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Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 6: 1692 - 1706

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.06.024

Rural Revitalization and Ecotourism

Does the Digital Economy Optimize Tourism Industry Structure? Effects and Mechanisms Based on Quantile Regression and Threshold Modeling

  • LIU Lei ,
  • SU Juan , * ,
  • XUE Xuanxuan
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  • College of Tourism, Jishou University, Zhangjiajie, Hunan 427000, China
* SU Juan, E-mail:

LIU Lei, E-mail:

Received date: 2023-09-27

  Accepted date: 2024-02-06

  Online published: 2024-12-09

Supported by

The National Natural Science Foundation of China(42261042)

The Hunan Provincial Natural Science Foundation(2024JJ7410)

The Hunan Provincial Graduate Student Research and Innovation Program(CX20221095)

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Abstract

A comprehensive evaluation index system is constructed, and the entropy weight TOPSIS method is used to measure the optimization level of the digital economy and tourism industry structure of 30 provinces in China from 2012 to 2021. Moreover, models such as quantile regression and panel threshold are used to explore the influence of the digital economy (DIG) on the optimization of the tourism industry structure (TIS) as well as its transmission mechanism. The study reveals that DIG significantly promotes TIS, which remains valid after endogeneity and robustness tests; the impact of DIG on TIS exhibited a “U-shape” effect that first decreases and then increases, and its highest significance is at the 90% quartile level. Threshold model tests revealed a nonlinear threshold effect with DIG and tourism total factor productivity (TTFP) as a single threshold and tourism technological progress index (TECH) as a double threshold, and the second threshold has the largest effect of 0.163. Mechanism analysis found that the mediating impact of the DIG on the TIS was mediated by increasing the TTFP, and the TECH accounted for the highest proportion of 12.15%. Regional analysis revealed that the role of DIG on the TIS is Central>East>West>Northeast, and the empowering effect is more significant in the high digital economy level area and the high tourism industry structure optimization area.

Key words： digital economy; tourism industry structure optimization; tourism total factor productivity; quantile regression; panel threshold model

Cite this article

LIU Lei , SU Juan , XUE Xuanxuan . Does the Digital Economy Optimize Tourism Industry Structure? Effects and Mechanisms Based on Quantile Regression and Threshold Modeling[J]. Journal of Resources and Ecology, 2024 , 15(6) : 1692 -1706 . DOI: 10.5814/j.issn.1674-764x.2024.06.024

1 Introduction

The emergent digital economy profoundly affects global economic and social development. It utilizes digitized knowledge and information as the key production factors and a modern information network as the important carrier to facilitate the intelligent development of industries through the application of digital technology such as artificial intelligence, Internet of Things, and big data (Xia et al., 2023). In recent years, countries around the world have been actively promoting digital industrialization and industrial digitization, as well as the deep integration of the digital economy and the real economy. Chinese government also attaches great importance to the development of the digital economy. The China Digital Economy Development Research Report (2023) mentions that the penetration rate of the digital economy in China’s primary, secondary and tertiary industries was as high as 10.5%, 24.0%, and 44.7% respectively in 2022. At present, digital intelligence, as a “new high-quality productivity”, combines revolutionary breakthroughs in technology with innovative allocation of production factors to promote in-depth transformation and upgrading of China’s industries and enhance economic growth momentum.
In the context of the booming digitalization of the industry, big data, cloud computing, VR, RFID, and metaverse, among other digital technologies, continue to promote innovation in tourism industry production, operation and management; enrich the forms of tourism products, services, and experience, facilitate the development of the tourism industrial models and structures (Zhao, 2022a). Currently, the digital economy also plays an increasingly important role in promoting the innovative development of tourism resources and lowering carbonization in tourism development (Wang et al., 2022a). However, with the booming development of the tourism industry, structural imbalances, such as scale system disorders and low efficiency of factor allocation have gradually arisen, and large-scale development and utilization of tourism resources have also led to the emergence of tourism ecosystem imbalance problems (Wang and Xie, 2023). These problems hinder the high-quality development of the tourism industry, and the allocation of tourism factors, the development of tourism resources and the ecological balance of tourism need to be deeply optimized. Therefore, the combination of the tourism industry and digitalization is crucial for transforming the tourism industry as well as improving its quality at the present time. How can the digital economy be fully exploited to optimize the structure of the tourism industry, clarifying the relationship between the integration of the digital economy and the tourism industry is becoming a hot topic.

2 Literature review

The academic community has explored topics such as the digital economy and industrial structure in depth, including the following aspects. First, the mechanism of how the digital economy empowers economic growth and industrial structure change and the associated path has been explored. Tapscott first put forward the concept of the digital economy in 1996 and stated that it would greatly affect the economic environment and social activities (Carlsson, 2004). With the development of the economy, the integration of the digital economy in the industry has gradually become widespread. At the microlevel, digital technology lowers enterprise costs, enhances the diversification of consumption scales, and expands the boundaries of enterprise supply and demand. It has led to significant increases in zero-cost enterprise production, data valorization, economies of scale, and economies of scope (Goldfarb and Tucker, 2019; Jing and Sun, 2019; Si et al., 2023). Macroscopically, the digital economy promotes regional economic growth through technological innovation and new factor allocation (Lyu et al., 2023). What’s more, digital economy enabled industrial development can be divided into two ways. The first is digital technology, which involves the role of technological innovation and promotes the development of industrial digitization and intelligence (Pan et al., 2022). The second is the data element, which introduces the factor integration effect and promotes the dynamic diversified equilibrium of supply and demand of factors in the whole industry chain (Liu and Chen, 2021).
The second aspect is research on the tourism industry structure. The theory of industrial economics, including the Petty-Clark theory, holds that economic growth is closely related to changes in industrial structure. Modern industrial economists believe that industrial structure optimization is an endogenous driving force behind industrial development, and its explicit characteristics are rationalization and seniority (Xue, 2009; Ju et al., 2015). The study of the tourism industry structure falls under industrial economics, reflecting the economic and technological links between tourism industries and their proportionality. With the improvement in the quality of tourism economic growth, the coordination ability and degree of association among industries within the tourism industry increase and evolve toward a higher state. The former represents the degree of rationality in the tourism industry structure, whereas the latter represents an advancement in the structure of the tourism industry (Yeh and Lin, 2013; Liu et al., 2014). However, with improvements in the quality of the economy and the promotion of Chinese-style modernization, only improving the rationality and advancing the structure of the tourism industry may be insufficient to improve the quality of the industry, and improvements to the structure of the tourism industry need to be combined with comprehensive developments in economics, politics, ecology, culture, and society (Sun, 2024). Therefore, efforts to improve the tourism industry structure in the modern era focus on enhancing tourism resource allocation efficiency as well as the innovative development and ecological exploitation of tourism resources (Tang et al., 2017; Liu et al., 2021). Therefore, this study divides the optimization of the tourism industry structure into rationalization, advancement, efficiency, and ecologization.
Considering the integration of the digital and real economies, the digital economy is currently penetrating the tourism industry. Digital technology promotes upgrades to tourism products, services, and supply chain models and generates new business models to promote structural innovation in the tourism industry. Data factors drive improvements in the tourism factor production efficiency and tourism resource allocation efficiency, and the efficient flow of factors promotes the dynamic equilibrium of the supply and demand structure of the tourism industry (Lin et al., 2023; Sun and Guo, 2023). The utilization of digital technology and data elements in the tourism industry promotes the coordination of the techno-economic linkages of the constituent elements within the industry, promotes the evolution of the tourism industry toward high value-added, improves the efficiency of tourism resource allocation, and facilitates the ecological and environmental protection of tourism under the goal of “double carbon” (Gössling, 2020; Wang et al., 2022b). The digital economy has become a crucial driving to optimizing the structure of the tourism industry in the modern era.
In summary, the existing research has provided inspiration for this study, but there are several research gaps need to be enriched. First, existing research on the digital economy and industrial structure focuses on agriculture, manufacturing, and other industries, whereas the tourism industry is neglected. In fact, the tourism industry, as an information-intensive industry, has a high degree of fitness with the digital economy, and the digital transformation of the tourism industry is flourishing. Moreover, theoretical rather than quantitative analysis has been used to investigate the effect of the digital economy on the tourism industry structure. By quantitatively analyzing the data, the relationship between the digital economy and tourism industry can be revealed more scientifically, making it easier to compare with other industries. Second, the digital economy has been evaluated from the perspective of digital economy connotations, but a broad overview of the application of the digital economy is lacking. The degree of application of digital economy reflects its development level and should be included in the measurement system. Third, research on the impact of the digital economy on the optimization of the tourism industry structure has focused on rationalization and seniority, but new perspectives on the development of tourism resources as well as tourism ecological development in the modern era are lacking. Currently, digital innovation has become an important driving force for tourism resource development, which helps to enhance the attractiveness of tourism resources and reduce the destruction of tourism ecological environment. Therefore, this study asks and will answer the following four questions: Does the DIG have an impact on TIS? What are the characteristics of the DIG impact on TIS? What are the pathways through which DIG impact on TIS? What are the regional differences in the DIG's impact on TIS?

3 Theoretical mechanism and hypotheses

3.1 Direct effect

The digital economy has a direct impact on the optimization of the structure of the tourism industry. The tourism industry is a pheromone-intensive industry, and digital knowledge and information processing technology facilitate information collection on tourism supply and demand; processing; and transmission of low-cost, cross-regional, cross-industry flow. The information flow guides the integration and convergence of tourism flows, including capital, labor, service, and material flows in the tourism industry (Jin et al., 2023). The interaction between information flow and tourism flow promotes the coordination of the proportional relationship and scaling speed of each industry within the tourism industry, as well as the rationalization of the internal structure of the tourism industry. Meanwhile, tourism is a rapidly developing industry, and its economic benefits are crucial (Zhao, 2022b). The development of digital industrialization and industrial digitization has broad implications for the tourism industry. The open and shared business architecture makes up digital platforms and intelligent decision-making hubs, among others. It accelerates the digital extraction of tourism resources and the digital combination of tourism elements and improves the digitalization and intelligence level of tourism enterprises’ operation decision-making, information consultation, booking and purchasing, and tourism payment (Wei, 2022). Therefore, it lowers the entry threshold of the traditional tourism industry, breaks through the boundaries of the traditional tourism industry chain (Zang and Hang, 2023), increases the output value of the highly elastic sector and the proportion of foreign exchange earnings from tourism, and advances the tourism industry sector. The following hypothesis is made according to the above theories.
Hypothesis 1: Digital economy has a positive impact on optimizing the tourism industry structure.

3.2 Indirect effect

A new economic growth theory proposes that economic output is closely related to factors such as capital, labor, and technology (Whiteley, 2000). The optimization of the tourism industry structure involves tourism resource factor reallocation, and improving tourism resource allocation efficiency and tourism eco-efficiency is necessary for the tourism industry in the modern era. On the one hand, the digital economy introduces technological innovation. The utilization of new digital technology in tourism improves the utilization efficiency of existing tourism resources and introduces technological progress to the industry (Gao, 2021). The current intelligent service involves upgrades to tourism scenic spots, travel agencies, hotels and other sectors as well as the interaction between intelligent virtual tourism, such as cloud viewing exhibition, immersive experience, VR scenic spots, digital museums, and the actual experience. Further, the technological advances in the tourism industry have accelerated the innovative development of tourism resources and the intelligent monitoring of the ecological development of tourism. On the other hand, the digital economy introduces factor integration and upgrading effects (Li and Ren, 2023). The convergence of data and tourism elements has led to drastic changes in the way tourism factors are combined as well as in the scale and speed of flows. The efficient interaction of new tourism scenes, products, and business forms with the traditional tourism industry improves the technical efficiency and scale efficiency of tourism resources under the existing technology level while increasing outputs from factor inputs (Sun et al., 2022), which improves the total factor productivity of the tourism industry. Enhance the total factor productivity of the tourism industry and optimize the structure of the tourism industry under the dual role of “technological innovation” and “data elements” of the digital economy. The following hypothesis is made according to the above theories.
Hypothesis 2: Digital economy contributes to the optimization of the tourism industry structure by promoting tourism total factor productivity.

3.3 Nonlinear impact

The digital economy has a nonlinear impact on the optimization of the tourism industry structure. On the one hand, the digital economy promotes technological innovation and factor allocation enhancement in the tourism industry, but the digital transformation of the tourism industry at the conceptual level among tourism consumers and the establishment of trust is a slow, delicate process (Hao et al., 2023). In the early stages, the digital economy promotes a rapid increase in tourism demand, which is incongruous with the slow increase in the supply capacity of labor, capital, and technology in the tourism industry, and the alignment between supply and demand in the digital economy-enabled tourism industry needs to be strengthened (Kim et al., 2021). On the other hand, a discrepancy between data and tourism elements exists in terms of spatial “flows” (Ma et al., 2023). Data elements enable the exchange, flow, and integration of tourism industry elements, such as information, capital, talent, and technology in the virtual digital space at a low cost. However, the life cycles of different tourism resources and tourism products are different, and same-frequency resonance between traditional tourism elements and digital elements is still far from being realized (Yuan et al., 2023). Therefore, the discrepancy between the supply and demand structure and spatial flow may result in a nonlinear relationship between the optimization of the tourism industry structure and the digital economy. The following hypothesis is made according to the above theories.
Hypothesis 3: Digital economy has a nonlinear impact on the optimization of the tourism industry structure (see Fig. 1).
Fig. 1 Effect and mechanism of digital economy on the optimization of tourism industry structure

4 Models and variables

4.1 Benchmark model

The basic regression model is constructed to study the impact of the digital economy on the optimization of the tourism industry structure. The individual and time double fixed-effects models are established to improve the reliability of the results.
T I S i t = α 0 + α 1 D I G i t + α 2 Z i t + μ i + τ i + σ i t
where T I S i trepresent the tourism industry structure optimization index; D I G i trepresent the digital economy development level index; Z i treflect the other control variables; α0 is the constant term; α1 reflects the degree of the digital economy’s influence on the optimization of the tourism industry structure; α2 reflects the degree of influence of control variables on the optimization of tourism industry structure; μi and τi represent the individual and time fixed effects; and σit is a random perturbation term; i is the province; t is the period.

4.2 Quantile regression model

Traditional OLS regression usually only reflects the results of the effect of homogenization between variables without capturing the differences in the influence of the explanatory variables at different levels of the explained variables. The quantile regression model proposed by Koenker and Bassett (1978) can reflect the relationship between the optimization of the tourism industry structure and the digital economy under different quartiles and alleviate the problem of the results being interfered for the extreme data values. Therefore, the quantile regression model (2) is as follows:
Q q ( T I S i t | D I G i t , Z i t ) = β 0 + β 1 D I G i t + β 2 Z i t + μ i t + τ i t + σ i t
where TISit, DIGit, Zit, μi , τi, and σit are the same as those in model (1); β0 is a constant term; β1 denotes the extent of the impact of the digital economy on the optimization of the tourism industry structure at different quantile levels; β2 denotes the degree of influence of control variables on the optimization of tourism industry structure at different quantile levels. The quantile regression coefficient β is estimated using the bootstrap method with put back sampling to obtain the confidence interval of the sample and then estimate the regression coefficients of the model.

4.3 Intermediary effect model

To explore the role of the digital economy on the optimization of the tourism industry structure path, theoretical analysis was utilized to construct the following step-by-step regression of the mediating effect model:
T I S i t = α 0 + c D I G i t + β Z i t + μ i + τ i + σ i t
M E D i t = α 0 + c D I G i t + β Z i t + μ i + τ i + σ i t
T I S i t = α 0 + b M E D i t + c ′ D I G i t + β Z i t + μ i + τ i + σ i t
where M E D i tis the mediating variables, including the total factor productivity of tourism (TTFP) and its decomposition variables, namely technical progress index (TECH), pure technical efficiency of tourism (PECH) and scale efficiency of tourism (SECH), c is the total effect coefficient of the DIG affecting the TIS, a is the impact coefficient of the DIG on MED, b is the impact coefficient of MED on TIS, and c' is the direct effect coefficient of the DIG affecting the TIS.

4.4 Panel threshold model

Hansen’s (1999) panel threshold model was used to test the nonlinear relationship between the digital economy and the optimization of tourism industry structure, and model (6) is constructed based on model (1):
$\begin{aligned} T I S_{i t}= & \delta_{0}+\delta_{1} D I G_{i t} \times I(\varphi \leqslant \gamma)+\delta_{2} D I G_{i t} \times I(\varphi>\gamma)+ \\ & \beta Z_{i t}+\mu_{i}+\tau_{t}+\sigma_{i t} \end{aligned}$
where φis the threshold variable and γis the specific threshold value. Additionally, the core explanatory variable D I Gas well as M E Dare used as threshold variables. I ( • )is an indicator function that takes the value of 1 or 0. The remaining variables are the same as those in model (1). Model (6) is used to represent the single-threshold case, and the multi-threshold model is not presented again.

4.5 Variable selection

4.5.1 Dependent variable—TIS

Optimizing the tourism industry structure is a dynamic process, which involves continuous coordination, upgrading, and increasing efficiency as well as an ecological approach to the growth, state and mode of the tourism industry (Liu et al., 2022b). Combined with existing studies, the following evaluation index system for optimizing the structure of the tourism industry is constructed, which considers rationalization, advancement, efficiency, and ecology (Table 1).
Table 1 Evaluation index system of tourism industry structure optimization
Target Subsystems Indicators Indicators interpretations
Tourism industry structure
optimization		 Rationalization Y1 Harmonization of proportional relationships in the tourism sector Calculated using the index TRit :
T R i t = ∑ i = 1 n Y i t Y Y i t / L i t Y / L − 1 2
where, T R i t = ∑ i = 1 n Y i t Y Y i t / L i t Y / L − 1 2, T R i t = ∑ i = 1 n Y i t Y Y i t / L i t Y / L − 1 2 (7)
Y2 Annual growth rate of tourism industry revenue Ratio of current gross tourism receipts minus previous year’s gross tourism receipts to previous year’s gross tourism receipts
Y3 Employment growth rate in tourism industry Ratio of persons employed in tourism to persons employed in the tertiary sector
Advancement Y4 Highly elastic sectoral income in the tourism industry Revenue from tourism, shopping, and entertainment sectors
Y5 Innovative output capacity of tourism industry Number of patent applications related to the tourism industry
Y6 Foreign exchange earning capacity of tourism industry Ratio of Inbound tourism revenue to total tourism revenue
Efficiency Y7 Tourism industry investment output rate Gross tourism revenue/Investment in fixed assets in tourism industry
Y8 Labor productivity in tourism industry Ratio of total tourism revenue to total number of employees in tourism
Ecologization Y9 Eco-efficiency of tourism industry Municipal nonhazardous waste disposal rate
Y10 Tourism industry environment investment rate Amount of environmental protection investment×(Total tourism revenue/GDP)

Note: In formula (7), TRit denotes the degree of coordination of the tourism industry sector, and Yit and Lit are the tourism output value and the number of employees at different times and sectors, respectively, this paper considered three sectors: tourist agencies, tourist attractions, and the hotel industry.

4.5.2 Core independent variable—DIG

Combined with the connotation and application characteristics of the digital economy, this study constructs the evaluation index system of the digital economy development level in Table 2, which contains 20 indicators in three dimensions: digital infrastructure, digital industry development, and digital economy application (Liu et al., 2022a). All indicators in the table are positive.
Table 2 Evaluation index system of digital economy development level
Target Subtarget Dimension Indicator Unit
Digital economy Digital infrastructure Hardware facilities X1 Long distance fiber optic cable line length 104 km
X2 Mobile phone base station 104 Individuals
X3 Internet broadband access port 104 Individuals
Software facilities X4 Number of Internet domain names 104 Individuals
X5 Number of IPv4 addresses 104 Individuals
X6 Number of Internet websites 104 Individuals
Digital industry development Digital industrialization X7 Total telecommunications business Billion yuan
X8 Software business revenue Billion yuan
X9 Employment in the information transmission, computer services and software industry People
Industrial digital X10 Websites per 100 businesses Billion yuan
X11 E-commerce sales Billion yuan
X12 Computers per 100 population People
Digital economy applications Digital users X13 Internet broadband access users 104 households
X14 Digital telephone users 104 households
Digital innovation X15 R&D investment intensity %
X16 Number of R&D organizations
X17 Number of patent applications granted		 Individual
Individual
Digital financial inclusion X18 Digital inclusion financial digitization index -
X19 Digital financial inclusion coverage breadth index -
X20 Depth of use index for digital financial inclusion -

4.5.3 Intermediary variables—TTFP, TECH, PECH, SECH

The essence of structural optimization is recombining resource elements, and TTFP is closely related to the optimization of the tourism industry structure. This study construct the TTFP measurement system presented in Table 3, which contains four input indicators and two output indicators (Ji and Li, 2022). The DEA Malmquist index method was used to measure the TTFP of 30 provinces in China from 2012 to 2021. TTFP was decomposed into TECH, EFCH, PECH, and SECH, where PECH×SECH = EFCH. A value of the index greater than 1 indicates an increase in the total factor productivity in tourism from t to t+1. When it is equal to or less than 1, it indicates no change or regression. This study utilized TTFP as well as TECH, PECH and SECH as mediating variables.
Table 3 Total factor productivity measures for the tourism industry
Target Dimensions Indicators
Input
indicators		 Capital factor Z1 Original value of fixed assets of travel agencies, star-rated hotels and star-rated scenic spots
Labor factor Z2 Number of employees in travel agencies, star-rated hotels and star-rated scenic spots
Resources factor Z3 Number of scenic spots of Grade 4A and above
Technology factor Z4 Harmless waste treatment rate of domestic waste
Output
indicators		 Earnings factor Z5 Gross international and domestic tourism receipts
Scale factor Z6 Total international and domestic tourism arrivals

4.5.4 Control variables

The optimization of the tourism industry structure is jointly influenced by external system factors and internal factors of the industry. For example, the level of economic development and government policy support are important external system factors, whereas tourism resource endowment, tourism human capital, and tourism consumption demand are important internal system factors. The control variables are: 1) Government support (GOV). The policy and financial support of the government guide the direction of industrial development and constitute the basic driving force for the optimization of the regional tourism industry structure, which is manifested in the amount of fixed asset investment in the tertiary industry (Lei and Xu, 2021). 2) Level of economic development (ED). The essence of economic development is the continuous innovation and structural optimization of industries. When economic development reaches a certain level, it will cause the industrial structure to evolve. This paper draws on the research from other scholars and uses real per capita GDP to measure economic development (Wang, 2023). 3) Tourism resource endowment (RES). Tourism resources are the basis for tourism activities and RES affects the distribution and flow of factors such as tourism capital, technology, and labor. It has an important impact on the technical and economic links between various sectors of the tourism industry as well as the ecological development of the tourism industry. This paper selects the number of A-class tourist attractions to measure RES (Wang, 2011). 4) Tourism human capital (HUM). Labor, as an important factor of production, has been at the core of industrial development, and tourism, as a labor-intensive industry, cannot be separated from the tourism human capital. Tourism human capital stock and quality are crucial to tourism economic growth. This paper uses the number of students enrolled in middle and higher tourism colleges and universities to measure this parameter (Wang and Gao, 2016). 5) Tourism consumption demand (TD). Tourism consumption demand is a form of consumption with comprehensive social benefits, and optimizing the tourism supply system based on the actual demand for tourism consumption development can fundamentally promote the balance of the supply and demand of tourism factors. This paper uses the per capita tourism consumption expenditure of residents to measure TD (Liu et al., 2021).

4.5.5 Instrumental variable

Instrumental variables are usually used to mitigate endogeneity problems, and their selection should fulfill the requirements of relevance and exogeneity. In this paper, we utilize the total telecommunications business (DX) as an instrumental variable for the core explanatory variable DIG. On the one hand, the development of DIG is closely related to the basic telecommunication business, and Internet telecommunication technology is essential for the development of digital technology. The construction of digital infrastructure in areas with better telecommunication equipment is also faster. On the other hand, satisfying the innovation needs of the tourism industry in the modern era using traditional telecommunication and fixed-line telephony has been difficult, and the digital transformation of the tourism industry needs emerging digital technology and data elements to empower the traditional elements of the tourism industry. Therefore, DX is used as a key instrumental variable to meet the requirements.

4.5.6 Data sources

Panel data were collected from the China Statistical Yearbook, China Science and Technology Statistical Yearbook, China Tourism Statistical Yearbook, China Culture and Tourism Statistical Yearbook, and China Digital Inclusive Finance Index website (https://tech.antfin.com/research/data). This mainly comprises data from 2012 to 2021 from 30 provinces in China (Hong Kong, Macao, Taiwan, and Tibet were excluded). Moreover, the entropy weight TOPSIS method was used to calculate the composite index of the digital economy and tourism industry structure optimization. As a summary, the statistical description is listed in Table 4.
Table 4 Descriptive statistics results of main variables
Variables Name Sample Mean Max Min S.D.
Dependent variable Tourism Industry Structure Optimization (TIS) 300 0.089 0.377 0.019 0.052
Independent variable Digital Economy (DIG) 300 0.135 0.567 0.017 0.108
Intermediary variables Tourism Total Factor Productivity (TTFP) 300 1.105 4.491 0.091 0.569
Technological Progress Index (TECH) 300 1.096 3.142 0.062 0.528
Pure Technical Efficiency (PECH) 300 0.981 1.436 0.486 0.152
Scale Efficiency (SECH) 300 1.092 9.931 0.224 0.721
Control variables Government Support (GOV) 300 0.139 0.001 0.792 0.103
Economic Development Level (ED) 300 0.248 0.937 0.014 0.175
Tourism Resource Endowment (RES) 300 0.241 1.022 0.002 0.174
Tourism Human Capital (HUM) 300 0.137 0.046 1.913 0.175
Tourism Consumption Demand (TD) 300 0.262 0.008 1.016 0.174

5 Empirical results

5.1 Baseline regression results

Models I-V in Table 5 present the regression results of mixed OLS, individual random effects, individual fixed effects, time fixed effects, and double fixed effects, respectively. These models are significantly effective at the 1% level, thus verifying Hypothesis 1. However, the coefficient of Model V is lower than that of Models III and IV, and the optimization of the tourism industry structure by the digital economy may be affected by heterogeneous fluctuations in the province and time. Among the control variables, the level of ED is more significant, suggesting that the optimization of the tourism industry structure by the digital economy is closely related to the level of economic development.
Table 5 Regression results of the direct impact of the digital economy on the optimization of the tourism industry structure
Variables Model I Model II Model III Model IV Model V
DIG 0.266***
(7.98)		 0.230***
(6.44)		 0.184***
(4.28)		 0.196***
(5.31)		 0.154***
(3.35)
GOV 0.075**
(2.22)		 0.066*
(1.78)		 0.032
(0.72)		 0.070**
(2.17)		 0.035
(0.95)
ED 0.042
(1.47)		 0.092
(2.56)		 0.195***
(3.08)		 0.062
(1.78)		 0.156***
(3.35)
RES ‒0.023
(‒1.22)		 ‒0.011
(0.49)		 0.382
(0.03)		 0.002
(0.08)		 0.013
(0.42)
HUM ‒0.010
(‒0.72)		 ‒0.009
(‒0.64)		 0.013
(0.87)		 ‒0.016
(‒1.30)		 ‒0.018
(‒1.36)
TD ‒0.030
(‒1.07)		 ‒0.064
(‒2.06)		 ‒0.121***
(‒2.99)		 0.015
(0.39)		 ‒0.008
(‒0.17)
Constant 0.047***
(8.70)		 0.047***
(6.68)		 0.046***
(4.51)		 0.050***
(6.09)		 0.043***
(4.26)
Regional effects Yes Yes No Yes
Time effects No No Yes Yes
Samples 300 300 300 300 300

Note: ***, **, and * denote significance levels of 1%, 5%, and 10%, respectively. The data in parentheses are the t-statistics, and the same goes for the next tables.

5.2 Quantile regression results

To reflect the characteristics of the impact effect of the digital economy on the optimization of the tourism industry structure accurately, this study adopts the 10%-90% quantile level for quantile regression. Table 6 lists the fluctuation of the digital economy influence coefficient with the increasing quartile level of tourism industry structure optimization. This paper determined that the integration of new technologies and elements into the tourism industry requires a certain foundation of innovation in tourism resources, and the digital transformation of the tourism industry is slower when the foundation of the structure of the tourism industry is weaker and vice versa. Figure 2 shows the changes in quantile regression coefficients with the quantile points, which shows that the digital economy has a significant positive impact on the optimization of the tourism industry structure and exhibits a “U-shape” change characteristic of decreasing and then increasing.
Table 6 Quantile regression results of the impact of the digital economy on the optimization of the tourism industry structure
Variables Dependent variable (TIS)
Level = 0.10 Level = 0.25 Level = 0.50 Level = 0.75 Level = 0.90
DIG 0.247***
(3.08)		 0.216***
(6.00)		 0.238 ***
(8.03)		 0.278***
(5.13)		 0.350***
(3.51)
GOV 0.037
(1.06)		 0.033
(1.07)		 0.023
(0.74)		 0.083
(1.29)		 0.188
(1.44)
ED 0.051**
(2.34)		 0.063**
(2.09)		 0.062**
(2.51)		 0.000
(0.01)		 ‒0.113*
(‒1.45)
RES ‒0.035
(‒1.23)		 0.005
(0.26)		 0.009
(0.60)		 0.019
(0.75)		 ‒0.064
(‒1.00)
HUM 0.021
(1.42)		 0.027
(1.40)		 0.019
(1.38)		 0.023
(1.43)		 ‒0.073***
(‒2.67)
TD ‒0.110***
(‒3.20)		 ‒0.069
(‒2.34)		 ‒0.056
(‒2.37)		 0.023
(0.79)		 0.113
(1.63)
Constant 0.036***
(7.24)		 0.032***
(5.82)		 0.041***
(13.11)		 0.055***
(8.87)		 0.085***
(4.64)
Regional effects Yes Yes No Yes
Time effects No No Yes Yes
R2 0.2018 0.2057 0.2742 0.2971 0.2726
Samples 300 300 300 300 300
Fig. 2 Quantile regression trend of the impact of the digital economy on tourism industry structure optimization

Note: The horizontal axis of the figure is the number of interquartile points at an interval, the vertical axis is the digital economy regression coefficient, the solid line is the result of the interquartile regression estimation, the dotted line is the digital economy regression estimation, and the two dotted lines between them indicate the confidence interval of the regression value (with a confidence level of 95%), the figure shows the trend of the change in the coefficient of the impact of the digital economy.

5.3 Nonlinear regression results

The process by which the digital economy optimizes the tourism industry structure is a dynamic and complex process. To explore the “U-shaped” characteristics of the threshold changes, this study takes DIG, TTFP, TECH, PECH, and SECH as the threshold variables for the threshold effect test. First, the bootstrp bootstrap method is used to test the threshold effect on the threshold variables. As listed in Table 7, DIG and TTFP both passed the 1% single-threshold effect test, indicating the presence of a single-threshold effect with DIG and TTFP as the threshold variables; TECH passed the 5% and 10% double-threshold tests. A double-threshold effect with TECH and single-threshold effects with DIG and TTFP was observed, and the plots of likelihood ratio functions for both single-threshold and double-thresholds are shown in Figs. 3, 4, 5, and 6, which can be uses to visualize the thresholds and confidence intervals for the threshold variables.
Table 7 Test of the threshold effect results
Threshold variables Type of threshold test Threshold P Crit10 Crit5 Crit1 Confidence interval
DIG Single threshold 0.402 <0.001 9.877 11.809 13.383 [0.370, 0.421]
TTFP Single threshold 1.110 0.003 9.511 11.189 14.723 [1.105, 1.111]
TECH Double threshold 1.105 0.030 15.789 18.558 22.462 [1.063, 1.111]
1.160 0.067 10.484 13.062 20.213 [1.590, 1.162]

Note: P-values were obtained by repeated sampling 300 times using the Bootstrap method.

Fig. 3 Threshold identification of DIG
Fig. 4 Threshold identification of TTFP
Fig. 5 First threshold identification of TECH
Fig. 6 Second threshold identification of TECH
Table 8 lists the estimation results of the threshold model after the threshold effect test. Results I and II show that the threshold effects of single threshold variables DIG and TTFP are characterized by “increasing marginal benefits,” and the double-threshold effect of TECH is characterized by an inverted U-shape of an increase followed by a decrease in result III, which verifies hypothesis 3.
Table 8 Regression results of the threshold effect model
Single threshold variable Estimated value I Double threshold variable Estimated value III
0≤DIG≤0.402 0.088 ***
(1.8)		 0≤TECH≤1.105 0.142**
(2.08)
DIG>0.402 0.263***
(4.58)		 1.105≤TECH> 1.160 0.613***
(12.36)
Single threshold variable Estimated value II TECH≥1.160 0.194***
(3.79)
0≤TTFP≤1.110 0.098**
(2.25)
TTFP>1.110 0.214***
(4.51)
Controls Yes Controls Yes
F-statistic 6.35 F-Statistic 3.52
The results of the threshold effect test reflect the universality and specificity of the digital economy and its ability to optimize the structure of the tourism industry. The influence mechanism of the digital economy on the optimization of the tourism industry structure is represented in the threshold of the digital economy, tourism total factor productivity, and tourism technology progress index. On the one hand, the application and penetration of the digital economy, a new economic form, in the tourism industry helps the generation of new business forms and new modes in tourism. On the other hand, digitized knowledge information and data elements can be integrated with traditional tourism factors, improve the configuration and combination of tourism factors under the existing technology level, and improve the pure technical efficiency and scale efficiency of tourism factors. Along with the improvement of total factor productivity of tourism industry, the structure of tourism industry is gradually optimized.

5.4 Endogeneity and robustness tests

This study partially tested the endogeneity problem. First, to address the measurement error, the National Statistical Yearbook was used as the base data to improve data quality, and the DEA-Malmquist model was used to measure total factor productivity in tourism. Second, to address the omission of explanatory variables during the estimation of the basic regression model, control variables were included, and a double fixed effects model was used. Third, to address the endogeneity problem caused by mutual causation, the core explanatory variable L-DIG with one period lag was selected as a substitute variable to be regressed again, and the results in column I of Table 9 show that the results are still significant. Further, the two-way causation problem was controlled. Fourth, the stability of the model was tested by replacing the core explanatory variables Z-DIG for the robustness tests.
Table 9 Robustness test results
Variables One period behind I Replacement of core explanatory variables II Instrumental variable
approach
First-stage III Second phase IV
L-DIG 0.231**
(2.07)
Z-DIG 0.245***
(2.92)
DX 0.881**
(2.20)
DIG 0.781*
(1.69)
Regional effects Yes Yes Yes Yes
Time effects Yes Yes Yes Yes
Controls Yes Yes Yes Yes
Constant 0.033**
(2.67)		 0.050***
(5.09)		 ‒0.162*
(‒1.70)		 ‒0.140
(‒1.30)
Samples 300 300 300 300
R2 0.6680 0.6500 0.4243 0.4975
Column II shows the results of re-regression by replacing the entropy method TOPSIS with principal component analysis, and the results reveal that Z-DIG is significant. Columns III and IV are the results of the two-stage least squares test using total telecommunication business DX as an instrumental variable; both the first and second stages are significant. Finally, the weak instrumental variable DX test revealed that the F-value was greater than 10, DX was not a weak instrumental variable, and the model was robust.

5.5 Intermediary effect regression results

We selected TTFP, TECH, PECH, and SECH as the intermediary variables based on the threshold regression and explored in depth the characteristics of the mechanism by which the digital economy promotes the optimization of the tourism industry structure under the mediation of the total factor productivity of the tourism industry. Table 10 lists the results of the stepwise regression. The first test was conducted to investigate the impact of the digital economy (DIG) on the mediating variables, and columns I, III, V, and VII present the results of the impact of DIG on TTFP, TECH, PECH, and SECH, respectively. Noticeably, the digital economy has a positive effect on TTFP, TECH, and PECH but a negative facilitating effect on SECH. This is attributable to the inability of the digital economy to expand the tourism industry quickly, resulting in a certain masking and lagging effect on the scale efficiency of tourism. Second, the mediating variables were input into the benchmark model to test the impact of the digital economy and mediating variables on the optimization of the tourism industry structure. The test results in columns II, IV, VI, and VIII reveal that the DIG coefficients were not significant, whereas the TTFP, TECH, PECH, and SECH coefficients passed the 5% significance test.
Table 10 Test results of indirect transmission mechanism of digital economy on tourism industry structure optimization
Variables Dependent variable (TIS)
TTFP TECH PECH SECH
I II III IV V VI VII VIII
DIG 0.185***
(3.27)		 0.007
(0.11)		 0.209***
(3.02)		 0.014
(0.23)		 0.051**
(1.80)		 0.011
(0.18)		 ‒0.063*
(‒2.01)		 ‒0.005
(‒0.07)
TTFP 0.120**
(2.30)
TECH 0.107**
(2.24)
PECH 0.373***
(3.16)
SECH ‒0.208**
(‒2.13)
Controls Yes Yes Yes Yes Yes Yes Yes Yes
Constant 1.234***
(5.11)		 1.982*
(7.95)		 1.064***
(3.82)		 ‒2.000***
(‒8.34)		 0.326
(3.64)		 ‒2.008***
(‒8.72)		 ‒0.143
(‒1.21)		 ‒1.916***
(‒8.13)
Intermediary effect Remain Remain Remain Remain
Percentage 12.06% 12.15% 10.33% 7.12%
Sobel-Z 2.013** 1.666* 1.266 0.095
R2 0.3169 0.5538 0..3263 0.5539 0.1230 0.5602 0.2017 0.4955
Samples 300 300 300 300 300 300 300 300
Therefore, the digital economy can promote the optimization of the tourism industry structure by improving the total factor productivity of the tourism industry. Hypothesis 2 is verified. However, this is a slow process, and it does not achieve productivity improvement as quickly as other industries. Additionally, TECH and PECH exert a positive fully mediated effect, and SECH exerts a negative fully mediated effect.
Finally, Sobel’s test revealed that only TTFP and TECH passed the significance test. This suggests that the technological progress in the tourism industry brought about by the DIG can rapidly revolutionize the technological structure of the tourism industry, innovate the production, operation and management mode of the tourism industry, and give rise to new forms and modes of tourism. However, the effects on increasing the technical efficiency of the tourism industry through the integration of data elements with traditional factors of production in tourism have been slower. The rapid flow of data elements and the integration of traditional tourism labor and capital elements still have a certain period of adaptation.

5.6 Regional heterogeneity test results

China was divided into east, central, west, and northeast regions to explore the regional heterogeneity, and the impact results are listed in I, II, III, and IV, respectively in Table 11. According to the level of DIG and TIS, this is divided into a high DIG area, a low DIG area, a high TIS area, and a low TIS area to explore the horizontal heterogeneity; the results are shown in columns V, VI, VII, and VIII, respectively, in Table 11. Columns I-IV of Table 11 reveal that the significance of the digital economy on the optimization of the tourism industry structure is greater in the east and central regions than the west and northeast regions and strongest in the central region. Meanwhile, columns V-VIII show that the effect of digital economy development on the optimization of the tourism industry structure is more significant in the high DIG area and the high TIS area.
Table 11 Regional heterogeneity test results of the digital economy on the optimization of tourism industry structure
Variables I II III IV V VI VII VIII
DIG 0.189**
(2.76)		 0.250***
(3.20)		 ‒0.196
(‒1.47)		 0.265
(0.88)		 0.146**
(2.94)		 0.228
(1.64)		 0.174**
(2.50)		 0.144
(1.50)
GOV 0.271
(1.55)		 0.007
(0.75)		 0.391**
(2.66)		 0.168
(0.67)		 0.100
(1.11)		 0.010
(0.26)		 0.131
(0.78)		 0.018
(0.65)
ED 0.115
(1.39)		 0.256**
(2.55)		 0.125
(1.32)		 ‒0.030
(‒0.19)		 0.091
(1.25)		 0.099
(1.16)		 0.183*
(1.81)		 0.019
(0.34)
RES ‒0.125
(‒1.96)		 ‒0.107
(‒1.25)		 0.057
(0.91)		 0.066
(0.29)		 ‒0.070
(‒1.44)		 0.085*
(1.94)		 ‒0.010
(‒0.16)		 0.014
(0.43)
HUM ‒0.009
(‒0.51)		 ‒0.026
(‒1.18)		 0.006
(0.15)		 0.253
(0.99)		 ‒0.018
(‒1.06)		 ‒0.023
(‒0.88)		 ‒0.013
(‒0.68)		 ‒0.049
(‒2.19)
TD 0.024
(0.16)		 ‒0.126
(1.50)		 ‒0.050
(0.84)		 0.399
(1.48)		 0.021
(0.18)		 ‒0.024
(‒0.45)		 ‒0.012
(‒0.16)		 ‒0.003
(‒0.05)
Constant 0.025
(0.88)		 0.020
(0.70)		 0.042**
(2.55)		 ‒0.066
(‒0.79)		 0.050
(2.55)		 0.051***
(3.42)		 0.032
(1.20)		 0.070***
(5.92)
Regional effects Yes Yes Yes Yes Yes Yes Yes Yes
Time effects Yes Yes Yes Yes Yes Yes Yes Yes
Samples 100 60 110 30 150 150 150 150
F-Statistic 6.23 6.04 6.39 7.02 9.72 5.26 7.48 7.86

Note: According to the regional development policy formulated by the State Council of China, the eastern region comprises 10 provinces, including Beijing and Tianjin; the central region comprises 6 provinces, including Shanxi and Henan; and the western region comprises 11 provinces, including Chongqing and Sichuan. The northeast region includes the provinces of Liaoning, Jilin, and Heilongjiang. Hong Kong, Macao, Taiwan, and Tibet were not considered due to their data being incomplete. The average value of the index of the digital economy development level and the index of tourism industry structure optimization of the 30 provinces from 2012 to 2021 were calculated and ranked. The top 15 provinces were regions with high digital economy development and high optimization of the tourism industry structure; the bottom 15 provinces were regions with low digital economy development and low optimization of the tourism industry structure.

The research results illustrate that there are regional differences in the optimization of tourism industry structure by digital economy. Noticeably, the eastern and central regions have more complete digital infrastructure construction and abundant digital application scenarios in the tourism industry, which is conducive to the promotion of digital economy on the optimization of tourism industry structure. However, in the western and northeastern regions, the development of digital economy is relatively slow, and there are still some challenges in the application of digital technology in the transformation of the tourism industry.

6 Discussion

Previous research on the digital economy-enabled industrial structure has revealed theoretical and practical findings that are relevant, laying the foundation for this study. However, in the research on the role of the digital economy in improving the tourism industry, scholars have focused on the opportunities, challenges, and paths of the digital transformation of the tourism industry from the theoretical perspective (Dang, 2023). In contrast, the focus from the practical perspective has been on the digital economy’s impacts on tourism economic growth (Ji and Li, 2022), development of the tourism industry quality, development of tourism resources (Yuan et al., 2023), and the sustainable development of the tourism industry (Su et al., 2023). However, research on the relationship between structural changes in the tourism industry and the digital economy has received little attention.
The main research contributions and novelty of this study are as follows. First, at the theoretical level, this study believes that the development of the tourism industry in the digital economy is not only driven by “digital technological innovation”, but also closely related to the role of data, a new factor of production, in improving the efficiency of factor allocation. Digital Intelligence, as a kind of “new quality productivity”, combines revolutionary breakthroughs in technology as well as innovative allocation of production factors to promote in-depth transformation and upgrading of the tourism industry, and increase the kinetic energy of optimizing the structure of the tourism industry. Secondly, about the indicator design, digital economic development measurement indexes were constructed from the multidimensional aspects of digital infrastructure, digital industry development, and digital economic applications. The efficient allocation of tourism resources and the ecological development of the tourism industry were incorporated into the evaluation system for optimizing the structure of the tourism industry by considering the rationalization and advancement of the industrial structure. Finally, at the methodological level, the double fixed effect, quantile regression, panel threshold, and mediation effect models were used, and several regression models were used to explore in detail the effect characteristics and transmission path of the optimization of tourism industry structure under the influence of the digital economy. In addition, the regional and horizontal heterogeneity were explored, which can provide empirical references for the integration of the tourism industry and the digital economy in each region.
Although this study found that DIG has a significant role in promoting TIS, this study also had some limitations, which will require further research. First, regarding the measurement of DIG, the literature adopts various methods, such as national economic accounting, value-added measurement, comprehensive indicator system, and digital economy satellite account. This study only used the comprehensive indicator system; the DIG measurement method will be enriched in a future study. Second, the measurement dimensions of TIS are limited, and this study constructed a four-level TIS system. However, the evaluation of the structure of the tourism industry will be enriched as the tourism industry develops. Third, the mediating effect analysis of this paper revealed that the digital economy promotes the optimization of tourism structure mainly through the technological progress of the tourism industry. However, the promotion of elemental upgrading through the efficiency of scale as well as the efficiency of pure technology is different from the rapid integration of industries such as agriculture and manufacturing with the digital economy. The contributions of digital elements to upgrades in the structural of the tourism industry will only yield dividends resulting from the digital economy when regional digital economy development and the total factor productivity of the tourism industry reach a certain level. Therefore, improving the use of data elements to upgrade tourism elements is crucial for improving the efficiency of tourism resources and promoting structural optimization within the tourism industry and with other industries, which should be further investigated in the future.

7 Conclusions

By utilizing the panel data of 30 provinces in China from 2012 to 2021, this study established an evaluation index system of DIG and TIS. Moreover, it systematically explored the impact and mechanism of DIG on TIS. The findings are as follows.
First, the digital economy has a significant effect on rationalizing the tourism industry structure as well as the advancement, efficiency, and ecologization of the composite optimization system. The quantile regression model revealed that the digital economy contributes most significantly to the optimization of the tourism industry structure at the 90% quantile level. Second, the panel threshold model found that the impact of DIG on TIS exhibits inverted “U” nonlinear characteristics. A single-threshold threshold for TTFP and DIG exists, whereas TECH exhibits a double threshold. Third, the mechanism by which DIG boosts the TIS is by improving TTFP. By decomposing TTFP into TECH, PECH, and SECH, the mediation test reveals a fully mediated effect of the DIG through TTFP to promote TIS, and TECH is the most significant and highest percentage. Fourth, the impact of the digital economy on the optimization of the tourism industry structure has regional variability, and the impact of the digital economy on the optimization of the tourism industry structure is greatest in the central region. Moreover, this region has a strong digital economy and high tourism industry structure optimization. Further, the role of “sending charcoal in the snow” in the west is lower than that of “adding flowers to the snow” in the east and central regions. Consequently, this also reflects the regional differences in the digital transformation of the tourism industry. The acquisition and release of the structural, technological, and factor dividends of the digital economy development are related to economic development and regional tourism development.
By comparing existing studies, this study found that the mechanisms and paths through which the digital economy drives total factor productivity gains in the tourism industry have both similarities and differences with other industries. On the one hand, the new digital technology can rapidly facilitate technological progress and innovation in the tourism industry and generate new intelligent tourism business and new models, similar to other industries (Ji and Li, 2022). On the other hand, the effect of data on the technical efficiency and scale efficiency of tourism factors is different from the rapid “feedback effect” in agriculture and manufacturing industries (Wang and He, 2023), and the integration of the traditional factors of tourism, such as land, capital, labor, and knowledge, with the data factor is susceptible to the impact of the digital divide, data silos, data monopoly, and the differences in tourism development, which results in a hidden and fluctuating conduction path. This explains why the mediation effect of PECH and SECH in Table 10 accounts for a lower proportion than TECH. In addition, the impact of the digital economy on the optimization of the tourism industry structure is most significant in the central region. The traditional industry structure optimization in the eastern region and the strong digital economy in the region with the fastest development (Liu and Chen, 2021) also reflect the uniqueness of the tourism industry. In summary, this paper found that the effect of the digital economy in facilitating tourism industry structure optimization lies in the improvement of the efficiency of tourism industry factor allocation, which is a slow process but one that is full of potential.
Based on the findings of this empirical study, the following policy recommendations are offered. First, the construction of digital infrastructure should be strengthened, the effectiveness of digital industrialization and industrial digitization improved, interconnection and sharing of tourism flow accelerated, and the boundaries of the tourism industry chain expanded. New forms and modes of “tourism+digital economy” should be actively innovated. Second, the quality of tourism should be steadily improved. Combining the scientific and technological innovation effect of digital technology and elements with the upgrading effect of elements can realize the simultaneous growth of technological progress, technological efficiency, and economies of scale. Meanwhile, the innovative development of tourism resources in the modern era should be focal, low-carbon approaches in the tourism industry should be developed, and emphasis placed on the ecological environment to realize the sustainable development of tourism resources. Finally, regional differences in the development of tourism resources should be emphasized. The east-central region should support tourism infrastructure in the western and north-eastern regions. Additionally, regions are concerned about the digital divide as well as data silos and data security in the tourism industry. The efficiency and security of inter-regional tourism resource flows should be prioritized so that tourism information and data can realize their true value. Local governments should strengthen the regulation of tourism digital platforms to maintain a better environment for the development of tourism resources.

Acknowledgement

We thank the reviewers for their valuable comments on earlier drafts of this manuscript that helped us improve the quality.

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