment opportunities for multinational enterprises [90]. Specific financial initiatives under this trade agreement that encourage tourism economic exchanges and development include tourism-specific loans and financing, tourism industry investment funds, and projects to facilitate tourism payments. Additionally, the Belt and Road Initiative has made a significant impact in promoting regional cooperation and advancing international tourism development [50]. Financial initiatives, such as the Belt and Road Development Fund, further contribute to the development of tourism economies in destination countries. In this study, tourism promotion and cooperation are therefore considered as independent variables. Furthermore, tourism policies and regulations are essential tools for promoting and safeguarding tourism economic growth. In-depth collaboration between China and the ASEAN on tourism policies not only facilitates travel but also enables citizens to participate in tourism-related economic activities, supporting sustainable regional economic development. Key policies include entry–exit management, tourism market regulations, specialized support for the border tourism industry, regional transportation connectivity policies, and tourism tax exemptions. Accordingly, we include tourism policy and regulations as independent variables.

Additionally, tourism resource endowment can reflect a country’s abundance of tourism resources. These resources help regulate tourists’ travel demands to some extent, thereby influencing interregional tourism economic flows [2,91]. Finally, this study used the China–ASEAN network as a case study because the South China Sea dispute is a critical factor hindering regional tourism development [32]. Considering that geopolitical issues may affect regional tourism economic development, geopolitical risk was included as an independent variable.

In summary, this study identified institutional, geographic, and economic distances, tourism resource endowment, tourism policies and regulations, tourism promotion and cooperation, and geopolitical risk as factors influencing the structure of the China–ASEAN tourism economic network. The following model was constructed to quantify the impact of these factors on the spatial correlation network of the tourism economy:

$$Y=\left(X_i\right)$$

(11)

where Y represents the matrix of spatial correlation relationships in the tourism economic space and Xi denotes institutional distance (X₁), geographic distance (X₂), economic distance (X₃), tourism resource endowment (X₄), tourism policies and regulations (X₅), tourism promotion and cooperation (X₆), and geopolitical risk (X₇). Regarding the measurement of geopolitical risk indicators, this study drew on the research by Hu and Li [92]. Based on the summaries of the core ideas of previous studies [93,94], relevant dimensions and algorithms were selected. All variable data were standardised using polar deviation to eliminate the influence of scale.

Table 3. Data sources and formulas of variable and measurement indexes.

Variable	Description	Data Source
Institutional distance	$$I{D}_{ıȷ}={\displaystyle \frac{1}{M}}{\sum }_{m=1}^M \left\{{\left(I_{mi}-I_{mȷ}\right)}^2/V_m\right\}$$ (12) where $I{D}_{ıȷ}\mathrm{denotes} \mathrm{the} \mathrm{mth} \mathrm{dimension} \mathrm{score} \mathrm{for} \mathrm{country} i; I_{mȷ}$ $\mathrm{denotes} \mathrm{the} \mathrm{mth} \mathrm{dimension} \mathrm{score} \mathrm{for} \mathrm{country} j; V_m$ denotes the variance of dimension m; and M denotes the number of distance dimensions for all regimes.	WGI, www.govindicators.org
Geographical distance	Geographic distances are expressed as air distances from national capitals.	https://www.timeanddate.com/worldclock/distance.html (accessed on 6 October 2024)
Economic distance	The economic distance is calculated with reference to Formula (4).	World Bank (https://data.worldbank.org.cn)
Tourism resource endowment	The sum of the number of World Heritage Sites, world geoparks, and world biosphere reserves represents the quantity of tourism resources	UNESCO (https://www.unesco.org/)
Tourism policy and regulations	Facilitation of visa policies, signing of tourism cooperation agreements, the number of support policies for tourism investment, and development of representative tourism policies and regulations.	ASEAN Main Portal (https://asean.org)
Tourism promotion and cooperation	If country i and country j jointly carry out activities such as marketing and tourism fairs in year t. Such activity is recorded as 1, a lack of such activity is recorded as 0, and the matrix is constructed accordingly.	ASEAN Main Portal (https://asean.org) and Stats.gov.cn (http://www.stats.gov.cn)
Geopolitical risks	All indicators were first standardized using the following formula: $$S_i={\displaystyle \frac{\left(U_i-L_i\right)\left(X_i-T_i\right)}{\left(U_i+L_i-2T_i\right)X_i+U_i{T}_i+L_i{T}_i-2U_i{L}_i}}$$ (13) where Ui, Li, and Ti represent the maximum, minimum, and mean values of the indicator Xi, respectively, and Si represents the standardized value of the indicator. Si represents the normalized value of the positive indicator, while -Si represents the normalized value of the negative indicator. Finally, the composite index is calculated using the following formula: $$S={\displaystyle \frac{\sum _{i\ne j}^{i,j} (S_i+1)(S_j+1)}{2n(n-1)}}$$ (14) where Si and Sj represent the i and j sub-indicators (i, j = 1, 2, …n,i ≠ j); n is the number of indicators; and S is the value of the composite index, ranging from 0 to 1; the larger the value is, the higher the geopolitical risk.	World Bank (https://data.worldbank.org.cn), Institute for Economics and Peace (www.economicsandpeace.org), U.S. Department of the Treasury (https://home.treasury.gov), Naciones Unidas (www.un.org/securitycouncil (accessed on 6 October 2024))

summarises the data sources and measurement methods used for these variables.

3.2. Data Source

As a case study, we selected 11 countries for empirical research: China, Singapore, Malaysia, Indonesia, Thailand, Cambodia, Myanmar, Vietnam, Laos, the Philippines, and Brunei. Data on inbound tourist arrivals and revenue were sourced from the World Tourism Organization “https://www.unwto.org/tourism-statistics/key-tourism-statistics (accessed on 8 December 2022)”. In contrast, GDP, per capita GDP, and the proportion of service employees to the total number of employees were obtained from the World Bank “https://data.worldbank.org.cn/indicator (accessed on 7 December 2022)”. Furthermore, the geographical distance between countries was measured by the air distance between their respective capitals using data from “https://www.timeanddate.com/worldclock/distance.html (accessed on 5 November 2022)”. The following procedures were applied for missing data: if data for a specific year were missing, the rolling mean substitution method was employed. The relative mean substitution method was applied if missing data could not be replaced using the rolling mean substitution method. If missing data remained unreplaced, data from neighbouring years were used as substitutes [95].

For the timeline, we selected four representative years to capture the evolution of the China–ASEAN tourism economic network under different contexts. The year 2010 marks the establishment of the China–ASEAN Free Trade Area, a milestone that accelerated economic development and integration across East Asia and serves as the starting point for this study. In 2013, China introduced the Belt and Road Initiative, which significantly influenced the tourism economic network between China and the ASEAN. In 2015, the upgraded China–ASEAN Free Trade Area agreement was formally signed, further advancing regional integration and growth. Lastly, the COVID-19 pandemic beginning in 2019 had profound impacts on both economic and tourism development [96]. Given the lack of comprehensive tourism and economic data from 2020 onward, we set 2019 as the endpoint of the study. Accordingly, we chose 2010–2019 as our study period, selecting data from 2010, 2013, 2015, and 2019 to assess the strength of tourism economic linkages between China and the ASEAN. This approach allows us to uncover the characteristics of the China–ASEAN tourism economic network before the COVID-19 pandemic and ensures data availability and comparability, providing a basis for future comparative studies.

Recent research suggests that binarising the tourism–economic linkage intensity matrix based solely on actual data may introduce bias and error if a truncated value is chosen when calculating the intensity of economic ties. Drawing on previous studies [97] and considering the characteristics of our research data, we adopted the average linkage value between each country and others as the truncation value to minimise errors. If the linkage value exceeded the truncation value, it was simplified to 1. If it fell below the truncation value, it was simplified to 0. This approach allowed us to filter out weaker connections, resulting in a more refined spatial network.

4. Results

4.1. Analysis of Tourism Economic Connection Strength and Tourism Economic Connection Quantity

The spatial relationship matrix of the tourism–economic connections between these countries was constructed using panel data from 11 countries in the China–ASEAN region and the modified gravity model. The visualisation tool in Gephi 0.9.2 software was employed to draw the network structure for four distinct time points, as illustrated in Figure 2.

The results indicate that from 2010 to 2019, the tourism–economic connections between China and the ASEAN became increasingly close, with a continuous rise in spatial interactions, displaying evident stage characteristics. Broadly, it can be categorised into three stages: (1) The rapid development stage from 2010 to 2013, during which the tourism–economic connections between China and the ASEAN experienced rapid enhancement. The spatial structure shifted from a China-centred point structure to a China-centred mesh structure. (2) The consolidation development stage from 2013 to 2015, where China–ASEAN tourism–economic connections significantly declined compared with those in the previous stage and the spatial network’s closeness decreased noticeably. (3) The sustained growth stage from 2015 to 2019 was marked by a substantial increase in China–ASEAN tourism–economic connections. The spatial structure gradually shifted from a China-centred network structure toward a multi-core spatial structure, with the Philippines–Vietnam connection as the central growth pole. The regional linkage network demonstrated a complex evolutionary trend.

First, regarding the strength of the tourism–economic connection, the influence of the ‘Belt and Road’ initiative led to a fluctuating and increasing trend in the strength of the tourism–economic connection between China and the ASEAN. This trend exhibited an ‘N’-shaped pattern of change. Notably, countries such as China, Thailand, Malaysia, and Indonesia, characterised by faster economic development and richer tourism resources, experienced the most substantial increases in connection strength. China engages in the most frequent economic interactions with all the ASEAN countries in the region. In contrast, economically underdeveloped countries, such as Brunei and Laos, have fewer links with other nations. Specifically (

Table 4. Dyadic pairs between the top six and bottom six of the China–ASEAN tourism–economic connection strength in 2010, 2013, 2015, and 2019.

Years	First Six Bipartite Pairs			Last Six Bipartite Pairs
2010	Rankings	Bipartite pairs	Tourism economic connection strength	Rankings	Bipartite pairs	Tourism economic connection strength
	1	Thailand–China	111,932,542.2888	105	Brunei–Cambodia	0.0032
	2	China–Thailand	94,207,388.3459	106	Cambodia–Brunei	0.0011
	3	Indonesia–China	8,408,158.4925	107	Brunei–Myanmar	0.0006
	4	China–Indonesia	6,890,626.7787	108	Brunei–Laos	0.0006
	5	Malaysia–China	1,195,676.6705	109	Myanmar–Brunei	0.0002
	6	Philippines–China	754,918.6784	110	Laos–Brunei	0.0002
2013	1	China–Thailand	143,915,162.0428	105	Brunei–Cambodia	0.0038
	2	Thailand–China	142,083,508.2517	106	Brunei–Myanmar	0.0033
	3	Indonesia–China	3,739,107.4933	107	Cambodia–Brunei	0.0015
	4	Malaysia–China	3,502,444.2343	108	Myanmar–Brunei	0.0013
	5	China–Indonesia	3,266,522.3392	109	Brunei–Laos	0.0009
	6	China–Malaysia	2,271,249.8812	110	Laos–Brunei	0.0002
2015	1	Thailand–China	27,011,860.7390	105	Brunei–Myanmar	0.0141
	2	China–Thailand	24,761,007.4235	106	Brunei–Cambodia	0.0097
	3	Malaysia–China	12,954,252.7732	107	Myanmar–Brunei	0.0055
	4	China–Malaysia	9,116,115.7138	108	Cambodia–Brunei	0.0039
	5	Philippines–Indonesia	2,684,181.7550	109	Brunei–Laos	0.0025
	6	Indonesia–China	2,304,992.4755	110	Laos–Brunei	0.0007
2019	1	Philippines–Vietnam	159,435,056.1461	105	Brunei–Cambodia	0.0322
	2	Vietnam–Philippines	97,095,209.8407	106	Brunei–Myanmar	0.0244
	3	China–Thailand	52,407,867.2976	107	Cambodia–Brunei	0.0157
	4	Thailand–China	50,721,941.7466	108	Myanmar–Brunei	0.0108
	5	Malaysia–China	45,631,087.9089	109	Brunei–Laos	0.0065
	6	China–Malaysia	35,462,774.6308	110	Laos–Brunei	0.0021

), there were only five China–ASEAN dyadic pairs with a regional tourism–economic connection strength of 1,000,000 in 2010. This figure increased to six pairs in 2013, eight in 2015, and fifteen in 2019. This represents an approximately 1-fold and 3-fold increase from 2015 to 2010, respectively. Notably, in 2019, the Philippines assumed growth pole status, replacing China as one of the core nodes in the spatial network. This shift propelled regional tourism and economic connections toward a multi-core network structure.

Second, the tourism–economic connection quantity aligns closely with the aforementioned changes in connection strength, emphasising a trend toward spatial equilibrium. In 2010, the top three countries (Figure 3) in terms of tourism economic connection quantity were Thailand (11,221,851), China (102,926,439), and Indonesia (8,724,166). Thailand accounted for 49.49% of the total, whereas Brunei, with a negligible contribution, accounted for less than 0.01%. By 2013, China’s share increased to 49.52% and Brunei remained in a position of minimal significance. In 2015, the total regional tourism–economic connection quantity witnessed a 70.15% decline, narrowing the gap between the tourism–economic connection quantities in each country. By 2019, the number of tourism–economic connections in the Philippines and Vietnam surpassed those of China and Thailand, reducing the ratio of extreme values to 34.89%. This indicates a moderation in the polarisation trend of the total tourism–economic connection quantity distribution.

4.2. Characterisation of the Structure of the China–ASEAN Network

4.2.1. Structural Characteristics of Overall and Individual Network

First, we calculate the network density of the China–ASEAN tourism economic network to determine its overall characteristics. The results (

Table 5. Network density of tourism economy connection in China–ASEAN in 2010, 2013, 2015, and 2019.

Years	Network Density	Number of Contacts
2010	0.15	17
2013	0.19	21
2015	0.18	20
2019	0.21	23

) shows that the overall network density of the China–ASEAN regional tourism–economic connections is low but in a state of fluctuating growth, indicating that the degree of China–ASEAN tourism cooperation has been gradually deepening. The spatial network density of the China–ASEAN tourism economic networks in 2010, 2013, 2015, and 2019 was 0.15, 0.19, 0.18, and 0.21, respectively, all less than 0.5. This indicates that the economic linkage is weak, implying that regional tourism economic flow is weak and the overall tourism economic network needs optimisation.

The degree centrality index (

Table 6. China–ASEAN degree centrality in 2010, 2013, 2015, and 2019.

Country	2010 Degree Centrality		2013 Degree Centrality		2015 Degree Centrality		2019 Degree Centrality		Average Value
	Out-Degree	In-Degree	Out-Degree	In-Degree	Out-Degree	In-Degree	Out-Degree	In-Degree
Philippines	0	1	1	3	2	3	2	1	1.63
Cambodia	1	2	1	2	1	1	1	5	1.75
Laos	0	2	0	2	0	2	0	2	1
Malaysia	1	1	1	1	2	1	2	1	1.25
Myanmar	1	3	1	3	1	1	1	4	1.88
Thailand	1	1	1	1	1	1	3	1	1.25
Brunei	0	2	0	2	0	1	0	1	0.75
Singapore	1	2	1	2	0	2	0	2	1.25
Indonesia	0	1	2	1	2	3	1	3	1.63
Vietnam	2	1	3	3	3	3	5	1	2.63
China	10	1	10	1	8	2	8	2	5.25
Average value	1.5	1.5	1.9	1.9	1.8	1.8	2.1	2.1	1.83

) showed that the degree centrality of China–ASEAN tourism–economic connections exhibited a positive fluctuating trend throughout the study period. This suggests an increasing interconnection and collaboration between China and ASEAN countries, establishing China as a ‘leader’ in the regional economic network. The analysis of out-degree and in-degree revealed the following: (1) China consistently held the highest out-degree from 2010 to 2019, highlighting its pivotal role as a hub country in the regional tourism economic network; (2) from 2010 to 2019, there was minimal variation in the inwardness values among the countries. For instance, the maximum difference in 2019 was four, indirectly indicating a negligible spatial disparity in the attractiveness of countries within the region.

In terms of the closeness centrality index, from 2010 to 2019, the out-degree of China–ASEAN regional closeness centrality consistently exceeded the in-degree (

Table 7. China–ASEAN closeness centrality and betweenness centrality in 2010, 2013, 2015, and 2019.

Country	2010			2013			2015			2019
	RP		RB	RP		RB	RP		RB	RP		RB
	In-Degree	Out-Degree		In-Degree	Out-Degree		In-Degree	Out-Degree		In-Degree	Out-Degree
Philippine	11	9	0	14	14	0	16	12	0	10	16	0
Cambodia	14	10	0	18	10	0	10	10	0	24	10	1
Laos	12	9	0	16	9	0	19	9	0	16	9	0
Malaysia	11	11	0	11	11	0	11	28	0	11	28	0
Myanmar	14	10	1	19	10	3	10	10	0	23	10	0
Thailand	10	53	0	10	53	0	11	27	0	11	29	0
Brunei	14	9	0	14	9	0	12	9	0	12	9	0
Singapore	12	10	1	12	10	1	12	9	0	12	9	0
Indonesia	11	9	0	11	16	0	16	12	0	16	11	0.5
Vietnam	11	12	0	14	14	6	16	13	2	10	17	2.5
China	10	100	9	10	100	9	11	33	13	11	33	11
Aggregate value	131	242	11	149	255	19	145	173	15	157	180	15
Average value	12	22	1	14	23	1.73	13	16	1.36	14	16	1.36

Note: RP stands for closeness centrality, RB stands for betweenness centrality.

), suggesting that the capacity for intra-regional tourism diffusion was higher than the tourism integration capacity. Specifically, the overall change in the degree of in-degree countries within the region was relatively modest, reflecting balanced development in the tourism economy. Despite this, the in-degrees of Cambodia and Myanmar increased by approximately 71% and 64%, respectively, from 2010 to 2019. It was also observed that the evolution pattern of the out-degree contrasts with that of the in-degree, forming a spatial structure characterised by clustering with China as the core. China had the highest mean out-degree value (66.5), followed by Thailand (40.5).

The betweenness centrality index (Table 7) revealed that China consistently held the highest betweenness centrality from 2010 to 2019, indicating its critical controlling role in the entire network. Vietnam closely followed, with the second-highest mediator centrality, establishing itself as a critical intermediary node. The distinct characteristics of the tourism resources in China and Vietnam enable them to act as intermediaries and bridges within the network structure. The betweenness centrality degrees for Vietnam, Indonesia, Cambodia, and China increased by 250%, 50%, 100%, and 18.18%, respectively, from 2010 to 2019. This increase in the diffusion capacity of their tourism economies provides an advantageous position for tourism economic exchanges. Conversely, some countries exhibited lower betweenness centrality degrees, or even zero, such as Brunei, whose tourism industry started later and had a smaller influence on the network.

4.2.2. Cooperative Status Index

According to the calculated China–ASEAN Tourism Economic Cooperation Status Index (

Table 8. China–ASEAN tourism economic cooperation status index in 2010, 2013, 2015, and 2019.

Country	2010			2013			2015			2019			Overall Avg	Overall Ranking
	In	Out	Avg	In	Out	Avg	In	Out	Avg	In	Out	Avg
Philippine	4.00	3.03	3.52	5.69	4.84	5.27	6.38	4.78	5.58	3.67	5.96	4.82	4.79	8
Cambodia	5.30	3.67	4.49	6.62	3.67	5.15	3.67	3.67	3.67	9.94	4.00	6.97	5.07	6
Laos	4.78	3.03	3.91	5.96	3.03	4.50	6.84	3.03	4.94	5.96	3.03	4.50	4.46	9
Malaysia	4.00	4.00	4.00	4.00	4.00	4.00	4.00	9.93	6.97	4.00	9.93	6.97	5.48	4
Myanmar	6.03	4.00	5.02	8.29	4.67	6.48	3.67	3.67	3.67	9.09	3.67	6.38	5.39	5
Thailand	3.67	17.88	10.78	3.67	17.88	10.78	4.00	9.34	6.67	4.00	10.52	7.26	8.87	2
Brunei	5.30	3.03	4.17	5.30	3.03	4.17	4.40	3.03	3.72	4.40	3.03	3.72	3.94	11
Singapore	5.12	4.00	4.56	5.12	4.00	4.56	4.78	3.03	3.91	4.78	3.03	3.91	4.23	10
Indonesia	4.00	3.03	3.52	4.00	5.88	4.94	6.38	4.78	5.58	6.54	4.16	5.35	4.85	7
Vietnam	4.00	4.78	4.39	7.69	7.69	7.69	7.04	5.83	6.44	4.50	8.06	6.28	6.20	3
China	6.67	39.67	23.17	6.67	39.67	23.17	8.70	18.11	13.41	8.04	17.44	12.74	18.12	1

), the 11 China–ASEAN countries are classified into the following categories: core leaders, important collaborators, general collaborators, and strategic collaborators.

From 2010 to 2019, China emerged as the central leader in China–ASEAN tourism–economic connections, with the average value of China’s cooperation status index ranking first at 18.12. Moreover, China’s out-cooperation status index consistently maintained a leading position. Important collaborators in the spatial context of China–ASEAN tourism–economic connections include Thailand, Malaysia, and Cambodia. Notably, Thailand’s cooperation status index was 8.87, ranking second only to China’s. Over the period 2010–2019, Malaysia and Cambodia experienced the most substantial growth in the cooperation status index, with increases of 74% and 55%, respectively. Myanmar and Vietnam are general cooperators in the spatial pattern of China–ASEAN tourism–economic connections. Vietnam (6.20) and Myanmar (5.39) are in the middle of the overall cooperative status index but exhibit lower growth rates. Indonesia, the Philippines, Laos, Singapore, and Brunei are strategic partners in the spatial dynamics of China–ASEAN tourism–economic connections with cooperation status indices of 4.85, 4.79, 4.46, 4.23, and 3.94, respectively. Their cooperation status indices suggest that urgent improvements are required for the autonomous capacity of tourism cooperation.

4.3. Analysis of Influencing Factors

4.3.1. Analysis of QAP Correlations

A QAP correlation analysis used the previously calculated matrix of differences in influencing factors as explanatory variables and the matrix of tourism economic connection intensity among countries as the response variables. In this analysis, 5000 random permutations were selected to investigate the structural evolution of the China–ASEAN tourism economic network. The results in

Table 9. QAP correlation analysis.

Independent Variable	2010		2013		2015		2019
	Correlation Coefficient	p-Values	Correlation Coefficient	p-Values	Correlation Coefficient	p-Values	Correlation Coefficient	p-Values
Institutional distance difference matrix	−0.107	0.096 *	−0.096	0.169	−0.146	0.015 **	−0.134	0.056 *
Geographic distance difference matrix	0.204	0.076 *	0.188	0.073 *	0.323	0.040 **	0.134	0.150
Economic distance difference matrix	−0.037	0.000 **	−0.036	0.000 ***	−0.060	0.000 ***	−0.063	0.000 ***
Tourism resource endowment difference matrix	−0.029	0.075 *	0.000	0.568	−0.035	0.052 *	−0.014	0.135
Tourism policies and regulations difference matrix	0.021	0.063 *	0.000	0.551	0.018	0.082 *	−0.045	0.063 *
Tourism promotion and cooperation difference matrix	0.031	0.083 *	0.000	0.356	0.035	0.089 *	−0.032	0.149
Geopolitical risk difference matrix	−0.001	0.404	−0.001	0.214	−0.020	0.102	0.016	0.275

Note: *, **, and *** indicated significance at 0.1, 0.05, and 0.01 levels, respectively.

indicate that, excluding geopolitical risk, the other independent variables exhibited varying degrees of correlation with the China–ASEAN tourism economic–spatial network across different years. This suggests that these variables contribute to forming the China–ASEAN tourism–economic relationship network to a certain extent, indirectly validating the rationality of the selected influencing factors.

The data analysis (Table 9) demonstrates that the significant correlations between the matrices of individual variables and the spatial correlation matrix in the tourism economy varied across different years. This reflects the differences in the primary factors influencing the formation of the spatial correlation network in the China–ASEAN tourism economy for each year. Consequently, analysing drivers for the spatial correlation network of China–ASEAN tourism–economic connections should be dynamic, considering inter-annual changes rather than focusing solely on a single year. Furthermore, it is necessary to explore the regression relationship between the influencing factors and the spatial correlation matrix in tourism–economic connections to mitigate the impact of multicollinearity. More specifically, building upon the QAP correlation analysis, the influence of each factor should be investigated using QAP regression analysis.

4.3.2. Analysis of QAP Regression

The regression results (

Table 10. QAP regression analysis.

Independent Variable	2010		2013		2015		2019
	Standardized Coefficient	p-Values	Standardized Coefficient	p-Values	Standardized Coefficient	p-Values	Standardized Coefficient	p-Values
Institutional distance difference matrix	−0.106	0.087 *	−0.094	0.134	−0.138	0.023 **	−0.124	0.062 *
Geographic distance difference matrix	0.204	0.075 *	0.187	0.076*	0.319	0.034 **	0.129	0.136
Economic distance difference matrix	−0.007	0.636	−0.013	0.596	−0.021	0.525	−0.028	0.558
Tourism resource endowment difference matrix	0.035	0.217	−0.004	0.218	−0.031	0.209	−0.018	0.350
Tourism policies and regulations difference matrix	−0.001	0.551	0.000	0.429	−0.013	0.125	−0.043	0.070 *
Tourism promotion and cooperation difference matrix	0.068	0.090 *	−0.005	0.205	0.017	0.316	−0.018	0.291
Geopolitical risk difference matrix	0.009	0.129	0.000	0.386	0.006	0.258	0.034	0.169
R-square	0.054		0.045		0.126		0.039
Adjusted R-square	−0.011		−0.021		0.066		−0.027
P (R-square)	0.044		0.054		0.026		0.041

Note: * and ** indicated significance at 0.1 and 0.05 levels, respectively.

) reveal that despite the relatively small coefficient of determination (R-Square) at all research stages, it is highly significant (i.e., P (R-Square) is below the 0.1 significance level for all cases). This implies that these models, as a whole, are reasonably valid to a certain extent [68,98]. In summary, the impacts of institutional and geographic distances were more pronounced, whereas other factors exerted less significant influence.

Specifically, within the study period, the institutional distance difference matrix was significant in certain years. In years when the institutional distance difference matrix was significant (2010, 2015, and 2019), its regression coefficient consistently exhibited a negative trend. This indicates that a smaller institutional distance between countries is more favourable for forming tourism economic networks. Notably, in 2019, only institutional distance and tourism policies and regulations were significant, emphasising the increasing importance of institutional factors in influencing the development of tourism economic networks. This provides partial support for Hypothesis 2. Essentially, a smaller institutional distance between countries may alleviate tourists’ risk perceptions [63,99] and facilitate the establishment of tourism economic networks. This partially supports Hypothesis 4.

The geographic distance difference matrix was significant in specific years (2010, 2013, and 2015) and its coefficient consistently exhibited a positive trend. The more significant the difference in geographical distance, the more it promotes the formation of tourism economic connection networks. Simultaneously, in cases of significant geographic differences in distance, countries with better transportation conditions were not constrained by the distance; instead, this turned advantageous by facilitating better tourism exchanges with other countries while contributing to the formation of spatial connection networks centred around that country. The difference matrices of economic distance and tourism resource endowment were not significant in any year. This indicates that although economic development and tourism resources are related to the network of tourism–economic connections, their impact on the network is relatively limited. Consequently, Hypothesis 5 was rejected, whereas Hypothesis 4 was partially rejected.

The geopolitical risk difference matrix was not significant in any study year. It is particularly noteworthy that although geopolitical risk does not have a significant impact on the formation of tourism–economic relationship networks, it does influence the significance of other factors. Specifically, including geopolitical risk indicators makes tourism policies and regulations (2019) and tourism promotion and cooperation (2010) significantly affect the formation of tourism–economic relationship networks. This demonstrates that the effect of geopolitical risk on the spatial network of tourism–economic relationships between countries is indirect rather than direct. Therefore, Hypothesis 1 was partially supported. Similar to Hypothesis 2, Hypothesis 3 was partially supported.

5. Discussions

Using the case study of China–ASEAN from 2010 to 2019, this paper explores an important topic related to tourism economic networks and geopolitics. This study expands existing literature in several aspects. First, it pioneers the inclusion of geopolitics in the study of tourism economic networks, examining the impact of geopolitical risks on tourism economy from a network connectivity perspective, thereby broadening current literature. While previous research on cross-border tourism [39,40] has considered geopolitical risks, it typically does so from a supply–demand perspective. Furthermore, although studies emphasize the value of geopolitical risks in revealing tourism demand and economic aspects, they often focus on the impact on domestic or individual country tourism economies [17,23]. Our research not only empirically constructs cross-border regional tourism economic networks but also addresses Mostafanezhad’s call to link tourism with broader regional geopolitical practices such as cooperation, exchange, and national development [100], thereby contributing to the existing literature.

In addition, this study reveals tourism economic network characteristics under significant geopolitical events at the cross-border level, thereby broadening the application context of tourism economic networks in the literature. Existing literature discusses how major political events often bring fluctuations to tourism development. For instance, Liu et al. found that political instability within ASEAN countries significantly impacts the growth rate of their tourism markets [101]. Yin et al. demonstrated that the establishment of the China–ASEAN Free Trade Area has propelled the development of tourism economy [84], while Li et al. indicated that the Belt and Road Initiative (BRI) has increased inbound tourism revenue for countries along the BRI routes by enhancing political and economic connectivity [102]. Liu et al. also noted a rationalization trend in Chinese tourist shopping behaviour after 2014, contributing to a slowdown in tourism economic growth between China and ASEAN countries [24]. Our study validates these findings by confirming distinct phased characteristics in the China–ASEAN tourism economic network. Specifically, we include temporal nodes of significant political events in our case studies, offering a more comprehensive and systematic analysis that serves as a reference for future studies exploring the impacts of major geopolitical events on tourism.

Furthermore, this study extends the application of distance decay theory. Existing literature discusses distance decay primarily in terms of tourism demand [103,104] and occasionally within the spatial dimension of the tourism market [105]. In contrast, our study applies distance decay theory to tourism economic networks and constructs a multidimensional distance framework for this purpose. Our findings both support distance decay theory, indicating that closer institutional distances lead to tighter connections in tourism economic networks, and respond to some scholars’ assertions that economic and social development, which they argue leads to “time–space compression,” weakens the influence of distance decay [106]. Specifically, our research results show that geographic distance has a positive impact on tourism economic networks.

6. Conclusions, Limitations and Future Research

This study explored the spatial structure and influencing factors of the China–ASEAN tourism economic network. First, based on the modified gravity model and social network analysis, we constructed the tourism economic network between China and the ASEAN and analysed the network structure characteristics. In addition, the QAP method was employed to validate the factors influencing the formation of a tourism economic network. The main conclusions are as follows.

Firstly, we found that the China–ASEAN tourism economic network exhibits an overall “N”-shaped fluctuating growth trend with distinct phases. Specifically, the period from 2010 to 2013 was characterized by rapid development, 2013 to 2015 saw consolidation, and from 2015 to 2019 there was sustained growth. At the same time, spatially, the network gradually evolved from a point-like structure towards a multi-core network. However, in terms of network characteristics, China displayed distinctive individual characteristics and occupied a core leadership position within the network. Additionally, in terms of power roles, China–ASEAN tourism economic cooperation can be categorized into four roles: core leaders, important collaborators, general collaborators, and strategic collaborators. Despite a recent decline in China’s core leadership position, it continues to play a crucial role in the integration and diffusion within the network.

Regarding influencing factors, we found that geopolitical factors do not independently impact the tourism economic network but rather interact through associations with tourism policies, regulations, promotion, and cooperation. Among the three dimensions of distance we examined, the effects of institutional distance and geographical distance were more significant. This is prominently reflected in the tourism economic network following the “distance decay” theory in institutional terms—where smaller institutional distances correlate with closer tourism economic network associations. Meanwhile, geographical distance has a noticeable positive effect on the tourism economic network.

Based on the above conclusions, and in order to promote regional tourism economic cooperation and further advance the implementation of a high-quality tourism development strategy, this paper proposes the following policy recommendations.

Firstly, communication networks should be improved. While the China–ASEAN tourism economic cooperation network is taking shape, significant differences still exist among individual characteristics and network effects of China and ASEAN countries. Therefore, it is necessary to continue enhancing and expanding platforms for tourism cooperation and exchange between China and ASEAN countries, continually increasing the scale of two-way tourism exchanges to promote the integrated development of China–ASEAN tourism.

Secondly, we have established a robust empirical relationship between tourism economic networks and distance. Governments and businesses need to focus on distance, particularly institutional distance, and establish cooperation mechanisms to reduce barriers caused by distance to tourism. Governments and destination management organizations often attribute tourism economic growth to their importance.

Lastly, geopolitics should be considered. It is crucial to address geopolitical issues such as the South China Sea territorial disputes and their impact on maritime tourism cooperation among countries and regions across the South China Sea. Leveraging the geostrategic advantages of China–ASEAN, emphasis should be placed on managing destinations through regional cooperation methods. This involves prioritizing factors that ensure geopolitical stability and necessitates the formulation of effective policies and contingency strategies to mitigate the impact of tensions on tourism economies.

This study had some limitations that should be addressed in future research. First, constrained by data availability, we analysed the characteristics of the tourism economic connection network in China–ASEAN from 2010 to 2019. If relevant post-COVID-19 pandemic data can be obtained and an analysis of tourism economic cooperation could be conducted of before and after the pandemic, further nuanced conclusions may be drawn. Second, owing to this article’s length constraint, we could not delve deeper into how geopolitical risk influences the strength of China–ASEAN tourism economic networks through its association with tourism policies and cooperation. Therefore, future research may consider adopting a geopolitical theoretical system as the research framework, integrating the research perspectives of micro-tourists and macroeconomics. An in-depth analysis of the indirect conduction mechanism of geopolitical risk in China–ASEAN tourism economic networks may be explored. Future studies should consider employing more effective methods to address this issue.