A Study on the Mechanisms of New Quality Productive Forces Enabling the Upgrading of the Modern Tourism System: Evidence from China

Abstract

Entering the new development stage, empowering the modern tourism system by upgrading it with new quality productive forces (NQPF) is of great significance in promoting the high-quality development of China’s tourism industry. Based on the panel data of 30 provinces in China between 2018 and 2022, the two-way fixed effects model, the mediated-effects model, and the spatial Durbin model SDM were constructed using STATA 16 for empirical analysis. Results indicated that NQPF have a significant facilitating effect on upgrading the modern tourism system, which is reflected in four aspects: industrial efficiency upgrading, industrial technology upgrading, industrial structure upgrading, and open sharing upgrading. The results of the mechanism test show that the dynamic capacity of the industry plays an important intermediary role in the process of NQPF promoting the upgrading of the modern tourism system. In addition, NQPF has a spatial spillover effect on upgrading the modern tourism system. Based on the above conclusions, strengthening the cultivation and development of NQPF, optimizing the industry dynamic capacity, promoting coordinated regional development, and optimizing the policy environment are proposed in order to further enhance the overall level of the modern tourism system and to realize the high-quality development of tourism.

1. Introduction

In May 2024, the Chinese government delivered an important instruction regarding tourism work, which explicitly requested the following: “strive to improve the modern tourism system, accelerate the construction of a strong tourism country, and promote the high-quality development of the tourism industry in a stable and far-reaching way,” which put forward new requirements for the improvement of the modern tourism system. The policy documents issued by the Chinese government, such as the “14th Five-Year Plan for Tourism Development” and the “14th Five-Year Plan for Culture and Tourism Development”, both emphasize the need to improve the modern tourism system, making it more robust and effectively supplying tourism offerings. Over the past few years, particularly from 2018 to 2022, China’s tourism industry has undergone significant changes and development [1]. According to official statistical bulletins, China’s inbound tourism market steadily entered a slow recovery phase in 2018, while outbound tourism saw rapid growth. The number of domestic tourist trips increased by 10.8% compared to the previous year, inbound tourist trips grew by 1.2%, and outbound tourist trips rose by 14.7%. Due to the impact of the pandemic, in 2022, the total number of domestic tourist trips decreased by 22.1% and domestic tourism revenue dropped by 30.0%. Notably, the COVID-19 pandemic has had profound effects on the global and domestic tourism systems in China, not only altering tourists’ behavior patterns but also accelerating the digital transformation of the tourism industry. Against this backdrop, exploring how to promote high-quality development of the tourism sector through enhancing industrial efficiency, technological applications, structural optimization, and open sharing becomes particularly crucial.

The modern tourism system is a concept encompassing the characteristics of the times, with a certain degree of relativity and dynamism [2], and, to a certain extent, its dynamic adjustment also highlights tourism to promote the country’s economic and social development [3]. The modern tourism system involves not only the structure of the tourism economy but also the main market participants and tourism-related industries. It is an organic system comprising interconnected and mutually supportive industry sectors, dominant forces, and elemental conditions. Academic research results concerning the modern tourism system mainly focus on its structural elements [4,5,6,7], influencing factors [8,9,10], development paths [11,12,13], and other aspects. Current research mostly studies and analyzes one area of tourism development but lacks a quantitative systematic analysis of the influence mechanism of modern tourism system upgrading.

Modernization toward socialism in world history mainly includes two aspects: the modernization of productivity levels and the modernization of the forms of communication. The proposal of new quality productive forces (NQPF) is a concrete manifestation of the modernization of productivity levels, which was born from revolutionary breakthroughs in technology, innovative allocation of production factors, and in-depth transformation and upgrading of industries. The development of workers, labor materials, labor objects, and their optimal combination is the basic connotation, and the significant increase in total factor productivity is the core aim. At present, there are not many studies on NQPF empowering the upgrading of the modern tourism system in the academic world, with most of the focus placed on analyzing the impact of productivity factors, i.e., laborers, labor materials, and labor objects, on the modern tourism system separately. First, the new type of labor factor mobility helps to break down the barriers to free labor mobility [14] so that tourism employees can reasonably move between different regions according to market demand. The application of digital technology further enhances this mobility, ensuring that high-quality talent can quickly flow to where they are most needed by reducing the cost of information searches and improving matching efficiency [15]. Especially in economically backward but tourism resource-rich regions, such labor return promotes the development of new types of business, such as rural tourism, increases tourism revenue in these regions, and narrows the gap with developed regions [16]. Second, new types of labor materials, including digital infrastructure and scientific and technological innovation transformation, are key forces driving the upgrading of modern tourism. Acting in the tourism field, using these new types of materials not only improves the user experience but also facilitates the emergence of new tourism consumption scenarios, such as digital scenic spots and immersive experiences [17]. For example, Jiangsu Province, as a pioneer in the development of smart tourism in China, has recognized 32 provincial smart tourism scenic spots, and a number of projects, such as the China Grand Canal Museum in Yangzhou and the Suzhou Bay Digital Art Museum, have been selected as pilot projects for the cultivation of immersive experience spaces for smart tourism in the country. Its Digital Culture and Intelligent Tourism Development Center promotes the application of provincial cultural and tourism data sharing, creating a provincial, municipal, county and enterprise data sharing system at four levels, accumulating 296 million articles and tourism industry data resources These data are used for passenger flow measurement, consumption analysis, scenic area monitoring, and risk early warning and other services, which has greatly enhanced the development of the province’s tourism industry.

In examining the relevant literature, deficiencies in the existing literature were found. First, there are fewer research studies focused on quantifying the level of development of the modern tourism system. Second, although existing studies analyze the development of modern tourism from multiple theories and are influenced by multiple factors, there is a lack of systematic analysis of the influence mechanism of modern tourism system upgrading. Third, existing studies have proposed the major strategy of NQPF to promote the construction of a modern tourism system as the background. However, the mechanism of how NQPF empower the upgrading of a modern tourism system is not clarified, which leads to a lack of corresponding theoretical support for current government policymakers and theoretical explorers when they study and formulate policies to promote the construction of the modern industrial system. Given this, how can modern tourism system be quantified? Can NQPF promote the upgrade of the modern tourism industry system? What is the mediator influencing the upgrade of the modern tourism system by NQPF? Additionally, considering the geographical issues among Chinese provinces, does spatial mutual influence exist?

Based on these questions, this study constructs a two-way fixed-effect model, a mediation effect model, and a spatial spillover effect model. It conducts empirical analysis using panel data from 30 provinces in China between 2018 and 2022. The main contributions of this research are as follows: firstly, it fills the gap in the quantitative study of the development level of the modern tourism industry system; secondly, it clarifies the specific mechanism through which NQPF can upgrade the modern tourism industry system; thirdly, it reveals the significant role played by industrial dynamic capabilities within this process; lastly, it identifies positive spatial spillover effects of new quality productivity on the upgrade of the modern tourism industry system. These findings not only provide new perspectives for theoretical discussions but also offer strong support to policymakers, aiding in the promotion of transformation and upgrading of China’s modern tourism industry system.

2. Research Hypothesis

2.1. NQPF

Productivity consists of three core elements: workers, means of labor, and objects of labor. It is characterized by two fundamental features: spatial and temporal heterogeneity and open development. Spatial and temporal heterogeneity are reflected in the unevenness of productivity levels across historical periods and national regions, while open development indicates that the evolution of productivity is not linear [18] and that its constituent elements are dynamically adjusted in response to technological advances and changes in social demand. To understand the meaning of NQPF, we need to start from the dimensions of “new” and “quality”. In the dimension of “new”, NQPF is reflected in the birth of new industries and the innovation of industrial organization. For example, strategic emerging industries, future industries, and the platform economy are new industrial carriers and organizational forms driven by cutting-edge technologies and disruptive technological innovations. These new industries not only break the boundaries of traditional industries but also reshape the operation logic of economic activities [19]. In the dimension of “quality”, NQPF is essentially different from traditional productivity, which is based on the technological conditions of digitization, networking, and intelligence, and realizes the qualitative leap of productivity. NQPF is not only a new economic foundation, but also a new type of innovation-driven productivity, which promotes the economic growth and high-quality development through technological innovation and have a significant impact on the three elements of productivity-labor and capital. The three elements of productivity—workers, labor materials, and labor objects—present higher quality requirements. Specifically, laborers need to have higher digital skills and innovation capabilities, labor materials need to be upgraded in the direction of intelligence and greening, and labor objects need to realize value reconstruction on the basis of new types of resources such as data and information [20].

2.2. Impact of NQPF Enabling the Modern Tourism System Upgrade

Existing studies have pointed out that the effective integration and optimal application of NQPF, a key driving force to promote the construction of the modern industrial system, is of great significance in enhancing the overall efficiency of the industry and achieving high-quality development [21]. In this paper, we will analyze the four dimensions of NQPF to empower the upgrading of the modern tourism system, including upgrading industrial efficiency, technology and structure, and open sharing.

First, we examine industrial efficiency upgrading. In terms of new types of workers, the number of high-quality workers has been increasing, which helps to open up high-value products [22]. Meanwhile, algorithmic management can positively influence the work of tourism practitioners by improving service efficiency, reliability, and customer-oriented behavior [23], which, in turn, improves the tourism service system. The information technology of new labor data enables efficient data collection, processing, and transmission [24,25]. By analyzing large quantities of user-generated content, social media data, and consumer behavior data, tourism enterprises are able to understand customer needs more accurately and reduce the information gap [26,27], thereby improving efficiency. It has been found that an increased level of digital infrastructure can significantly promote the integration of the digital economy and the real economy, which, in turn, effectively enhances the utilization efficiency of various factors, optimizes the structure of factor allocation, and improves the efficiency of factor allocation [28,29]. In addition, regarding capital factors, NQPF optimizes how capital is allocated and improves the efficiency of capital use in the tourism industry. The efficient flow of capital also helps to break geographical boundaries and realize cross-regional cooperation and development, thus enhancing the competitiveness of the whole industry [30]. Second, regarding industrial technology upgrading, NQPF can horizontally extend the boundary of industrial technology with the help of digital technology. It can also dig deeper into the depth of industrial technology vertically to promote the scientific and technological innovation of the industrial chain with new technology, drive the industry to carry out independent innovation and subversive technological innovation, and break through the bottleneck of basic fields [31]. Technological advances supported by NQPF, such as artificial intelligence, cloud computing, virtual reality, augmented reality, and other digital technologies, have been widely used in the tourism industry [32,33], thus driving the upgrading of the tourism system. Third, concerning industrial structure upgrading, tourism NQPF promotes the industrial structure of rationalization and seniority through innovating and optimizing new products, services, modes, and industry chains [34]. NQPF development makes the industrial structure more rational. In addition, NQPF emphasizes digital development, which promotes industrial transformation through the deep integration of digital technology and the real economy, improves production efficiency and product and service quality, and opens up a new path for the advanced industrial structure [35]. Finally, regarding industrial open sharing and upgrading, it has been found that NQPF promotes both an increase in the total amount of social wealth and the formation of a fairer and more reasonable distribution mechanism, thus significantly contributing to the level of industrial sharing [36]. Based on this, the following hypothesis is proposed.

Hypothesis H1:

NQPF will significantly empower the upgrading of the modern tourism system.

2.3. The Mediating Effect of Industrial Dynamic Capabilities

The theory of dynamic capability was first proposed by Teece et al. in 1997 [37]. It refers to the ability of enterprises to integrate, establish, and reconstruct internal resources to adapt to environmental changes to continuously obtain new competitive advantages. Later, the theory was gradually applied to economic and management-related fields, and studies have found that industrial dynamic capabilities are of great significance to the construction of high-level professional clusters in the regional industrial chain [38]. Dynamic capabilities emphasize the impact of changes in the underlying environment, especially in terms of market and technological forces, and its core components include “perception,” “development,” and “reconstruction” [39]. The industry addresses opportunities in the market by mobilizing resources, embracing innovative prospects, and executing actions to optimize these opportunities and capture value [40]. The NQPF, which includes components such as new technologies, new manufacturing, new services, new forms of business, and new scenarios of application [41], further contributes to the enhancement of the dynamic capabilities of the tourism industry, bringing a new developmental dimension to the development of modern tourism. NQPF also brings a new development environment to modern tourism development. NQPF empowers the comprehensive upgrading of the modern tourism system by promoting the enhancement of the industry’s dynamic capabilities. This process will not only help to improve the quality of tourism services and expand market coverage but also stimulate more innovative potential, laying a solid foundation for the sustainable development of modern tourism. By strengthening the dynamic capabilities of the tourism industry, it will be better able to adapt to the uncertainty of the future market, seize the opportunities brought by emerging technologies, and achieve a higher level of specialization and international development. Based on this, this paper proposes the following hypothesis.

Hypothesis H2:

NQPF further empowers the upgrading of the modern tourism system by promoting the enhancement of the industry’s dynamic capabilities.

2.4. Spatial Spillover Effects of NQPF on the Upgrading of the Modern Tourism System

According to the theory of technology diffusion, once new technologies or knowledge are applied within a specific region or enterprise, they are likely to diffuse to other regions or enterprises through a variety of channels, such as trade, direct investment, technology transfer, and personnel mobility [42]. Such diffusion not only promotes the popularization and application of technology but also accelerates the dissemination of knowledge and the diffusion of innovation, which, in turn, promotes the progress of the whole industry and the development of the regional economy. NQPF, as a new form of productivity, has the same strong potential for diffusion of the new technology and new knowledge inherent in it. Existing studies have shown that digital NQPF significantly impacts the modernization of the manufacturing industry chain [43]. In the modern tourism industry, the role of NQPF should not be ignored. The development of NQPF can significantly enhance the productivity of all factors in the tourism industry, reduce information asymmetry, and improve the efficiency of resource allocation by deeply integrating information technology into all aspects of tourism services. This not only helps to optimize the structure of the tourism industry and improve the level of industrial agglomeration but also effectively avoids the problem of resource wastage caused by improper spatial allocation [44]. In addition, from a broader geographical perspective, the construction and development of tourism digital platforms are reshaping the pattern of inter-regional tourism flows. By providing efficient online booking services, personalized travel recommendations, and real-time travel information updates, these platforms have strengthened the links and interactions between different regions and promoted the effective integration and sharing of tourism resources. This change has transformed the value chain of the tourism industry from the traditional local single-chain model to a complex network model across regions, further promoting the expansion of the tourism production frontier and realizing the optimization of tourism’s kinetic energy [45]. The development of NQPFs not only has a positive impact on upgrading the local modern tourism system but also can drive neighboring and even more distant regions through spatial spillover effects and the synergistic development of tourism. Based on this, this paper proposes the following hypothesis.

Hypothesis H3:

NQPF has a significant spatial spillover effect on upgrading the modern tourism system.

The theoretical model is illustrated in Figure 1.

3. Research Design

3.1. Model Construction

3.1.1. Baseline Model

Based on the above analysis, a two-way fixed effects model was developed to test the direct impact of NQPF development on upgrading the modern tourism system. The two-way fixed effects model reduces omitted variable bias by controlling for individual fixed effects and time fixed effects. This approach allows for the mitigation of unobserved heterogeneity that is constant over time or across entities. By incorporating fixed effects, the model can better address endogeneity problems, thus offering more precise causal relationship estimates.

$${\mathrm{U}\mathrm{M}\mathrm{I}\mathrm{T}\mathrm{S}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\alpha }}_1{\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{F}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\alpha }}_2{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

In Equation (1), ${\mathrm{U}\mathrm{M}\mathrm{I}\mathrm{T}\mathrm{S}}_{\mathrm{i}\mathrm{t}}$ and ${\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$ denote the upgrading of the modern tourism system and the development of NQPF in province i in year t, respectively. ${\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ is the set of control variables, including the level of economic development, the degree of openness to the outside world, the level of financial development, and the level of urbanization. The province is denoted by i, and t denotes the year. The intercept term is denoted by ${\mathsf{\alpha }}_0$, and ${\mathsf{\alpha }}_1$ and ${\mathsf{\alpha }}_2$ are the estimated coefficients of the independent variables and control variables, respectively. The individual fixed effects, year fixed effects, and random perturbation terms are ${\mathsf{\mu }}_{\mathrm{i}},{\mathsf{\lambda }}_{\mathrm{t}}$, and ${\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$, in that order.

3.1.2. Models of Mediating Effects

In order to test the mechanism of the role of industrial dynamic capacity in the upgrading of NQPF and the modern tourism system, drawing on the study by Wen Zhonglin et al. (2004) [46], the following model is constructed:

$${\mathrm{I}\mathrm{D}\mathrm{C}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{F}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(2)

$$\mathrm{U}{\mathrm{M}\mathrm{I}\mathrm{T}\mathrm{S}}_{\mathrm{i}\mathrm{t}}={\mathsf{\gamma }}_0+{\mathsf{\gamma }}_1{\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{F}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\gamma }}_2{\mathrm{I}\mathrm{D}\mathrm{C}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\gamma }}_3{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(3)

By analyzing the role of mediating variables, the interrelationships between different factors can be explored in depth, providing a basis for the development of targeted strategies. In Equations (2) and (3), ${\mathrm{I}\mathrm{D}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$ represents the mediating variable industry dynamic capacity, ${\mathsf{\beta }}_0$ and ${\mathsf{\gamma }}_0$ are intercept terms, and ${\mathsf{\beta }}_1$, ${\mathsf{\beta }}_2$, ${\mathsf{\gamma }}_1$, and ${\mathsf{\gamma }}_2$ are the estimated coefficients of the corresponding variables. The rest of the variables are interpreted the same way as in Equation (1).

3.1.3. Model of Spatial Spillover Effect

Due to the vastness of China’s provincial areas, and considering the possible interactions between geographically neighboring regions, a spatial effects model is introduced to assess the effects of the diffusion of NQPF within and across regions, and to enhance the practical application value of the model [47]. The following model is constructed by introducing the spatial interaction term of each variable in Equation (1):

$$\mathrm{U}{\mathrm{M}\mathrm{I}\mathrm{T}\mathrm{S}}_{\mathrm{i}\mathrm{t}}=\mathsf{\rho }\mathrm{W}\times {\mathrm{U}\mathrm{M}\mathrm{I}\mathrm{T}\mathrm{S}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\tau }}_0+{\mathsf{\tau }}_1{\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{F}}_{\mathrm{i}\mathrm{t}}+{{\mathsf{\tau }}_2\mathrm{W}\times {\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{F}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\tau }}_3{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+\mathsf{\mu }}_{\mathrm{i}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(4)

In Equation (4), $\mathsf{\rho } \mathrm{d}\mathrm{e}\mathrm{n}\mathrm{o}\mathrm{t}\mathrm{e}\mathrm{s}$ the spatial autocorrelation coefficient, W is the spatial economic distance matrix, and ${\mathsf{\tau }}_1$, ${\mathsf{\tau }}_2$, and ${\mathsf{\tau }}_3$ are the estimated coefficients of the corresponding variables. The rest of the variables are interpreted the same way as in Equation (1).

3.2. Variable Selection

(1)

Explained variable: the modern tourism system (UMITS). Zhang et al. [48] pointed out that the evaluation of the modern industrial system should be constructed to reflect the overall development trend of the industrial system to a certain extent, and the evaluation indexes were constructed from the dimensions of economic level, industrial structure, industrial development, industrial innovation, and industrial association. This combines the existing research and the characteristics of the tourism industry to construct the evaluation index system of the modern tourism system from four dimensions: industrial efficiency upgrade, industrial technology upgrade, industrial structure upgrade, and open and shared superiority (

Table 1. Modern tourism system evaluation index system.

Modern Tourism Industry System
Primary Indicators	Secondary Indicators	Indicator Description	Weight
Industrial efficiency upgrading	Tourism industry efficiency	DEA model calculation of tourism industry efficiency	3.35%
Industrial technology upgrading	Industrial innovation level	Tourism R&D expenditure	10.24%
		Tourism invention patents	15.21%
Industrial structure upgrading	Rationalization of industrial structure	Degree of coupling and coordination between tourism industry and regional economy	3.89%
	Advanced industrial structure	Consumption of high value-added segments	9.92%
		Per capita consumption of tourists	6.95%
Openness and sharing upgrading	Tourism open development	Tourism arrivals	22.06%
		Tourism foreign exchange income as a percentage of GDP	18.99%
	Tourism sharing development	Forest coverage rate	6.19%
		Green space per capita	3.20%

).

Industrial efficiency reflects the degree of effective utilization of tourism resources. It is calculated using the DEA model with tourism employees, tourism resources, and tourism fixed assets as input indicators, and total tourism revenue, total tourism trips, and value-added of the tertiary industry as output indicators.

The level of industrial innovation reflects the technological progress and innovation ability within the tourism sector. This is measured by tourism R&D expenditure and tourism invention patents. Since there are no directly relevant statistics on tourism R&D, estimates are based on total tourism revenue as a percentage of GDP [49]. The number of tourism-related invention patents is obtained from the official website of the State Intellectual Property Office (SIPO) by searching keywords such as “tourism”, “travel”, “attractions”, “hotel”, and “B&B”.

Industrial structure shows the relationship between different components within the tourism industry and their interaction with the whole economic system. It includes two dimensions. The first is structural rationalization, calculated by the coupling and coordination degree between total tourism revenue and gross regional product. Secondly, the structure is advanced, focusing on the development of high value-added segments, with reference to Guo et al. [50] study, measured by the share of shopping and entertainment per capita per day spent by inbound overnight travelers.

The level of tourism openness and sharing indicates the degree of tourism opening up to the outside world and the sharing status of social resources/tourism open development reflected by the number of inbound tourists and the proportion of foreign exchange income to regional GDP. Tourism shared development takes into account ecological and environmental protection factors, characterized by the forest coverage rate and green space per capita. These metrics not only help enhance tourists’ experience but also contribute to the realization of sustainable development goals.

(2)

Explanatory variables: new quality productive forces (NQPF). Academics have not yet reached a unified understanding of the indicators of NQPF, which belongs to the integration of complex systems and is difficult to use a single indicator to express. At present, some scholars take factor structure and value leap as the basic dimensions to construct the evaluation index system [51], but more scholars construct the index measurement system of NQPF in three dimensions: new quality laborers, new quality labor objects, and new quality labor materials from the factors of productivity [52]. Based on the existing research, this paper constructs the NQPF evaluation indexes as shown in the table below (

Table 2. NQPF evaluation index system.

NQPF
Primary Indicators	Secondary Indicators	Indicator Description	Weight
New type of workers	Quality of workers	Number of people with bachelor’s degree or above/number of people in labor force	5.52%
	Labor productivity	Real GDP/total employment	7.01%
New labor resources	Digital infrastructure	Number of Internet broadband access ports	4.41%
		Length of fiber optic cable lines Area	2.81%
	Capital investment in science and technology innovation	R&D expenditure/real GDP	31.12%
	Degree of transformation of scientific and technological achievements	Technology market turnover/real GDP	15.86%
New types of labor objects	Development level of strategic emerging industries	Revenue of software and information technology service industry/real GDP	17.71%
	Development level of future industries	Number of intelligent robots installed in the region	2.90%
	E-commerce development level	Total e-commerce sales/real GDP	12.66%

). The development level of NQPF is calculated using the entropy value method.

The new type of workers indicator is measured by worker quality and worker productivity. Worker quality is measured by calculating the percentage of people with a bachelor’s degree or higher relative to the total workforce. This reflects the proportion of highly qualified individuals and their impact on the overall workforce. Worker productivity is assessed by dividing real GDP by the total number of people employed. This demonstrates the average level of output and the economic contribution of each worker.

New labor resources include digital infrastructure and capital investment in science, technology, and innovation. Digital infrastructure is measured by the number of Internet broadband access ports and the length of fiber optic cable lines in the area. These metrics reflect the level of digitalization development and the efficiency of information transmission. Capital investment in science and technology innovation is measured by the ratio of R&D expenditure to real GDP, indicating the strength of support for technological innovation. The degree of transformation of scientific and technological achievements is assessed by the ratio of technology market turnover to real GDP. This reflects the ability to transform scientific and technological achievements into economic benefits.

The new types of labor objects indicator focuses on three areas. Firstly, development level of strategic emerging industries, measured by the ratio of income from software and information technology services to real GDP, reflecting the importance of high-value-added industries. Secondly, development level of future industries, measured by the number of intelligent robots installed, indicating the development potential of smart manufacturing. Thirdly, development level of e-commerce, measured by the ratio of total e-commerce sales to real GDP, showing the activity of the online trading market.

(3)

Mediating variable: industrial dynamic capability (IDC). The core of IDC lies in “perception”, “development”, and “reconstruction”. Combined with the existing research, this paper constructs the evaluation index system of IDC from the three dimensions of opportunity perception capability, opportunity development capability, and resource reconstruction capability. This paper constructs the evaluation index system of IDC from the three dimensions of opportunity perception, opportunity development, and resource reconstruction capability. Finally, the entropy weight method is used to calculate the comprehensive score of IDC (

Table 3. Evaluation index system of industrial dynamic capability.

Industry Dynamic Capabilities
Primary Indicators	Secondary Indicators	Indicator Description	Weight
Opportunity sensing ability	Attention allocation	Baidu news keyword search volume	4.48%
Opportunity development ability	Industry factor level	(Number of A-grade scenic spots, travel agencies, and star-rated hotels in each province)/(Sum of the number of A-grade scenic spots, travel agencies, and star-rated hotels in the country)	18.16%
	Industry agglomeration level	(Total tourism income/gross regional product of each province)/(National total tourism income/gross domestic product)	26.60%
Resource reconstruction capacity	Financial resources	Share of total tourism revenue in value added of tertiary industry	29.52%
	Information resources	Number of web pages per 100 enterprises	8.63%
		Internet penetration rate	12.61%

).

Opportunity sensing ability reflects the market’s attention to a specific industry or field. Measured by the search volume of Baidu news keywords, this indicator captures the heat of public and media attention within a company’s field, thus indirectly reflecting a company’s sensitivity and responsiveness to changes in the external environment. Among the Baidu news keywords were new quality productivity, scientific and technological innovation, digitalization, high-quality development, modern industrial system, industrial upgrading, tourism industry, smart tourism, virtual tourism, and so on.

Opportunity development capability includes two dimensions: the level of industrial elements and the level of industrial aggregation. The level of industrial elements demonstrates the richness and development potential of each province in terms of tourism resources. It is measured by the ratio of the number of A-grade scenic spots, travel agencies, and star-rated hotels in each province to the corresponding national total. The industry agglomeration level further explores the importance of tourism in the local economy and its relative position in the national context. It is measured by the ratio of total tourism revenue to gross regional product in each province relative to the national average.

Resource reconfiguration capacity includes financial resources and information resources. Financial resources emphasize the contribution of tourism to the value add of the tertiary industry, which is reflected by calculating the proportion of total tourism revenue to the value add of the tertiary industry. Information resources are measured by the number of web pages per 100 enterprises and the Internet penetration rate.

(4)

Control variables: to avoid the bias of regression results due to the omission of variables, this paper further quotes the control variables. The control variables are ① the level of economic development (ECO), expressed as the logarithm of the regional per capita GDP; ② the degree of openness to the outside world (OPEN), measured by the degree of dependence on foreign trade, which is equal to the total number of imports and exports/GDP; ③ the level of financial development (FIN), measured by the ratio of the balance of loans to GDP; and ④ the level of urbanization (UR), measured by the proportion of the resident population to the total population of the region.

3.3. Data Sources

The concept of the modern industrial system was first proposed in the report of the 19th CPC National Congress in October 2017. Therefore, this article selects 2018 as the starting point of the research period. Based on the availability of data, this article uses panel data from 2018 to 2022 across 30 provinces in China (excluding the Tibet Autonomous Region, Taiwan Province, Hong Kong Special Administrative Region, and Macao Special Administrative Region) as the research sample. The Baidu news index was obtained using a Python v. 3.10 crawler, and other data were primarily obtained from the China Statistical Yearbook, China Tertiary Industry Statistical Yearbook, China Social Statistical Yearbook, China Culture and Tourism Statistical Yearbook, the official website of the National Bureau of Statistics, and official statistical yearbooks of provinces and cities, etc., in which some missing data were processed by using the analogical or interpolation methods.

4. Empirical Test and Result Analysis

4.1. Analysis of Benchmark Regression Results

Panel regression using Equation (1) is conducted to test whether NQPF drives the upgrading of the modern tourism system (

Table 4. Benchmark regression results.

	UMITS
	(1)	(2)	(3)	(4)	(5)
NQPF	0.4697 ***	0.4730 ***	0.3473 **	0.3390 **	0.3452 **
	(3.09)	(3.09)	(2.16)	(2.09)	(2.11)
ECO		0.0268	0.0381	0.0394	0.03548
		(0.39)	(0.35)	(0.58)	(0.52)
OPEN			2.3164 **	2.2431 **	2.1375 *
			2.22	(2.12)	(1.97)
FIN				0.1240	0.1251
				(0.48)	(0.48)
UR					2.5493
					(0.51)
ID FE	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES
cons	7.9448 *** (7.9)	7.6268 *** (5.88)	7.7665 *** (6.08)	7.6339 *** (5.83)	6.0691 * (1.83)
N	150	150	150	150	150
adj. R2	0.4189	0.4146	0.4286	0.4426	0.4391

Note: * p < 0.1, ** p < 0.05, *** p < 0.01, t-values in parentheses.

).

Column (1) is the two-way fixed effects regression result of only adding explanatory variables without adding various control variables. Its regression coefficient of the explanatory variables is 0.4697; it is significant at the 1% level. Columns (2) to (5) are the results of two-way fixed effects regression with the gradual addition of four control variables: ECO, OPEN, FIN, UR. The regression coefficients of productivity are in the same direction and have roughly the same significance, indicating that NQPF is positively driving the upgrading of the modern tourism system. The control variables OPEN and UMITS are both significantly positive at the 5% level, indicating that the degree of openness to the outside world is also positively driving the upgrading of the modern tourism system. Thus, H1 is verified.

4.2. Robustness and Endogeneity Test

The robustness test is specified as follows. First, the samples of municipalities directly under the central government and special year samples are excluded. Due to the differences in the level of NQPF development among Chinese provinces, in order to verify the generality of the results, we chose to exclude the samples of Beijing, Shanghai, Tianjin, and Chongqing, which are directly under the municipalities. Additionally, due to the large impact of the 2020 New Crown Epidemic on the tourism industry, we excluded that year before conducting the test. The regression results are shown in columns (1) and (2) of

Table 5. Endogeneity test and robustness test.

	Excluding Municipality Samples	Excluding 2020	Shrinkage Treatment	Instrumental Variable Approach
	(1)	(2)	(3)	(4)
NQPF	0.3352 ** (2.24)	0.3741 ** (2.06)	0.3866 ** (2.34)
L.NQPF				0.2818 ** (2.33)
Control variable	YES	YES	YES	YES
ID FE	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES
Constant	10.5637 ** (2.34)	28.8310 ** (2.34)	20.2599 * (1.83)	9.6536 *** (3.61)
N	130	120	150	120
R-squared	0.4902	0.4507	0.4339	0.4820

Note: * p < 0.1, ** p < 0.05, *** p < 0.01, t-values in parentheses.

. The estimated parameters and significance levels have not changed significantly, indicating that the robustness test is passed. Second, all the data are shrink-tailed. To further improve the robustness of the results, all data were subjected to a shrinkage process. The regression results are shown in column (3) of Table 5, where the estimated parameters and significance levels remain stable, further validating the reliability of the results.

At the same time, the possible endogeneity problem triggered by bidirectional causality between NQPF and modern tourism system upgrading is considered, leading to biased results. Therefore, the instrumental variable method is applied to test the endogeneity problem by considering the NQPF lagged one period as an instrumental variable (L.NQPF), and the results are shown in column (4) of Table 5, indicating that the hypothesis that NQPF empowers the upgrading of the modern tourism system is still valid after dealing with the endogeneity problem.

4.3. Testing the Mechanism of Action

The mediating effect of NQPF on industrial dynamic capability in the process of upgrading the modern tourism system is further tested by taking IDC as the mediating variable; the results are shown in

Table 6. Intermediate effect test.

	IDC	UMITS
	(1)	(2)
NQPF	0.1195 *** (2.81)	0.3491 ** (2.09)
IDC		0.0714 ** (2.06)
Control variable	YES	YES
ID FE	YES	YES
YEAR FE	YES	YES
Constant	4.7673 *** (5.51)	6.0261 (1.63)
N	150	150
R-squared	0.7143	0.4252

Note: ** p < 0.05, *** p < 0.01, t-values in parentheses.

.

In column (2), the regression coefficients of NQPF and the mediating variables are significantly positive. Meanwhile, adding the mechanism variable of IDC increases the regression coefficient of the explanatory variables from 0.3452 to 0.3491. Therefore, NQPF can empower the upgrading of the modern tourism system by enhancing the dynamic capability of the industry, and H2 has been proved.

4.4. Spatial Spillover Effect Test

Before analyzing the spatial spillover effect, Moran’s I index method was used to calculate the spatial effect of the spatial economic distance matrix NQPF and the modern tourism system. The results show (

Table 7. Global Moran index of NQPF and modern tourism system, 2018–20022.

Year	NQPF		UMITS
	Moran’I	Z	Moran’I	Z
2018	0.081	3.249 ***	0.095	3.634 ***
2019	0.088	3.465 ***	0.085	3.356 ***
2020	0.110	4.075 ***	0.101	3.832 ***
2021	0.114	4.176 ***	0.106	3.955 ***
2022	0.118	4.298 ***	0.116	4.254 ***

Note: *** p < 0.01.

) that the Moran’s I index value of NQPF was significantly positive during 2018–2022 and shows an increasing trend year by year during the observation period. The Moran’s I index value of the modern tourism system is notably positive and shows a fluctuating increasing trend during the observation period. The above results indicate a spatial agglomeration of NQPF and modern tourism system development at the provincial level during 2018–2022, and the agglomeration characteristics of both have been enhanced. Overall, there is a spatial agglomeration phenomenon for both, and the spatial spillover effect of the digital economy on the resilience of the tourism economy can be further examined using spatial econometric models.

The generalized spatial econometric model was subjected to an LM test, an LR test, a Hausman test, and a Wald test in accordance with the testing process of Elhorst (2014) [53]. According to the test results, it was finally determined that the spatial Durbin model (SDM) with double fixed effects was used under the spatial economic distance matrix.

As can be seen from the results (

Table 8. Decomposition results of spatial spillover effect.

	(1) Main	(2) Wx	(3) Spatial	(4) Variance	(5) Direct	(6) Indirect	(7) Total
NQPF	1.013 *** (7.19)	2.688 * (1.82)			0.6177 *** (3.96)	1.4742 (1.52)	2.0919 ** (2.00)
rho			−0.6607 ** (1.98)
Sigma2_e				0.0457 *** (8.57)
Control variable	YES	YES	YES	YES	YES	YES	YES
ID FE	YES	YES	YES	YES	YES	YES	YES
YEAR FE	YES	YES	YES	YES	YES	YES	YES
N	150	150	150	150	150	150	150

Note: * p < 0.1, ** p < 0.05, *** p < 0.01, t-values in parentheses.

), the spatial autoregressive coefficient is significantly negative, indicating that there is a significant spatial correlation effect in the upgrading of the modern tourism system, i.e., the level of development of modern tourism in a region is negatively affected by areas with strong economic ties. There are multiple possible reasons for this. First, resource competition: the development of modern tourism often depends on limited resources, such as attractions, transportation facilities, and accommodations. When neighboring regions vigorously develop tourism, competition for resources may develop, resulting in a region not being able to obtain sufficient resources to support the growth of its tourism industry, thus negatively affecting the local tourism industry. The second possible reason is market saturation. The simultaneous development of tourism in two or more geographically proximate regions may lead to the fragmentation of tourist choices and relative market saturation. This means that each region may attract fewer tourists, affecting the region’s tourism revenue and development potential. Third, there is a lack of complementarity in economic structure. If there is a lack of complementarity in the economic structure of neighboring regions, they will have fewer opportunities for cooperation. Meanwhile, the regression coefficient of the spatial lag term of NQPF is 2.688, which is significant at the 10% level, indicating that the development of the modern tourism system will be positively affected by the NQPF of neighboring provinces. Further decomposition of the spatial spillover effect shows that the total effect is significantly positive at the 5% level, implying that the overall impact of NQPF on the development of the modern tourism system is positive, and thus H3 is established.

5. Conclusions and Discussion

5.1. Research Conclusions

The development of NQPF is a major strategic proposition made in light of the brand-new situations, development trends, and challenges faced by China as it enters a new stage of development. As a new form reflecting the qualitative leap and structural transformation of productivity, the core of NQPF lies in the drive for scientific and technological innovation, the rise of high-end industries, and the in-depth fusion and optimization of the various elements of productivity. The specific embodiment of NQPF in the tourism industry includes both the formation of new industry carriers such as new tourism forms, intelligent services, and green tourism modes, as well as the upgrading of productivity elements such as the improvement of the quality of laborers, the efficient use of tourism resources, and the wide application of digital technology. Based on the panel data of 30 provinces in China from 2018 to 2022, this paper deeply explores the mechanism of NQPF empowering the upgrading of the modern tourism system. The research yielded various findings.

First, it is shown that NQPF has a significant promoting effect on upgrading the modern tourism system. Consistent with the findings of Shao et al. [54], NQPF can improve the unreasonable state of industrial structure and accelerate its transformation. NQPF significantly improves the overall level of the modern tourism system in four aspects: industrial efficiency, industrial technology, industrial structure, and open and shared upgrading. NQPF puts forward higher requirements for the quality of laborers, the quality of labor materials, and the efficiency of the utilization of labor objects, which leads to quality change, efficiency change, and power change. High-quality labor groups, the promotion of digital infrastructure construction, and the development of strategic and future industries work together to provide strong support for the overall improvement of the modern tourism system. A typical case in line with this conclusion is Guangdong Province, where early on quality projects and cultivation of smart tourism were carried out to give full play to its technological advantages and promote the innovative application and demonstration of information technology such as big data, artificial intelligence, and cloud computing in the field of culture and tourism. At the same time, the joint establishment of a talent pool of innovation-leading cultural and tourism experts in the Greater Bay Area has fostered a large number of innovative, composite, and outward-looking cultural and tourism cross-border scientific and technological talents, which has become a leading force in the transformation of the tourism industry.

Second, the dynamic capacity of the industry is shown to play an important intermediary role in the process of NQPF in promoting the upgrading of the modern tourism system. Previous research has found that the key to promoting industrial upgrading lies in technological progress, and the crux of technological progress is the accumulation of human capital [55]. Technological innovation can generate momentum that has a driving effect [56]. This study further discovers that NQPF not only promotes the upgrading of the industry directly but also indirectly promotes the advancement of the tourism industry by enhancing the industry’s opportunity awareness capacity, opportunity development capacity, and resource reconstruction capacity. This means that NQPF can enhance the internal capacity of the industry, enabling industrial development to capture market opportunities more keenly and transform these opportunities into actual products and services quickly and effectively, thus further promoting the modernization of the tourism industry.

Third, NQPF is shown to have a positive spatial spillover effect on the upgrading of the modern tourism system, which suggests that NQPF not only promotes the upgrading of the local tourism industry but also drives the development of the tourism industry in neighboring regions through the spatial spillover effect, forming a good situation of synergistic regional development. This conclusion is consistent with the actual development situation. For example, Chengdu and Chongqing, as the core cities of the Chengdu-Chongqing Twin Cities Economic Circle, have attracted a large number of tourists in recent years through the promotion of high-end tourism projects such as smart tourism and digitalized scenic spot management by NQPF. Chengdu has attracted a large number of tourists by building the “Country of Heavenly Capital” tourism brand, while Chongqing has competed with it through the “Mountain City” specialty tourism, which has led to a diversion of tourists between the two cities. The development of NQPF in Chengdu and Chongqing has had a positive spillover effect on the neighboring areas (e.g., Leshan in Sichuan, Wanzhou in Chongqing). The development of NQPF in Chengdu and Chongqing has had positive spillover effects on neighboring regions (e.g., Leshan in Sichuan and Wanzhou in Chongqing). For example, Chengdu’s digital tourism platform and technical support have helped Leshan improve its tourism services, while Chongqing’s smart tourism program has led to the upgrading of Wanzhou’s tourism industry. Similarly, consistent with the findings of Gao et al. on the modernization of agricultural industries, NQPF is shown not only to drive the modernization of local industrial chains and supply chains but also have a positive impact on the modernization of industrial chains and supply chains in surrounding areas [57]. This finding emphasizes that NQPF is not limited to a specific geographic region but can have a positive impact on a wider scale, contributing to a more balanced and coordinated national tourism layout.

5.2. Theoretical Contributions

First, the conceptual framework of NQPF is innovated. This study deepens the understanding of NQPF, defines it as a new form reflecting the qualitative leap and structural transformation of productivity, and emphasizes that its core lies in the drive for scientific and technological innovation, the rise of high-end industries, and the in-depth integration and optimization of all factors of productivity. By introducing the concept of NQPF, this study not only enriches the discussion on productivity development in the existing literature but also proposes a new analytical framework, which helps to better explain the driving mechanism behind the upgrading of the modern tourism system.

Second, it explains the mechanism by which NQPF empowers the modern tourism system. For the first time, this study systematically explores the impact of NQPF on the modern tourism system via the four dimensions of industrial efficiency upgrading, industrial technology upgrading, industrial structure upgrading, and open sharing upgrading. Quantitative methods such as panel data regression analysis, a mediation effect test, and spatial spillover effect analysis are used to provide quantitative evidence for the mechanism of NQPF’s empowerment of the upgrading of the modern tourism system.

Third, it expands the research boundary of dynamic capability theory. Dynamic capability theory is mostly used to study the internal and external integration of enterprises to face changes in the market environment, and the study expands the research boundary of dynamic capability theory to the tourism industry system. The evaluation index system of IDC is constructed from the three dimensions of dynamic capability: opportunity perception, opportunity development, and resource reconstruction. The mediating role of IDC in the process of NQPF promoting the upgrading of the modern tourism system is explored in depth. It further complements the empirical studies in the existing literature on how NQPF indirectly supports tourism development by enhancing the internal capacity of the industry and provides a new perspective for understanding the internal mechanism of industrial upgrading.

5.3. Practical Implications

To further utilize the enabling role of NQPF for the modern tourism system, the following suggestions are made.

First, the cultivation and development of NQPF should be strengthened by combining government-led and market mechanisms. The government should increase its support for tourism science and technology innovation and encourage enterprises and scientific research institutions to carry out technological research and development as well as contributing to applications in the field of tourism by setting up special funds and providing tax incentives. Special attention should be paid to the research and development of cutting-edge technologies such as digital technology, artificial intelligence, and virtual reality, bringing tourists a brand-new experience and significantly improving the tourism industry’s operational efficiency and service quality. At the same time, the government should play the role of a bridge to promote cooperation among industries, universities, and research institutes and accelerate the transformation and application of scientific and technological achievements. Regarding talent cultivation and skills, education and training are key to upgrading the quality of the industry. The popularity of digital travel platforms may have an impact on traditional tourism players such as small travel agents and tour guides. The government and society should work together to improve tourism employees’ professional skills and comprehensive quality, especially digital skills and innovation ability, through various channels such as university curricula, vocational training institutions, and online learning platforms. In addition, a continuous career development path should be established to motivate employees to continuously update their knowledge and skills to meet the rapidly changing needs of the tourism industry. Personalized training programs should be designed to meet the needs of talents at different levels to ensure that the supply of talent matches the development of the industry.

Second, it is vital to optimize the dynamic capabilities of the industry. To improve data-driven decision support, advanced technologies such as big data and cloud computing should be utilized to construct an intelligent tourism information system to strengthen the monitoring and analysis of tourism market dynamics. This could be achieved by mining and analyzing massive data, capturing market trends and consumer preferences in a timely fashion, and providing accurate decision-making support for enterprises and the government. For example, data analysis can be used to predict peak and off-peak seasons, rationally plan the allocation of tourism resources, and avoid the waste of resources or potential overcrowding.

Furthermore, tourism enterprises should be encouraged to continuously innovate and develop new products and services that meet market demand, such as personalized and customized trips, theme parks, cultural festivals, and events. At the same time, they should focus on improving service quality, such as intelligent customer service and barrier-free tourism facilities, to meet diversified and personalized consumer demand. In addition, brand building should be strengthened to create tourism brands with local characteristics and cultural connotations to enhance market appeal.

The flow and allocation of key factors such as capital, talents, and technology through policy guidance and market regulation need to be optimized by establishing a fair and transparent competitive environment to attract more social capital to enter the tourism industry, improving the mechanism for introducing and retaining talents to solve the problem of talent shortage, promoting the transformation of scientific and technological achievements, and improving technological innovation. The ultimate goal is to realize efficient synergy between the various links within the tourism industry and form a complete industrial chain.

Third, regional coordinated development must be established. It is important to note that while the rapid development of NQPF has brought about significant upgrading of the tourism industry, it may also lead to the concentration of tourism resources and revenues in more developed regions, thus exacerbating inter-regional inequalities. For example, in the Yangtze River Delta integration region, core cities such as Shanghai, Hangzhou, and Nanjing are able to utilize NQPF to promote tourism upgrading more efficiently due to their superior economic conditions and technological advantages, while neighboring regions such as Huangshan in Anhui and Lishui in Zhejiang may find it difficult to benefit from it due to a lack of resources. For this reason, the following two strategies are proposed to promote coordinated regional development. Firstly, a sound regional tourism cooperation mechanism should be created, breaking the restrictions of administrative divisions and promoting the sharing and complementarity of tourism resources. Signing cooperation agreements, the joint organization of large-scale activities, and other forms of integration of high-quality tourism resources in the region will create cross-regional boutique lines and brand projects. For example, establishing a “one-card” system can be explored to facilitate the free passage of tourists between multiple regions, increasing the convenience and attractiveness of tourism. Interconnectivity can be strengthened by increasing investment in infrastructure construction and improving transportation conditions, especially accessibility in remote areas. Cross-regional tourism information platforms and networks should be built to realize instant exchange and sharing of tourism information. Tourism procedures can be simplified, and the tourist experience can be enhanced through digital means, such as online booking systems and mobile payments. In addition, publicity and promotion can be carried out through social media and other internet channels to attract more domestic and foreign tourists.

Finally, the policy environment should be optimized by emphasizing policy support and market regulation. More policies and measures should be introduced to support the development of the tourism industry, along with favorable financial subsidies, tax breaks and exemptions, and financial credits. For start-ups and small and micro enterprises, more flexible financing channels and support measures should be provided to lower the threshold of entrepreneurship. At the same time, to avoid long-term pressure on local finances, a policy performance evaluation system can be established, implementing the dynamic adjustment of support. For example, the financial subsidy policy for regular evaluation optimizes the allocation of funds to ensure the efficient use of resources. It is also essential to diversify funding sources and attract social capital to participate in tourism development through public–private partnership models to reduce fiscal pressure. For example, private enterprises are encouraged to invest in the construction and operation of tourism infrastructure, and the government provides support through policy guidance and risk-sharing mechanisms to ensure the financial sustainability of policy support. Meanwhile, a sound tourism market supervision system should be established to strengthen the standardized management of travel agencies, scenic spots, hotels, and other industry entities while cracking down on illegal business practices and maintaining a good market order. The legitimate rights and interests of consumers should be protected by strict laws and regulations to create a healthy and orderly tourism consumption environment.

A favorable social atmosphere should be created through publicity and education activities, raising public awareness of the importance of tourism and advocating civilized tourism. All sectors of society should be encouraged to participate in developing and supervising the tourism industry to create a favorable situation in which the whole society cares about and supports the tourism industry. In addition, we will actively promote international exchanges and cooperation, draw on the advanced experience of other countries, and continuously enhance the internationalization of China’s tourism industry.

5.4. Research Limitations and Future Directions

Due to the relatively new concepts of new quality productivity and the modern tourism industry system, academic exploration of their definitions and connotations is still in its infancy. The indicator systems constructed for new quality productivity, the modern tourism industry system, and industrial dynamic capabilities in this study also need continuous optimization in future research. Limited by data availability, this study only utilized panel data from 30 provinces across China between 2018 and 2022. As these concepts deepen, future research could expand the time frame and refine the research samples down to the city level, which would help derive more robust conclusions. Further explorations can also be made regarding the mediating and regulating mechanisms of the new quality productivity influencing the modern tourism industry system. At the same time, it is worth noting that challenges such as digital inequality [58], tourist data breaches [59], or potential automation that may threaten traditional tourism employment require further consideration.