Spatio-temporal effects and influence mechanism of digital technology on tourism efficiency in Chinese provinces

Fan Zhang; Qian Cheng

doi:10.1038/s41598-024-74367-8

. 2024 Oct 3;14:22975. doi: 10.1038/s41598-024-74367-8

Spatio-temporal effects and influence mechanism of digital technology on tourism efficiency in Chinese provinces

Fan Zhang ^1,^✉, Qian Cheng ¹

PMCID: PMC11450143 PMID: 39363093

Abstract

Digital technology serves as a new industrial driving force, playing a crucial role in promoting the efficiency of regional tourism. To explore the inherent logic between digital technology and tourism development, the study employed the DEA-BCC model and the entropy-weighted TOPSIS method to measure the tourism efficiency and digital technology development level of 31 provinces (municipalities and districts) in China from 2011 to 2022. The evolutionary characteristics of the relationship between digital technology and tourism efficiency were analyzed from the spatio-temporal dimension, and empirical tests were carried out using the spatial error model (SEM) and the spatial lag model (SLM) to explore the mechanism of the impact of digital technology on tourism efficiency. The results show that: during the study period, the overall trend of digital technology development and tourism efficiency is upward, but there is a certain degree of spatial mismatch between the two; digital technology is not only conducive to the improvement of tourism efficiency in the province, but also acts on neighboring provinces through spatial spillover effects, mechanism of action tests show that digital technology can positively moderate the effects of tourism economic growth and tourism industry structure on tourism efficiency; in western and northeast China, the positive effect of digital technology on tourism efficiency is more obvious. The conclusions provide a new perspective for understanding and analyzing the development of provincial tourism in China, as well as a reference for the rational use of digital technology to promote tourism development.

Keywords: Digital technology, Tourism efficiency, Spatial error model, Spatial lag model, Spatio-temporal characteristics

Subject terms: Environmental social sciences, Socioeconomic scenarios, Sustainability

Introduction

With the acceleration of a new round of technological change, Chinese society is rapidly moving into the digital era, data has become an important factor in production, digital technology has become a driving force for innovation, and the promotion of digital technology to better serve the practice of tourism development will help to promote efficient regional economic development¹. China’s digital economy reached 50.2 trillion yuan in 2022, while the online tourism market reached 746 billion yuan in the same year². Digital technology construction has become a crucial investment in social development and an effective tool for economic growth. In the context of China’s 14th Five-Year Plan for Tourism Development, there is a strong emphasis on fostering innovation-driven, high-quality tourism³. Tourism is tasked with a vital mission: reviving rural areas and stimulating consumption. Nevertheless, the rapid growth of the tourism sector has led to the over-exploitation of regional tourism resources and a decline in the ecological environment. Furthermore, the uneven development among provinces has posed a significant obstacle to the sustainable progress of the tourism industry⁴. An integrated approach to tourism development that emphasizes both efficiency and quality is necessary, and therefore tourism cannot be developed without the enabling impact of digital technology, which is particularly important in the context of shrinking consumer demand and increasing ecological pressures⁵. In essence, digital transformation is an urgent necessity for the tourism sector to navigate the industry’s current crisis successfully.

The current development of tourism suffers from such problems as slow transformation and upgrading, insufficient cultivation of new forms of business, and structural imbalance between supply and demand, etc. It is a key issue that needs to be urgently resolved at this stage for the development of the tourism industry to shift from the pursuit of “scale and speed” to “quality and efficiency”^6,7. The crucial factor in achieving efficient development on the basis of steady growth in the total volume of tourism lies in the balance between supply and demand in the tourism market. Too little input and an imbalance in output will result in insufficient impetus for the growth of the tourism economy and inefficiency in the tourism industry⁸. Therefore, a strong external force is urgently needed to break through the limit of supply and demand in the tourism market and promote tourism efficiency. In the digital era, digital technology, as a pioneering force driven by innovation, can help promote the tourism industry from factor-driven to innovation-driven, break through the limit of tourism growth, achieve a balance between supply and demand in the market, and thus improve tourism efficiency⁹. Ultimately realizing multi-dimensional coordination between tourism and the economy, society and the environment. To prevent ongoing unrefined development, the intervention of digital technology can refine and innovate the tourism industry¹⁰. Digital infrastructure resources are critical in the early stage of development, with communication technology, information technology, and Internet technology serving as the primary digital infrastructure that acts as a foundation for tourism activities and product innovation, as digital finance, innovative technology, and other resources continue to deepen^11,12, the marginal cost of the integration of digital technology and developing the tourism industry will gradually decrease.

The academic community has reached a consensus on the impact of digital technology on tourism economic growth. Based on information and communication technology, digital financial inclusion and innovative scientific and technological elements¹³, digital technology penetrates into tourism activities in an all-round way, driving the circulation of new factors of production such as knowledge, technology and information in the tourism industry, thereby enhancing the efficiency of production factor combination in the tourism industry and increasing the proportion of high-level elements in the tourism industry¹⁴. Ultimately, this provides dynamic energy for promoting the improvement of tourism efficiency through factor allocation. Digital technology primarily promotes the enhancement of tourism efficiency through economies of scale, supply and demand matching, long-tail effects, technological innovation, organizational change, service enhancement, structural improvement, and management upgrading^15–17. At the macro level, the development of the digital economy brings about efficiency improvement in the tourism industry, optimizing industry structure and enhancing the level of tourism economic development. The development of the digital economy brings new input factors, resource allocation efficiency, and total factor productivity, opening up new prospects for the development of the tourism industry¹⁸. Furthermore, the complementary role of digital technology in virtual tourism industry clusters prompts the tourism industry to exhibit new characteristics in the interaction between the real and virtual, breaking traditional boundaries of the value chain¹⁹. At the meso-level, digitalization drives industrial structure upgrading, reshapes the organizational structure of the tourism industry chain, enhances cross-border integration capabilities, and stimulates the emergence of new tourism formats^20,21. At the micro level, digital technology overcomes temporal and spatial limitations in knowledge acquisition for tourism practitioners^22,23. It also strengthens the collective learning mechanism of the tourism industry, ultimately improving the quality of human capital and output performance in tourism²⁴.

As a result, digital technologies play a pivotal role in advancing tourism development by facilitating technological upgrades and generating economies of scale, and the tourism industry’s economic growth can reflect the dynamic process from inputs to outputs in the development of tourism industry, which is the premise of tourism efficiency, the tourism industry structure reflects the structural problems of industrial development, which reflects the matching and combining relationship of tourism efficiency. Given that the overall efficiency of tourism hinges on the interplay of these factors, both technological advancements and economies of scale are indispensable for enhancing tourism efficiency²⁵. Existing studies have confirmed that digital technology can reduce the cost of information transmission while breaking down barriers of information asymmetry, thereby improving technical efficiency²⁶. Digital technology can also promote the aggregation of physical entities and virtual clusters in the tourism industry, driving diversification of virtual clusters in the tourism industry and related industries, leveraging economies of scale, and thus enhancing the scale efficiency of regional tourism^27,28. However, there is a lack of empirical testing at the macro level, with most attention focusing on the specific forms of digital technology, such as artificial intelligence, cloud tourism, and the application of big data, on the quality of tourism experiences^29–31. Whether digital technology can promote the improvement of tourism efficiency and through what means is still under exploration. Additionally, this study believes that ignoring spatiotemporal characteristics and regional heterogeneity may lead to estimation bias. This is because the “spatiotemporal compression” effect caused by the efficient information transmission function of digital technology is profoundly changing traditional geographical spaces, altering the interaction and correlation of tourism industries among regions³².

Given this, this paper integrates statistical yearbook data, web mining data and other multi-source data to construct a provincial level digital technology and tourism efficiency index evaluation system, adopts DEA-BCC model and entropy weighted TOPSIS method to scientifically measure the tourism efficiency and digital technology development level of China’s 31 provinces in the period of 2011–2022, and visualizes its spatial and temporal characteristics with the help of ArcGIS10.8. Secondly, the spatial effect of digital technology on tourism efficiency and spatial heterogeneity are empirically analyzed by spatial econometric panel model; finally, the mechanism of digital technology affecting tourism efficiency is explored, and validating the moderating role of tourism economic growth and tourism industry structure. This study’s innovation lies in several aspects: (1) in terms of research perspective, it innovatively integrates digital technology and tourism efficiency into the same theoretical framework, expanding the understanding of factors influencing the quality of tourism industry development at the macro level. It empirically explores the characteristics of the effect of digital technology on enhancing tourism efficiency, enriching and expanding the theoretical system and empirical evidence of existing research. (2) in terms of research methods, it utilizes SEM and SLM to enhance the credibility of the core conclusion that digital technology promotes tourism efficiency. Heterogeneity analysis is particularly noteworthy for China, which exhibits severe regional development imbalances and prominent inter-provincial differences. (3) in terms of indicator measurement improvement, further decomposition of tourism efficiency into tourism overall efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE), and the development of digital technology includes three dimensions: internet infrastructure, digital finance, and innovation capability. This provides a more comprehensive explanation of the impact of digital technology on the tourism industry. The purpose of this paper is to reveal the inner law and action mechanism of researching digital technology affecting tourism efficiency, and to explore its realistic fit at the regional level. At the same time, it provides decision-making reference for other regions to cultivate digital tourism.

Research design

Measurement of tourism efficiency

Tourism efficiency reflects the inputs of tourism resources, their allocation, and utilization³³. Additionally, tourism efficiency aids in appropriately adjusting the intensive use of capital in tourism based on the inputs and outputs of tourism resources. Various methods are commonly used to assess the efficiency of the tourism industry, including Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA), and DEA-Malmquist models based on production frontier analysis. These methods are widely employed in the study of traditional tourism industry sectors like travel agencies, tourism hotels, and tourism transportation, as well as in the field of tourism destinations^34–37.

DEA (data envelopment analysis) has a unique advantage when dealing with the problem of economics production function and economies of scale, and the result of its assessment of efficiency is a comprehensive indicator. Since DEA does not need to set the weights of input and output variables and the form of the production function, especially in the case of small sample capacity, the research problem is dominated by multiple inputs and multiple outputs^19,38. Based on the greater advantages of the DEA method in evaluating tourism efficiency, this paper will use this method to evaluate the tourism efficiency of Chinese provinces.

Methods of measuring efficiency

This paper uses the DEA-BCC method to measure efficiency in China’s provincial tourism industry and decomposes it into pure technical and scale efficiency³⁹. The CCR model and BCC model in the DEA methodology are two basic models commonly used to measure the efficiency of similar producing individuals. The CCR model produces under constant returns to scale (CRS), but in practice, producing individuals often have variations in returns to scale, so this leads to scale efficiency being mixed in the efficiency measure. The BCC model takes into account variable returns to scale (VRS), removes the CRS assumption from the CCR model, and measures the relative efficiency values of producing individuals at different returns to scale. BCC model assumes that the returns to scale are variable, so the comprehensive efficiency is decomposed into two parts, scale efficiency and pure technical efficiency, scale efficiency can compare the difference between the existing scale and the optimal scale, reflecting the degree of rational allocation of inputs and outputs; pure technical efficiency measures technical efficiency after removing the influence of the business scale factor, it is the assessment of the efficiency of the existing input resource allocation⁴⁰. In this paper, the tourism industry is an input-led industry, and the tourism industry can minimize inputs without changing the quantity of outputs in a certain period of time.

Model for measuring efficiency

In Eq. (1), it is assumed that there are X kinds of inputs and Y kinds of outputs in each decision-making unit, n represents the number of decision-making units, θ is the efficiency of DMUs, λ is the index combination coefficient of DMUs, and the slack variable S⁻ and the residual variable S⁺ represent the amount of output insufficiency and input redundancy, respectively. If the efficiency value of this DMU is equal to 1, it can be considered that this DMU is located on the frontier and is in an effective state of comprehensive technical efficiency; the closer it is to 1, the greater the relative efficiency value of this DMU is, The tourism comprehensive efficiency in the final measurement results refers to the comprehensive measurement and evaluation of the resource allocation capacity of the decision-making unit and the efficiency of the resource utilization and other aspects. Comprehensive efficiency can be further decomposed into pure technical efficiency (PTE) and scale efficiency (SE). Pure technical efficiency is the production efficiency influenced by factors such as management and technology, while scale efficiency is the production efficiency influenced by the size of the enterprise.and the model formula is as follows:

Efficiency assessment is based on a system of two indicators of economic productivity, inputs and outputs. Land, labor, and capital are usually defined in the Cobb-Douglas production function as the most basic inputs to economic production activities. Few studies have included land elements in the input index system due to the lack of statistical data on land use in tourism therefore, land elements were not included in the input index system in this study³⁷. Labor and capital are considered as input indices, and travel agencies, star hotels, and scenic spots are the main components of tourism economic development, the available statistical data proved that the abundance of these sectors adequately reflects the core service and resource elements of tourism economic development⁴¹. As a result, the number of travel agencies, star-rated hotels and A-class scenic spots as well as the original value of investment in tourism fixed assets in tourism are defined as capital inputs, and the number of employees in the tourism industry is defined as labor inputs; tourism revenue and the number of tourists intuitively reflect the economic benefits of the tourism industry, and are defined as output indicators. The input and output indicators chosen in this paper have been verified by relevant studies to be scientific and able to reflect the resource allocation efficiency of the tourism industry in a certain period of time^19,25. Specific indicators and calculation methods are shown in (Table 1).

Table 1.

Selection of input-output indicators.

Indicator type	Variable Name	Measurement Method
Input indicators	Reception service capacity	Sum of the number of travel agencies and star-rated hotels
	Resource endowment	Number of A-class scenic spots
	Fixed asset investment	Original value of investment in tourism fixed assets
	Human capital	Number of employees in the tourism industry
Output indicators	Tourism revenue	Total income from tourism
Output indicators	Tourism scale	Total number of tourists

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Measurement of digital technological development

Methods of digital technological development

Most of the current measurements of digital technology use the digital finance inclusion index or internet indicators, and related research is relatively one-sided. Drawing on previous research results^21,23., the evaluation system of indicators for the level of digital technology development is presented in (Table 2). The system is constructed around three key aspects: digital basic resources, digital technical support, and digital innovation potential. The index reflecting the level of digital economic development, derived from processing the following 12 indicators using the entropy weight TOPSIS method, is denoted as ‘Digiti’ and simplified to “DIG” in the model set as a core explanatory variable. In instances where individual data points were missing for any given year, this paper employed the linear interpolation method to fill in the gaps.; Furthermore, statistical data in US dollars were consistently converted to RMB based on the average exchange rate between the US dollar and the RMB for the respective year.

Table 2.

Selection of indicators for the digital technology development index.

	Variable name	Measurement method
Digital technology comprehensive development	Digital basic resources	Mobile phone penetration rate
		Number of Internet broadband access ports
		Length of long-distance fiber-optic cable lines
		Number of Internet domain names
	Digital technology support	Breadth of digital financial coverage
		Depth of digital financial usage
		Digital finance digitization level
		Level of online mobile payment
	Digital innovation potential	Internal expenditure on R&D funding
		R&D personnel full-time equivalent
		Number of patents granted
		R&D intensity

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Standard deviational ellipse

Using the center coordinates, azimuth, area, long and short semi-axes of the standard deviation ellipse analysis method⁴², the standard deviation ellipse is able to present the spatial distribution law of regional geographic elements in a visual way, and the study respectively describes the spatial characteristics and evolution trends of the development of tourism efficiency and digital technology in China’s provincial areas. The average distribution centers of tourism efficiency (TE)) and digital technology (DIG) are taken as the center of gravity, and the direction of movement of the center of gravity reflects the spatial trajectory of the changes in the distribution of TE/DIG; the direction of the main trend is the azimuth angle, and the long and short semiaxes reflect the degree of dispersion and agglomeration in the spatial distribution.

Model setting

Standard panel model setting

The extent of the role of digital technology on tourism was estimated using OLS with the following model:

Where: Inline graphic as the explained variable is tourism efficiency; TE, SE, PTE are used as the explained variables respectively, and the core explanatory variable DIG is the level of digital technology development. In addition, the evolution of the spatial pattern of tourism efficiency is a complex process, and in the process of evolving from the original spatial pattern to the existing pattern, it will be affected by a combination of factors, such as the regional economy, finance, and urbanization levels, etc. The following control variable indicators were selected drawing on relevant studies⁴³: the level of economic development (PGDP), the level of financial development (FIR), the degree of openness to foreign business (OPEN), and the industrial structure (IND), urbanization level (UR). α₀, α₁, ., α₅ represent the elasticity coefficients of each variable, respectively; i represents the study area; t represents the year; and Inline graphic is the random error term.

Spatial measurement model construction

Fully consider the spatial effect of regional digital technology and tourism development, through the form of spatial weight matrix embodied in the spatial lag term or spatial error term, its estimation results are more realistic⁴⁴. According to the different impacts of spatial terms, spatial measurement models are mainly categorized into spatial lag models and spatial error models.

Spatial lag model (SLM), which reflects that the tourism efficiency of the region is not only a function of the local tourism revenue, but also a function of the local tourism income.is not only a function of local tourism revenue, but also affected by the tourism efficiency of neighboring regions, with strong interregional spatial autocorrelation⁴⁵. The regional economic efficiency has strong spatial autocorrelation. The model is as follows:

Where: W_ij is an element of the spatial weight matrix W; ρ is the spatial lag (autocorrelation) coefficient; and the rest is the same as Eq. (2).

Spatial error model (SEM), which fully takes into account the fact that the tourism efficiency of the province is affected by some hidden and unquantifiable variables which are spatially correlated. At the same time, factor fluctuations within a certain space may spread to neighboring regions through the spatial transmission mechanism, resulting in the existence of spatial spillovers of random error shocks between regions. The model is as follows:

Where: Inline graphic denotes the error term of spatial autocorrelation; λ is the spatial error (autocorrelation) coefficient; and the rest is the same as Eq. (2).

The study introduces tourism economic growth (TEG) and tourism industry structure (TIS) as moderating variables, and in accordance with the setting conventions of established research results, TEG is measured with reference to Robertico’s approach, using the ratio of the total output value of the tourism industry to the GDP⁴⁶, and TIS is measured with reference to Gan and Ji’s approach^24,47, using the total revenue of the tourism industry, total employment in the tourism industry, the number of travel agencies, number of star hotels and tourist attractions industry, reconstructing the Tel index measure and normalizing the indicators. In order to empirically test whether digital technology has an impact on the improvement of tourism efficiency through the positive moderating effect of TEG and TIS, regression analyses are conducted in a spatial panel, adding the interaction terms of the core explanatory variables with the moderating variables (DIG × TEG, DIG × TIS) to verify whether there is a moderating effect.

Data sources

This paper chooses 31 provinces in China as the research object. The main reasons are: 1) Provincial units are the most important source markets, the unique location elements, resource flow and agglomeration functions of the provinces make them an important support for tourism development, Chinese provinces have more complete government management elements, and the popularization of digital technology promoted by provincial governments is more common, which is an ideal object for the observation and practice of this study. 2)The study aims to explore the enabling role of digital technology in China’s tourism industry, so it needs to take into account the national development examples, so that the samples are similar and comparable to each other; 3) Full digital technology popularization needs financial security. Provinces with higher tourism revenues usually receive more attention and resources, enabling better infrastructure development. In addition, considering that the level of the national tourism economy was affected to different degrees during the COVID-19, it is difficult for the indicators of recent years to objectively reflect the strength of the tourism economy, so the 12-year indicators are chosen to avoid the errors caused by chance factors.

Studying the mechanism of digital technology on tourism efficiency in 31 Chinese provinces from 2011 to 2022, the research includes pertinent data, without involving data from Hong Kong, Macao, and Taiwan, to form a balanced panel of 372 province-year observations. All data from China Tourism Statistical Yearbook 2001–2018, China Culture and Tourism Statistical Yearbook 2019–2023 and copies, statistical yearbooks of provinces and cities 2012–2023, China Science and Technology Statistical Yearbook, China Fixed Asset Investment Statistical Yearbook. some of the missing data come from the statistical bulletins of national economic and social development of provinces and municipalities, the CEIC database, and the statistical yearbooks and statistical bulletins of the relevant prefectures and municipalities, the rest of the missing data were filled in using interpolation.

Spatio-temporal effects of digital technology and tourism efficiency

Temporal characteristics of tourism efficiency and digital technology

The temporal characteristics of the development of digital technology and tourism efficiency were analyzed by plotting box plots and histograms with the help of Origin2022 (Fig. 1). From Fig. 1a, it can be seen that the tourism efficiency rises slowly in fluctuation during the study period, but the development gap between the provinces and regions is always existing, and the average value of tourism efficiency reaches the highest value of 0.893 in 2019 year, but the value of tourism efficiency in 2020–2022 is unstable due to the influence of Covid-19, indicating that the layout of digital technology continues to develop steadily as an emerging economy in recent years, while tourism development may be relatively slow due to the constraints of industrial transformation and upgrading ; from Fig. 1b, it can be seen that from 2011 to 2022, the average value of digital technology in China’s provinces and regions shows a slow-growing trend characteristic, with the average value increasing from 0.249 in 2011 to 0.270 in 2022. It is worth mentioning that the development of digital technology and tourism efficiency both declined in 2017–2018, which may be due to the fact that China’s economic development entered the “new normal” in that period, and the development of the industry has not yet established an endogenous growth mechanism to prevent and buffer against the rapid economic deceleration, thus showing a downward trend in that time period, but it also precisely shows that the development trend of tourism efficiency and digital technology coincides with the overall environment of the nation’s economic development.

Fig. 1 — Box plot of level of digital technology development and tourism efficiency.

In terms of the contribution of PTE and SE to tourism efficiency, scale efficiency is significantly higher than pure technical efficiency (Fig. 2). During the study period, the mean value of pure technical efficiency is 0.841 and the mean value of scale efficiency is 0.874, the value of scale efficiency rises from 0.832 in 2011 to 0.919 in 2022, which indicates that scale efficiency has a greater impact on comprehensive efficiency, and also reflects that during the study period, the majority of provinces as a whole are in the stage of increasing returns to scale, and the development of tourism focuses on relying on factor inputs rather than the improvement of technology level. To further compare the tourism efficiency of provinces in different periods, according to the regional division of the National Bureau of Statistics of China, the 31-province sample was divided into the eastern, central and western and the northeast. Taken together, the northeast has the highest comprehensive efficiency, with a mean value of 0.811, the central region has the second highest comprehensive efficiency, with a mean value of 0.789, and the western region has the lowest, with a mean value of 0.670; the scale efficiency of the central, western, and northeast regions is higher than their purely technical efficiencies, while the purely technical efficiency of the eastern region is higher than its scale efficiency, which reflects that the eastern region’s tourism inputs make efficient use of resources and have a higher technological level, but scale benefits are not utilized, while the central, western and northeast regions have redundancy in tourism inputs and relatively backward technical elements. Although the value of tourism efficiency in the western region is low, it has the fastest development speed, and the comprehensive efficiency rises sharply, from 0.598 in 2011 to 0.761 in 2022. On the contrary, in the northeast region, the tourism efficiency declines sharply in 2020–2022, and the tourism efficiency decreases from 0.849 in 2011 to 0.644 in 2022, the main reason for this is due to the uneven development of the three provinces in the northeast, while the continuing impact of Covid-19 has also exposed the lack of depth in the integration of digital technology and tourism, leading to problems such as the limited transformation and upgrading of industries in the northeast.

Spatial characteristics of tourism efficiency and digital technology

The spatial display is visualized using ArcGIS10.8 software (Fig. 3).In 2011, the high value areas of China’s provincial tourism efficiency were mainly distributed in the southwestern and northeast provinces, and the tourism efficiency was significantly improved in 2015, and the high value areas of tourism efficiency were gradually spreading to the northwestern part of the country in 2019, and in 2022, the spatial distribution of the tourism efficiency presents a big change, and the high value areas gradually tend to be in the western part. on the one hand, due to the impact of Covid-19, on the other hand, also because the western provinces have high-quality natural resources, the rapid development of the tourism industry in recent years, Xinjiang and Tibet and other western provinces and municipalities of the tourism resources by to the full use of the tourism industry as a whole is in the stage of increasing returns of the scale, and the provinces in the eastern and central regions due to the tourism industry started early, has entered the stage of diminishing marginal returns; From 2011 to 2022, regions with higher levels of digital technology development are mainly concentrated in the Yangtze River Delta and the Pearl River Delta, and the differences between the East, the Middle East and the West are more prominent, which may be due to the fact that the development of digital technology is closely related to the economic foundation of the region, and there is a regional rootedness, however, there is a certain degree of spatial mismatch between tourism efficiency and digital technology, which may be due to the fact that the development modes and stages of different regions are also different, thus leading to the development of digital technology and tourism.

Further with the help of using the ArcGIS10.8 spatial standard deviational ellipse analysis technique to analyze the spatial distribution and evolutionary trend of digital technology and tourism efficiency in typical years (Fig. 4). Firstly, from the perspective of overall spatial distribution, the standard deviational ellipse of tourism efficiency is widely distributed, with the main axis distributed along the northeast-northwest-southwest direction, which indicates that the central and eastern parts of China are the agglomeration areas of tourism efficiency, and the outer part of the standard deviational ellipse gradually spreads from Sichuan to Qinghai, Tibet and other provinces, which indicates that the radiation scope of tourism efficiency is gradually expanding; the center of gravity of tourism efficiency is shifted from Henan to Hubei, and finally shows a clear bias towards the southwest, and the ellipse area keeps expanding, indicating that the spatial distribution of tourism efficiency tends to expand from agglomeration; the standard deviational ellipse distribution of digital technology development are all located in the central and southeastern regions, and the main axis is distributed along the northeast-northwest-southeast direction, thus indicating that the central-east is the agglomeration area of digital technology development, from the perspective of the center of gravity, the center of gravity of digital technology development in the four years is located in Henan Province, which eventually shows the result of obvious deviational to the northwest and southeast, and the ellipse area shows a tendency of expanding and shrinking, indicating that the spatial distribution of digital technology gradually tends to agglomerate.

Fig. 4 — Standard deviation ellipse and center of gravity of tourism efficiency and digital technology.

Empirical results and discussion

The overall impact of digital technology on tourism efficiency

The spatial correlations reflected by SLM and SEM models are global, thus the variables in the spatial regression models may have endogeneity problems, and the coefficient estimates are biased or invalid if OLS is used, and need to be measured by two-stage least squares (2SLS), generalized method of moments (GMM), or the method of great likelihood (ML), etc. Anselin suggested that ML be used to estimate the parameters of SLM and SEM⁴⁸, because ML estimation can effectively avoid the problem of endogeneity of variables, and at the same time, it can scientifically reflect the degree of dependence of the tourism efficiency of neighboring provinces, and accurately measure the impact of tourism efficiency of neighboring provinces on the local area. Therefore, this paper adopts the ML method for estimation, and the OLS estimation results are listed in the text at the same time for easy comparison. Using Matlab 2016b and its spatial measurement software package, the inverse geospatial distance matrix is selected to estimate and test the relevant models of the impact of digital technology on tourism efficiency in China’s 31 provinces as a whole (Table 3).

Table 3.

Econometric Estimates of Tourism Efficiency and Digital Technology Development in 31 provinces of China. *, **, and *** are significant at the significance level of 10, 5, and 1%, respectively. The “t“ statistic is in parentheses.

Variable	TE
Variable	OLS	SEM	SLM
DIG	0.194^*** (3.1954)	0.572^* (1.810)	0.582^* (1.771)
PGDP	0.007^*** (3.221)	0.004^** (2.232)	0.006^** (2.274)
FIR	0.013 (0.615)	0.018 (0.816)	0.014 (0.601)
OPEN	0.087 (1.257)	0.050 (0.398)	0.034^* (2.127)
UR	0.030^** (2.233)	0.024^** (2.521)	0.035^*** (3.618)
IND	0.045 (0.810)	0.025^*** (2.595)	0.038^*** (4.950)
Wdep.var.*			0.371^*** (4.692)
spat.aut.		0.458^*** (5.968)
Log-L	34.922	174.08	176.77
R ²	0.024	0.503	0.378
LM-LAG			18.550^***
R-LMLAG			3.820^*
LMERR		16.296^***
R-LMERR		1.026

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From Table 3, it can be seen that the R² and the Log-L values of SLM and SEM are higher than the estimates of the traditional OLS model, indicating that the estimation of the spatial econometric model is more reliable than that of the traditional model. Further model selection is based on LMLAG, LMERR and their robustness statistics, the results show that both pass the 1% significance level test, but LMLAG and its robustness statistics are higher than LMERR, which indicates that the SLM is better than the SEM model. Therefore, this paper chooses SLM as the explanatory model when analyzing the overall impact of digital technology on provincial tourism efficiency. The coefficient of SLM, W*dep.var., passes the test at 1% significance level and is positive, indicating that there is an obvious positive spatial spillover effect of China’s provincial tourism efficiency, and that the tourism efficiency of each province is not only affected by the local digital technology and other related factors, but also affected by the neighboring regions. The coefficients of the control variables in the SLM model are all significantly positive, indicating that each factor has a positive impact on tourism efficiency, but the overall effect is not strong. The effect of digital technology on tourism efficiency is that for every 1% increase in digital technology, tourism efficiency changes positively by 0.582%, and among the related control variables, the positive effects on tourism efficiency are IND, UR, OPEN and PGDP, for every 1% increase in each of these factors, the comprehensive efficiency changes positively by 0.038, 0.035, 0.034, and 0.006%. This result shows that digital technology is the main factor driving the development of tourism, which is mainly manifested in the updating of technology level, the increase of scale efficiency due to the input of digital factors, thus the factors of production are enhanced and the efficiency of tourism is increased; the FIR contributes the least to the efficiency of tourism, which is mainly due to the slow speed of the operation of tourism capital, the slowdown of the market scale and growth rate at the present stage, and thus the value of tourism resources can’t be realized, hindering the development of tourism.

Robustness test

In order to further verify the robustness of the spatial spillovers of digital technology on tourism efficiency, this paper re-estimates the model by replacing the spatial weight matrix (Table 4). The spatial measurement matrix used in the study is the geographic distance matrix, which is replaced by a nested matrix of economic and geographic distances in the robustness test. The results of the robustness test show that the impact of digital technology on tourism efficiency has not changed fundamentally, which is consistent with the results of the main regression as well, and confirms that the conclusions of the previous paper have a certain degree of robustness.

Table 4.

Robustness test. *, **, and *** are significant at the significance level of 10, 5, and 1%, respectively. The “t “statistic is in parentheses.

Variable	TE
Variable	OLS	SEM	SLM
DIG	0.130^** (2.522)	0.427^* (1.900)	0.447^* (1.919)
PGDP	0.003 (0.379)	0.003^** (2.273)	0.004^** (2.300)
FIR	0.041^*** (4.991)	0.025 (1.575)	0.022 (1.411)
OPEN	0.118^** (2.017)	0.147^* (1.655)	0.149^* (1.679)
UR	0.014^*** (3.035)	0.014 (0.261)	0.015 (0.263)
IND	0.012 (0.660)	0.021 (0.521)	0.016 (0.420)
Log-L	37.069	127.620	135.622
R ²	0.298	0.362	0.380

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Mechanism of action tests

To deeply explore the mechanism of digital technology affecting tourism efficiency, DIG and TEG, TIS interaction terms were introduced into the spatial panel for analysis, and the regression results are shown in Table 5, which shows that the two interaction terms, respectively, passed the significance level test of 5 and 1%, which further confirms the proof of the positive regulating effect of digital technology on tourism economic growth and tourism industry structure, and is able to drive the improvement of tourism efficiency. The possible reason is that digital technology helps to solve the problem of input-output balance and the rationalization of factor resource allocation, thus ensuring the value-added industrial chain and the optimization of industrial structure, and influencing the improvement of tourism efficiency.

Table 5.

Moderating effects test. *, **, and *** are significant at the significance level of 10%, 5%, and 1%, respectively. The “t “statistic is in parentheses.

Variable	TE
Variable	TEG(1)	TIS(2)
DIG	0.984^*** (3.094)	0.920^*** (2.850)
TEG	0.308^* (1.739)
DIG×TEG	2.028^** (2.249)
TIS		0.679^** (2.297)
DIG×TIS		2.575^*** (4.184)
Control	YES	YES
cons	0.643^*** (5.960)	0.356^*** (2.702)
R²	0.518	0.615
N	372	372

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Heterogeneity test of digital technology on tourism efficiency

For the analysis of regional heterogeneity, the study selects the explanatory models for the eastern, central, western and northeast regions (Table 6). The R² and Log-L values of the SLM and SEM models for the four major regions are higher than the estimates of the traditional OLS model, indicating that all the spatial econometric models are better than the traditional models in their estimation. Model selection was further based on LMLAG, LMERR and their robustness statistics, and SLM, SEM, SLM and SLM were selected as the explanatory models for the eastern, central, western and northeast regions, respectively, based on the significance level and the magnitude of the robustness statistics. The spatial autoregressive coefficient W*dep.var. and the error spatial autocorrelation coefficient spat.aut. of the four regions are both significantly positive, which further verifies the existence of spatial correlation and positive spatial spillover effect of provincial tourism efficiency in the four regions.

Table 6.

Heterogeneity test results. *, **, and *** are significant at the significance level of 10, 5, and 1%, respectively. The “t “statistic is in parentheses.

Variable	Eastern			Central			Western			Northeast
Variable	OLS	SEM	SLM	OLS	SEM	SLM	OLS	SEM	SLM	OLS	SEM	SLM
DIG	0.450^*** (2.714)	0.485^*** (4.553)	0.631^*** (4.419)	0.469 (0.131)	0.213^* (1.741)	0.276^** (2.511)	0.541^*** (5.302)	0.949^*** (3.600)	0.850^*** (3.085)	0.463 (1.321)	0.734^** (2.226)	0.821^* (1.926)
PGDP	0.121^** (2.433)	0.309^*** (3.557)	0.312^* (1.900)	0.122^** (2.250)	0.134^* (1.746)	0.180 (1.152)	0.011 (0.076)	0.013^*** (4.966)	0.041^*** (8.306)	0.017 (1.165)	0.057^** (2.561)	0.068^*** (2.873)
FIR	0.072^*** (3.054)	0.121^*** (6.341)	0.078^*** (5.093)	0.157^*** (4.762)	0.194^*** (6.936)	0.145^*** (4.303)	0.031^** (2.440)	0.095^*** (2.630)	0.033 (1.451)	0.055 (0.589)	0.055 (0.487)	0.041^*** (2.783)
OPEN	0.096 (1.331)	0.150^*** (2.609)	0.152 (0.887)	0.914^* (1.678)	0.668^*** (3.552)	0.667^* (1.665)	0.202 (0.776)	0.101 (0.375)	0.172 (1.398)	1.308^** (2.538)	0.886 (1.241)	0.024 (0.128)
UR	0.009 (1.213)	0.002 (0.180)	0.013^*** (6.326)	0.030 (1.296)	0.037 (0.995)	0.026 (0.846)	0.001 (0.403)	0.018 (1.231)	0.065^*** (9.689)	0.016 (0.185)	0.061 (0.463)	0.025 (0.499)
IND	0.207^*** (3.588)	0.107 (1.324)	0.025 (0.455)	0.397^*** (4.331)	0.160 (1.613)	0.211^** (2.205)	0.118^* (1.717)	0.331^*** (4.241)	0.175^*** (4.921)	0.360^*** (4.160)	0.808^*** (5.418)	0.396^*** (4.303)
Wdep.var.*			0.899^*** (6.633)			0.648^*** (7.091)			0.745^*** (7.921)			0.973^*** (7.561)
spat.aut.		0.821^*** (7.170)			0.825^*** (13.342)			0.756^*** (7.724)			0.926^*** (11.671)
Log-L	37.531	135.03	120.82	38.59	128.23	140.22	38.714	139.71	212.74	42.30	153.09	215.90
R ²	0.418	0.742	0.725	0.382	0.825	0.753	0.230	0.818	0.646	0.307	0.821	0.815
LM-LAG			3.976^**			7.355^**			13.570^***			9.495^***
R-LMLAG			1.196			1.366			5.062^**			2.988^*
LMERR		3.827^*			10.180^***			8.759^**			6.567^**
R-LMERR		1.047			3.191^*			1.251			1.060

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The positive impact of digital technology on tourism efficiency in the four major regions have passed the significance level test, fully illustrating the significant pulling effect of digital technology; it is worth noting that the intensity of the effect of digital technology on tourism efficiency in the western and northeast regions is higher than that in the central and eastern regions, which is probably due to the fact that China’s economic development has shown different characteristics in the regions, with tourism resources in the western region gradually being fully utilized and the tourism industry still in the stage of increasing returns to scale; while tourism efficiency in the northeast region has declined in recent years, but due to the more complete support system of digital technology, the positive impact on tourism efficiency is still significant, Undeniably, there is often a conversion process from “quantitative change” to “qualitative change” in factor inputs, in which there will be a certain lag. Some provinces in the central and eastern regions have already entered the stage of diminishing marginal returns due to the early start of tourism.

Among the relevant control variables, the positive effect of economic development, financial development and urbanization on tourism efficiency in the eastern region decreases in descending order, as shown in the positive change of 0.312, 0.078 and 0.013% respectively for every 1% increase in each factor; the economic development, financial development and degree of openness of the central region have a positive effect on tourism efficiency, as shown in the positive change of 0.134%, 0.194% and 0.668 respectively for every 1% increase in each factor; The economic development, urbanization level and industrial structure in the western region have a positive influence on tourism efficiency, and for every 1% increase in each factor, the overall economic efficiency changes positively by 0.041%, 0.065% and 0.175%; the economic development, financial development and industrial structure in the northeast region have a positive influence on tourism efficiency, and for every 1% increase in each factor, the overall economic efficiency changes positively by 0.068, 0.041 and 0.396%.

Conclusions

Main conclusion

Research on the relationship between the digital economy and the high-quality development of the tourism industry is still in the theoretical exploration stage. Therefore, based on the provincial panel data of China from 2011 to 2022, this paper innovatively analyzes the mechanism of digital technology’s impact on tourism efficiency, thus obtaining estimation results that are more in line with the economic reality. The main conclusions are as follows: t.

In terms of spatio-temporal effects, China’s provincial tourism efficiency slowly rises in fluctuation during the study period, and the spatial distribution pattern of high-value areas of tourism efficiency gradually disperses to the west; the level of digital technology development as a whole shows a gradual upward trend, and the high-value areas are stably distributed in the southeastern coastal provinces, with stronger spatial agglomeration, but the low-value areas of the central and western regions shrink significantly over time, and the gap between the provinces is always there. Among the four regions, the northeast has the highest level of integrated efficiency, while the eastern and central regions have comparable levels of integrated efficiency, and the western is low but growing fastest; the scale efficiency has a greater role in improving the comprehensive efficiency of tourism compared to the pure technical efficiency, which is due to the fact that the overall development of the tourism industry in most of China’s provinces still focuses on relying on factor inputs rather than the improvement of the technological level.

In terms of the impact mechanism, digital technology has a significant positive impact on tourism efficiency and a significant spatial spillover effect, tourism efficiency in each province is not only affected by local digital technology and other related factors, but also by related factors in neighboring regions, these findings are consistent with previous studies^21,25, the test of the mechanism of action shows that digital technology can positively moderate the effect of tourism economic growth and tourism industry structure on tourism efficiency, which are the two paths of action of digital technology to promote tourism efficiency; in contrast to previous studies, the analysis of regional heterogeneity found that the positive effect of digital technology for the western and northeast regions is greater than that of the central and eastern regions⁴¹, which echoes the results presented by the spatio-temporal effect,. This also verifies that the tourism industry in the western provinces is still in the stage of increasing returns to scale, and the role of digital technology helps to increase the flow of tourists and reduce the marginal cost to obtain economies of scale; as for the northeast region, there is a certain lag in the positive impact of digital technology on tourism efficiency. With the adaptation of digital technology and the production factors of the tourism industry, the production factors of the tourism industry gradually break through the spatial and temporal limitations under the support of digital technology, which helps to solve the problems of diminishing marginal effect of tourism inputs and imbalance between inputs and outputs of the regional tourism and thus promotes the enhancement of tourism efficiency. In the eastern and central regions, the development of digital technology has entered the maturity stage, and is at the stage of constant or decreasing marginal returns, with the marginal productivity of digital technology factors decreasing.

In summary, the mechanism of digital technology’s overall enhancement of China’s tourism efficiency is mainly realized by economies of scale and technological progress²⁴, the sustained and steady growth of the tourism economy and the optimization of the tourism industry structure are also the path of action of digital technology to promote the efficiency of tourism. Through the decomposition of tourism efficiency and the visualization of spatial and temporal effects, it can be judged that, due to the late intervention of digital technology in the western region, the imbalance between the total supply and demand of the tourism industry and the structural imbalance at the early stage of the development is an important obstacle to the improvement of the efficiency, so the positive effect of digital technology on the tourism industry of the western provinces has a longer period of reflection, the process of integrating digital technology with tourism in the northeast may result in a disorganized allocation of short-term tourism production factors. In addition, digital technology has the economic characteristics of “natural monopoly”, for the relatively mature digitalization of the eastern and central regions, the previous resource elements may not be able to keep up with the pace of innovation and development of digital tourism, resulting in tourism efficiency below the effective scale of production operation.

Theoretical contributions

The main contributions of this study are as follows: (1) Decomposing tourism efficiency values into pure technical efficiency and scale efficiency for further measurement and comparative analysis, and encompassing the multidimensional indicators covered by digital technology, To explore the intrinsic mechanism of the impact of digital technology on tourism efficiency with tourism economic growth and tourism industry structure as the moderating variables, aiming to make the logic behind clearer, make up for the limitations of the existing research in exploring the impact mechanism from a single perspective, and provide an important basis for empirically exploring the relationship between the two^13,21;(2) Analyzed the spatial heterogeneity among the four regions over time, with a horizontal and vertical perspective, repeatedly comparing the 31 research samples to obtain more practical and universal theoretical basis, dynamically displaying the spatiotemporal evolution characteristics of China over the past decade; (3) From a macro spatial perspective, expanding the understanding of the impact of digital technologies on different regions at different levels, and including regional heterogeneity in the examination further deepens the understanding of the impact of digital technologies on tourism.

Practical implications

According to the spatial and temporal evolution characteristics of digital technology and tourism efficiency in 31 provinces, firstly, in the area of active development of digital tourism, the provinces represented by Zhejiang and Guangdong province, the digitization construction started early and advanced in an orderly manner, and it is recommended to maintain the strategic stability, continue to promote the digitization innovation based on the circulation and sharing of data and knowledge, and explore the value of travel-related data to cash in, so that it can move steadily forward in an era of digital uncertainty, avoiding the phenomenon of a large number of blind investment in digitization leading to excess capacity, information surplus and loss of value^10,49;Secondly, the system of policies, regulations and industry standards related to digital technology is not yet sound in a few provinces, such as the Tibet Autonomous Region, where policymakers try as much as possible to avoid the digital dilemma of heavy development and light planning or development first and planning later, resulting in a large investment with a low return; In addition, the rough construction of digitalization is also a prominent problem that restricts the high-quality development of tourism, with reference to provinces such as Shandong and Anhui, where the level of development of digital technology is high but the efficiency of tourism does not match, and there may be problems such as “digital blindness” or “digital traps"^50,51, so that administrators should coordinate the direction of the application of digital technology and the practical benefits, and strengthen the guidance of digital tourism; at the stage of digital tourism integration in the northeast region, especially the Jilin Province, we need to give full play to the “positive externalities” of digital technology, realize the deep integration of digitalization and tourism, and promote the digital, networked and intelligent development of the tourism industry by focusing on the current uncoordinated development of the tourism industry and weakening the sustained impact of Covid-19 on the tourism industry.

Attention to the heterogeneous character of the impact effects of digital technologies. Policy makers should take into account the actual development situation according to the local conditions³³. In less developed regions in the west, the positive effect of digital technology is not obvious for the short term, but in the long term digital technology can positively promote the development of tourism, and managers should deeply understand and predict tourists’ behaviors, preferences, and needs based on big data and machine learning algorithms, and innovate products and services accordingly, such as in Sichuan and Shaanxi provinces, where large investments in digitization have already been quite effective; Simultaneously, efforts are made to accelerate the digitization of infrastructure in western regions, expand smart public services, increase the scale of physical industry aggregation, and promote the diversification and virtual aggregation of the tourism industry and related industries²³, this approach leverages economies of scale to enhance regional tourism efficiency. In the more developed regions of eastern China, such as Zhejiang and Guangdong, the ability of digital technology to transform into advanced productivity in the tourism industry has already been demonstrated, but there are more provinces at the stage of diminishing returns to scale, so consideration can be given to scaling down the scale of inputs, optimizing the innovation ecosystems related to digital tourism, and encouraging market players to explore the types of innovations on the basis of the existing technological elements. In addition, the uneven development of the three provinces in the northeast region should perhaps take into account the differences in tourism factor endowments and economic foundations among the provinces; Jilin should focus on the technological innovation effect as a breakthrough, and Liaoning and Heilongjiang should focus on optimizing the factor structure and accelerating the construction of a system of high-quality development of the tourism economy through the enhancement of tourism efficiency.

Research limitations

The study has certain research limitations and shortcomings that warrant further exploration in the future. Firstly, the study is macroscopic and examines the province level, the lack of city-level tourism statistics makes it difficult to test and analyze the input and output elements of tourism production, as the tourism industry satellite account improves, or using multiple sources of data for side-by-side comparisons, but also data such as those of listed tourism companies, a more microscopic research object will be chosen for measurement. Secondly, given the availability of indicators related to tourism development, the study did not select mediating variables to explore the mechanism of mediating effects, which can be used in the future to further investigate the multiple transmission paths of digital technology on tourism development through mediating effects modeling. Finally, the empirical study is conducted only from a linear perspective, but considering that the network externality characteristic of digital technology may have a non-linear effect on tourism development, it can be verified in the future with the help of panel smoothing transformation model and other models.

Acknowledgements

This research is supported by the General Project of National Social Science Foundation of China (No.23BGL322).

Author contributions

FZ: conceptualization, charting, data curation, and writing—original draft preparation. QC: editing and writing reviewing.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

[CR1] 1.Dang H. The underlying logic of digital economy enables transformation of tourism industry. Econ. Probl. 03 45–50 + 121. 10.16011/j.cnki.jjwt.2023.03.015 (2023). [Google Scholar]

[CR2] 2.China Academy of Information and Communication Technology. White paper on the development of China’s digital economy. Shanghai: China Academy of Information and Communication Technology. http://www.caict.ac.cn/kxyj/qwfb/bps/202207/t20220708_405627.htm.

[CR3] 3.Ministry of Culture and Tourism of the People’s Republic of China. The status of tourism as a strategic pillar industry of the national economy is more consolidated. Beijing: Ministry of Culture and Tourism, PRC, https://www.mct.gov.cn/preview/special/xy20d/9672/202210/t20221013_936444 (2022).

[CR4] 4.Xu L., Wang S., Li J., Tang L., & Shao Y. Modelling internation tourism flows to China: a panel data analysis with the gravity model. Tourism Econ. 25(7), 1047–1069. 10.1177/1354816618816167 (2019). [Google Scholar]

[CR5] 5.Sorooshian S. Implementation of an expanded decision-making technique to comment on Sweden readiness for digital tourism. Systems 9(3), 50 10.3390/systems9030050 (2021). [Google Scholar]

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PERMALINK

Spatio-temporal effects and influence mechanism of digital technology on tourism efficiency in Chinese provinces

Fan Zhang

Qian Cheng

Abstract

Introduction

Research design

Measurement of tourism efficiency

Methods of measuring efficiency

Model for measuring efficiency

Table 1.

Measurement of digital technological development

Methods of digital technological development

Table 2.

Standard deviational ellipse

Model setting

Standard panel model setting

Spatial measurement model construction

Data sources

Spatio-temporal effects of digital technology and tourism efficiency

Temporal characteristics of tourism efficiency and digital technology

Fig. 1.

Fig. 2.

Spatial characteristics of tourism efficiency and digital technology

Fig. 3.

Fig. 4.

Empirical results and discussion

The overall impact of digital technology on tourism efficiency

Table 3.

Robustness test

Table 4.

Mechanism of action tests

Table 5.

Heterogeneity test of digital technology on tourism efficiency

Table 6.

Conclusions

Main conclusion

Theoretical contributions

Practical implications

Research limitations

Acknowledgements

Author contributions

Data availability

Declarations

Competing interests

Footnotes

References

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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