Can digital economy improve tourism economic resilience? Evidence from China

Abstract

The digital economy has become an important driver of resilient growth in tourism economy. Taking China as the research object, it is found that the digital economy not only has a positive spatial spillover effect on tourism economic resilience but also mitigates the negative impact of the COVID-19 pandemic. The heterogeneity test shows that the digital economy has a spatial diffusion effect on tourism economic resilience in the eastern, western, and southern regions and a spatial siphoning effect on the non-pilot areas of the National Smart Tourism Pilot Policy. The result of influence mechanism test proves that the digital economy can contribute to the tourism economic resilience by stimulating tourism fiscal expenditure, guiding tourism industry cluster, and increasing tourism consumption. The findings of the study have important policy implications for China and other countries around the world in using the digital economy to promote sustainable tourism development.

Keywords

COVID-19 digital economy influence mechanism spatial diffusion effect tourism economic resilience

Introduction

Tourism is an important industry that promotes political, economic, and cultural exchanges between countries around the world, which is a driving force for shared global prosperity. Tourism economy is crucial to national economic development and is gradually becoming a pillar industry for the economic development of each country. China attaches great importance to tourism economy development and emphasizes its resilience cultivation (Sun and Song, 2021). Under the impact of multiple shocks such as the COVID-19 pandemic, trade disputes, and economic crisis, China’s tourism economic resilience is facing great challenges and pressure. Tourism is an integrated industry sector that provides tourism products and services to consumers, and the various links of industry chain are highly interdependent and vulnerable to crises (Rui, 2021). Take the COVID-19 epidemic as an example, compared to 2019, international tourist arrivals and tourism spending (in US dollars) in China fell by 88% and 49% in 2020 due to strict epidemic prevention and control policies, and the tourism economy suffered huge losses.¹ How to enhance tourism economy resilience and bring into play the driving force of tourism industry on national economy is an important issue that needs to be urgently considered for the sustainable development of China’s tourism industry.

The digital economy has become a key force in reorganizing global factor resources, reshaping global economic structure, and changing global competitive landscape with the advancement of digital technology (Amine et al., 2023). The digital economy is characterized by high innovation, strong penetration, wide coverage, and is not only a reliance for the transformation and upgrading of traditional industries but also a momentum for the shift and upgrade of tourism economy (Rui, 2022). The digital economy has exerted a profound influence on all actors in tourism economy. Thanks to the rapid spread of digital technologies, tourists have reduced the cost of information acquisition and improved the efficiency of travel decisions. Tourism companies innovate products and services through digital construction to improve business environment and increase effective supply (Zhang and Shang, 2022). The digitalization process of tourism industry has contributed significantly to the increased tourism economy resilience. The literature on the tourism economic resilience in the context of digitalization includes the following categories: Firstly, the concept of tourism economic resilience. The concept of tourism economic resilience is controversial. More recent literature defines tourism economic resilience in terms of engineering resilience at the organizational level, and there is a lack of understanding of its recovery and change characteristics (Hall et al., 2023). Secondly, the different impact of tourism economic resilience. While socio-ecological system resilience is crucial for tourism destinations, socio-ecological system resilience is more important for individuals and organizations in the short term, and policy makers should be flexible in using digital tools to enhance tourism economic resilience to improve individual, organizational, community, and destination development (Girish, 2023). Thirdly, the ways to improve tourism economic resilience. Measures such as the construction of smart tourism cities and the digital transformation of tourism enterprises all contribute to the recovery of tourism economy from external shocks (Kim and Kim, 2023; Schönherr et al., 2023). Therefore, are there differential impacts of the digital economy on the tourism economic resilience? Are there different channels through which the digital economy enhances the tourism economic resilience? These are questions that deserve to be explored in depth. This paper focuses on the effect and mechanism of digital economy affecting tourism economic resilience in China, which has important theoretical significance and practical value for tourism industry to accelerate digital transformation and promote high-quality tourism development. In addition, this paper provides a more mature theoretical analysis of the integration of digital economy and tourism development form a spatial perspective by enriching the understanding of the consequences of the influence of digital economy on tourism economic resilience and conducting a detailed empirical test of their interaction mechanism, which is a valuable complement to existing studies. This paper indicates that digital economy facilitates the tourism economic resilience with a significant spatial diffusion effect and can mitigate the negative impact of COVID-19 pandemic. Expanding tourism fiscal expenditure, tourism industry cluster, and increasing tourism consumption are the main channels through which the digital economy enhances the tourism economic resilience.

The remainder of this article is organized in the following manner. The proceeding section includes the theoretical background and hypothesis. Section 3 is methodology, which explains the econometric model and variables. Section 4 is empirical test. Section 5 is further discussion, focusing on the influence mechanism of digital economy on tourism economic resilience. Finally, section 6 concludes the analysis.

Theoretical background and hypothesis

Theoretical background elaboration

The concept of resilience was first introduced from engineering by Holling (1973) and has been widely applied to related disciplines. Subsequently, Aura et al. (2001) introduced the idea of economic resilience to describe the process by which an economic system withstands and recovers from crisis shocks. A key feature of economic resilience is the ability of regional economies to adapt to a complex and volatile external environment (Simmie and Martin, 2010), and regions with strong economic resilience are able to quickly recover their growth path and avoid falling into recession (Ron and Peter, 2015). In recent years, the topics related to economic resilience have received much attention (Gerwyn, 2023), and the exploration of economic resilience basically covers various fields of agriculture, manufacturing, and services (Ye et al., 2022; Xu et al., 2022). The factors influencing economic resilience is one of the research hotspots, in addition to traditional factors such as government regulation (Ema et al., 2022; Lee and Wang, 2023) and industrial structure (Shutters et al., 2022), the role of technological progress in enhancing economic resilience has become increasingly significant (Lei et al., 2023; Myrto et al., 2023).

Tourism is extremely sensitive to changes in the external environment and it is urgent to improve the tourism industry resilience to enhance its risk management capacity (Aithal et al., 2023; Kocak et al., 2023). Both sudden natural disasters and public health events have put great pressure on tourism economic development (Shi et al., 2023), and tourism market players have had to seek multiple ways to improve tourism economic resilience. Factors such as tourism industry specialization (Yang et al., 2022a), policy support (Mehrdad et al., 2022), and cultural element infusion (Meng and Zollet, 2023) could exert a positive effect on tourism economic resilience at the level of tourism destinations, tourism communities, and tourism enterprises. The digital economy has become an inevitable choice to strengthen the tourism economic resilience with the increasingly developed digital technology. Numerous studies have also demonstrated the importance of digital economy for tourism economic resilience (Saputra and Pitanatri, 2022; Huda, 2023).

The existing literature has laid the foundation for this study, but there are still the following deficiencies: First, tourism economic resilience is an emerging research topic, relevant research is in its infancy, and the theoretical research results are not abundant. Second, some scholars have only theoretically elaborated on the influence of digital economy on tourism economic resilience, and the specific role of digital economy on tourism economic resilience is lacking empirical support. Third, the academic community ignores a systematic discussion about the influence mechanism of digital economy on tourism economy resilience, resulting in the lack of guidance and feasibility of policy suggestions. Therefore, this paper takes China as the research object and focuses on the effect and mechanism of digital economy on tourism economic resilience, so as to provide decision-making reference for their coordinated development and further releasing the driving force of digital economy on tourism economy resilience.

Theoretical hypothesis

The crises, such as COVID-19 pandemic, trade disputes, terrorism, and natural disasters, has led to profound changes in both the external environment faced by tourism industry and the internal structure of tourism organizations. In the face of external shocks, tourism economic actors could use digital technology to explore new growth paths, gradually breaking away from the “low-end lock” of traditional markets and improving resilience to risk (Rui, 2023a). Digital economy could enhance the independent innovation capacity of tourism economic participants, prompting different types of tourism enterprises to actively engage in product and service innovation. Meanwhile, the digital economy facilitates tourist consumption, expands tourism market transactions, and improves the equilibrium level of tourism economy (Tsourgiannis and Valsamidis, 2019). The advancement of digital technology has accelerated the cross-regional dissemination of tourism elements, generating significant knowledge spillover effects. Both sides of supply and demand of tourism industry rely on geographical proximity to learn from each other, which promotes a rational division of labor in regional tourism and accelerates the coordinated development of regional tourism economies (Tang et al., 2022).

Hypothesis 1

Digital economy is conducive to tourism economic resilience with a significant spatial diffusion effect.

Industrial policy theory states that scientific and reasonable industrial policies are an important guarantee for the improvement of industrial competitiveness (Simone, 2023). Facing the complex and changeable external environment, the healthy development of tourism economy depends on the policy support. First, the development of digital economy promotes the construction of digital government and improves the administrative efficiency of government (Anna et al., 2023). Relying on digital technology, government can accurately identify the development status and trend of tourism industry, and cultivate the tourism industry as a leading industry. The policy support for tourism industry could improve tourism economic resilience (Kalbaska et al., 2017). Second, the digital economy has the nature of public goods, and the government-led digital infrastructure construction provides the physical basis for information and communication, data processing, and emergency management in the tourism industry. The digital transformation of government functions is driving innovation in tourism governance models, and the learning and application process of digital technology has positive externalities for social development (Vassilis, 2023). Policies related to guiding the development of digital economy will not only improve the social welfare but also contribute to the resilient growth of tourism economy.

Hypothesis 2

The digital economy will lead government to make policy interventions to improve the tourism economic resilience.

According to industry cluster theory, geographical concentration of industries causes incremental returns to scale and drives overall progress in related industries (Marcelo et al., 2023). Tourism industry agglomeration is conducive to tourism enterprises, practitioners, and management sector to learn and absorb professional technology and knowledge. The digital economy has given rise to a diverse pool of technologies (Ron and Simona, 2009), providing SMEs with more opportunities for technology options to incubate and grow. The networks of production, service, and exchange concluded by digital economy strengthen the links between the various sectors of tourism industry and promote formal or informal communication and collective learning between the various factors of tourism economy. And tourism industry cluster amplifies the spillover effect of tacit knowledge, reducing the risk of failure of tourism enterprises in the cluster process (Pedro et al., 2017). The tourism industry cluster guided by digital economy creates a cumulative “self-reinforcing” effect that persists over time and influences the trajectory of tourism economic resilience (Martin, 2010).

Hypothesis 3

The digital economy can enhance the tourism economic resilience by promoting tourism industry cluster.

Active tourism consumption energizes the tourism economy development and is the main driver of its resilience, and the digital economy plays an important role in stimulating tourism consumption (Cozzio et al., 2023). Expectation confirmation theory suggests that consumers compare pre-purchase expectation with post-purchase performance, and it indicates increased consumer satisfaction and consumption if post-purchase performance exceeds pre-purchase expectation (Fadel et al., 2022). Multi-disciplinary integration of digital technology and tourism can improve tourists’ post-purchase performance. For tourists, the development of digital finance provides a more efficient and convenient means of payment for tourists, optimizing the consumption environment and leading an increase in the total utility of tourism consumption (Qin et al., 2022). Tourists will also be able to make decisions based on the vast amount of information available on the tourism digital platform, making tourism consumption more comfortable and secure. For tourism companies, the digital transformation of tourism service and consumption scenes brings a new experience to tourists, improves tourists’ satisfaction, and increases their repurchase rate of tourism products and services. The increased efficiency of tourism companies through digital transformation has helped them to improve after-sales service, which in turn has increased tourists’ satisfaction. For government, advancement in digital regulatory instruments has maintained order in the tourism market, created a good consumption environment, and stimulated tourism consumption (Guo and Lin, 2022). Tourism consumption growth leads to consumption upgrading and promotes sustainable development of tourism economy.

Hypothesis 4

The digital economy can improve tourism economic resilience by boosting tourism consumption.

The theoretical research idea of this paper is illustrated in Figure 1.

Figure 1.

Theoretical research framework.

Methodology

Econometric model setting

The Spatial Durbin Model (SDM) is set to test the influence of digital economy on tourism economic resilience in China. The SDM model is a general form of the Spatial Lag Model (SLM) and the Spatial Error Model (SEM), which reveals the correlation between the spatial lag term of the independent variable and the dependent variable, and can better reflect the spatial interaction pattern between different variables. Res_it is tourism economic resilience and W_ijRes_it is its spatial lag term in equation (1). The inverse distance spatial weight matrix $W_{i j}^{d}$ is selected for SDM test, and d_ij is the geographic distance between region i and j.

W_{i j}^{d} = 1 / d_{i j}, i \neq j; W_{i j}^{d} = 0, i = j

(1)

Equation (2) is the specific form of the Spatial Durbin Model. Digital_it represents digital economy, which is the core explanatory variable and X_it is a control variable. region_i and year_t are region effect and year effect, respectively. The purpose of controlling region effect is to control the effect on the dependent variable of some characteristics that do not vary over time (e.g., cultural and geographical) in different regions. The purpose of controlling year effect is to control the effect of time factors on the dependent variable that does not vary with individuals. The sample includes 30 provinces, cities, and autonomous regions in China² and the research period is from 2013 to 2020.

\begin{array}{l} R e s_{i t} = α + ρ \sum_{j = 1}^{N} W_{i j} R e s_{i t} + β_{1} D i g i t a l_{i t} + β_{2} \sum_{j = 1}^{N} W_{i j} D i g i t a l_{i t} + η X_{i t} \\ + η_{1} \sum_{j = 1}^{N} W_{i j} X_{i t} + r e g i o n_{i} + y e a r_{t} + ε_{i t} \end{array}

(2)

Calculation of tourism economic resilience

The tourism economic resilience is the ability of the tourism economy to withstand risk shocks and return to growth. That is, the ability of the tourism economy to cope with crisis risks and to restore system functions in the event of a disruptive shock. The key to building the resilience is how to emerge from the crisis rapidly and to achieve sustainable development. The regional growth structure of tourism economy is an important dimension in determining the tourism economic resilience. A higher growth rate of the tourism economy than the whole country leads to lasting regional tourism growth dynamics, indicating a more resilient tourism economy and it can be reflected in the rate of change of tourism economic growth (Cheng et al., 2022). Using changes in core indicators of economic development to reflect the ability of country (region) to cope with shocks is a common approach to measure resilience. This paper follows the ideas of Simmie and Martin (2010) to calculate the tourism economic resilience using the sensitivity of total tourism revenue in different provinces, cities, and autonomous regions of China. The formula is as follows:

R e s_{i} = \frac{Δ r e s_{i} / r e s_{i}}{Δ r e s_{n} / r e s_{n}}

(3)

In equation (3), Res_i is the tourism economic resilience in region i,

Δ r e s_{i} / r e s_{i}

is the rate of change of tourism economy in region i, and

Δ r e s_{n} / r e s_{n}

is the rate of change of tourism economy in China. From a dynamic perspective, equation (3) reflects the changes of tourism economic resilience in one place and the whole country across the development cycle, which can capture the changing patterns of tourism economic resilience in different regions and is applicable to the scientific assessment of changes in tourism economic resilience (Yang et al., 2022b). The tourism economy in region i grows faster than the whole country and the tourism economic development shows a strong resilience if Res_i is greater than 1, while the tourism economy is less resilient if Res_i is less than 1 (Mao et al., 2022). It is important to note that the tourism economy in China experienced negative growth in 2020 due to the COVID-19 pandemic, and a larger Res_i indicates a greater decline in tourism economy in region i. Therefore, the value of Res_i in 2020 is taken as the opposite number to measure tourism economic resilience. The tourism economy is measured by regional total tourism revenue and the data is from the official statistical yearbooks of each province, city, and autonomous region. The official statistical yearbooks can be found on the official websites of the statistical offices of each region.

Construction of the evaluation system of digital economy

The digital economy is a new economic form with data resources as the key element, modern information networks as the main carrier, and the digital transformation of all factors as an important driving force. The evaluation system of digital economy is constructed from four aspects: policy support, infrastructure, industry inputs, and market benefits (Rui, 2023b). The policy points out the direction of digital economy development, promotes the construction of digital economy infrastructure, and guides enterprises to invest in digital construction, thereby generating economic benefits. The evaluation system covers and reflects the whole process and dynamicity of digital economy development in a scientific and reasonable way, which is shown in Table 1. The entropy weighting method can remove human subjectivity and objectively reflect the variability and importance of different indicators, and the calculation results are more accurate (Xi et al., 2022). Therefore, the entropy weighting method is used to assign weights to each tier 2 indicator and calculate the total score (ranges from 0 to 1) of digital economy development level and logarithmize the final score, and higher score indicates a more developed digital economy. The data of indicators is from EPS (Easy Professional Superior) DATA.³

Table 1.

The evaluation system of digital economy.

Tier 1 indicators	Tier 2 indicators	Direction of indicators
Policy support	Science and technology financial expenditure	+
Infrastructure	Total business volume of post and telecommunications	+
	Cell phone penetration rate	+
	Number of Internet users	+
	Length of fiber optic cable line	+
Industry inputs	Number of employees in information transmission, software, and information technology services	+
	Number of employees in scientific research and technology services	+
	Fixed asset investment in information transmission, software, and information technology services	+
	Fixed asset investment in scientific research and technology services	+
	Enterprise R&D expenditure	+
Market benefits	Technology market turnover	+
	Number of patent applications granted	+
	Software business revenue	+
	Express business revenue	+

Note: The symbol of + indicates that the indicator is positive.

Control variables

Control variables include tourism resources (Resource), resident income (Income), openness to the outside world (Open), traffic accessibility (Traffic), and environmental regulations (Regulation). Tourism resources are the basis for tourism development. High-quality tourism resources are highly attractive to tourists and contribute to the regional tourism economic resilience (Nelson and Sumesh, 2023). The total number of 5A tourist attractions in each region is used to represent tourism resources. Grade 5A is the highest level of tourist attractions in China, and 5A tourist attractions are the most high-quality tourism resources in the destination. Increasing resident income generates a higher marginal propensity to consume and promotes tourism consumption. Meanwhile, the growth of residents’ income indicates that the tourism industry is able to provide satisfactory industry compensation, accumulate labor factors, and contribute to the sustainable development of tourism economy (Chi, 2016). The income level of residents is measured by disposable income per capita. The expanded openness to the outside world has promoted the development of inbound and outbound tourism, while facilitating the introduction of global leading technologies and management models to enhance the resilience to risk of tourism industry (Tang, 2021). The openness to the outside world is measured by external dependence (the ratio of total imports and exports to GDP). Convenient transportation can bring incremental visitors to destinations and is an important guarantee for tourism economic development (Reynolds, 2021; Algieri and Álvarez, 2023). Traffic accessibility is measured by road density (the ratio of the sum of rail and road miles to land area). A good ecological environment is the natural substrate for cultivation of tourism economic resilience. Government environmental regulation is crucial for ecological protection (Huang and Tian, 2023), which is measured by investment in industrial pollution control as a share of GDP. Take logarithm of all variables. Except that the data of 5A tourist attractions are collected manually, the data of other control variables came from EPS (Easy Professional Superior) DATA. All variables are at the provincial level and do not require the summation of data from municipalities under provincial jurisdiction.

Data description

Table 2 demonstrates the measurement and the result of descriptive statistics of each variable. The mean value of Res is 1.309 during the observation period, and China’s tourism economy has strong development resilience. However, the minimum value of Res is negative, indicating that the tourism economic resilience in some regions lags behind the national average and is sensitive to risk. The average value of Digital is only 0.187, most regional digital economy development is relatively backward, and the difference between the minimum and maximum values also indicates that there is a large gap in digital economy development among different regions. The results for other variables show that the distribution of tourism resources in China is characterized by spatial unevenness. The variables of Income, Open, and Traffic have large extreme differences, with significant disparities between different regions in terms of income levels, openness to the outside world, and traffic accessibility. The mean and maximum values of Regulation are 0.001 and 0.009, respectively, indicating that environmental regulation in China is relatively limited.

Table 2.

The result of descriptive statistics.

Variable	Description	Mean	Std. Dev	Min	Max
Res	Calculated according to equation (3)	1.309	2.146	−12.338	12.655
Digital	Calculated according to evaluation system of digital economy	0.187	0.181	0.009	0.800
Resource	The total number of 5A tourist attractions	7.645	4.326	2	25
Income	Disposable income per capita	32,739.48	10,295.41	19,597	73,849
Open	The ratio of total imports and exports to GDP	0.280	0.296	0.007	1.449
Traffic	The ratio of the sum of rail and road miles to land area	0.994	0.525	0.099	2.222
Regulation	Investment in industrial pollution control as a share of GDP	0.001	0.001	8.60 × 10⁻⁶	0.009

Empirical test

Figure 2 illustrates the correlation between the digital economy and changes in value added in different sectors. The left axis of the figure below shows the units of value added of sector, while the right axis shows the national sum of the digital economy scores of each region calculated by the entropy method. China’s digital economy score fell slightly after 2014 and then continued to rise. The primary sector had the lowest value added with little change, and the digital economy had a weaker influence on agriculture. The trend of the digital economy score and the secondary and tertiary sectors is basically the same, confirming their strong correlation. As an important part of the tertiary sector, tourism is increasingly linked to the digital economy. The digital economy is gradually becoming an important driving force for the modernization and transformation of the tourism industry.

Figure 2.

Trends between the digital economy and the value added of three industries.

Spatial correlation test

Regarding the spatial correlation test of tourism economic resilience, the Moran’s I index of tourism economic resilience is significantly positive in most years during the observation period, indicating that China’s tourism economic resilience has a strong positive spatial correlation and shows an obvious synergistic development trend, which lays the foundation for spatial econometric test (the result is shown in Table 1 in Appendix).

Baseline regression

The influence of digital economy on tourism economic resilience is tested using OLS and SDM. From Table 3, the estimated coefficient of Digital is significantly positive with or without the inclusion of control variables, indicating that the digital economy can enhance tourism economic resilience. In the spatial econometric model, the estimated coefficient of the spatial lag term of digital economy, which is W_ Digital,⁴ is also significantly positive, meaning that there is a spatial diffusion effect of the positive influence of digital economy on tourism economic resilience.⁵ This finding is consistent with Hypothesis 1, and the promotion of digitalization has become a consensus to enhance the tourism economic resilience in various regions. The digital economy has significant scale effect and network externalities, and the tourism digital transformation of destination has led to the co-development of surrounding areas. Regarding control variables, factors such as improvement in tourism resources quality, growth in disposable income, expansion of openness to the outside world, and strengthening of environmental regulations are all conducive to the resilient growth of tourism economy. The estimated coefficient of W_ Resource is significantly negative, indicating that tourism resources have a significant “brand masking effect” and the prominent brand dominance of tourism destinations can be detrimental to tourism economic resilience in surrounding areas (Lee and Kim, 2021). The “brand masking effect” is particularly pronounced when there is strong substitution of tourism resources in neighboring areas. Tourism destinations with outstanding tourism resources, strong branding, and market competitiveness may not be perceived by tourists, or may not be able to attract tourists once they are perceived (Liu et al., 2018). The estimated coefficient of W_ Open is significantly positive, and the increased tourism economic resilience is benefited from the deepening of opening up.

Table 3.

The result of baseline regression.

Variable	OLS		SDM
Digital	0.471*** (0.134)	0.359* (0.214)	0.457*** (0.164)	0.379*** (0.030)
Resource		0.269*** (0.093)		0.324 (0.386)
Income		0.639*** (0.026)		1.016*** (0.019)
Open		0.184*** (0.026)		0.324*** (0.024)
Traffic		0.266 (0.232)		0.205 (0.353)
Regulation		0.765*** (0.152)		0.192*** (0.018)
Constant term	0.305 (0.315)	6.473 (6.751)		−7.660 (13.484)
$ρ$			0.699*** (0.071)	0.341** (0.138)
W_ Digital			1.053* (0.652)	4.401* (2.315)
W_ Resource				−3.707* (1.897)
W_ Income				1.523 (1.567)
W_ Open				1.063*** (0.349)
W_ Traffic				−3.264 (2.582)
W_ Regulation				0.622 (0.592)
Hausman test value			−0.16	−15.87
R ²	0.455	0.541	0.772	0.324
Observations	240	240	240	240
LR test (SAR)			7.66***	16.42**
LR test (SEM)			7.85***	18.87***
LM-lag			10.38***	15.61**
LM-err			10.86***	16.66**
Region and year effects controls	Yes	Yes	Yes	Yes

Note: ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

The outbreak of COVID-19 pandemic in 2020 posed a great challenge to tourism economic resilience, and this paper incorporates the impact of COVID-19 pandemic into the analytical framework to explore whether digital economy can mitigate the negative impact of the epidemic on tourism economic resilience. Set Covid-time as a dummy variable, and it takes a value of 1 for 2020 and 0 for the rest of the years. Meanwhile, the total number of confirmed epidemic cases and deaths in 2020 is used to measure the COVID-19 pandemic shock, which expressed as Covid-number and taken as logarithm. The data is from the official website of health committee in each region. In Table 4, the column of Impact of the COVID-19 pandemic demonstrates the impact of the epidemic on tourism economic resilience, and the estimated coefficients of both Covid-time and Covid-number are significantly negative, indicating that the epidemic undermined tourism economic resilience growth. In the column The impact of digital economy and epidemic, the interaction term between epidemic shock and digital economy is constructed and it is found that the estimated coefficients of Digital × Covid-time and Digital × Covid-number are both significantly positive, meaning that digital economy could mitigate the negative impact of COVID-19 pandemic on tourism economic resilience. However, the coefficients of spatially lagged terms of two variables are not significant, confirming that there is no spatial spillover effect of the mitigation effect of digital economy. How to make full use of the spatial correlation effect of digital economy to resist the risk of public health emergencies will be one of the priorities for strengthening tourism economic resilience in the future.

Table 4.

The impact of digital economy and epidemic on tourism economic resilience.

Variable	Impact of the COVID-19 pandemic		The impact of digital economy and epidemic
Digital			0.413 (0.311)	0.441*** (0.038)
Covid-time	−0.839*** (0.102)		−0.133* (0.084)
Covid-number		−0.324** (0.140)		−0.289 (0.238)
Digital × Covid-time			0.605* (0.320)
Digital × Covid-number				0.083* (0.048)
Constant term	−28.306 (19.817)	−25.471 (19.822)	−9.583 (21.055)	−9.381 (21.064)
$ρ$	0.373*** (0.134)	0.383*** (0.132)	0.327** (0.140)	0.344** (0.138)
W_ Digital			4.603* (2.407)	4.183* (2.387)
W_ Covid-time	1.315 (0.872)		−18.275*** (4.580)
W_ Covid-number		−0.199** (0.083)		−0.098 (0.825)
W_ Digital × Covid-time			−0.095 (2.227)
W_ Digital × Covid-number				0.006 (0.409)
Hausman test value	−13.56	−1.09	−13.21	−1.86
R ²	0.304	0.313	0.332	0.332
Observations	240	240	240	240
LR test (SAR)	14.13**	18.25***	10.29*	11.18*
LR test (SEM)	14.35**	11.47*	10.28*	12.93*
LM-lag	15.18**	18.11***	21.54***	10.91*
LM-err	18.56***	19.45***	25.10***	11.27*
Other variables controls	Yes	Yes	Yes	Yes
Region and year effects controls	Yes	Yes	Yes	Yes

Note: ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

Robustness checks

Firstly, the inverse distance spatial weight matrix is replaced by the economic distance matrix for test, and the expression of economic distance matrix is shown in equation (4). In equation (4), $\bar{Y_{i}}$ and $\bar{Y_{j}}$ are the mean values of GDP per capita in region i and j in the observation period. If the two regions are not the same, $W_{i j}^{e}$ is their inverse of the absolute value of the difference between the mean values of GDP, and this calculation is able to measure the economic gap between the two regions. If the two regions are the same, the value of $W_{i j}^{e}$ is 0.

W_{i j}^{e} = \frac{1}{| \bar{Y_{i}} - \bar{Y_{j}} |}, i \neq j; W_{i j}^{e} = 0, i = j

(4)

Secondly, replace the variable of digital economy with digital finance. The digital finance has benefited from advances in digital technology, and there is an extremely strong correlation between them. The development of digital finance has facilitated the means of payment for tourists, expanded the scale of online tourism transactions, and boosted the tourism economy. Digital finance is denoted by Finance and the data is from Peking University Digital Inclusive Finance Index (Guo, 2020). Considering that tourism arrivals and tourism revenue are highly correlated, tourism arrivals can be used as a better proxy to measure tourism economic resilience. The ratio of the growth rate of tourism arrivals in each region to the growth rate of national total tourism arrivals is used as the dependent variable for regression, which is represented by Tourist and the data is from the national economic and social development statistical bulletin of each region. In addition, the tourism economic resilience is recalculated with reference to the resilience measure of Yi et al., 2023 and tested as a new dependent variable (Res1).⁶ Finally, the four municipalities of Beijing, Shanghai, Tianjin, and Chongqing are removed from the sample and re-examined due to the fact that the municipalities in China differ significantly from other regions in terms of policy orientation, land area, economic power, and population size, which may lead to extreme values. Table 5 shows the robustness test results with different methods, and the estimated results are basically consistent with the baseline regression. There is a significant boosting effect and spatial diffusion effect of the digital economy on tourism economic resilience, confirming that the estimated results are robust and reliable.

Table 5.

The result of robustness checks.

Variable	Economic distance matrix	Independent variable substitution	Dependent variable is tourist	Dependent variable is Res1	Sample adjustment
Digital	0.375*** (0.031)		0.262*** (0.061)	0.045*** (0.016)	0.587*** (0.079)
Finance		6.702** (3.008)
$ρ$	0.218*** (0.074)	0.389*** (0.133)	0.215*** (0.005)	0.485*** (0.118)	0.950*** (0.352)
W_ Digital	0.522** (0.260)		3.618** (1.827)	0.175*** (0.014)	8.600* (4.638)
W_ Finance		5.861*(3.835)
Hausman test value	−14.30	−0.10	−5.84	−28.25	70.74***
R ²	0.553	0.302	0.789	0.350	0.525
Observations	240	240	240	240	208
LR test (SAR)	28.92***	18.68***	19.16**	18.36***	18.81***
LR test (SEM)	34.05***	18.01***	10.02*	13.59**	17.96***
LM-lag	33.01***	18.80***	19.00***	18.83***	19.09***
LM-err	39.08***	18.14***	19.37***	11.52*	18.13***
Other variables controls	Yes	Yes	Yes	Yes	Yes
Region and year effects controls	Yes	Yes	Yes	Yes	Yes

Note: ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

Endogeneity test

The digital economy as an independent variable may give rise to endogeneity problems. In this paper, the annual average temperature volatility of different regions is selected as an instrumental variable for digital economy. (a) The digital economy development depends on information network equipment, and stable temperature helps information network equipment to save energy and reduce consumption, leading to the decreasing development cost of digital industry. There is a strong correlation between climate environment and digital economy, and smaller temperature fluctuation favoring the digital economy development. (b) Temperature fluctuation can affect the digital economy development but not vice versa; thus, the two-way causality problem is overcome. (c) Temperature change is highly random within a certain range, independent of the heterogeneity of individuals and groups, but can influence the process of digital economy development at the same time. It follows that temperature fluctuation can serve as a better instrumental variable for digital economy. The standard deviation of temperature change per year is calculated based on the monthly data of temperature to measure the degree of temperature fluctuation in each region, which is expressed by the variable VAR. According to the result of two-stage least squares (2SLS) method in Table 6, the estimated coefficients are significantly negative for VAR and significantly positive for Digital, indicating that smaller temperature change created good natural conditions for digital economy to promote tourism economic resilience. In addition, the limited information maximum likelihood method, which is insensitive to weak instrumental variable, reveals that the estimated result is similar to 2SLS, indicating that there is no weak instrumental variable problem. Meanwhile, the Anderson Lagrangian statistic test result is 27.484, which rejects the original hypothesis at the 1% level and avoids the under-identification problem.

Table 6.

The result of endogeneity test.

Variable	Digital (first stage)	Res (second stage)
VAR	−0.691*** (0.153)
Digital		2.320** (0.947)
Constant term	−11.341*** (1.778)	−16.412 (13.389)
Other variable controls	Yes	Yes
R ²	0.681	0.673
Prob > F	0.000	0.000
Observations	240	240

Note: ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

Heterogeneity test

This section examines heterogeneity from both regional and policy perspectives, and Table 7 shows the detailed results for the regional perspective. Digital economy has given a significant boost to tourism economic resilience in eastern, central, and western regions. However, the estimated coefficients of W_ Digital are significantly positive only in eastern and western regions. The eastern provinces are mostly developed coastal areas with a strong tourism innovation atmosphere and a high degree of integration between digital technology and tourism. The tourism economy is generally more resilient to risk. The digital transformation of tourism enterprises in western region has been faster in recent years. Under the influence of a series of regional policies (such as the Belt and Road Initiative), extensive cooperation networks have been built in various fields of digital economy and tourism industry, and a synergistic development situation has been formed. At present, China’s north-south economic divide is becoming increasingly apparent, with economic growth slowing in the north and the economic center gradually shifting southwards. North-south economic differences also provide perspectives for regional heterogeneity test. According to the results, the digital economy in the north can only positively affect the local tourism economic resilience, while the digital economy in the south has a significant spatial spillover effect. The positive effect of the digital economy on tourism economic resilience is closely related to the economy development, and the degree of integration of the digital economy and tourism in economically disadvantaged regions has yet to be improved.

Table 7.

The result of regional heterogeneity test.

Variable	East	Central	West	North	South
Digital	0.136*** (0.030)	2.730* (1.455)	0.836** (0.347)	1.504*** (0.556)	0.074*** (0.012)
Constant term	−16.895** (7.832)	−64.441 (41.114)	14.076 (15.389)	−35.426 (28.363)	0.594 (7.598)
$ρ$	0.473*** (0.094)	0.176* (0.089)	0.448*** (0.120)	0.069** (0.029)	0.738*** (0.063)
W_ Digital	2.654*** (0.997)	−2.683 (5.223)	4.645** (1.854)	−4.034 (3.414)	0.135* (0.099)
Hausman test value	−10.29	−29.71	−6.72	14.16	−14.54
R ²	0.689	0.960	0.609	0.667	0.843
Observations	88	64	88	120	120
LR test (SAR)	28.71***	16.80**	21.30***	12.07*	10.76*
LR test (SEM)	28.02***	16.74**	25.13***	12.49*	13.33**
LM-lag	37.94***	17.19**	22.04***	10.92*	11.28*
LM-err	31.72***	16.48**	26.27***	11.47*	13.80**
Other variables controls	Yes	Yes	Yes	Yes	Yes
Region and year effects controls	Yes	Yes	Yes	Yes	Yes

Note: ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

From the policy perspective, China’s free trade area (FTA) policy has had a profound influence on digital economy and tourism industry. Governments in FTAs have introduced a number of policies that are conducive to tourism development, such as allowing foreign travel agencies to engage in outbound tourism and promoting the development of shopping tourism. The establishment of the FTA has facilitated the opening up of the local area to the outside world and the flow of factors, creating a favorable environment for the digital economy to drive the tourism economic resilience. Regions with FTAs are classified as With FTA group and the rest as Without FTA group. Table 8 shows that the influence of the digital economy on tourism economic resilience is not affected by the FTA, and that China’s insistence on opening up allows for more equal access to external resources across different regions, reinforcing the driving force of the digital economy on the tourism economic resilience. In addition, this paper divides the provinces, cities, and autonomous regions into Pilot areas and Non-pilot areas according to the policy of National Smart Tourism Pilot.⁷ Smart tourism is a new model of tourism development based on cloud computing, with mobile terminal applications as the core and information services as the characteristics, which promotes the deep integration of the digital economy and the tourism economy. The National Smart Tourism Pilot has shortlisted a number of cities for early and pilot implementation in tourism digital transformation, with a view to fostering the digital economy as a driving force for sustainable development of the tourism economy. The estimated coefficients of Digital for both Pilot areas and Non-pilot areas are significantly positive, but the estimated coefficients of W_ Digital are insignificant in Pilot areas and significantly negative in Non-pilot areas, indicating that the policy effect is not as effective as desired. Although the pilot policy is conducive to unleashing the driving force of digital economy, it is unable to drive tourism economy to share the dividends of digital technology in the surrounding areas. Policy bias causes a concentration of resources to some specific regions, weakening the spatial diffusion effect of digital economy on tourism economic resilience. The lack of scientific guidance of policy in non-pilot areas may generate vicious competition in tourism industry, leading to a spatial siphoning effect of the digital economy on tourism economic resilience.

Table 8.

The result of policy heterogeneity test.

Variable	With FTA	Without FTA	Pilot areas	Non-pilot areas
Digital	0.226*** (0.075)	1.976*** (0.712)	0.591*** (0.079)	1.284*** (0.473)
Constant term	−16.714 (14.483)	17.530 (24.328)	−20.302 (15.820)	−21.084 (27.948)
$ρ$	0.187** (0.074)	0.351*** (0.131)	0.200*** (0.033)	0.365** (0.141)
W_ Digital	1.895*** (0.069)	4.123** (1.681)	−0.218 (3.085)	−4.048* (2.042)
Hausman test value	−3.68	−25.53	−12.22	−57.29
R ²	0.211	0.872	0.593	0.512
Observations	160	80	152	88
LR test (SAR)	11.63*	21.13***	26.42***	17.35**
LR test (SEM)	12.55*	23.28***	26.63***	11.97*
LM-lag	11.35*	22.80***	31.03***	17.24**
LM-err	11.00*	21.10***	26.22***	11.55*
Other variable controls	Yes	Yes	Yes	Yes
Region and year effects controls	Yes	Yes	Yes	Yes

Note: ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

Further discussion

This section focuses on the influence mechanism of digital economy on tourism economic resilience by using tourism fiscal expenditure, tourism industry cluster, and tourism consumption as mediating variables and applying the mediating effect model to investigate whether the digital economy can influence tourism economic resilience through different mediating variables. Tourism financial expenditure and tourism consumption are expressed as Expenditure and Consumption, respectively, and the data are from the China Statistical Yearbook. The formula for tourism industry cluster is as follows:

C l u s t e r_{i} = \frac{E_{i, t} / E_{i}}{E_{t} / E}

(5)

In equation (4), E_it and E_i are the number of people employed in tourism in region i and the total number of people employed in region i, and E_t and E are the number of people employed in tourism nationwide and the total number of people employed nationwide. The proportion of tourism employees in region i exceeds that of the whole country and the degree of tourism industry cluster is higher if Cluster is greater than 1. On the contrary, the degree of tourism industry cluster is lower if Cluster is smaller than 1. Data of the number of employees in tourism is from the China Culture and Tourism Yearbook, and data of the number of people employed nationwide is from the China Statistical Yearbook. Taking tourism fiscal expenditure as an example, the mediating effect model is as follows:

E x p e n d i t u r e_{i t} = α_{0} + β_{1} D i g i t a l_{i t} + β_{2} X_{i t} + r e g i o n_{i} + y e a r_{t} + ε_{i t}

(6)

R e s_{i t} = α_{0} + β_{1} D i g i t a l_{i t} + β_{2} E x p e n d i t u r e_{i t} + β_{3} X_{i t} + r e g i o n_{i} + y e a r_{t} + ε_{i t}

(7)

As shown in Table 9, the digital economy can stimulate tourism fiscal expenditure, drive tourism industry cluster and increase tourism consumption. All mediating variables are significantly positive, confirming the existence of mediating effects. The expansion of tourism fiscal expenditure, tourism industry cluster, and tourism consumption growth are important channels through which the digital economy increases tourism economic resilience. Hypotheses 2, 3, and 4 are proved.

Table 9.

The result of mediating effect test.

Variable	Expenditure	Res	Cluster	Res	Consumption	Res
Digital	0.540*** (0.025)	0.303 (0.368)	0.248*** (0.037)	0.111 (0.230)	0.011** (0.004)	0.321 (0.209)
Expenditure		0.553*** (0.104)
Cluster				0.998*** (0.372)
Consumption						3.275*** (0.934)
Constant term	0.327 (0.800)	6.508 (6.767)	−3.448*** (1.170)	9.917 (6.786)	−3.359*** (0.462)	−4.530 (7.301)
R ²	0.840	0.543	0.966	0.795	0.822	0.967
Observations	240	240	240	240	240	240
Other variable controls	Yes	Yes	Yes	Yes	Yes	Yes
Region and year effects controls	Yes	Yes	Yes	Yes	Yes	Yes

Note: ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

IV-based causal mediating analysis is used to identify the causal relationship between digital economy and tourism economic resilience to overcome the endogeneity problem of the mediating effect test (Dippel et al., 2020). The total effects corresponding to three mediating variables are significantly positive, verifying the positive influence of digital economy on tourism economic resilience. The estimated coefficients of indirect effect are also significantly positive, which proves the existence of mediating effect, and the three mediating variables explain 50.941%, 88.651%, and 89.568% of the tourism economic resilience, respectively. The first stage F-statistic value is 28.270, which is close to 30, indicating that the estimated results tend to be close to the true value. The mediating effect test is scientifically sound (the result is shown in Table 2 in Appendix).

Concluding remarks

This paper examines the effect and mechanism of digital economy affecting the tourism economic resilience, with China as the research subject. It is found that the digital economy can promote tourism economic resilience with a significant spatial diffusion effect and can also mitigate the negative impact of COVID-19 pandemic on tourism economic resilience. Heterogeneity test indicates that the spatial diffusion effect of digital economy on tourism economic resilience is significant only in the eastern, western, and southern regions. The spatial spillover effect of digital economy in policy pilot areas is not obvious, while the digital economy in non-pilot areas has a spatial siphoning effect on tourism economic resilience, meaning that policy effectiveness still needs to be improved. The mediating effect model suggests that the digital economy can enhance tourism economic resilience by stimulating tourism fiscal expenditure, guiding tourism industry cluster, and increasing tourism consumption, which provides strong policy insights. The innovation of this paper is that the digital economy and tourism economic resilience are incorporated into a unified analytical framework to examine the digital economy as an influencing factor for tourism economic resilience, which helps to recognize the importance of digital economy for tourism. Regarding empirical test, heterogeneity test from angles of region and policy pilots reveals variability in the influence of digital economy on tourism economic resilience, which broadens the research perspective. The examination of influence mechanism based on industrial policy theory, industry cluster theory, and expectation confirmation theory deepens the application of classical theories in tourism research and lays a theoretical foundation for the exploration of the interaction law between digital economy and tourism economic resilience.

The research conclusions have important implications for integrated development of digital economy and tourism economy and some suggestions are provided: (a) government should introduce policies to accelerate the digital transformation of tourism industry and provide institutional guarantees for digital technology to promote tourism economy development. Provide financial subsidies to tourism enterprises that are operating in difficulty. Set standards for digital transformation of enterprises, and reward enterprises that qualified. (b) Focus on cultivating tourism industry clusters, piloting the construction of smart tourism parks, and providing space carriers for cooperation between digital enterprises and tourism enterprises. Encourage tourism enterprises to jointly carry out industry technological innovation, and realize the knowledge spillover effect generated by agglomeration to improve tourism economic resilience. (c) In the post-epidemic era, tourism companies should grasp the shift in market demand and use digital technology to innovate consumption and service scenarios to improve consumer satisfaction and increase the scale of tourism consumption. Relying on digital technology to tap the market potential and promote the resilient growth of tourism economy will be one of the important tasks for tourism companies.

Several limitations are noted here. First, the measurement of tourism economic resilience can be improved, and economic resilience can be assessed from the perspectives of recovery, stability, and growth to highlight the dynamic nature of economic resilience. Second, the research object of this paper is China, and other countries can be included in the sample to analyze the influence of digital economy on global tourism economic resilience and enhance the generalizability of research findings. Third, this paper explores the influence mechanism based on industrial policy theory, industry cluster theory, and expectation confirmation theory, and the research perspective is not focused enough. It would help to improve the theoretical content if a specific theory were to be explored in order to investigate the influence mechanism of digital economy on tourism economic resilience.

Supplemental Material

Supplemental Material - Can digital economy improve tourism economic resilience?—Evidence from China

Supplemental Material for Can digital economy improve tourism economic resilience?—Evidence from China by Rui Tang in Journal of Tourism Economics

Footnotes

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by PRC National Social Science Foundation Youth Program Research on the Mechanisms and Effects of Resilience Enhancement of Tourism Enterprises under Digital Transformation: [23CJY047].

ORCID iD

Rui Tang

Supplemental Material

Supplemental material for this article is available online.

Notes

Author biography

Rui Tang (1988-) male, Hefei, Anhui, Ph.D., Lecturer, Academy of Strategy for Innovation and Development, Anhui University, main research direction is industrial economics. ORICD ID: 0000-0003-3189-6340. Rui Tang received his master’s degree in management and PhD in economics from East China Normal University. From 2019, Rui Tang has published several papers in SSCI journals, including Tourism Management Perspectives, Journal of the Asia Pacific Economy, International Journal of Tourism Research, Economic Research-Ekonomska Istraživanja, and Current Issues in Tourism. He also serves as an external reviewer for several SSCI journals, including Emerging Markets Finance and Trade, SAGE Open, and Tourism Management Perspectives.

References

Aithal

Anil

Angmo

(2023) Rural tourism in India: case studies of resilience during crisis. Worldwide Hospitality and Tourism Themes 15(1): 63–73. DOI: 10.1108/WHATT-08-2022-0104.

Algieri

Álvarez

(2023) Assessing the ability of regions to attract foreign tourists: the case of Italy. Tourism Economics 29(3): 788–811. DOI: 10.1177/13548166211068669.

Belhadi

Kamble

Benkhati

, et al. (2023). Does strategic management of digital technologies influence electronic word-of-mouth (eWOM) and customer loyalty? Empirical insights from B2B platform economy. Journal of Business Research 156: 113548. DOI: 10.1016/j.jbusres.2022.113548.

Sanina

Balashov

Rubtcova

(2023) The socio-economic efficiency of digital government transformation. International Journal of Public Administration 46(1): 85–96. DOI: 10.1080/01900692.2021.1988637.

Aura

de Graaff

Peter

(2001) Resilience: An Evolutionary Approach to Spatial Economic Systems. Amsterdam: Tinbergen Institute. Tinbergen Institute Discussion Papers 01-100/3.

Cheng

Zhao

(2022) Exploring the spatio-temporal evolution of economic resilience in Chinese cities during the COVID-19 crisis. Sustainable Cities and Society 84: 103997. DOI: 10.1016/J.SCS.2022.103997.

Chi

(2016) Research note: employment and wage sensitivity to tourism activities – the case of US tourist arrivals and expenditure in Hawaii. Tourism Economics 22(1): 171–178. DOI: 10.5367/te.2014.0422.

Cozzio

Tokarchuk

Maurer

(2023) All-inclusive holiday packages, tourist consumption and spending patterns at tourism destinations. Tourism Review 78(1): 89–100. DOI: 10.1108/TR-12-2021-0572.

Dippel

Ferrara

Heblich

(2020) Causal mediation analysis in instrumental-variables regressions. STATA Journal: Promoting communications on statistics and Stata 20(3): 613–626. DOI: 10.1177/1536867X20953572.

10.

Ema

C-R

Hamdouch

Iațu

(2023) Innovation in urban governance and economic resilience. The case of two Romanian regional metropolises: timișoara and Cluj Napoca. Cities 132: 104090. DOI: 10.1016/j.cities.2022.104090.

11.

Fadel

Meservy

Kirwan

(2022) Information filtering in electronic networks of practice: an fMRI investigation of expectation (dis) confirmation. Journal of the Association for Information Systems 22(2): 491–520. DOI: 10.17705/1jais.00731.

12.

Gerwyn

(2023) An analysis on the economic resilience and vulnerability of local economies in the Philippines to hydrometeorological disasters. International Journal of Disaster Risk Reduction 84: 103447. DOI: 10.1016/j.ijdrr.2022.103447.

13.

Girish

(2023) Tourism resilience in the ‘new normal’: beyond jingle and jangle fallacies? Journal of Hospitality and Tourism Management 54: 513–520. DOI: 10.1016/j.jhtm.2023.02.006.

14.

Guo

(2020) Measuring China’s digital financial inclusion: index compilation and spatial characteristics. China Economic Quarterly (04): 1401–1418.

15.

Yifan

Bei

(2022) Influencing factors and multiple paths of construction ability of digital government: qualitative comparative analysis based on 31 Chinese provinces. Procedia Computer Science 199: 1213–1220. DOI: 10.1016/j.procs.2022.01.154.

16.

Hall

Safonov

Naderi Koupaei

(2023) Resilience in hospitality and tourism: issues, synthesis and agenda. International Journal of Contemporary Hospitality Management 35(1): 347–368. DOI: 10.1108/IJCHM-11-2021-1428.

17.

Holling

(1973) Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4(1): 1–23. DOI: 10.1146/annurev.es.04.110173.000245.

18.

Huang

Tian

(2023) How does heterogeneous environmental regulation affect net carbon emissions: spatial and threshold analysis for China. Journal of Environmental Management 330: 117161. DOI: 10.1016/j.jenvman.2022.117161.

19.

Huda

(2023) Digital marketplace for tourism resilience in the pandemic age: voices from budget hotel customers. International Journal of Organizational Analysis 31(1): 149–167. DOI: 10.1108/IJOA-10-2021-2987.

20.

Kalbaska

Janowski

Estevez

, et al. (2017) When digital government matters for tourism: a stakeholder analysis. Information Technology & Tourism 17: 315–333. DOI: 10.1007/s40558-017-0087-2.

21.

Kim

(2023) Spatial spillovers of sport industry clusters and community resilience: bridging a spatial lens to building a smart tourism city. Information Processing & Management 60(3): 103266. DOI: 10.1016/j.ipm.2023.103266.

22.

Kocak

Okumus

Altin

(2023) Global pandemic uncertainty, pandemic discussion and visitor behaviour: a comparative tourism demand estimation for the US. Tourism Economics 29(5): 1225–1250. DOI: 10.1177/13548166221100692.

23.

Lee

Kim

(2021) Brand tourism effect in the luxury hotel industry. The Journal of Product and Brand Management 30(1): 90–103. DOI: 10.1108/JPBM-09-2019-2574.

24.

Lee

Wang

(2023) Impacts of political fragmentation on inclusive economic resilience: examining American metropolitan areas after the great recession. Urban Studies 60(1): 26–45. DOI: 10.1177/00420980211064455.

25.

Lei

Liang

Ruan

(2023) Evaluation on the impact of digital transformation on the economic resilience of the energy industry in the context of artificial intelligence. Energy Reports 9: 785–792. DOI: 10.1016/j.egyr.2022.12.019.

26.

Liu

Huang

Bao

, et al. (2018) On amendment of the theory of evolution of nearby tourism destinations’ spatial competition. Tourism Science 32(05): 44–53. DOI: 10.16323/j.cnki.lykx.2018.05.004.

27.

Mao

Yafei

(2022) The development of digital industry and urban economic resilience. Finance & Economics (08): 60–75.

28.

Marcelo

Salume

Barbosa

, et al. (2023) The path to digital maturity: a cluster analysis of the retail industry in an emerging economy. Technology in Society, 72: 102191. DOI: 10.1016/j.techsoc.2022.102191.

29.

Martin

(2009) Roepke lecture in economic geography—rethinking regional path dependence: beyond lock-in to evolution. Economic Geography 86(1): 1–27. https://www-jstor-org.web.bisu.edu.cn/stable/27806893

30.

Mehrdad

Heidary Dahooie

Skare

(2022) COVID-19 crisis and resilience of tourism SME’s: a focus on policy responses. Economic Research-Ekonomska Istraživanja 35(1): 5556–5580. DOI: 10.1080/1331677X.2022.2032245.

31.

Meng

Zollet

(2023) Neo-endogenous revitalisation: enhancing community resilience through art tourism and rural entrepreneurship. Journal of Rural Studies 97: 105–114. DOI: 10.1016/j.jrurstud.2022.11.016.

32.

Myrto

Pierri

Timmer

(2023) IT shields: technology adoption and economic resilience during the COVID-19 pandemic. Labour Economics 81: 102330. DOI: 10.1016/j.labeco.2023.102330.

33.

Nelson

Sumesh

(2023) International tourism: inimitable vs imitable core tourism resources and destination image. Journal of Destination Marketing & Management 27: 100756. DOI: 10.1016/j.jdmm.2022.100756.

34.

Pedro

MG-V

Elche

Martínez-Pérez

, et al. (2017) Determinants of radical innovation in clustered firms of the hospitality and tourism industry. International Journal of Hospitality Management 61: 45–58. DOI: 10.1016/j.ijhm.2016.11.002.

35.

Qin

Yang

Yue

(2022) Research on development of digital finance in improving efficiency of tourism resource allocation. Resources, Environment and Sustainability 8: 100054. DOI: 10.1016/j.resenv.2022.100054.

36.

Reynolds

(2021) The role of air transport in tourism market access: a framework for capturing spatial, temporal and industry variability in air traffic flows. In: Ferrante

Fritz

Öner

(eds) Regional Science Perspectives on Tourism and Hospitality. Advances in Spatial Science. Cham: Springer. DOI: 10.1007/978-3-030-61274-0_6.

37.

Ron

Peter

(2015) On the notion of regional economic resilience: conceptualization and explanation. Journal of Economic Geography 15(1): 1–42. DOI: 10.1093/jeg/lbu015.

38.

Ron

Simona

(2009) Related variety, trade linkages, and regional growth in Italy. Economic Geography 85(3): 289–311. DOI: 10.1111/j.1944-8287.2009.01034.x.

39.

Rui

(2021) Trade facilitation promoted the inbound tourism efficiency in Japan. Tourism Management Perspectives 38: 100805. DOI: 10.1016/j.tmp.2021.100805.

40.

Rui

(2022) A study of the effects and mechanisms of the digital economy on high-quality tourism development: evidence from the Yangtze river Delta in China. Asia Pacific Journal of Tourism Research 27(11): 1217–1232. DOI: 10.1080/10941665.2023.2174033.

41.

Rui

(2023a) Digital economy drives tourism development—empirical evidence based on the UK. Economic Research-Ekonomska Istraživanja 36(1): 2003–2020. DOI: 10.1080/1331677X.2022.2094443.

42.

Rui

(2023b) Digital economy and total factor productivity of tourism enterprises in China: the perspective of market competition theory. Current Issues in Tourism: 1–16. DOI: 10.1080/13683500.2022.2159338.

43.

Saputra

IGG

Pitanatri

PDS

(2022) Digital acceptance and resilience in rural tourism destination: a case of Bali. In: Hassan

Sharma

Kennell

, et al. (eds) Tourism and Hospitality in Asia: Crisis, Resilience and Recovery. Singapore: Springer. DOI: 10.1007/978-981-19-5763-5_18.

44.

Schönherr

Eller

Kallmuenzer

, et al. (2023) Organisational learning and sustainable tourism: the enabling role of digital transformation. Journal of Knowledge Management 27(11): 82–100. DOI: 10.1108/JKM-06-2022-0434.

45.

Shi

Gong

Wang

, et al. (2023) Heterogeneity of inbound tourism driven by exchange rate fluctuations: implications for tourism business recovery and resilience in Australia. Current Issues in Tourism 26(3): 450–467. DOI: 10.1080/13683500.2021.2023478.

46.

Shutters

Waters

(2022) Industrial structure and a tradeoff between productivity and economic resilience. Studies in Business and Economics 17(3): 224–240. DOI: 10.2478/sbe-2022-0057.

47.

Simmie

Martin

(2010) The economic resilience of regions: towards an evolutionary approach. Cambridge Journal of Regions, Economy and Society 3(1): 27–43. DOI: 10.1093/cjres/rsp029.

48.

Cameli

. (2023). A complexity economics framework for 21st-century industrial policy. Structural Change and Economic Dynamics, 64:168–178. DOI: 10.1016/j.strueco.2022.11.007.

49.

Sun

Song

(2021) Sustainable tourism development from a resilience perspective. Tourism Tribune (05): 8–10. DOI: 10.19765/j.cnki.1002-5006.2021.05.004.

50.

Tang

(2021) Does trade facilitation promote the efficiency of inbound tourism? The empirical test based on Japan. International Journal of Tourism Research 23(1): 39–55. DOI: 10.1002/jtr.2390.

51.

Tang

Cai

(2022) Does the digital economy improve urban tourism development? An examination of the Chinese case. Sustainability 14(23): 15708. DOI: 10.3390/su142315708.

52.

Tsourgiannis

Valsamidis

(2019) Digital marketing in tourism: why Greek tourists use digital marketing applications like Airbnb. International Journal of Culture, Tourism and Hospitality Research 13(4): 473–486. DOI: 10.1108/IJCTHR-03-2019-0043.

53.

Vassilis

(2023) Digital age: the changes in economy, society, politics. Journal of Balkan and Near Eastern Studies 25(1): 160–165. DOI: 10.1080/19448953.2022.2132004.

54.

Qin

Feng

(2022) The improved entropy weighting model in water quality evaluation based on the compound function. Environmental Monitoring and Assessment 194: 662. DOI: 10.1007/s10661-022-10304-4.

55.

Yan

, et al. (2022) Can coordinated development of manufacturing and information communication service industries boost economic resilience? An empirical study based on China’s provinces. Sustainability 14(17): 10758. DOI: 10.3390/su141710758.

56.

Yang

Kim

Hwang

. (2022a). The spatial moderating effect of environmental pollution on the relationship between tourism and community resilience. Tourism Management 93: 104554, DOI: 10.1016/j.tourman.2022.104554.

57.

Yang

Zhao

, et al. (2022b) Study on urban resilience from the perspective of the complex adaptive system theory: a case study of the Lanzhou-Xining urban agglomeration. International Journal of Environmental Research and Public Health 19: 13667. DOI: 10.3390/ijerph192013667.

58.

Zou

Zhang

, et al. (2022) Spatial–temporal evolution characteristics of agricultural economic resilience: evidence from Jiangxi province, China. Agronomy 12(12): 3144. DOI: 10.3390/agronomy12123144.

59.

Lee

C-C

Peng

. (2023). Does regional integration improve economic resilience? Evidence from urban agglomerations in China. Sustainable Cities and Society 88: 104273. DOI: 10.1016/j.scs.2022.104273.

60.

Zhang

Shang

(2022) The influence and mechanism of digital economy on the development of the tourism service trade—analysis of the mediating effect of carbon emissions under the background of COP26. Sustainability 14: 13414. DOI: 10.3390/su142013414.

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