Research on Path Configuration and Mechanism of Digital Economy-Driven Resilience Enhancement in Tourism Industry

Abstract

The deep integration of the digital economy and the tourism industry has reconstructed the ecological pattern of the industry, and the tourism industry urgently needs to explore ways to improve resilience in the post-epidemic era to cope with uncertainties and risks. Relying on the FIIF framework, this study uses the statistical data of 30 provinces in China to verify the path configuration of the digital economy on the resilience improvement of the tourism industry and the synergistic effect between various variables, aiming to reveal the multi-configuration effect of the digital economy, driving the resilience improvement of the tourism industry, analyzing the regional differentiated path selection mechanism, and providing a theoretical framework and practical guidance for the resilience improvement and digital transformation of the tourism industry. The results show that (1) the variables of “digital infrastructure” and “digital innovation expenditure” constitute the necessary conditions for the high-resilience level of the tourism industry, and “digital talent shortage” and “digital innovation investment” are the key factors for the formation of low-resilience configuration. (2) There are three configuration paths for high resilience, which are manifested in the two types of “digital infrastructure and digital innovation driven type” and “digital infrastructure and digital industry driven type”. The low-resilience configuration has three paths and has an asymmetric relationship with high-resilience configuration paths. (3) The tourism industry’s resilience configuration in the eastern and central–western regions shows significant differences.

1. Introduction

As a strategic pillar industry of the national economy, tourism is an important engine for China’s high-quality development. However, under the impact of the COVID-19 pandemic, the tourism system is facing many risks and uncertainties, and the tourism market is experiencing the recurring cycle of “shock-recover-develop” [1]. At the same time, China is experiencing a critical period of socio-economic development and transformation in the new era, and the tourism industry is also facing the challenges of transformation and upgrading. Under the new development paradigm, the digital economy empowers the transformation and upgrading of traditional industries, constantly fostering new industries, new business forms, and new models [2]. Big data, artificial intelligence, and cloud computing are constantly applied to the traditional tourism industry to help solve issues like resource mismatch, market distortion, and information asymmetry, and are constantly stimulating the vitality and resilience of the tourism industry. Under endogenous chronic pressure and exogenous acute impact, facing the trend of deep integration of the digital economy and tourism, how to transform the tourism industry chain with the help of the digital economy and forge industrial resilience is an important issue for high-quality development. To address this issue, three critical questions still need to be answered. First, how do digital economy elements synergistically configure to drive the resilience of the tourism industry? Second, what differentiated choices should various regions in China make to enhance the resilience of the tourism industry? Third, how should policymakers optimize factor allocation in a localized manner? By answering these questions, not only will new theoretical paradigms be provided for the study of the digital economy and tourism resilience, but operational regional development strategies will also be formed, aiding the resilience transformation of China’s tourism industry in the post-pandemic era.

From the perspective of existing research, the research on tourism resilience presents a multi-dimensional theoretical perspective and empirical methods. In terms of conceptual understanding, Luthe defines tourism resilience as “the ability of the tourism system to cope with stress” [3], emphasizing the immediate responsiveness of the system under shocks. Bowen proposed that “tourism resilience is the ability of the tourism industry to recover to an ideal state” [4], which focuses on the stability of resilience and the ability to restore paths. Prayag further expands this to “tourism resilience is the ability of the tourism industry to withstand shocks and restore its growth path” [5], which clearly highlights the adaptability and innovation reconstruction ability of the tourism industry’s resilience. Obviously, although the three scholars have different expressions of the concept of tourism resilience, their core is similar, that is, “the speed and degree of recovery of the tourism system in the face of internal and external shocks or disturbances, and the ability to reorganize tourism resources and adjust its own structure to adapt to the new external environment after the shock, so as to maintain the sustainable and healthy development of the tourism system”. In terms of resilience measurement, Wang et al. proposed a static measurement based on the four-dimensional indicator system of “resistance-recovery-update-reconstruction” [6]. Ye et al. proposed a resilience deconstruction framework of “supply-demand-structure-management” [7], emphasizing the dynamic adaptation process. Fang Yelin et al. evaluated the resilience of the inbound tourism industry from the perspective of industrial specialization and industrial diversification [8]; Zhu Jingmin et al. constructed an index system for the resilience of the urban tourism environmental system that includes three aspects: economy, society, and ecology [9]. The selection of different indicators provides a selective perspective for resilience measurement, but it is also questioned whether the theoretical basis for the selection of measurement indicators is insufficient. In terms of measurement methods, there are various research options. Pang et al. used the entropy method to construct economic resilience indicators with the proportion of tourism revenue and per capita GDP as the core, but did not cover the socio-ecological dimension [10]. Dong et al. use the DEA-Tobit model to measure resilience efficiency and focus on resource allocation optimization, but it is difficult to capture nonlinear dynamics [11]. Du et al. used geographic detectors to analyze the spatial differentiation of resilience, which can identify regional differences but ignores the synergistic effect of the factors [12]. These differences reflect the multidisciplinary nature of tourism resilience research but also lead to insufficient theoretical integration. In addition, relevant studies also believe that the application of digital technology, economic development, climate change, tourism resource endowment, tourism planning and development, and government policies are important factors affecting tourism resilience [13,14,15,16]. These factors involve not only external risk factors but also the tourism industry itself. Therefore, it is of great significance to scientifically evaluate the resilience of regional tourism and its driving factors to promote the construction of resilience capacity for the regional tourism industry.

The enabling effect of the digital economy tourism industry has also been widely verified. Sharma et al. found that digital technology innovation significantly improves the resilience of the tourism economy after the epidemic [17]. Sheng Yanchao et al. confirmed that the digital economy directly enhances the anti-risk ability of enterprises by optimizing the supply chain [18]. Tang et al. revealed that the digital economy has a positive effect on China’s tourism resilience [19], but only linear regression is used to verify the overall effect. In the analysis of the influencing mechanism, Tang Rui found that the digital economy can promote the high-quality and coordinated development of the cultural tourism industry by promoting the innovation of cultural tourism products and enhancing market potential [20]. Xia Jiechang et al. [2] and Cai Shangwei [21] believe that the digital economy has caused profound changes in the organization and industrial structure of the tourism industry, and digitalization is an important support for the high-quality development of the culture and tourism industry. Zhu Jingmin [9] found that the digital economy can promote the resilience of the tourism economy by improving the level of tourism economic growth in the dimension of resistance, the technology carrier in the dimension of resilience, and the human capital in the dimension of restructuring capacity. Although it has become an academic consensus that the digital economy empowers the resilience of the tourism industry [17,18], there are still significant limitations in the existing research. First, most of the literature focuses on the linear relationship between the digital economy and tourism resilience, but ignores the nonlinear interaction and configuration effect between the factors. Second, most of the existing studies have explored the mechanism from a holistic perspective, but the impact of inter-regional heterogeneity of digital economy development on the path of resilience improvement has not been systematically revealed. Third, the existing analysis does not allow enough synergy between the internal elements of the digital economy, which leads to an insufficient explanation of the complexity of the relationship between the factors. In general, the existing tourism disciplines have not paid enough attention to how the digital economy can empower the resilience of the tourism industry, and the path and mechanism of the digital economy to drive the resilience of the regional tourism industry are not clear.

This study intends to continuously deepen and improve the research through the following three aspects. First, the fuzzy set qualitative comparative analysis (fsQCA) method is used to analyze the relationship between the digital economy and the resilience of the tourism industry, which can effectively reveal the synergistic and substitution relationships between multiple variables, identify the asymmetric impact of different condition combinations on the results, and break through the dependence of traditional methods on a single causal chain. The second is to use the four-dimensional collaborative framework of “digital infrastructure-digital industry-digital innovation-digital finance” (FIIF) to systematically integrate the four core elements of the digital economy, clarify the mapping relationship between each element and the resilience dimension (resistance, recovery, adaptation, and innovation), and reveal the synergistic mechanism between the elements. The third is to deepen the study of regional heterogeneity, conduct configuration analysis based on sub-samples, reveal the differences in the paths of Eastern, Central and Western China, provide a precise policy toolbox for policymakers in the eastern, central and western regions of China, and provide a basis for precise policy implementation in different resource-endowed regions.

2. Theoretical Framework

The development of the digital economy has become a high point in the information age for all countries in the world to improve the quality of economic development and compete for the right to speak in the international economy. This paper draws on the “FIIF” framework of digital economy proposed by Xu Xianchun [22] and deconstructs the theory of digital economy-driven resilience improvement of the tourism industry in combination with the current development of the digital economy. The core logic under the framework is as follows: digital infrastructure as the technical foundation of the digital economy, digital industry as the specific manifestation of the industrialization of data elements, digital innovation capability as the foundation of the sustainable development of the digital economy, and digital finance as the development guarantee of the digital economy. It is clear that the four elements of the digital economy under the FIIF framework are not isolated but form a closed-loop mechanism for resilience improvement through dynamic collaboration and have a close correspondence with the four dimensions of resilience (resistance, recovery, adaptation, and innovation) of the tourism industry specifically.

First, digital infrastructure serves as a technology foundation to directly enhance the resilience of the tourism system by providing high-speed networks, smart platforms, and 5G application scenarios. Digital infrastructure can improve the level of networking, intelligence, and collaboration of tourism infrastructure by promoting the transformation and upgrading of tourism emergency command centers, smart gates, smart screens in scenic spots, ticketing systems, electronic explanations, etc. Digital infrastructure is also beneficial to the construction of smart tourism platforms, which provide one-stop services by integrating all types of tourism resources, optimizing resource allocation, and improving the quality of tourists’ travel experience. In addition, the coverage and optimization of 5G networks, as well as the application of 5G technology in video surveillance, real-time transmission, and unmanned driving, can also provide new service scenarios and experiences in the field of tourism and enhance the resilience of the adjustment of the tourism industry.

Second, the digital industry (including telecommunications, e-commerce, and information industries) will enhance the resilience of the system by promoting the digital integration of the tourism industry chain. The in-depth integration of the telecommunication industry and the tourism industry promotes the development of intelligent tourism. By providing a high-speed and stable network connection, the telecommunication industry enables tourism information to be quickly and accurately delivered to tourists and tourism enterprises, helps tourism enterprises realize the intelligent management of tourism resources, and improves the quality and efficiency of tourism services; the e-commerce industry provides tourism enterprises with more convenient and broader sales channels by building an online tourism sales platform. Through the use of big data analysis technology, e-commerce enterprises can gain a deep understanding of tourists’ tourism needs and preferences, develop new tourism products that are more in line with market demand, and better realize the seamless and efficient circulation of tourism products; the information industry provides more intelligent and convenient services for the tourism industry. Using information technology, tourism enterprises can realize the transformation from a traditional service mode to a digital and intelligent service mode. The information industry also plays an important role in guaranteeing tourism information security. By using encryption technology, firewalls, and other information security means, the information industry can ensure the confidentiality, integrity, and availability of tourism information, protect the legitimate rights and interests of tourists and tourism enterprises, and maintain the stability and healthy development of the tourism market.

Third, digital innovation is the core driving force, and the adaptability of the tourism industry is strengthened through technology research and development and business restructuring. On the one hand, increasing investment in research on digital technologies, development, and applications can accelerate the digitalization of tourism products and services, improve the operational efficiency of tourism enterprises, reduce costs, and provide more personalized and efficient services. For example, the immersive experience created by virtual reality technology can flexibly adapt to changes in tourist demand, and artificial intelligence algorithms can be used to optimize passenger flow prediction, so that tourism enterprises can dynamically adjust the service supply to maintain competitiveness in a fluctuating environment. On the other hand, utilizing digital technologies such as the internet, mobile technology, artificial intelligence, and virtual reality to create new tourism products and services can improve the sense of tourist experience and increase operational efficiency. In addition, the improvement of digital technology’s innovation ability can help the tourism industry better respond to market changes, such as during the epidemic, by developing online tourism, virtual tourism, and other new forms to adapt to changes in tourists’ demand, and enhance the industry’s ability to adjust and innovate.

Finally, digital finance can ensure the innovation capacity of the tourism industry by broadening financing channels and optimizing risk management. In terms of service breadth, digital finance breaks the geographical limitations of traditional financial services, making it possible to finance tourism projects in remote areas and international tourism cooperation projects, enhancing their resistance and resilience in the face of external shocks. Digital finance has also greatly improved the experience of tourism consumers. Through digital financial tools such as mobile payments, online booking, and virtual currencies, consumers can plan their trips, pay for expenses, enjoy services, leave reviews, and obtain feedback more conveniently. In terms of service depth, digital finance, through big data, artificial intelligence and other technical means, can deeply analyze the operating conditions of tourism enterprises, market trends, and consumer behavior, providing more accurate risk assessment and management services for the tourism industry and enhancing the resilience of the tourism industry against shocks. Innovative products of digital finance, such as travel insurance, prepaid cards, travel credit, etc., not only enrich the payment and protection choices of tourism consumers but also improve the efficiency of capital flow and risk resistance of each link in the tourism industry chain.

In the context of regional differences, the synergies of the various elements under the FIIF framework are particularly significant. In economically developed areas, relying on complete infrastructure and active financial policies to achieve “two-wheel drive” will help the tourism industry to achieve innovative development faster. Well-developed digital infrastructure provides rich application scenarios for digital innovation, while developed digital finance provides sufficient funding for innovative projects, which promote the continuous innovation and development of the tourism industry and enhance the resilience of the tourism industry. In economically underdeveloped regions, the resilience of the tourism industry can be achieved through the “dislocation and complementarity” of industry and innovation (such as the telecommunications industry to make up for the shortcomings of infrastructure). These regions can use the development of the telecommunications industry to make up for the lack of digital infrastructure, and on this basis, carry out digital innovation, promote the digital transformation of the tourism industry, improve the adaptability and resilience of the tourism industry, and then, enhance the resilience of the tourism industry. In short, digital infrastructure provides an operating platform for the digital industry, the development of the digital industry provides practical scenarios and demand orientation for digital innovation, the results of digital innovation promote the innovative application of digital finance, and digital finance provides financial support and risk protection for the other three elements, which interact and influence each other to jointly shape the resilience of the tourism industry. See Figure 1.

3. Methods

3.1. Method Selection

This study uses fuzzy-set qualitative comparative analysis (fsQCA) to explore the complex causal mechanisms of the digital economy, driving the improvement of tourism industry resilience. Adopting this method is primarily based on three considerations. First, fsQCA is suitable for studies with 15~50 medium sample sizes [23], which is highly consistent with the data volume of 30 provinces in China and avoids the limitations of traditional regression analysis on small samples. Second, the digital economy drives the resilience of the tourism industry, involving the nonlinear synergy or substitution relationship of multiple factors (infrastructure, industry, innovation, and finance), and fsQCA can effectively identify the specific combination structure of the conditional variables, reveal the synergy and interaction between multiple variables, and help this paper identify and reveal the variable relationship between the digital economy and the resilience of the tourism industry. Third, fsQCA can effectively reveal the differences in development paths in different regions through a configuration analysis by sub-sample, which provides an effective basis for differentiated policy formulation and the regional adaptability of different provinces in the Chinese context.

3.2. Sample and Data

The “Four Forces” model constructed by Fang Yelin [24] is considered to be a powerful tool for measuring the resilience of the tourism industry in different provinces in the Chinese context. Based on the four dimensions of “resistance-resilience-adaptability-innovation”, the model comprehensively considers the ability of the tourism industry to maintain its own stability and quickly recover to its original state in the face of internal and external shocks, as well as to actively adjust its structure and develop new development models in a changing environment. The higher the level of regional economic development, the richer the resource endowment, which is considered to have more advantages in resisting risks. Sound tourism reception facilities, abundant tourism practitioners, and a beautiful environmental foundation are conducive to the rapid recovery of the tourism industry after the impact. Complete fixed-asset investment and diversified tourist reception capacity will help the adjustment and optimization of the tourism industry. The abundant talent pool and advanced tourism teaching level are conducive to the innovation and development of the tourism industry. Drawing on the practice of Fang Yelin [24], a multi-index evaluation system of tourism economic resilience composed of 10 secondary indicators is constructed from the four dimensions of “resistance-resilience-adjustment-innovation”, and the TOPSIS model based on the entropy method is used to measure and avoid the bias caused by subjective weighting.

Under the “FIIF” framework, the digital economy is manifested in the coordinated development of digital infrastructure, digital industry foundation, digital innovation potential, and digital inclusive finance. Based on the previous theoretical analysis and with reference to scholars such as Liu Jun [25] and Wang Jun [26], digital infrastructure and a digital talent pool are used to evaluate the digital foundations of different provinces. Use the development data on the information industry, telecommunications industry, and e-commerce industry to measure the development level of the digital industry. Measure the digital innovation potential of the region by using digital innovation expenditure and digital technology innovation capabilities. Digital financial inclusion is measured in terms of depth and coverage of finance. Taking 30 provinces (municipalities) and autonomous regions on the Chinese mainland as samples (due to the lack of statistical data in Tibet, this study is not used as a sample for this study) and considering that the impact of digital economy on the resilience of the tourism industry has a certain time lag, in order to avoid the singleness of the data, the result variables from 2021 and 2022 are selected to match the mean values of the conditional variables from 2021 and 2020, and the values of the conditional variables involving multiple indicators are measured by principal component analysis.

Table 1. Measurement index system of digital economy and tourism industry resilience.

Variables	Primary Indicators	Secondary Indicators	Indicator Content and Unit	Data Sources
Resilience level of tourism industry	Tourism industry resistance	The richness of tourism resources	Number of national A-level scenic spots (number)	‘China Cultural Relics and Tourism Statistical Yearbook’
		Tourism economic development index	The proportion of tourism income to GDP (%)
		Regional economic development index	GDP per capita (million yuan)	‘China Statistical Yearbook’
	Tourism industry recuperability	Greening comprehensive index	Forest Coverage (%)	‘China Forestry Statistics Yearbook’
		Tourism Facilities Index	The proportion of star hotels/the number of hotels (%)	‘China Cultural Relics and Tourism Statistical Yearbook’
		Tourism employment index	The proportion of tourism practitioners/the number of practitioners (%)
	Tourism industry adjustment	Tourism economic reconstruction ability	Proportion of fixed-asset investment (%)
		Tourism reception adjustment ability	Number of inbound tourists/total tourist reception
	Tourism industry innovation	Tourism talent reserve situation	Number of students in tourism colleges/universities
		The development level of tourism education	The number of tourism colleges and higher vocational colleges
Development level of digital economy	Digital foundation	Digital infrastructure	Mobile phone switch capacity, internet broadband access port, internet domain name number, long-distance optical cable line length	‘China Statistical Yearbook’, ‘China City Statistical Yearbook’
		Digital talent pool	The number of R&D personnel, information transmission, software, and information technology service industry practitioners in urban units
	Digital industry development	Information industry development	Mobile phone penetration rate, internet broadband penetration rate
		Telecommunication industry development	Telecommunications business volume, software business income
		E-commerce industry development	Per capita postal business income (million yuan), e-commerce sales
	Digital innovation potential	Digital innovation expenditure	Science and technology expenditure (billion yuan)
		Digital technology innovation	Comprehensive index of regional enterprise innovation	‘Regional innovation capability evaluation report’
	Digital inclusive finance	Digital financial coverage breadth	Digital inclusive financial coverage breadth index	‘Peking University Digital Inclusive Finance Index’
		Digital financial coverage depth	Digital inclusive financial usage depth index

shows the selection of indicators and data sources at each level.

3.3. Measurement and Calibration

In fsQCA, each condition and result can be regarded as a set, and each case has membership scores in all sets, and calibration is the process of assigning set scores to cases. Calibration is the process of assigning set scores to the cases. According to case realities and the distribution of variable values, the three breakpoints of “full membership”, “crossover point”, and “full non-membership” are determined as “anchors” and then calibrated into fuzzy sets with values from 0 to 1. Following the method of Schneider C Q [27], the three calibration points of complete membership, intersection, and complete non-membership for the conditional variables and the result variables were set, respectively, as the 90th percentile, 50th percentile, and 10th percentile of the sample data. Based on the fsQCA3.0 software, the result variables and the conditional variables were calibrated, and the results are shown in

Table 2. Calibration of outcome variables and condition variables.

Variable Classification	Indicator Description	Variable Name and Description		Fully in	Crossover	Fully out
Outcome variable	Tourism industry resilience	Y	Tourism industry resilience	0.754	0.301	0.017
Condition variable	Digital foundation	X1	Digital infrastructure	0.514	0.177	0.031
		X2	Digital talent pool	0.549	0.201	0.062
	Digital development	X3	Information industry development	0.620	0.288	0.075
		X4	Telecommunication industry development	0.711	0.370	0.104
		X5	E-commerce industry development	0.684	0.315	0.083
	Digital innovation ability	X6	Digital innovation expenditure	4002.2	901.80	6.0
		X7	Digital technology innovation	61.665	25.234	14.643
	Digital finance level	X8	Digital financial coverage breadth	433.42	372.719	292.06
		X9	Digital financial coverage depth	510.69	361.409	262.72

.

4. Empirical Analysis

4.1. Necessity Analysis of Single Condition

After checking for multicollinearity, it was found that the variance inflation factors were all below 5, far less than the critical value of 10, and none of the correlation coefficients exceeded 0.5. Therefore, the problem of multicollinearity can be preliminarily excluded, which provides a basis for further testing the rationality of the theoretical model. Identifying and testing whether there are necessary conditions in the condition variables that constitute the outcome variable is called the necessity test. If there is a necessity condition, it means that there is a condition variable that plays a necessary role in promoting the resilience enhancement of the tourism industry. This test is mainly measured by the consistency index and coverage index, and it is usually considered that, when the consistency test value is higher than 0.9, the condition variable constitutes a necessary condition for the outcome variable. Coverage is a measure of the importance of the combination of condition variables, which is usually considered to be no less than 0.5 [24,27]. The results of the necessity analysis (

Table 3. Results of single-variable necessity analysis.

Variables	Variable Description	High Tourism Industry Resilience		Low Tourism Industry Resilience
		Consistency	Coverage	Consistency	Coverage
X1	Digital infrastructure	0.9633	0.9096	0.8844	0.7758
~X1	Digital infrastructure	0.5401	0.5821	0.5173	0.4601
X2	Digital talent pool	0.8865	0.7876	0.9230	0.9144
~X2	Digital talent pool	0.6162	0.5620	0.5475	0.8967
X3	Information industry development	0.5259	0.5120	0.7118	0.6819
~X3	Information industry development	0.4233	0.6107	0.4379	0.4581
X4	Telecommunication industry development	0.8307	0.8184	0.7055	0.5908
~X4	Telecommunication industry development	0.5465	0.4011	0.4798	0.4825
X5	E-commerce industry development	0.7111	0.6456	0.8803	0.4810
~X5	E-commerce industry development	0.5588	0.5136	0.4297	0.5142
X6	Digital innovation expenditure	0.9459	0.8436	0.9268	0.9138
~X6	Digital innovation expenditure	0.5662	0.5103	0.6117	0.5684
X7	Digital technology innovation	0.4814	0.5474	0.6442	0.7479
~X7	Digital technology innovation	0.3179	0.4312	0.3764	0.5342
X8	Digital financial coverage breadth	0.8001	0.8586	0.8023	0.8264
~X8	Digital financial coverage breadth	0.4893	0.5748	0.4527	0.5232
X9	Digital financial coverage depth	0.5836	0.6603	0.5343	0.4532
~X9	Digital financial coverage depth	0.3173	0.4531	0.3126	0.4532

) show that the consistency of “Digital Infrastructure” and “Digital Innovation Expenditures” is higher than 0.9, indicating that the two variables constitute a high level of resilience in the tourism industry. Variables constitute the necessary conditions for a high level of resilience in the tourism industry. The consistency levels of “digital talent pool”, “telecommunication industry development”, and “financial coverage breadth” are all higher than 0.8, indicating that these three factors have a significant impact on tourism industry resilience. In the low-level tourism industry resilience necessity conditions analysis, “digital talents” and “digital innovation expenditure” are above the threshold of 0.9, indicating that these two conditions are necessary conditions for low tourism industry resilience.

Although the single condition variables have a certain degree of explanatory power for the results, they still cannot effectively explain all the triggering conditions for digital economy-driven resilience enhancement in the tourism industry. Therefore, in order to more comprehensively explore the mechanism of the digital economy’s role in promoting tourism industry resilience, it is necessary to further analyze the configuration formed by multiple condition variables and their interactive and synergistic effects (i.e., conditional configuration effect analysis) and, thus, reveal the complex influence relationship of the digital economy in promoting tourism industry resilience.

4.2. Sufficiency Analysis of Conditional Configuration

The analysis of the sufficiency of the condition configuration can reveal the impact of various interlinked conditions on the results. Based on the combination of antecedent conditions in the research, first of all, the original consistency threshold is set at 0.8. A threshold of 0.8 means that, in the analysis, at least 80% of the cases are required to conform to the hypothesized relationship between the condition combination and the result. This can ensure that the data have a high quality and reliability and help avoid false relationships caused by accidental factors or data errors from being included in the analysis. Second, the consistency threshold (PRI) is set at 0.7. The consistency threshold (PRI) can more accurately reflect the causal relationship between the conditions and the result. When this value reaches 0.7, it indicates that the explanation of the result by the condition combination has a high degree of credibility and is not caused by random factors, which helps to improve the reliability and robustness of the research conclusions. Finally, the minimum case frequency threshold is one. Through standardized calculations, complex solutions, simple solutions, and intermediate solutions are obtained, which are used to distinguish the core and peripheral conditions affecting the resilience of the tourism industry, and to obtain the computational results of the condition configurations for the high and low resilience of the tourism industry in each province.

Table 4. Configuration results of digital economy-driven resilience enhancement in tourism industry.

Condition Variables		High-Resilience Configuration in Tourism Industry			High-Resilience Configuration in Non-Tourism Industry
		H1	H2	H3	L1	L2	L3
Digital foundation	Digital infrastructure	★	★	★	⊙	◎	⊙
	Digital talent pool	▲	▲	▲	◎	◎	◎
Digital development	Information industry development			★	▲	⊙
	Telecommunication industry development	▲	▲		⊙		▲
	E-commerce industry development			★	⊙	◎	▲
Digital innovation ability	Digital innovation expenditure	★	★	▲	◎	◎	◎
	Digital technology innovation		▲	▲	◎	⊙	⊙
Digital finance level	Digital financial coverage breadth	▲			◎	▲
	Digital financial coverage depth	▲	▲		▲		⊙
Consistency		0.947	0.922	0.935	0.889	0.932	0.950
Raw coverage		0.312	0.293	0.332	0.434	0.372	0.294
Single coverage		0.177	0.010	0.057	0.143	0.082	0.043
Provinces		Guangdong Shandong Shanghai Hunan Sichuan Henan Shaanxi	Beijing Tianjin Chongqing	Zhejiang Fujian Jiangsu Liaoning Anhui Hubei	Jiangxi Guizhou Hainan Hebei Yunnan	Qinghai Shanxi Ningxia Inner Mongolia	Xinjiang Guangxi Jilin Gansu Heilongjiang
Total consistency		0.511			0.532
Total coverage		0.932			0.901

★ indicates the core condition, ▲ indicates the auxiliary condition, ◎ indicates the core condition missing, ⊙ indicates the auxiliary condition missing, and a space indicates that the condition can either be present or missing without affecting the accuracy of the paths, and 3 decimals are retained.

presents the configurational analysis of the nine measurement variables of the digital economy on the enhancement of the resilience of the tourism industry. The results show (Table 4) that, when the digital economy drives the enhancement of the resilience of the tourism industry, the various condition variables play different roles in different paths, with three high configurational paths and three low configurational paths. The total coverage rate of the high configurational paths is 0.511, and the total consistency is 0.932, with the consistency and total consistency of all three paths exceeding 0.92. The total coverage rate of the low configurational paths is 0.532, and the total consistency is 0.901, both meeting the sufficiency requirements of the condition configurational analysis.

4.2.1. Configuration Analysis of Digital Economic Development in the High Resilience of the Tourism Industry

Table 4 shows that there are three highly resilient digital economic development configurations for the tourism industry. Configuration H1 has a consistency of 0.947 and a raw coverage of 0.312, explaining 31% of the sample cases, with digital infrastructure, digital innovation spending, and financial coverage at the core. The consistency of configuration H2 is 0.922, and the original coverage is 0.293. And 29% of the sample cases can be explained. The core conditions include digital infrastructure and digital innovation expenditure, and the auxiliary conditions include digital talent reserve. The consistency of configuration H3 is 0.935. The original coverage is 0.332, and it can explain 32% of the sample cases. The core conditions are digital infrastructure, etc., and the auxiliary conditions include internet and digital transaction infrastructure. By comparing the differences between the core and auxiliary conditions of the three configurations and combining the distribution of case provinces, the paths H1 and H2 are named as “digital foundation and digital innovation-driven type”, and the path H3 is named as “digital foundation and digital industry-driven type”.

(i)

“Digital Foundation and Digital Innovation Driven” covers configurations H1 and H2. Configuration H1 takes digital infrastructure and digital innovation expenditure as the core conditions, supplemented by digital talent reserve, telecommunication industry development, and digital financial coverage breadth and depth, which can drive the high-resilience development of the tourism industry. The core conditions of configuration H2 are consistent with those of H1, and the auxiliary conditions are more digital technology innovation. The core driving and auxiliary conditions of the two are basically the same. In the configuration H2 path, the government leads the construction and maintenance of digital infrastructure, and the industry continues to invest in digital innovation expenditure based on this and promotes the integration of the digital economy and the tourism industry by cultivating digital talents and developing the telecommunications industry. At the same time, we will use digital financial policies and special funds to develop the tourism industry and form a driving model to enhance the resilience of the tourism industry. This model relies on a positive interaction between government infrastructure, innovation spending, and matching funds. In provinces with rich integration scenes of digital economy and tourism economy, local governments and enterprises can easily grasp the technical needs of new tourism scenes and new formats, so as to actively invest in innovation and practice, and the development of new tourism formats requires a large amount of funds, prompting relevant organizations and enterprises to actively support and use funds. With the improvement of digital foundation, the government and enterprises are more active in carrying out scene, product, service, and format innovation, promoting the integration of digital and tourism industries, enhancing the ability of tourism industry to resist competition, coping with emergencies and adapting to market changes, and improving the resilience of tourism industry. That is, digital infrastructure provides integration scenarios, digital innovation investment catalyzes tourism innovation, and digital finance provides a financial guarantee.

Beijing City and Guangdong Province are typical provinces of this configuration. Taking Beijing as an example, in recent years, it has continuously expanded the coverage of its 5G network, promoted the construction of a “double gigabit” network, realized high-quality coverage of the 5G network in key areas and classic scenarios within and outside the Fifth Ring Road, built 104,000 5G base stations accumulatively, built 313,000 communication base stations, and fixed internet broadband access users reached 9.334 million. This not only brings convenient online navigation and a realistic virtual reality experience for tourists, it also promotes tourism services and management facilities, such as intelligent tour guides to improve management efficiency. At the same time, Beijing has continuously increased its investment in basic research and digital industry innovation. In 2022, its financial science and technology expenditure reached CNY 284.33 billion, ranking third in China. The improvement of digital infrastructure and investment in digital innovation create favorable conditions for the innovative development of Beijing’s tourism industry. In 2022, Beijing’s tourism reception revenue will reach CNY 622.5 billion, ranking first in China.

(ii)

“Digital Infrastructure and Digital Industry-Driven” corresponds to configuration H3. Configuration H3 takes digital infrastructure, the development of the telecommunications industry, and the development of e-commerce as core conditions, with digital talent reserves, digital innovation expenditures, and digital technological innovation as auxiliary conditions, to achieve high-resilience development of the tourism industry. Under this configuration, the government focuses on a high-quality allocation of digital infrastructure, cultivates a sound telecommunications and e-commerce digital industry, and promotes the tourism industry to be sensitive to digital opportunities and make timely adjustments, driving high-quality development of the tourism industry and enhancing resilience levels. Compared to paths H1 and H2, this configuration highlights the deep integration of the digital industry and the tourism industry more. Although the government actively builds digital infrastructure and application scenarios, the direct goal is not simply to provide high-quality tourism services, but rather, when external resources are limited, the government pays more attention to exploring development opportunities internally. The government leverages digital transformation and utilizes data platforms to perceive the market, update business models, and enhance reconstruction capabilities. At the same time, the industrial end relies on the resources of the telecommunications and e-commerce industry chains to achieve the precise matching of tourism supply and demand. Under this configuration, the supply of tourism services shows a segmented service supply derived from public resources, such as focusing on local or surrounding tourists’ needs, providing localized cultural tours, boutique tours, in-depth tours, short-distance characteristic tours, and other tourism products.

Zhejiang and Fujian provinces are typical provinces in this configuration. Taking Zhejiang as an example, its industrial digitalization index leads the country, with 263 intelligent factories, 12 future factories, and 210 provincial industrial internet platforms cultivated. Zhejiang’s e-commerce industry is relatively developed, with cross-border e-commerce pilot zones covering the entire province, and projects such as the World Bank Global Digital Finance Center, giving rise to emerging industries such as “Internet + Health”. Relying on solid digital foundations and complete digital industries, Zhejiang focuses on digital cultural tourism, with creative culture and technological innovation as the direction, integrating cultural and tourism resources such as science and technology museums and leveraging the “Cultural Tourism Cloud” platform that was jointly developed with the Big Data Group to promote digital reforms around the benefits of cultural tourism, forming a series of tourism projects. Through digital transformation, Zhejiang’s cultural tourism industry achieves the integration of tradition and innovation, promotes the sustainable development of the industry, and becomes a model of “digital cultural tourism” integration and development in our country.

4.2.2. Configuration Analysis of Digital Economic Development in the High Resilience of the Non-Tourism Industry

The low-resilience configuration also contains three paths. The total consistency of these paths is 0.901, and the total coverage is 0.532, which can explain 53% of the sample cases. The configuration L1 exhibits 0.889 consistency, 0.434 raw coverage, and 0.143 single coverage, with Jiangxi province being the typical case. The second low-configuration path L2 shows consistency, single coverage, and raw coverage of 0.932, 0.372, and 0.082, respectively, and the typical cases are Shanxi Province and Qinghai Province. The consistency, single coverage, and raw coverage of the third low-configuration path L3 are 0.950, 0.294, and 0.043, and the typical cases are Xinjiang Autonomous Region and Guangxi Province. From the comparison of the three low-configuration patterns, it can be found that the lack of a digital talent pool and digital innovation expenditure are the main reasons for the formation of low-configuration patterns. The low-resilience configuration state is not equivalent to a lower level of digital economy development in the low-configuration state regions or a lower resilience of the tourism industry in these regions but rather reflects the insufficient leading role of the digital economy in promoting the resilience of the tourism industry or the obstacles and challenges that exist in the specific enhancement process. Overall, the results of the low-configuration path suggest that the lack of digital talent and digital innovation spending are the key factors that make it difficult to enhance the resilience of the tourism industry. Therefore, it is necessary to pay attention to and solve the problems of digital talent cultivation and digital innovation investment encountered by these regions in the process of digital economy development in order to contribute to the tourism industry resilience enhancement.

4.3. Analysis of Configuration Substitution Relationship

The high configuration and low configuration of the digital economy to promote the resilience enhancement of the tourism industry are not antagonistic relationships, and there is an obvious substitution relationship between the condition variables under certain conditions. A comparison of configuration 1 and configuration 2 can be found, for the digital infrastructure and digital innovation expenditure of the basic convergence of the region, and the breadth of digital financial coverage and digital technological innovation can replace each other. Regions with wider digital financial coverage tend to have more active economic activities and more frequent innovative applications of fintech, and if the actual digital technological innovation investment in the place is insufficient, digital finance can be substituted to a certain extent. Correspondingly, if a region’s level of investment in digital technology innovation is higher, the reliance on digital financial coverage will be reduced accordingly. The comparison between Guangdong Province and Beijing City can prove this. There is little difference between Guangdong and Beijing in terms of digital infrastructure and digital development, but Guangdong’s digital technology innovation is weaker than Beijing’s, while its digital finance level is higher than that of Beijing. As one of the most active regions in China’s opening up to the outside world, Guangdong is able to develop and utilize digital finance activities more flexibly, actively, and fully, and the abundance and flexibility of funds provide valuable soil for digital infrastructure and digital innovation spending. And Beijing, as the concentration of technical and innovative talents in the country, leads the country in digital technology innovation level, and the spillover of digital innovation capability reduces the dependence on digital finance to a certain extent.

Comparing configuration 1, configuration 2, and configuration 3, it can be found that, for regions with a more complete digital infrastructure, digital development and digital innovation spending can substitute for each other. For most regions, digital infrastructure is the basic prerequisite for the digital transformation of the tourism industry, while how to carry out the transformation requires the integration of complementary industries, as well as the innovative application of digital technology. For regions with a better foundation for digital development, their digital industry can be integrated with the tourism industry faster, while for regions with a poorer foundation for digital development, if their digital innovation spending can be effectively improved, they can directly apply digital technology to the tourism industry, realizing the direct integration of technology and industry, thus realizing the substitution of digital technology for digital industry. This can be verified by the comparison between Beijing and Zhejiang. Both Beijing and Zhejiang have solid digital infrastructures, but the development of Beijing’s two major digital industries, namely the telecommunications industry and the e-commerce industry, is significantly weaker than that of Zhejiang. However, Beijing has a large amount of data innovation expenditures, thus forming a technological advantage and accomplishing the substitution of digital industry development.

4.4. Robustness Test

To ensure the generalizability and applicability of the research results, two ways are used to test the robustness of the analysis results. First, the approach of replacing the threshold is adopted. Drawing on Yu [23], the PRI consistency threshold was adjusted from 0.8 to 0.85 for the cohort analysis. The high-configuration paths after adjusting the PRI are three, and the core conditions and consistency remain consistent with the above analysis, proving the robustness of the results. Second, “tourism industry resilience” is replaced with the scores of the same year, one-period lag, and two-period lag as the outcome variables for analysis, and the results are still robust.

4.5. Further Analysis: The Variability of High-Resilience Configuration of Tourism Industry in Different Regions of China

Due to the vastness of China, there exists a large variability in the foundation of tourism industry development in different regions, and thus, there may be a large difference in the short-term driving path of the current digital economy to promote the tourism industry resilience enhancement. The sample is further analyzed for subregional configurations based on the division into east, central, and west regions by the National Bureau of Statistics of China. Following the approach of Schneider C Q [27], calibration was continued with a 90% quantile, a 50% quantile, and a 10% quantile as the three data anchor points of complete membership, crossover, and complete non-membership. The analysis results in

Table 5. Configuration analysis of tourism industry resilience (producing high-level results) in eastern, central, and western regions.

Condition Variable		Eastern Region			Central Region	Western Region
		HE1	HE2	HE3	HM1	HW1
Digital foundation	Digital infrastructure	★	★	★		◎
	Digital talent pool	▲	▲	◎	★	◎
Digital development	Information industry development	▲			★	★
	Telecommunication industry development	▲	▲	▲	▲	★
	E-commerce industry development		▲	★	⊙	⊙
Digital innovation ability	Digital innovation expenditure	★	★	★	▲	⊙
	Digital technology innovation	▲	◎	◎	⊙	⊙
Digital finance level	Digital financial coverage breadth		▲	★	▲
	Digital financial coverage depth	▲	▲		⊙	▲
Consistency		0.916	0.927	0.931	1	1
Raw coverage		0.337	0.411	0.428	0.223	0.354
Single coverage		0.031	0.117	0.437	0.030	0.084
Provinces		Beijing Tianjin Shandong	Fujian Guangdong	Zhejiang Shanghai Liaoning Jiangsu	Henan Hunan Hubei	Sichuan Shaanxi Chongqing
Total consistency		0.545			0.561
Total coverage		0.914			1

Note. ★ indicates the core condition, ▲ indicates the auxiliary condition, ◎ indicates the core condition missing, ⊙ indicates the auxiliary condition missing, and a space indicates that the condition can either be present or missing without affecting the accuracy of the paths, and 3 decimals are retained.

show that the core condition of the high-resilience configuration of the tourism industry in the eastern region lies in the existence of two paths with solid digital infrastructure and high digital innovation investment. The core condition of the high-resilience configuration of the tourism industry in the central region lies in the effective combination of digital talents and the information industry. The core condition of the high-resilience configuration of the tourism industry in the western region lies in the organic combination of the information industry, the telecommunication industry, and digital innovation investment. The high-resilience paths in both the central and western regions show a single state.

4.5.1. High-Resilience Configurational Analysis in the Eastern Region Tourism Industry

There are three configurations (HE1, HE2, and HE3) to realize the high resilience of the tourism industry in the eastern region. Both HE1 and HE2 take digital infrastructure and digital innovation expenditure as the core conditions and digital talent pool, telecommunication industry development, and depth of financial coverage as the periphery conditions. As the most active region in the development of China’s tourism industry, the eastern region has more diversified, personalized, and quality tourism requirements. Therefore, the core requirement for enhancing the tourism industry resilience in the eastern region lies in the formation of a high-quality tourism service supply and its scenario combination, which can be transformed into the various types of services required by tourists to satisfy their differentiated tourism demands. Similar to the H1 configuration, the government will invest in digital infrastructure construction and digital innovation to provide digital public infrastructure services for enterprises to develop “digital + tourism”, while enterprises will carry out tourism innovation and scenario development through technological development and application to form a “dual co-driven model” for improving tourism industry resilience. Beijing, Guangdong, and Fujian are the representative provinces of HE1. HE3 configuration performance has more e-commerce industry development than HE1 and HE2, and other core conditions are consistent, with Zhejiang as the representative province. This configuration has a certain degree of regional characteristics. Zhejiang, as the birthplace and the most developed place of China’s e-commerce industry, is now continuing to lead the development direction and path of China’s e-commerce industry. The high-quality development of the e-commerce industry has also brought opportunities for the development of the tourism industry, and new forms, such as “virtual tourism”, “live tourism”, and “sitcom tourism”, are relying on the relevant technologies and platforms of the e-commerce industry.

4.5.2. High-Resilience Configurational Analysis in the Central and Western Regions Tourism Industry

The path (HM1) of the high-resilience configuration in the central region is characterized by the core condition of the information industry and digital talent pool, and the auxiliary condition is telecommunication industry development, digital innovation expenditure, and financial coverage breadth. Obviously, compared to the eastern region, the role of digital talent and digital industry development is more prominent in the central region, where digital technology investment and digital innovation are limited. Take Hubei Province as an example. As one of the most developed provinces in China’s electronic information industry, Hubei Province has successfully attracted the National Memory Base, National Intelligent Networked Vehicle Demonstration Zone, National Cybersecurity and Talent Innovation Base, National Information Opto-electronics Innovation Center, and the China Information and Communication Science and Technology Group to settle in Wuhan. Relying on the strong telecommunications industry, Hubei Province accelerates the application of cloud computing, big data, artificial intelligence, and other new technologies in the field of digital tourism, intelligent tourism, tourism meta-universe, etc., and cultivates nationally renowned intelligent cultural and tourism digital service platforms, such as “Juyou Technology”, and developed digital exhibition halls, multimedia interactive equipment, projection interactive games, drawing games, holographic classrooms, somatosensory games, AR games and other new tourism products, and has become the first place in the country to derive a new digitalized tourism business.

The path of high-resilience configuration (HW1) in Western China is characterized by the information industry and the telecommunications industry as the core conditions and the depth of financial coverage as the auxiliary condition. The configuration characteristics of the western region show that the local governments in the western region should fully understand the development role of tourism in the region, give full play to the technical service capabilities of the digital industry, rely on the policy inclination of digital finance in the western region, implement diversified, precise, and characteristic tourism services driven by digital industry, and make up for the shortcomings of their own tourism development environment through good industrial chain cooperation. Taking Shaanxi Province as an example, due to the incentive of the prominent position of the regional tourism industry, Shaanxi Province actively promotes the deep integration of information technology and the tourism industry and responds to tourists’ digital consumption needs through business model innovation [28]. Under the guidance of the government, Xi’an, the provincial capital, relies on the local telecommunications industry and an internet platform (Douyin) to build a digital cooperation model for various departments to collaborate. On the government side, the Xi’an Tourism Development Committee is responsible for coordination, and cultural and museum institutions explore the characteristics of cultural and tourism integration. In terms of the market, it is dominated by Douyin, providing support such as traffic, video production, and internet celebrity resources, and in terms of technology, the local telecommunications department provides technical services and updates. And multi-party cooperation creates a new marketing model for the cultural tourism industry, incubating internet celebrity cultural tourism products such as “Datang Never Sleeps City”, “Little Sister Tumbler”, and “Shengtang Secret Box”. Thanks to the government’s planned and large-scale promotion, a large number of tourism enterprises and tourists have been stimulated to participate in the creation of cultural tourism content, and various “new ways of cultural tourism” have been derived. Various forms of high-quality tourism content have been continuously output, which has significantly improved tourists’ sense of participation, achievement, and happiness, and has also greatly improved the resilience of the local tourism industry.

Although compared with the eastern region, the central and western regions are lagging behind in terms of digital infrastructure, digital talent pools, and digital innovation inputs. In terms of the abundance and uniqueness of tourism resources, the central and western provinces of China have a higher value and greater potential for the differentiated development of tourism resources. With the deepening integration of the digital economy and tourism and the continuous improvement of tourism infrastructure and tourism reception capacity in the central and western regions, the resilience of their tourism industries is gradually narrowing the gap with the eastern regions. In order to better enable the digital economy to enhance tourism industry resilience, each region still needs to formulate and implement corresponding digital economy development policies according to its own characteristics and advantages, so as to give full play to their respective advantages and promote synergistic development among regions.

5. Conclusions and Recommendations

5.1. Conclusions

Under the background of vigorous development of the digital economy and the transformation and upgrading of the tourism industry, this study takes 30 provinces (autonomous regions and municipalities directly under the Central Government) of China as the research objects to deeply explore the resilience change and differentiation path of the tourism industry, driven by the digital economy, and obtains the following achievements:

(i)

First, the research defines the key elements and necessary conditions for the resilience enhancement of the tourism industry driven by the digital economy. Based on the FIIF analytical framework, key elements of the digital economy affecting the resilience of the tourism industry are identified, covering data infrastructure, digital industry development, digital innovation potential, and digital inclusive finance. Through the fsQCA method, it is found that digital infrastructure and digital innovation expenditure are the necessary conditions to form the high-resilience configuration of the tourism industry. This means that, in promoting the highly resilient development of the tourism industry, solid digital infrastructure is the foundational support, and continuous digital innovation expenditure is the key driving force. The shortage of a “digital talent reserve” and “digital innovation expenditure” is the necessary condition for the low-resilience configuration of the tourism industry, which reveals the key constraints of the low resilience of the tourism industry in some regions. This conclusion provides the core basis for accurately analyzing the resilience relationship between the digital economy and the tourism industry, which is helpful in making accurate efforts in the subsequent development;

(ii)

Second, the research clearly reveals the configuration path and factor substitution relationship of the digital economy to drive the resilience improvement of the tourism industry. The study finds that there are three paths for the high-resilience configuration of the tourism industry, which can be summarized into two modes: “digital foundation and digital innovation” and “digital foundation and digital industry driven”. This provides a clear direction for the development of the tourism industry, and different regions can choose the appropriate path according to their own resource endowment and development stage. For example, regions with innovation advantages can focus on digital foundation and digital industry-driven models, while regions with good digital industry foundations can choose a digital foundation and digital industry-driven models. At the same time, there are three low-resilience configurations of the tourism industry, and there is an asymmetric relationship with the driving path of high-resilience configurations. In addition, there is a substitution relationship between the elements of the digital economy in the process of driving the resilience of the tourism industry, and the breadth of digital financial coverage and digital technology innovation can replace each other in regions where the digital foundation and digital innovation expenditure are basically converged. For regions with a better digital infrastructure, digital industry and digital innovation spending can be substituted for each other. This finding breaks the fixed model of the role of factors in traditional cognition and provides more flexible strategies for the development of the tourism industry. In actual development, when the development of one element is restricted, the substitution of other factors can ensure the improvement of industrial resilience and improve the ability of the tourism industry to cope with the complex environment;

(iii)

Third, the research clarifies the differentiated path of the tourism industry’s toughness-upgrading configuration in the eastern, central, and western regions of China from the perspective of regional difference. Relying on the advantages of digital talent reserve, digital industry development, digital finance, digital infrastructure, and digital innovation investment, the eastern region has formed a pattern of multi-drive tourism industry resilience improvement of the digital economy. This model is the result of the eastern region making full use of its own advantageous resources and provides an example of high-end development for other regions. Relying on the advantages of digital industry development and talent reserve, the central region presents a path of a dual element driving tourism industry resilience promotion, giving full play to the synergy of its own characteristic resources, and providing a feasible path for the development of the tourism industry in the central region. Under the condition of insufficient digital infrastructure construction, the western region mainly relies on the telecommunication industry and the information industry, showing the characteristics that are dual dependent and path driven. This shows that the western region can tap the potential of key industries and realize the development of the tourism industry in combination with its own reality. These conclusions provide strong support for regional differentiated development, and local governments can formulate policies according to local conditions to achieve precise development.

5.2. Policy Recommendations

Based on the research findings, the following insights are proposed for tourism industry resilience enhancement for tourism purposes.

First, it is necessary to strengthen the coordination of digital elements and plan development paths according to local conditions. The eastern region should rely on the advantages of the existing digital infrastructure, focus on supporting digital innovation in tourism scenarios (such as virtual reality tourism and AI tour guides), and set up special funds to encourage enterprises to develop smart cultural tourism products. We will continue to promote the in-depth integration of e-commerce platforms and tourism resources and create new business formats, such as “live tourism” and “immersive cultural tourism IP”. For example, we can learn from the “cultural tourism cloud” model of Zhejiang Province to integrate government, enterprise, and platform resources to establish a regional digital tourism–ecological alliance. The central region should take the development of the digital industry as the core, combine the advantages of digital talents, and strengthen regional tourism cooperation. Establish a cross-regional digital tourism alliance, integrate the surrounding tourism resources, and jointly develop digital tourism products. For example, the “Central Digital Cultural Tourism Corridor” will be jointly built to showcase regional characteristic culture through digital technology, attract more tourists, and enhance the overall resilience of the tourism industry. The western region should integrate digital infrastructure, industry, and innovation elements to tap characteristic tourism resources. The use of digital technology to create tourism IP with western characteristics, such as the development of digital cultural and creative products, virtual tourism projects with Dunhuang culture as the theme, and the transformation of resource advantages into industrial advantages, achieves leapfrog development in the tourism industry. Certainly, policymakers could draw insights from Dubai’s “Dubai Metaverse Strategy”, which leverages the creation of virtual attractions (e.g., a VR-enabled Burj Khalifa experience) and NFT-based travel memorabilia to pre-engage potential tourists in the metaverse, thereby stimulating synergistic growth in both virtual and physical tourism revenue streams.

Second, strengthen the construction of digital infrastructure and narrow the digital divide between regions. The governments in the western region should increase investment in infrastructure, build high-speed and stable network facilities, achieve full coverage of 5G networks in key tourist attractions, and improve the level of intelligence in the attractions. For example, Spain’s implementation of the ‘Smart Tourism Destination (DTI)’ national initiative consolidates real-time data (e.g., visitor flow, transportation, and hotel bookings) into a unified platform, reduces resource waste through data-driven precision management, and enhances smart service capabilities. At the same time, encourage enterprises to participate in the construction of digital infrastructure, attract investment in the construction of data centers, cloud computing platforms, and other projects through policy preferential treatment and tax reductions, and provide support for the digital transformation of the tourism industry. In addition, regions can also strengthen the cultivation of digital talents, such as formulating plans for the cultivation of digital talents, cooperating with universities and vocational colleges to offer “digital + tourism” related professional courses, increasing practical teaching, and cultivating compound talents who understand the tourism business and possess digital technology skills. Moreover, an effective talent incentive mechanism should also be established, improving the salary and treatment of digital tourism talents, providing a good career development space, and attracting and retaining talents.

Third, it is necessary to guard against “digital traps” and implement precise policies to enhance industrial resilience. Local governments should strengthen the top-level design for the development of digital cultural tourism and formulate scientific and reasonable development plans. Before project development, fully conduct market research and a feasibility analysis to avoid blind investment in the construction of digital projects and ensure the sustainability of the project. Follow-up visits should also be carried out in the middle and later stages of the project, focusing on the actual effect of the project and avoiding the ineffective use of resources. At the same time, the national level will accelerate the formulation of policies and regulations related to digital cultural tourism, standardize the market order, and protect the rights and interests of consumers. For example, clarify the quality standards and data security specifications of digital tourism products. By establishing an industry supervision mechanism and strengthening the review and supervision of digital tourism projects, problems such as surplus digital scenarios and data leakage can be effectively prevented.

Fourth, it is imperative to confront the potential challenges posed by digitalization. Regions vary significantly in economic development levels, infrastructure, and cultural contexts, leading to divergent risks when implementing digital transformation strategies. In economically advanced regions, while talent concentration is high, industries face fierce competition for digital professionals, requiring substantial costs to attract and retain skilled workers. In less-developed regions, limited awareness and application capabilities in digital technologies among residents and employees necessitate massive investments in training and education to improve overall digital literacy. In culturally distinct regions, digital transformation risks introduce external cultural values that may clash with local traditions. For instance, traditional handicraft industries that adopt digital marketing might struggle to preserve their cultural essence and uniqueness during modernization. In resource-dependent regions, reliance on natural resource extraction and single-industry structures often results in insufficient foundational capacity and motivation for digital transformation. Addressing these challenges scientifically will profoundly shape the resilience of local tourism industries.

5.3. Limitations and Prospects

This study rigorously addressed all stages of research design, from topic conceptualization and data collection to analytical argumentation. However, academic inquiry is an iterative process, and several limitations remain unavoidable. First, given the availability of the data, there is still room for further discussion on tourism industry resilience, particularly in the operationalization of resilience metrics for tourism sectors, with insufficient measurement indicators. Second, while emphasizing digital economy variables, the study inadequately accounts for external shocks impacting tourism resilience (e.g., geopolitical risks and climate change). Furthermore, non-digital government policies (e.g., subsidies and tax incentives) are mentioned but not thoroughly analyzed. Third, reliance on secondary statistical data from official sources may fail to fully capture the firm-level or microeconomic dynamics influencing tourism resilience. The exclusion of Tibet due to data limitations also risks compromising the generalizability of the findings for Western China. Acknowledging these limitations not only clarifies the scope of this study but also highlights critical avenues for future scholarly refinement.