The Impact of Digital Technology on Tourism Economic Growth: Empirical Analysis Based on Provincial Panel Data, 2010–2022

Abstract

Through empirical analysis, this paper uses panel data from 30 provinces in China (excluding Hong Kong, Macao, and Taiwan, as well as Tibet) from 2010 to 2022 to explore in-depth and empirically test the relationship between the impact of digitalization technology on tourism economic growth and the intrinsic mechanism of its action. The results found that: first, digital technology has a significant impact on tourism economic growth, and second, digital technology has a significant impact on tourism economic growth. Significant promotional utility and, secondly, tourism industry efficiency, play a partial mediating role in the relationship between digital technology and tourism economic growth, i.e., digital technology can promote tourism economic growth by positively affecting tourism industry efficiency. This paper also reveals the role of digital technology in promoting the efficient development of the tourism industry, including the optimal allocation of resources and the improvement of service efficiency, which in turn promotes the innovative development of the tourism economy. Finally, in response to the challenges posed by digital transformation, suggestions are made to strengthen regulation and standardize the market order to ensure the sustainable and high-quality development of the tourism industry.

1. Introduction

Since the “Belt and Road” initiative was put forward, all parties have made efforts to promote the infrastructure construction of countries along the route and facilitate inter-country exchanges, which has continuously improved the basic conditions limiting the development of tourism (X. Li et al., 2022). Although digital tourism is still on the periphery of the Belt and Road construction, there is a certain interdependency relationship between the two concepts, which brings a strategic opportunity for their integrated development. The Belt and Road brings together many countries and nationalities, becoming the most dynamic and potential golden tourism road in the world, with rich tourism resources. As the tourism industry advances to a higher stage of development, high value-added, high-technology and high-knowledge digital tourism will become a pioneer industry in the synergistic development of the Belt and Road.

At the same time, digital tourism promotes the synergistic development of the Belt and Road economy. The growth in demand from tourists and the advancement of digital tourism have promoted the construction of tourism information infrastructure, large-scale digital tourism projects, and smart cities in key regions, thereby improving the situation of impoverished areas along the Belt and Road and realizing common prosperity. Digital tourism provides an innovative platform for tourism development in the Belt and Road partner countries, and the countries along the route have been able to engage in a more effective dialogue with Chinese tourists through digital ecological changes. Emerging payment and booking methods meet the diverse and fast-changing consumption needs of tourists, enabling more consumption on the ground, and countries are seeking cooperation with Chinese companies to join the digital tourism bandwagon. In addition, digital tourism not only provides a sustainable tourism model for the Belt and Road countries but also promotes closer cooperation between countries, which also profoundly interprets the concept of “community of human destiny” (X. Y. Zhang, 2020).

Since the 1980s, tourism has played a vital role in improving the quality of life of residents and tourists (Uysal et al., 2016; Mamirkulova et al., 2020). The impact of its development quality on the sense of gain and happiness of the people even surpasses that of other industries. Today, tourism has become one of the four major application areas of e-commerce, along with finance, software and publishing. After entering the 21st century, the gradual maturity of technologies such as electronic maps has accelerated the digitization of tourism information. Tourism enterprises and government management departments have also developed tourism information management systems accordingly, realizing the digitization of tourism resources and statistical information. For example, as early as the end of the 20th century, the United States began to study the application of satellite technology to tourism information services, combining e-commerce with multimedia display, which promoted the integration of information and visualization of tourism services.

At present, tourism information on the Internet in China is becoming increasingly rich, tourism e-commerce is gradually scaling up, and the profit model of tourism websites tends to mature, with the emergence of well-known brands such as Huaxia Tourism Network, Ctrip Tourism Network, Yilong Tourism Network, and Youth Travel Online. With the integration of digital technology, e-commerce, network technology, and spatial information technology, the number, content, and expression of tourism websites are constantly developing, showing a trend towards a unified platform for the network and a large, shared development.

At present, the tourism industry is showing the development trend of popularization, casualization, personalization, and networking, and the Internet has become the preferred channel for young people to obtain tourism information. This requires the use of new technologies to comprehensively upgrade the tourism industry, establish real digital tourism, and promote the progress of the current tourism informatization construction. The rapid development of computer hardware, “3S” technology, network broadband technology, multivariate database technology, virtual reality technology and e-commerce technology has provided a technical foundation for the informatization and digitization of the tourism industry, and therefore, the digitization of tourism has become an inevitable trend for the development of the industry.

The rapid development of digital technology has profoundly affected various industries, and the tourism industry is no exception. This study empirically examines the impact of digital technology on the growth of China’s tourism economy and clarifies its internal mechanism. Specifically, we will test whether digital technology significantly promotes tourism growth, explore its impact mechanism (focusing on the mediating role of tourism industry efficiency), and test the hypothesis that tourism industry efficiency is a mediating variable in the relationship between digital technology and tourism economic growth. Our research results aim to provide theoretical support and policy recommendations for promoting the sustainable development of China’s tourism industry.

2. Literature Review and Hypotheses

2.1. A Review of Foreign Literature on the Development of Tourism Economy

Compared with domestic research, there are relatively fewer studies on the driving mechanisms of tourism economic development in foreign countries. For instance, Nazneen et al. (2019) took corridor regions as an example to explore how local residents’ perception of road infrastructure development affects tourism development. Through a review of relevant foreign tourism economic literature in the past five years, it was found that most foreign scholars analysed the role of policy and social environmental factors in driving tourism demand to economic growth from a macro perspective. For example, Adedoyin et al. (2021) found that, for every 10% increase in the Maldives tourism tax, tourism demand would decrease by 5.4%. Durani et al. (2023) explored the impact of strict environmental policies on inbound tourism in G7 countries, and the results showed that strict environmental policies had a significant negative impact on high tourist arrival countries, while the impact on medium and low tourist arrival countries was relatively small. Gozgor et al. (2019) pointed out that the improvement of the legal system quality and property rights protection level could effectively promote inbound tourism. Demir et al. (2019), based on panel data analysis of 18 countries from 1955 to 2016, found that geopolitical risks had a significant negative impact on inbound tourism. In addition, Nguyen et al. (2020) divided research subjects into middle-low income economies, middle-high income economies, and high-income economies and found that economic uncertainty would inhibit outbound tourism, while economic stagnation would promote domestic tourism. Although most studies have shown that visa policies have a positive effect on the entry of international tourists, Yudhistira et al. (2021) pointed out that visa-free policies are not omnipotent. Further, Álvarez-Díaz et al. (2017) found that visa opening and political instability and civil unrest in alternative destinations would attract more Russian tourists to Spain, thereby promoting local economic growth and reducing unemployment.

Tourism has a significant linkage-driven effect and has been widely recognized as an important driving force for economic progress in developing countries or regions (Durbarry, 2004). Several studies (Dwyer & Forsyth, 1998; Narayan, 2004; Briedenhann & Wickens, 2004) have confirmed the important role of tourism in regional economic development. Durbarry’s (2004) study has shown that, for every 1% growth in tourism, Mauritius’ economy will grow by 0.8%. Buhalis and Amaranggana (2014), on the other hand, describe in detail the business components of smart tourism, including dynamically interconnected stakeholders, digitized core business processes, and organizational flexibility in the face of rapid change. Barišić and Cvetkoska (2020) examined the efficiency of tourism and travel management in EU countries, emphasising the significant contribution of this sector to GDP and employment. Through a non-parametric data envelopment analysis, their study assesses tourism consumption and capital investment as inputs and the overall contribution of tourism to GDP and employment as outputs during 2017, providing valuable insights and recommendations for policymakers. Cvetkoska and Barišić (2017) explore the efficiency of the tourism industry in the Balkan region, where, despite the increasing number of tourist arrivals and expenditures, its overall efficiency is still a cause for concern. The results of the study show that Albania was the most efficient throughout the observation period, while Montenegro was the least efficient, suggesting that there is room for improvement in tourism management in the region. Soysal-Kurt (2017) measured the relative efficiency of 29 European countries in 2013 through a data envelopment analysis and made recommendations for improvement for the less efficient countries. The results showed that 16 countries were relatively efficient and 13 countries were relatively inefficient, providing an important contribution to the literature on macro-level efficiency assessment in the tourism sector.

2.2. A Review Study on Domestic Literature on the Development of Tourism Economy

Domestic research on the development of the tourism economy mainly focuses on two aspects. On one hand, the research concentrates on the influence of policies, systems, guidelines and measures on the development of the tourism economy. For instance, R. M. Liu et al. (2020) regarded the cultural system reform as a dummy variable and found that it could promote the integration of the cultural industry and tourism industry, thereby driving the development of the regional tourism economy. J. Liu et al. (2022) conducted a quasi-natural experiment based on the selection of civilised cities and found that the selection of civilised cities could promote the development of the tourism economy through channels such as brand signal transmission, public value enhancement, industrial structure optimisation and technological innovation. Similarly, Chen et al. (2022) also conducted a quasi-natural experiment based on a “civilised city” and confirmed the promoting effect of urban honours on the growth of the tourism economy. Moreover, X. Huang et al. (2020) studied the impact of the “Belt and Road Initiative” on the inbound tourism market of countries and regions along the route. Most of these studies analysed data using dummy variables as explanatory variables. Meanwhile, there are also studies that focus on the role of specific variables in the growth of the tourism economy. For example, Bao and Huang (2023) found that the ecological wealth of cities could promote the development of tourism economy through information technology penetration and the vitality of the tourism market; C. Y. Yang et al. (2020) studied the impact of high-speed rail on the growth of domestic and inbound tourism; Tian et al. (2023) explored the role of upgrading transportation infrastructure in promoting the high-quality development of local tourism economy; and Shi et al. (2021) analysed the impact of the educational background, political capital and personal characteristics of government officials on the growth of tourism economy.

On the other hand, research is dedicated to exploring the role of environmental quality in the growth of the tourism economy. For instance, J. Liu et al. (2019) studied the impact of air quality on the tourism industry, finding that greenhouse gases have a relatively minor influence on tourism, while air pollution has a more significant negative impact on it. Among them, sulphur dioxide has the most significant impact on tourists’ demand, followed by PM2.5, nitrogen dioxide, and PM10. The influences of CO and ozone are relatively smaller. Zhou et al. (2019) also discovered that air pollution has a significant negative impact on tourism flow, and the impact on inbound tourism is greater than that on domestic tourism. N. Zhang et al. (2020) utilized monthly data from 58 major cities in China to find that the impact of air pollution can last for two months. Su and Lee (2022) further analysed the impact of air quality on international tourists using national panel data and spatial econometric models, discovering that for middle-income countries, low-income countries, countries with higher PM2.5 concentrations, and countries with fewer tourists, the air quality of these countries has a significant negative impact on the attractiveness to neighbouring countries’ tourists.

2.3. Research on the Impact of Digital Technology on the Development of the Tourism Economy

Before evaluating the impact of the digital economy on tourism, the academic community has extensively explored the relationship between digital technology and tourism. R. Huang and Li (2021) pointed out that the digital economy, with digital technology as its core driving force, can break the traditional path dependence of the tourism industry and reshape its organisational structure, thereby enhancing the efficiency of the tourism industry. Chen et al. (2022) emphasized that digital technology further promotes the improvement of the tourism industry’s efficiency by optimising the combination of production factors, stimulating the innovation vitality of the industry, and accelerating supply-side structural reform. Meanwhile, Z. Liu et al. (2022) discovered that the digital economy, through the analysis and utilisation of data information and the optimisation of tourism industry structure via the Internet, stimulates market vitality and drives tourism economic growth. Y. Yang (2022) argued that the rapid development of the digital economy has facilitated cross-regional mobility of tourists, reshaped the regional tourism economic geography pattern, and become an important means to alleviate the regional development gap in China’s tourism economy. In recent years, scholars have continuously deepened their research on the relationship between the digital economy and tourism economic growth. C. Y. Yang et al. (2020) found that tourism technological innovation and industrial upgrading have significant promoting effects on tourism economic growth. Ji et al. (2022) pointed out that digital infrastructure construction has a significant promoting effect on tourism economic growth and further incorporated digital infrastructure into the digital economic development indicator system to analyse the impact and mechanism of the digital economy on tourism economic growth from a more macroscopic perspective. Wei et al. (2023), from the perspective of tourism safety, proved that the digital economy, by reducing regional theft crime behaviours, promotes the development of the regional tourism economy. In addition, with the in-depth application of digital technology in the tourism field in China, Lu et al. (2022) observed the significant promoting effect of digital music products and other digital content on regional tourism economic development.

In the study of tourism development, digital technology is regarded as a core factor driving tourism economic growth, while smart tourism serves as a key driving force for the integration of this technology with industry applications. Hojeghan and Esfangareh (2011) explore the impact of the digital economy on the tourism industry, demonstrating the critical role of information and communication technology in promoting innovation, transformation, and economic growth while analysing the related challenges and opportunities. They focus on the application of the digital economy, e-commerce, and information technology in the tourism sector, as well as the coordination of policies and directions for technological development. L. Li (2016) pointed out that the network platform has brought new opportunities for the tourism industry, by using network data analysis to study in depth the factors affecting the structure of the tourism industry and constructing an evaluation index system for the structure of the regional tourism industry with the help of the principal component analysis (PCA) method, which provides valuable insights into the decision-making of the tourism industry. Morabito (2015) argued that the rise of smart tourism has prompted enterprises to reassess their business models and strategic importance, promoted innovation and transformation and upgrading of the tourism industry, and forced traditional tourism enterprises to redefine their development concepts and value creation methods. Sigala (2015) points out that changes in information and communication technology have significantly altered travellers’ travel patterns, needs and the shape and structure of the tourism industry, making the market more diverse and flexible. Song and Song (2011). incorporated spatial factors into the model and used spatial econometric analysis methods to explore the impact of provincial tourism innovation on tourism economic growth in my country. Their study found that tourism innovation not only promotes the growth of the province’s tourism economy but also has a positive spillover effect on the economic growth of neighbouring provinces through spatial transmission mechanisms.

In this context, in the emerging smart tourism economy, many companies such as Uber and Airbnb rely on online technology platforms and use information technology to expand new markets, proving the huge potential of digital technology in economic growth. The UK Smart Tourism Organization (2012) called this phenomenon “digital tourism” or “smart tourism”, further highlighting the important impact of information technology in the tourism industry. J. Liu et al. (2022) emphasised the importance of the tourism innovation level in enhancing the competitiveness of the tourism industry. Wei et al. (2020) explored the relationship between the economic development level and the tourism industry structure, pointing out that economic maturity affects the transformation of tourism patterns. Fang and Huang (2020) analysed the contribution of tourism industry efficiency to regional economic growth and revealed the necessity of effective resource allocation. These studies show that digital technology is not only a product of technological progress but also an important driving force for the development of the tourism industry. Wang et al. (2016) proposes building a new platform for smart tourism public service from a resource platform, cloud platform, and application platform, emphasizing that this initiative is conducive to the innovation of the tourism industry; although it does not directly elaborate on the impact on the efficiency of the tourism industry, the innovation of the new industry is likely to become a key driving force to improve the efficiency of the industry.

Hadad et al. (2012) found that globalisation and accessibility are critical to the efficiency of the tourism sector in developing countries and that labour productivity can be a good indicator of the overall efficiency of the tourism industry. Pantano and Stylidis (2021) noted that the tourism industry’s efforts to innovate in new technologies have developed practical new tourism resources. Based on cross-sectional data, Deng and Li (2015) analysed the factors influencing the tourism economy in 22 provinces in China. However, the existing literature mostly focuses on the impact of material conditions such as tourism infrastructure and tourism resources on the development of the tourism economy, and there is insufficient research on soft factors such as innovation and talent, which leaves room for exploration of the research on the impact of digital technology on the efficiency of the tourism industry. K. Liu et al. (2021) confirmed that the regional differences in the coupling level of transportation accessibility and the intensity of tourism economic linkage are affected by factors such as industrial structure, market openness, tourism resource endowment and transportation capacity, and their spatial intensity, suggesting that the synergistic effect of transportation and other related factors with digital technology needs to be taken into account in the study of digital technology’s impact on the efficiency of the tourism industry.

Although existing literature has revealed the potential impact of digital technology on the tourism economy, there is still insufficient empirical evidence on how it can indirectly drive economic growth through improving industrial efficiency. In addition, existing studies have mostly focused on a single region or industry, lacking systematic tests based on provincial panel data. To fill this gap, this study will construct a multidimensional indicator system and a dynamic panel model, combined with spatiotemporal data from 30 provinces in China, to empirically verify the direct effect of digital technology on the efficiency of the tourism industry and its intermediary mechanism in economic growth, thereby deepening the understanding of the “technology-efficiency-growth” transmission path.

Digital technology has been deeply integrated into the tourism industry, and it is closely related to the growth of the tourism economy and the efficiency of the tourism industry. Regarding Hypothesis 1, digital technology can broaden the channels for the dissemination of tourism information, increase the exposure of tourism destinations, attract more tourists, and thereby promote the growth of the tourism economy. According to Hypothesis 2, it can optimise the allocation of production factors in the tourism industry, give rise to innovations in intelligent services, reduce operating costs, and enhance the efficiency of the tourism industry. Hypothesis 3 is based on the principles of industrial economics, which state that the improvement of industrial efficiency is an important driving force for economic growth. After digital technology enhances the efficiency of the tourism industry, it can reduce enterprise costs, attract investment, and drive the growth of the tourism economy. Therefore, it is proposed that the efficiency of the tourism industry plays a mediating role in the relationship between the two.

2.4. Hypothesis

Hypothesis 1.

Digital technology positively affects tourism economic growth.

Hypothesis 2.

Digital technology positively affects the efficiency of the tourism industry.

Hypothesis 3.

Tourism industry efficiency mediates the positive correlation between digital technology adoption and tourism economic growth. Digital technology has a positive impact on the tourism industry’s efficiency, which in turn has a positive impact on tourism’s economic growth.

2.5. Hypothetical Conceptual Model

The conceptual model describes the relationship between digital technology, tourism industry efficiency, and tourism economic growth (Figure 1). The model assumes that digital technology has a positive impact on both tourism industry efficiency and tourism economic growth. Crucially, the model proposes that tourism industry efficiency plays a mediating role, implying that the positive impact of digital technology on tourism economic growth is partly realised through its positive impact on tourism industry efficiency. This suggests a chain reaction: improved digital technology leads to improved efficiency in the tourism industry, which in turn leads to improved tourism economic growth.

3. Research Design

3.1. Sample Selection and Description

Based on the research gap revealed in the literature review—the transmission mechanism of digital technology on industrial efficiency and the cross-regional heterogeneity effect—this study uses panel data from 30 provinces (except Hong Kong, Macao, Taiwan, and Tibet) in China from 2010 to 2022, and systematically examines the following issues through a fixed effect model:

3.1.1. Digital Technology Has a Positive Impact on Tourism Economic Growth

Digital technology has a positive impact on the tourism industry’s efficiency.

3.1.2. Tourism Industry Efficiency Plays a Mediating Role in the Positive Correlation Between Digital Technology Application and Tourism Economic Growth

Digital technology has a positive impact on the tourism industry’s efficiency, and tourism industry efficiency, in turn, has a positive effect on tourism economic growth.

The sample selection is based on two considerations: first, 2010–2022 is a period of explosive growth of digital technology in China, covering the policy cycle from the “12th Five-Year Plan” to the “14th Five-Year Plan”; second, provincial panel data can effectively control regional heterogeneity and avoid accidental bias in cross-sectional data. The data mainly come from the China Statistical Yearbook, the Tourism Statistical Yearbook, the Wind database and the National Bureau of Statistics, and are cleaned and standardised using Stata 14.0 to ensure the robustness of the empirical results.

3.2. Variable Selection

3.2.1. Core Explanatory Variables

In this paper, we study the impact of digital technology on the tourism economy, and the explanatory variable DT (Digital Technology) is constructed by the entropy method, which integrates the following three indicators: mobile telephone exchange capacity (unit: 10,000 households), the length of long-distance fibre optic cable lines (unit: kilometres), and cell phone penetration rate (unit: unit/100 people). These indicators refer to the study of Ji et al. (2022) on digital infrastructure construction and tourism economic growth and comprehensively reflect the development level of regional digital technology.

Specifically:

Mobile Telephone Exchange Capacity (unit: 10,000 households) refers to the maximum number of subscribers that can be supported by the switching equipment in the mobile communication network of a certain region. This indicator reflects the infrastructure scale and service capacity of the mobile communication network and is an important parameter for measuring the development level of regional communication technology.

Long-Distance Optical Cable Length (Long-Distance Optical Cable Length, unit: kilometres) refers to the total length of optical cable lines used for long-distance communication in a certain region. As the core infrastructure for information transmission, the length of optical cable is directly related to the coverage of regional communication networks and data transmission capacity and is important hardware support for the development of digital technology.

Mobile Telephone Penetration Rate (unit: units/100 people) refers to the number of mobile telephones per 100 people in a certain region. This indicator reflects the degree of penetration of mobile communication technology and the level of use of digital technology by the population and is a key indicator of the breadth of digital technology application in a region.

The digital technology index (DT), constructed through the entropy method of comprehensive measurement of the above three indicators, can comprehensively reflect the development level of regional digital technology and provide a scientific and reliable quantitative basis for the study of the impact of digital technology on the tourism economy.

3.2.2. Dependent Variable

In this article, tourism economic growth (TEG) is not a single indicator but rather a calculated ratio: the ratio of the output of a region’s tourism industry to the region’s GDP. This means that TEG is not simply a measure of the growth in the value of the tourism industry itself but rather a measure of the change in the value of the tourism industry as a proportion of the region’s overall economic output (GDP). A region’s tourism industry output may grow, but if the GDP grows faster, then the ratio of TEG may decrease, and vice versa. By using this ratio, the authors are attempting to fully examine the extent to which tourism contributes to the local economy. Growth in tourism industry output alone may not fully reflect the actual impact of tourism on the regional economy, as GDP reflects the overall economic activity of all industries in the region. Therefore, the use of the TEG ratio allows for a more accurate assessment of the relative contribution of tourism development and avoids masking the actual contribution of tourism due to the rapid growth of other industries.

In the national and international literature, tourism economic growth is usually measured in several ways. For example, Robertico et al. (2020) propose the use of the tourism professional level (regional tourism industry output/GDP), Lee and Chang (2008) use tourism income per capita (regional tourism income/number of tourists), and the tourism trip ratio is proposed by Corte (2008). However, the tourism trip ratio only considers the number of tourists and the number of trips, and fails to include the amount of money spent by tourists, which makes the indicator of tourism revenue per capita potentially one-sided in assessing the growth of the tourism economy. Therefore, based on the comprehensive analysis of various indicators, this paper chooses the tourism professional level (regional tourism industry output/GDP) as the explanatory variable in the research model, which is expressed as TEG, to reflect the evaluation of tourism economic growth more comprehensively. Through such an innovation, we can assess the economic contribution of the tourism industry more accurately.

3.2.3. Mediator Variable

In this study, we hypothesise that tourism industry efficiency (TIE) plays a mediating role in the positive impact of digital technology on tourism economic growth. Drawing on the research of Y. Liu and Han (2020), we define tourism industry efficiency (TIE) as “the tourism benefits created per unit of tourism service infrastructure”. On this basis, we innovatively quantify tourism industry efficiency as the ratio of tourism revenue to fixed assets of the accommodation and catering industry above the quota, taking into account the data disclosure calibre and data availability of the China Statistical Yearbook. This indicator can be interpreted as the turnover rate of tourism service infrastructure, i.e., the output of tourism revenue per unit of fixed asset input.

From an econometric point of view, this quantitative approach is not only highly operational but also capable of capturing more accurately the economic connotations of tourism industry efficiency. Specifically, tourism revenue reflects the output level of the tourism industry, while the fixed assets of the accommodation and catering industry above the quota represent the infrastructure input of the tourism industry. By calculating the ratio of the two, we can effectively measure the utilisation efficiency of tourism infrastructure, thus providing a reliable empirical basis for studying the mediating effect of digital technology in enhancing the efficiency of the tourism industry and promoting the growth of the tourism economy.

In addition, the design of this indicator takes into account the continuity and comparability of the data, ensuring the robustness of the findings. By introducing this mediating variable, we can more precisely test the mechanism of digital technology’s effect on tourism economic growth and provide more targeted recommendations for policymakers.

3.2.4. Control Variables

In the process of modelling research, it is necessary to consider that tourism economic growth is affected by factors other than digital technology development and tourism industry efficiency and to incorporate control variables. In this paper, tourism development and tourism industry structure are selected as control variables. The following briefly describes the reasons for selecting these two control variables and how they are calculated.

Tourism Development (TD): This variable indicates the foundation of local tourism development, which potentially affects the level of tourism economic growth under different tourism development foundations and therefore needs to be controlled. This variable consists of five indicators: the number of tourism employees, the number of tourism colleges and universities, the number of students in tourism colleges and universities, the amount of investment in tourism fixed assets, and the number of tourism-related patent applications, which can better represent the basic conditions of the local tourism development; the entropy method is used to determine the weights of the indicators and to calculate the total score.

Tourism Industry Structure (TIS): According to C. Liu et al. (2014), the change in the structure of China’s tourism industry has a significant impact on tourism economic growth. To measure the structure of the tourism industry, the study used indicators related to three major industry sectors: star-rated hotels, A-class tourist attractions, and travel agencies. Since different tourism industry structures may have potential impacts on tourism economic growth, it is necessary to control this variable in the analysis. Specifically, the study uses the number of star-rated hotels, the number of A-grade tourist attractions, and the number of travel agencies as the indicators of measurement, determines the weights of each indicator through the entropy value method, and finally calculates a comprehensive score of the tourism industry structure.

3.3. Model Construction

The model was constructed to examine the relationship and mechanism of the impact of digital technology on tourism economic growth, according to the assumptions and selected variables described in the previous section. Hypothesis 1, Hypothesis 2, and Hypothesis 3 need to be separately verified, so the following three linear regression models need to be constructed to test the relationship between digitalisation technology (DT) and tourism economic growth (TEG), the relationship between digitalisation technology (DT) and tourism industry efficiency (TIE), and the relationship between digitalisation technology (DT), tourism industry efficiency (TIE), and tourism economic growth (TEG), respectively. The following fixed-effect model is constructed:

$${TEG}_{i,t}={\alpha }_0+{\beta }_1·{DT}_{i,t}+{\beta }_2·{TD}_{i,t}+{\beta }_3·{TIS}_{i,t}+\epsilon$$

(1)

$${TIE}_{i,t}={\alpha }_1+{\gamma }_1·{DT}_{i,t}+{\gamma }_2·{TD}_{i,t}+{\gamma }_3·{TIS}_{i,t}+\epsilon$$

(2)

$${TEG}_{i,t}={\alpha }_2+{\delta }_1·{DT}_{i,t}+{\delta }_2·{TIE}_{i,t}+{\delta }_3·{TD}_{i,t}+{\delta }_4·{TIS}_{i,t}+\epsilon$$

(3)

In Equations (1)–(3): subscript i denotes province and t denotes year; the explanatory variable (TEG) is tourism economic growth; the core explanatory variable (DT) is digitisation technology; the mediator variable (TIE) is the efficiency of the tourism industry; α, β, γ, and δ are the coefficients to be estimated, respectively; and ε is the random error term.

4. Empirical Results and Analysis

4.1. Descriptive Statistics

4.1.1. Definition of Variables

The main research variables and symbols in this paper are defined in the

Table 1. Description of variables.

Indicator Category	Indicator Name	Variable Symbol
Explanatory variable	Tourism economic growth	TEG
Core explanatory variables	Digitization technology	DT
Intermediary variable	Efficiency of the tourism industry	TIE
Control variable	Tourism development	TD
	Structure of the tourism industry	TIS

below.

4.1.2. Descriptive Statistics of Digitisation Techniques

The digital technology index (DT) is a composite measure comprised of three metrics: cellular switching capacity (MPSC), long optical cable length (LOCL), and cell phone penetration rate (MPPR). These metrics are weighted using the entropy method to reflect their relative importance.

Table 2. Descriptive statistics of digital technology indicators.

Variable	Obs	Mean	Std. Dev.	Min	Max.
MPSC	390	7590.665	5210.811	549.000	40,856.900
LOCL	390	31,557.295	18,353.365	916.000	125,361.000
MPPR	390	100.166	26.508	40.870	189.460

shows the descriptive statistics for these three components.

The data and the resulting composite DT index.

Together, the descriptive statistics of these three sub-indicators show that there is a significant difference in the level of digital infrastructure development in different provinces of China during the study period. This variation is an important factor to consider in the study, as it may affect the effectiveness of digital technology in influencing tourism economic growth and tourism industry efficiency. The analysis that follows will utilise this composite DT index to examine its impact on tourism economic growth and control for other possible influences.

Determination of the Weights of the Sub-Indicators (Entropy Method)

To create a comprehensive digital technology index (DT), we use the entropy method to assign weights to three constituent indicators: cell phone switching capacity (MPSC), long-distance fibre optic cable length (LOCL), and cell phone penetration rate (MPPR). The entropy method is an objective assignment method, which assigns weights according to the variability or the amount of information on the indicators; the greater the variability and the more information, the higher the weights. This avoids the influence of the researcher’s subjective preference on the results and enhances the objectivity of the study compared with the subjective assignment method. The specific steps are as follows:

Data standardisation: First, the raw data for each indicator (MPSC, LOCL, MPPR) are standardised to ensure that all indicators are comparable. The minimum–maximum standardisation method was used to convert the raw data to a standardised value ($x_{ij}^{\prime }$) between 0 and 1, calculated as follows:

$$x_{ij}^{\prime }={\displaystyle \frac{x_{ij}-x_{min}}{x_{max}-x_{min}}},$$

where $x_{ij}$ denotes the original value on the ith province and the jth indicator, and $x_{min}$ and $x_{max}$ denote the minimum and maximum values of the jth indicator, respectively.

Calculation of the ratio: Next, the ratio of each province’s standardised value for its respective indicator to the sum of the standardised values for all provinces ($y_{ij}$) is calculated using the following formula:

$$y_{ij}={\displaystyle \frac{x_{ij}^{\prime }}{{\sum }_{i=1}^m{x}_{ij}^{\prime }}}\left(0\le y_{ij}\le 1\right),$$

where m denotes the number of provinces (30 in this study).

Entropy calculation: Calculates the entropy ($e_j$) of each indicator to measure the degree of uncertainty or variability of the indicator. The higher the entropy value, the more variable the indicator is and the more information it contains. The formula is as follows:

$$e_j=-K\sum _{i=1}^m{y}_{ij}\ln y_{ij},$$

where $K={\displaystyle \frac{1}{\ln m}}$ is a constant to ensure that the entropy value is between 0 and 1.

Calculation of information utility: Calculate the information utility ($d_j$) of each indicator, which represents the amount of useful information contained in the indicator. The higher the information utility, the greater the contribution of the indicator to the composite index. The formula is as follows:

$$d_j=1-e_j$$

Weight assignment: Finally, the weight (${\omega }_j$) of each indicator is calculated based on the information utility with the following formula:

$${\omega }_j={\displaystyle \frac{d_j}{\sum d_j}},$$

where j denotes the number of indicators (3 in this study). With this formula, information utility is normalised to a weight value that sums to one.

Analysis of results: The final calculation results are shown in

Table 3. Indicator weights for each subcomponent of digitisation technology.

Variant	MPSC	LOCL	MPPR
weights	0.4526	0.3576	0.1898

, and the weights of cell phone switch capacity (MPSC), long-distance optical cable line length (LOCL), and cell phone penetration rate (MPPR) are 45.26%, 35.76%, and 18.98%, respectively. This indicates that cell phone switch capacity (MPSC) has the highest weight, followed by long-distance optical cable line length (LOCL), and cell phone penetration rate (MPPR) has the lowest weight in constructing the comprehensive digital technology index (DT). This result reflects the results of the objective analysis of the data and shows that cell phone switch capacity has the greatest impact on the degree of digitisation, with the other two indicators having the second greatest impact.

Finally, using the calculated weights and the normalised data, the composite digital technology index DT for each province can be calculated with the following formula:

$${DT}_i=\sum _{j=1}^n{\omega }_j·x_{ij}^{\prime }.$$

where ${DT}_i$ denotes the composite digital technology index of the ith province, ${\omega }_j$ is the weight of the jth indicator, and $x_{ij}^{\prime }$ is the normalised value of the ith province on the jth indicator.

Through the above steps, we constructed an objective and scientific comprehensive digital technology index (DT), which provides a reliable quantitative tool for studying the impact of digital technology on the tourism economy.

4.1.3. Tourism Development

The Tourism development index (TD) is a composite index composed of five sub-indices: tourism fixed asset investment (TFAI), tourism patent applications (TPA), number of tourism schools (TSN), number of tourism students (TSSN) and number of tourism employees (TEN). Similar to the construction of the digital technology index (DT), these sub-indices are weighted using the entropy method to objectively reflect their contribution to the overall tourism development.

Table 4. Descriptive statistics of tourism development.

Variable	Obs	Mean	Std. Dev.	Min	Max.
TFAI	390	24,932.052	29,850.338	686.944	111,427.410
TPA	390	188.259	255.748	0	1589
TEN	390	85,831.138	66,247.413	6503	623,267
TSN	390	154.708	531.382	1	8736
TSSN	390	25,702.910	47,515.862	53	674,255

provides descriptive statistics for these five sub-indicators as well as for the composite tourism development index TD.

Descriptive statistical analysis in Table 4 reveals significant regional disparities in tourism development levels across Chinese provinces, as indicated by the tourism development index (TD). The standard deviations of the five constituent indicators—tourism fixed-asset investment (TFAI), tourism-related patent applications (TPA), number of tourism employees (TEN), number of tourism colleges (TSN), and number of tourism students (TSSN)—are all significantly larger than their respective means, highlighting substantial heterogeneity in tourism development across provinces. Specifically, TFAI reveals that tourism investment in some provinces far exceeds the average, while others lag. TPA demonstrates significant variations in investment in tourism technological innovation across provinces. TEN reflects considerable regional differences in the scale of the tourism workforce, potentially linked to the size, development stage, and employment structure of the local tourism industry. TSN and TSSN highlight disparities in the emphasis placed on tourism talent cultivation and the allocation of educational resources across provinces. The substantial differences in the number of tourism colleges and students underscore the uneven spatial distribution of tourism education resources in China. These significant variations are crucial considerations in subsequent empirical analyses, as they may interact in a complex way with the impact of digital technologies on tourism economic growth and improvements in tourism industry efficiency. Future research should delve deeper into the underlying causes of these regional disparities, including factors such as natural resource endowments, government policies, economic development levels, and market demand.

4.1.4. Descriptive Statistics of Tourism Industry Structure

Based on the descriptive statistics in

Table 5. Descriptive statistics of tourism industry structure.

Variable	Obs	Mean	Std. Dev.	Min	Max.
TAN	390	1072.179	721.361	95	3608
SHN	390	322.667	171.271	65	959
ATN	390	350.451	279.153	25	3469

, we can observe that the structure of China’s tourism industry has significant regional heterogeneity among provinces. This heterogeneity is reflected in three sub-indicators, namely, the number of travel agencies (TAN), the number of star-rated hotels (SHN), and the number of A-grade tourist attractions (ATN).

Table 5 shows the descriptive statistics of these three sub-indicators as well as the composite tourism industry structure index TIS. Similar to the tourism development index, the tourism industry structure index has significant differences among different provinces, and the standard deviation of each sub-index is relatively large, which suggests that there is significant regional heterogeneity in the industrial structure of China’s tourism industry. Specifically:

TAN (number of travel agencies): the average number is 1072, with a standard deviation as high as 721, and the minimum and maximum values are 95 and 3608, respectively, showing the great difference in the number of travel agencies in different provinces. This may be due to the differences in the scale of tourism, market demand, policy environment, and other factors in each province.

SHN (number of star-rated hotels): the average number is 323, the standard deviation is 171, and the minimum and maximum values are 65 and 959, respectively. This also shows that there are significant differences in the demand and supply of high-class accommodations in different regions, which are closely related to the level of local economic development, the positioning of the tourism market, and other factors.

ATN (number of A-class tourist attractions): The average number is 350, with a standard deviation as high as 279. This shows that there are significant differences in the development and utilisation of tourism resources in different regions. Provinces rich in natural scenery and regions with more historical and cultural resources may have more A-grade attractions, reflecting differences in resource endowment.

Overall, the variability of these indicators reveals the diversity in the structure of the tourism industry across China’s provinces. This diversity may be due to a combination of multiple factors such as natural resource endowment, industrial policy, level of economic development, and historical development, and by adjusting these factors, the tourism industry can be further optimised and developed. In terms of policy formulation and resource allocation, strategies suitable for the development of each region can be formulated according to its strengths and characteristics to achieve the goal of optimising resources and promoting the development of the local tourism economy.

4.1.5. Descriptive Statistics of Model Variables

Descriptive statistics reveal significant patterns and changes in the 30 Chinese provinces included in this study (390 observations in total, covering the period 2010–2022). The results reveal significant differences in tourism economic growth, digital technology adoption, tourism efficiency, tourism development, and tourism structure across the 30 provinces of China over the period 2010–2022. Highlights the heterogeneity of the study area and justifies the subsequent use of panel data analysis.

The descriptive statistical analysis in

Table 6. Descriptive statistics of variables.

Variable	Sample Size	Average Value	(Statistics) Standard Deviation	Minimum Value	Maximum Values
TEG	390	4.792	0.956	2.967	8.387
DT	390	0.243	0.102	0.049	0.63
TIE	390	23.840	18.881	2.186	107.553
TD	390	0.088	0.083	0.002	0.673
TIS	390	0.212	0.127	0.003	0.536

shows the key variables in the study—tourism economic growth (TEG), digital technology application level (DT), tourism industry efficiency (TIE), and the control variables tourism development (TD) and tourism industry structure (TIS). TEG has a mean growth rate of 4.79% but a standard deviation of 0.96%, indicating large fluctuations in the growth rate, which is closely related to the resource endowment, policy environment, and market conditions of each province. DT has a mean of 0.24, which seems to be moderate, but its standard deviation is as high as 0.1, and spans from 0.05 to 0.63, which is a very high standard deviation, reflecting great differences in digital infrastructure and technological capabilities, which are closely related to technological investment, infrastructure construction, and labour quality. The mean value of TIE is 23.84, but its standard deviation is as high as 18.88, with a range spanning from 2.19 to 107.55, suggesting that there are great differences in the efficiency of provinces in the use of tourism resources and infrastructures, which may be caused by the differences in resource allocation, management levels, and market conditions, and therefore supports the use of TIE as a mediating variable in the analysis.TD and TIS also show large variability, further suggesting that there are significant differences in the level of tourism development and the industrial structure of China’s provinces. These significant regional differences emphasise the necessity of using a panel data model analysis to better control for individual effects and more accurately assess the relationships between variables.

4.2. Correlation Analysis and Multicollinearity Test

4.2.1. Relevance Analysis

Definition:

The Pearson correlation coefficient between two variables is defined as the product of the covariance of the two variables divided by the standard deviation:

$${\mathsf{\rho }}_{X,Y}={\displaystyle \frac{\mathrm{cov}\left(X,Y\right)}{{\mathsf{\sigma }}_X{\mathsf{\sigma }}_Y}}={\displaystyle \frac{E\left[\left(X-{\mathsf{\mu }}_X\right)\left(Y-{\mathsf{\mu }}_Y\right)\right]}{{\mathsf{\sigma }}_X{\mathsf{\sigma }}_Y}}$$

In this section, correlation analysis and multicollinearity tests were conducted to assess the relationship between the model variables and the presence of multicollinearity problems.

Correlation Analysis: To analyse the impact of digital technology on tourism economic growth in China over the period 2010 to 2022, the study uses contemporaneous panel data from 30 provinces (excluding Hong Kong, Macau, Taiwan, and Tibet). The Pearson correlation coefficients reflect the overall relationship between the variables in these datasets and are not specific to any single year or province. As shown in

Table 7. Variable correlation analysis.

Variant	TEG	DT	TIE	TD	TIS
TEG	1.000
DT	0.122	1.000
TIE	0.012	0.171	1.000
TD	0.276	0.570	0.231	1.000
TIS	0.187	0.655	−0.080	0.614	1.000

, the results of the correlation analysis show that all the independent variables (DT, TIE, TD, and TIS) are positively correlated with the dependent variable TEG (tourism economic growth), but the correlations are relatively weak (all less than 0.3), indicating that there is no strong linear relationship between them and TEG. However, a moderately strong positive correlation (0.570) between TD (tourism development) and DT (digital technology) is of interest. This may indicate that provinces with higher levels of tourism development usually have better digital infrastructure, and the relationship may be bidirectional, i.e., the development of tourism promotes the development of digital infrastructure, and the improvement of digital infrastructure, in turn, contributes to the development of tourism.

Multicollinearity test: To assess multicollinearity, the variance inflation factor (VIF) method was used. A VIF value greater than 5 usually indicates a serious multicollinearity problem. In conclusion, the correlation analysis shows that there is a positive correlation between the independent variables and the dependent variable, but the correlation is weak, and the risk of multicollinearity is low.

4.2.2. Multicollinearity Test

To test for the presence of multicollinearity in the model, the variance inflation factor (VIF) was used. Multicollinearity refers to the presence of a high linear correlation between independent variables, which affects the stability of the regression model and the reliability of parameter estimates. The VIF value is used to measure the degree of multicollinearity between the independent variables. A VIF value of 1 indicates that no multicollinearity exists, while a VIF value greater than 5 is usually considered to be a serious multicollinearity problem.

Table 8. Results of multiple covariance test.

Variable	VIF	1/VIF
TIS	2.361	0.424
DT	1.999	0.500
TD	1.912	0.523
TIE	1.211	0.825
Mean VIF	1.871

shows the VIF values and their inverse (1/VIF) for each independent variable. The VIF values for all the independent variables are much less than 5, ranging from 1.211 to 2.361, with an average VIF value of 1.871. This indicates that there is no serious problem of multicollinearity among the independent variables in the model. Although there is some correlation between the independent variables (as described in the previous correlation analysis), this is not to the extent that it affects the reliability of the model estimates. Therefore, the regression results of the model can be considered reliable and open to further analysis and interpretation. The lower VIF value also indirectly supports the judgment of a lower risk of multicollinearity in the previous correlation analysis.

4.3. Selection of Linear Regression Models

Three diagnostic tests were conducted to determine the most appropriate regression model: the F-test for fixed effects, the Lagrange multiplier (LM) test for random effects, and the Hausman test comparing fixed and random effects models.

4.3.1. F-Test for Fixed Effects

This test is used to determine whether there is a significant individual effect (i.e., province share effect) in the model.

Table 9. F-test for fixed effects model.

R-squared	0.520	Number of Obs.	390
F-test	96.483	Prob > F	0.000

shows that the F-test statistic is 96.483 with a p-value of 0.000 (p < 0.05), which indicates that the fixed effects are significant, rejecting the original hypothesis (no individual effects) and supporting the use of a fixed effects model. The R-squared value is 0.520, which indicates a good model fit.

4.3.2. LM Test for Random Effects

This test is used to determine whether a random effects model should be used.

Table 10. LM test for random effects model.

	Var	sd = sqrt(Var)
TEG	0.9147027	0.9564009
e	0.1506586	0.3881476
u	0.5425807	0.7366007

Test: Var(u) = 0, chibar2(01) = 1130.42, Prob > chibar2 = 0.0000.

shows that the Chi-square statistic of the LM test, χ²(1) = 1130.42, has a p-value of 0.000 (p < 0.05), which suggests the presence of a significant random effect, and strongly recommends the use of a model containing a random effect, i.e., a fixed-effects model or a random-effects model, rather than an ordinary least squares (OLS) model. In the test results, Var(u) denotes the variance of individual effect, and it is significantly greater than 0, which further supports the conclusion that there is an individual effect.

4.3.3. Hausman Test

This test is used to compare the fixed effect model with the random effect model. If the Hausman test is significant (p < 0.05), it means that the fixed effect model is more appropriate; conversely, if the test is not significant, it means that the random effect model is more appropriate.

Table 11. Hausman test results.

	Coef.
Chi-square test value	26.033
p-value	0

shows that the Hausman test with Chi-square statistic χ²(k) = 26.033 and a p-value of 0.000 (p < 0.05) is significant, thus the researchers chose the fixed effect model over the random effect model.

Overall, after integrating the results of the F-test, LM-test, and Hausman test, the fixed-effect model was finally chosen as the optimal model for this study. This suggests that when studying the relationship between tourism economic growth and digital technology in Chinese provinces, it is necessary to consider individual differences at the provincial level (e.g., resource endowment, policy environment, etc.), and that the fixed-effect model can effectively control the influence of these individual differences, thus obtaining more reliable estimation results.

4.4. Empirical Regression and Results

This study empirically tests the impact of digital technology on tourism economic growth and its mechanism using a fixed-effects model to verify the three hypotheses. The results show that digital technology has a significant positive impact on both tourism economic growth and tourism industry efficiency, and that tourism industry efficiency plays an important mediating role between the two.

Model 1. Direct impact of digital technology on tourism economic growth: This model examines the direct impact of digital technology (DT) on tourism economic growth (TEG). The coefficient of DT is significantly positive at the 1% significance level (β = 3.854, t = 8.21, p < 0.01), which supports Hypothesis 1 and suggests that digital technology has a significant contributing effect on tourism economic growth. Meanwhile, the coefficients of the control variables tourism development (TD) and tourism industry structure (TIS) are also significantly positive, indicating that the optimisation of the level of tourism development and industrial structure also has a positive effect on tourism economic growth.

Model 2. Impact of Digital Technology on Tourism Efficiency: This model examines the impact of digital technology (DT) on tourism efficiency (TIE). The DT coefficient is significantly positive at the 1% significance level (β = 120.544, t = 8.32, p < 0.01), which supports Hypothesis 2 and suggests that digital technology significantly improves the efficiency of the tourism industry. The TD coefficient is also significantly positive, suggesting that a higher level of tourism development contributes to the improvement of tourism efficiency, while the TIS coefficient is not significant, indicating that the impact of tourism structure on efficiency is not significant.

Model 3. Mediation effect: This model examines the mediating role of tourism industry efficiency (TIE) between digital technology (DT) and tourism economic growth (TEG). The coefficient of DT is significantly positive at the 1% significance level (β = 2.518, t = 5.22, p < 0.01) and the coefficient of TIE is significantly positive (β = 0.011, t = 6.86, p < 0.01), which supports Hypothesis 3, indicating that digital technology promotes tourism economic growth by improving tourism efficiency, in which TIE plays a significant mediating role.

Overall, as shown in

Table 12. Model regression results.

Variables	(1)	(2)	(3)
	TEG	TIE	TEG
DT	3.854 ***	120.544 ***	2.518 ***
	(8.21)	(8.32)	(5.22)
TIE			0.011 ***
			(6.86)
TD	2.684 ***	44.969 ***	2.186 ***
	(6.20)	(3.37)	(5.28)
TIS	2.429 ***	−29.566	2.757 ***
	(3.18)	(−1.25)	(3.82)
Constant	3.104 ***	−3.126	3.139 ***
	(19.70)	(−0.64)	(21.15)
Observations	390	390	390
R-squared	0.457	0.292	0.520
Number of ID	30	30	30

t-statistics in parentheses, *** p < 0.01.

, the empirical results strongly support the three hypotheses, confirming the significant positive impact of digital technology on tourism economic growth and revealing the mediating role of tourism industry efficiency in it. This emphasises the importance of investing in digital infrastructure and improving the tourism industry’s efficiency to promote sustainable tourism development in China. The R-squared value of the model is also relatively high, indicating the strong explanatory power of the model.

4.5. Heterogeneity Analysis

According to the results of the fixed-effects model in the

Table 13. Heterogeneity test.

Variables	East (1)	Medium (2)	West (3)
DT	1.18 *	4.162 ***	3.747 ***
	(0.96)	(1.16)	(1.03)
TIE	0.0188 ***	0.0139 ***	0.00
	(0.00)	(0.00)	(0.00)
TD	4.561 ***	1.70	1.225 **
	(0.96)	(1.10)	(0.59)
TIS	2.41	4.62	2.043 **
	(1.27)	(3.15)	(1.01)
Constant	3.558 ***	2.294 ***	3.265 ***
	(0.37)	(0.53)	(0.18)
Observations	104.00	143.00	104.00
R-squared	0.64	0.55	0.47
Number of ID	8.00	11.00	8.00

Note: Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1

, the technological innovation indicator (TIE) shows a significant positive impact in the regressions of the eastern, central, and western regions1, verifying the role of technological innovation in promoting regional economic growth However, the coefficients of the regions are different, indicating that there is a significant difference in the degree of impact of technological innovation in different regions, which further verifies Hypothesis 3, that there is a difference in the impact of technological innovation in different regions. In the eastern provinces, the level of technological innovation has the most significant impact on economic growth, with a coefficient of 0.0188, indicating that technological innovation can significantly enhance economic growth in more economically developed regions. The high level of infrastructure construction, developed market, and perfect policy environment in the eastern provinces enable technological innovation to be quickly transformed into a driving force for economic growth. The coefficient of 0.0139 for the central provinces, although still significant, is slightly lower than that of the eastern provinces, reflecting that, although the central provinces have made progress in technological innovation, their overall innovation capacity and related policy support have not yet reached the level of the east. The coefficient of the western provinces is 0.0040, which is less significant, indicating that although the economic foundation of the western region is relatively weak, it has begun to gradually promote economic growth in recent years by increasing investment and policy support for technological innovation. Although technological innovation in the western region has a smaller direct role in promoting the economy, it has great potential in the long term, especially in the context of the government and enterprises actively promoting the development of innovation. Overall, there are significant differences in the promotion of technological innovation in different regions, mainly due to differences in the level of economic development, infrastructure development, policy support, and market environment in each region, and these differences reflect the different stages of economic development and innovation capacity in each region.

The fundamental reasons for regional differences lie in the following: The eastern region has a developed economy, complete infrastructure, a concentration of talents and technologies, and favourable policies, which are conducive to promoting economic growth through technological innovation. The central region is slightly inferior in the above aspects, with relatively weak ability to convert technological innovation into practical applications, but it is gradually improving. The western region has a weak economic foundation, and the infrastructure, talent, and market conditions for technological innovation are insufficient, which have limited impact. However, with policy support, its potential is gradually emerging.

4.6. Robustness Tests

To test the robustness of the findings, the researchers redefined the measure of the mediating variable, tourism industry efficiency (TIE), and re-ran the regression analysis. Initially, TIE was defined as tourism revenue per unit of fixed assets; in the robustness test, TIE (renamed TIE2) was redefined as the number of tourists per unit of fixed assets. This change maintains the consistency of the inputs (fixed assets) but changes the measure of outputs as a way of testing the sensitivity of the model results to different measures of efficiency.

Model 1 (Robustness test): Redefining the TIE does not affect the results of Model 1 since it is not included in Model 1. The DT coefficient remains significantly positive (β = 3.854, t = 8.21, p < 0.01), which again validates the direct positive impact of digital technology on tourism economic growth (Hypothesis 1).

Model 2 (Robustness test): The model examines the relationship between DT and the redefined TIE2. The DT coefficient remains significantly positive at the 1% significance level (β = 938.730, t = 6.38, p < 0.01), suggesting that there is a significant positive correlation between DT and the new efficiency metric, TIE2, which supports Hypothesis 2 (DT improves tourism efficiency). Although the control variables TD and TIS are no longer significant, the overall conclusion remains the same, i.e., digital technology improves tourism efficiency. The change in the coefficients highlights the impact of the efficiency measures on the results.

The robustness test (Model 3) further validates the mediating role of tourism industry efficiency (TIE) between digital technology (DT) and tourism economic growth (TEG). Even with the alternative efficiency indicator TIE2 (number of tourists/fixed assets), the DT coefficient remains significantly positive (β = 3.051, t = 6.38, p < 0.01), as does the TIE2 coefficient (β = 0.001, t = 5.25, p < 0.01). Although the DT coefficient decreased compared to the baseline model (3.854 vs. 3.051), the mediating effect remained significant. This suggests that the impact of digital technology on tourism economic growth is not entirely direct but is partly indirect through the enhancement of tourism industry efficiency (TIE). Even after controlling for the impact of digital technology, the improvement of tourism industry efficiency still significantly contributes to tourism economic growth, which confirms the key role of TIE as a mediating variable that amplifies the positive impact of digital technology on tourism economic growth and is not simply a superimposed effect. TIE plays a bridging role between DT and TEG, connecting the causal chain of digital technology, enhancing efficiency and driving economic growth.

The results of the robustness test generally support the conclusions of the baseline model. As shown in

Table 14. Robustness test results.

Variables	(1)	(2)	(3)
	TEG	TIE2	TEG
DT	3.854 ***	938.730 ***	3.051 ***
	(8.21)	(6.38)	(6.38)
TIE2			0.001 ***
			(5.25)
TD	2.684 ***	−27.763	2.708 ***
	(6.20)	(−0.20)	(6.48)
TIS	2.429 ***	45.559	2.390 ***
	(3.18)	(0.19)	(3.24)
Constant	3.104 ***	−0.010	3.104 ***
	(19.70)	(−0.00)	(20.42)
Observations	390	390	390
R-squared	0.457	0.143	0.496

t-statistics in parentheses, *** p < 0.01.

, although the coefficients and significance of some variables changed after redefining TIE, this mainly reflects the sensitivity of the efficiency measurement method rather than the fundamental conclusion of the model. Therefore, the research conclusion that digital technology has a significant positive impact on tourism economic growth and that tourism industry efficiency plays a mediating role in this processis robust.

4.7. Robustness Test of Alternative Indicators

From the perspective of robustness, both DIL1 (the number of patent applications and grants) and DIL2 (the number of utility model patent grants) serve as alternative indicators of the level of digital technology development. As shown in

Table 15. Robustness test of alternative indicators results.

Variables	(1) TEG (DIL1 Model)	(1)′ TEG (DIL2 Model)
DIL1	0.251 ***
	(7.44)
DIL2		0.290 ***
		(7.60)
TIE	0.0096 ***	0.0093 ***
	(6.08)	(5.92)
TD	1.731 ***	1.789 ***
	(4.24)	(4.43)
TIS	2.033 ***	2.030 ***
	(2.86)	(2.87)
Constant	1.510 ***	1.006 ***
	(5.25)	(2.94)
Observations	390	390
R-squared	0.553	0.556
Number of ID	30	30

Note: Standard errors in parentheses; *** p < 0.01.

, In the regression analysis, they both exhibit significant and directionally consistent positive effects. with coefficients significant at the 1% level. This result indicates that the driving role of digital technology in promoting tourism economic growth is stable and widespread. The underlying reason is that digital technology fundamentally stimulates regional innovation capabilities and promotes the intelligence of tourism service scenarios, thereby improving the allocation of industrial resources and the mode of service supply. The number of innovative patent applications, as an important indicator for measuring the technological activity of a region, precisely captures the real changes under the trend of digital transformation. In other words, regardless of whether it is the comprehensive digital infrastructure index or the patent grant number with more technological innovation attributes, the path logic of “technology-driven-efficiency improvement-economic growth” remains consistent, indicating that the core mechanism has strong robustness.

Furthermore, the alternative uses of different types of patents further enhance the robustness of the results: DIL1 is more inclined towards comprehensive technological achievements, while DIL2 reflects practical technological improvements. Both of these jointly demonstrate a positive and significant impact, indicating that the absorption capacity of tourism for digital achievements is not limited to a certain type of technological form, but shows a universal response ability to various technological innovations. Combining the continuous and stable mediating effect of TIE in the two sets of models also shows that improving the efficiency of the tourism industry has always been the key link of digital technology’s effect on the tourism economy, indicating that the mediating mechanism is not disturbed by index changes and has a certain structural stability.

To summarise, the robustness test not only verified the consistency of the results but also strengthened the credibility of the conclusion by starting from the logical consistency between the alternative indicators and the core variables. This further supported the “technology-efficiency-growth” transmission mechanism proposed in this paper.

4.8. Endogeneity Test

To address the potential endogeneity problem, this study adopts the two-stage least squares method (2sls) and introduces two exogenous instrumental variables, namely the provincial “Internet penetration rate and per capita telecommunications business revenue, for the exogenous processing of the level of digital technology development (DT). As shown in

Table 16. Endogeneity test t results.

Variables	(1) TEG	(2) TEG
Internet penetration rate as an instrumental variable	5.358 ***
	(5.33)
Internet penetration rate as an instrumental variable		9.359 ***
		(3.07)
TIE	0.007 ***	0.002
	(3.53)	(0.41)
TD	1.433 ***	0.373
	(2.92)	(0.39)
TIS	1.648 **	0.085
	(1.99)	(0.06)
Observations	390	390
R-squared	0.474	0.25
Number of ID	30	30

Note: Standard errors in parentheses; *** p < 0.01, ** p < 0.05.

, In Model 1, the estimated coefficient of digital technology is 5.358, and the t-value is 5.33, indicating that, after controlling for endogeneity, digital technology still has a significant positive promoting effect on the growth of the tourism economy. In Model 2, the coefficient of digital technology further increases to 9.359, and the corresponding t-value is 3.07. The result is still significant, which further verifies the robustness of digital technology as a driving factor of economic growth.

In terms of mediating variables, the tourism industry efficiency (TIE) in Model 1 is statistically significant, with a coefficient of 0.007, indicating that tourism industry efficiency does indeed play a partial mediating role in the path of promoting tourism economic growth through digital technology. In contrast, although the estimated value of TIE in Model 2 is positive, it does not reach statistical significance. This indicates that, when per capita telecommunications business revenue is used as an instrumental variable, the transmission path of tourism industry efficiency is relatively weak. This may be related to the fact that the digital technology level represented by this variable is more focused on the usage behaviour at the consumer end, weakening its explanatory power for the improvement of efficiency at the production end.

In terms of control variables, both the tourism development level (TD) and the tourism industrial structure (TIS) show positive influences in Model 1, indicating that tourism human resources, infrastructure input, and industrial structure optimisation support the growth of the tourism economy. By comparison, in Model 2, the coefficients of these two control variables decreased significantly, suggesting that, in the digital consumption environment represented by per capita telecommunications business revenue, there is a considerable difference between per capita telecommunications business revenue and the input of traditional industries, further reflecting the influence of the choice of instrumental variables on the explanatory power of control variables.

To summarise, the regression results of the two sets of instrumental variables have strengthened the core conclusion that digital technology plays a key driving role in the growth of China’s tourism economy and, to a certain extent, achieves an indirect promoting effect by improving the efficiency of the tourism industry. This provides a theoretical basis for the subsequent policy-making and emphasises the strategic significance of promoting the popularisation of digital communication and enhancing the digital capabilities of industries in the high-quality development of the regional tourism economy.

5. Discussion

5.1. Innovativeness

The innovativeness of this study is reflected in the following three aspects: First, in relation to methodological innovation, based on a panel data model controlling for regional heterogeneity and time effects, this is the first empirical verification of the significant contribution of digital technology to tourism economic growth, and the findings are highly robust. Second, in revealing the mechanism, through the test of the intermediary effect, the key role of tourism industry efficiency in the transmission path of “digitalisation technology → efficiency enhancement → economic growth” has been clarified, which makes up for the insufficiency of existing studies in exploring the intermediary mechanism. Third, in relation to path expansion, the study has analysed the role path of digital technology from the three dimensions of resource allocation, marketing efficiency, and service experience, providing a systematic theoretical framework for the digital transformation of the tourism industry.

5.2. Theoretical Implications

This study provides a new theoretical perspective for understanding the relationship between digital technology and tourism economic growth. When compared with previous theoretical achievements, it highlights various innovations. Previous studies mostly explored the impacts on tourism economic growth of macro policies, social environmental factors, or single material conditions. This study focuses on digital technology as an emerging key factor and delves into its internal influence mechanism, which is one of the innovations. This study has confirmed that digital technology not only directly promotes tourism economic growth but also indirectly boosts it by improving the efficiency of the tourism industry, which has not been fully revealed by previous studies. In terms of research methods, existing theories are mostly based on data from a single region or industry and lack universality. This study uses panel data from 30 provinces, controls for regional and temporal differences, and constructs a comprehensive index system and model for empirical analysis, making the research results more persuasive and universal. This is another important innovation point. Moreover, existing theories have obvious gaps. In the research on the relationship between digital technology and tourism economic growth, there is insufficient attention to soft factors such as innovation and talent, and the way digital technology affects tourism economic growth through these soft factors has not been systematically analysed. This study deeply analyses the dimensions of resource allocation, marketing efficiency, and service experience, filling this gap and providing more comprehensive theoretical support for the digital transformation of the tourism industry. In summary, in terms of theoretical achievements, whether in terms of research perspective, methods, or exploration of the influence mechanism, this study is significantly different from previous studies and makes new contributions to the theoretical development of this field.

5.3. Limitations

However, this study still has room for improvement in the following aspects: First, the geographical coverage is limited by data availability, and the research sample does not include Hong Kong, Macao, Taiwan, and Tibet. The tourism formats in these regions have significant particularities (such as cross-border tourism, plateau ecotourism, etc.), that may affect the universality of the research conclusions. Second, data timeliness is limited. Although the latest available data, from 2010 to 2022 are used, the accelerated penetration of new technologies, such as generative AI (e.g., ChatGPT-4 and the metaverse, in the tourism industry after 2023 has not been fully reflected. Third, although the research method controls individual heterogeneity through a fixed-effect model, the potential two-way causal relationship between digital technology and the tourism economy still needs to be more rigorously tested using the instrumental variable method. In addition, the analysis of the intermediary mechanism focuses on the dimension of industrial efficiency, and the discussion of potential action paths, such as cultural communication and environmental sustainability, needs to be deepened.

5.4. Future Research

Future research should further explore the heterogeneous impacts of digital technologies on different regions and sub-sectors (e.g., rural tourism, cultural heritage tourism), and introduce dynamic panel models to capture the long-term effects of technology iteration. It is also crucial to discuss the influence of emerging technologies like generative AI and the metaverse on the tourism industry. For example, generative AI can be used to create personalised travel itineraries, provide intelligent customer service, and generate realistic virtual tourism experiences. The metaverse can offer immersive virtual tourism, allowing tourists to visit destinations remotely and interact with virtual environments. By incorporating these emerging technologies into the research, a more refined basis for policy formulation can be created.

6. Conclusions

This study employs rigorous econometric methods and panel data from 30 mainland Chinese provinces (excluding Hong Kong, Macao, Taiwan, and Tibet) from 2010 to 2022 to conduct an in-depth empirical analysis of the relationship between digital technologies and tourism economic growth. The results demonstrate that digital technologies have a significant positive impact on tourism economic growth, and this impact is achieved indirectly by improving tourism industry efficiency. Specifically, the study finds that digital technologies optimise resource allocation, enhance marketing efficiency, and improve tourist experiences, ultimately leading to increased tourism industry efficiency and driving economic growth in the tourism sector.

In terms of outlook, future research can further explore the heterogeneous impacts of digital technologies on different regions and subsectors (e.g., rural tourism, cultural heritage tourism), and introduce dynamic panel models to capture the long-term effects of technology iteration, providing a more refined basis for policy formulation.