Digitalization and Culture–Tourism Integration in China: The Moderated Mediation Effects of Employment Quality, Infrastructure, and New-Quality Productivity

Abstract

The digital economy is significantly transforming the global economic environment and has emerged as the primary driver behind China’s high-quality development. The comprehensive melding of the cultural and tourism sectors (CTI) serves as a strategic approach to boost regional competitiveness and enhance public welfare. This study investigates the mechanisms and boundary conditions through which the growth of the digital economy across China’s 31 provinces from 2011 to 2023 impacts CTI, aiming to address existing research gaps related to micro-level transmission mechanisms and the analysis of contextual variables. Utilizing a two-way fixed-effects moderated mediation model complemented by instrumental variable (IV-2SLS) regression for testing endogeneity, the research uncovers intricate interactions among the digital economy, CTI, and significant influencing factors. The results strongly suggest that advancements in the digital economy substantially facilitate the integration of cultural and tourism sectors. This beneficial effect is partially mediated through two primary channels: the construction of new infrastructure and enhancements in employment quality, underscoring the critical role of both material and human capital in digital empowerment. Significantly, this research uniquely identifies that new quality productive forces (NQP) have a notable negative moderating impact on the link between the digital economy and cultural–tourism integration. This indicates that in provinces exhibiting high levels of NQP, the positive influence of the digital economy on cultural–tourism integration is considerably diminished. This unexpected finding can be interpreted through mechanisms such as resource dilution, varied integration pathways or maturity effects, along with differences in developmental stages and priorities. Furthermore, it resonates well with the resource-based view, innovation ecosystem theory, and dynamic capability theory. Instrumental variable regression further substantiates the notable positive influence of the digital economy on the integration of cultural tourism. This approach effectively tackles potential endogeneity concerns and reveals the upward bias that may exist in fixed-effects models. The findings contribute significantly to theoretical frameworks by enhancing the understanding of the intricate mechanisms facilitating the digital economy and, for the first time, innovatively designating NQP as a surprising key boundary condition. This enriches theories related to industrial advancement and resource allocation in the digital age. On a practical note, the research provides nuanced and differentiated policy guidance aimed at optimizing pathways for integration across various Chinese provinces at different stages of development. Additionally, it underscores significant implications for other developing nations engaged in digital tourism growth, thereby improving its global relevance.

1. Introduction

The digital economy is profoundly reshaping the global economic landscape with its transformative power, serving as the core engine for China’s high-quality development [1]. Concurrently, the deep integration of the cultural and tourism industries (CTI) is recognized as a strategic direction for enhancing industrial resilience, optimizing regional competitiveness, and improving public welfare [2]. This study focuses on the enabling role of the digital economy in CTI across China’s 31 provinces and municipalities—a topic of significant theoretical and practical importance. However, despite the profound impact of digitalization, the specific mechanisms through which the digital economy drives the complex systemic transformation of CTI remain unclear. Particularly at the provincial level, challenges in data circulation, industrial coordination, and talent structure may hinder the full realization of digital dividends [3]. Therefore, there is an urgent need for a more in-depth and nuanced analysis of the pathways and contextual conditions through which the digital economy influences CTI.

Existing research acknowledges the positive impact of the digital economy on industrial development; however, significant gaps remain regarding its role in the integration of culture and tourism [4]. First, few studies specifically explore the concrete mechanisms through which the digital economy drives this integration, particularly overlooking the roles of new infrastructure and employment quality as key mediating variables. Most existing literature remains at the macro-level correlation stage, failing to fully elucidate specific transmission chains [5]. Second, discussions on “New Quality Productivity” (NQP)—China’s recently proposed strategic economic development concept emphasizing innovation-led, high-quality growth—remain largely at the macro level. Its role as a moderating variable within specific integration industries lacks empirical validation. Crucially, academia has yet to fully elucidate the potentially complex and counterintuitive negative moderating effects of NQP on the digital economy’s promotion of integration [6]. This constitutes a significant area for advancement in understanding the boundary conditions of digital economy effects and how economic development priorities subtly reshape digital empowerment.

Given these research gaps, this paper aims to address these deficiencies. It empirically assesses the impact of digital economic development on the integration of cultural and tourism sectors at the provincial level in China. Furthermore, it explores the mediating roles of new infrastructure and employment quality, while critically examining the moderating effect of new productive forces on this relationship [7]. This study not only situates China’s experience within a global context but also compares international reports and global cases to enhance the universality of the research and the breadth of its policy implications [8]. Furthermore, viewed through the lens of the United Nations Sustainable Development Goals (SDGs), this research holds profound intrinsic relevance. The integration of culture and tourism itself represents a key pathway to achieving “Sustainable Cities and Communities” (SDG 11), particularly in safeguarding and passing on cultural heritage (SDG 11.4), as well as promoting “Decent Work and Economic Growth” (SDG 8.9). The digital economy empowerment mechanisms explored in this study fundamentally examine how digital technology, as a key driver, enhances the efficiency (economic dimension), inclusiveness (social dimension), and innovation (cultural dimension) of the cultural and tourism industry, thereby directly contributing to multiple sustainable development goals. More critically, examining the moderating effects of new-quality productive forces will reveal how to prevent the cultural and tourism industry’s potential for sustainable development from being marginalized in the pursuit of technology-driven growth (SDG 9). This holds significant theoretical value for reconciling potential tensions among different Sustainable Development Goals.

The core argument of this study is that the digital economy significantly promotes the integration of culture and tourism through key pathways, such as the development of new infrastructure and the enhancement of employment quality. However, this promotional effect is negatively moderated by new-type productive forces [9]. This finding not only deepens our understanding of the complex and nonlinear mechanisms through which the digital economy drives industrial upgrading but also offers refined and differentiated strategic recommendations for Chinese provinces to optimize their integration pathways at various developmental stages and with differing industrial priorities [10].

The paper’s structure proceeds as follows: Section 2 reviews the literature and identifies research gaps; Section 3 constructs the theoretical framework and research hypotheses; Section 4 details the model design and indicator selection; Section 5 presents empirical results; Section 6 discusses findings, theoretical contributions, practical implications, and policy recommendations; Section 7 summarizes conclusions, limitations, and future research directions.

2. Literature Review

2.1. Digital Economy and Culture–Tourism Integration: Existing Research and Limitations

The digital economy, an emerging economic paradigm centered on digital technology with data as a key production factor, is profoundly reshaping global economic structures and social frameworks through its transformative power [11]. Its pervasive, integrative, and innovative characteristics position it as a core engine driving high-quality economic development and industrial transformation. Concurrently, the deep integration of cultural and tourism industries (Cultural Tourism Integration, CTI) is recognized as a strategic choice to enhance industrial resilience, optimize regional competitiveness, and improve public welfare [12]. The essence of CTI lies in dismantling traditional industrial barriers, fostering the organic integration and mutual empowerment of cultural and tourism elements, ultimately giving rise to new industrial chains and restructuring value chains—shifting from offering single products to delivering composite experiences. From the perspective of industry integration theory, digitalization in this process facilitates cross-boundary synergies, enables economies of scope, and drives innovative restructuring of value chains [13].

Existing research widely acknowledges the significant promotional role of the digital economy in industrial development and integration, with its impact pathways being multidimensional and complex. Digital platforms play a pivotal role in information dissemination and market expansion, effectively overcoming traditional information asymmetries [14]. They substantially broaden the channels for promoting and marketing cultural and tourism products, enabling distinctive resources from remote areas to gain widespread recognition and reach global consumers. The digital economy also makes substantial contributions to enhancing efficiency and reducing costs. Advanced technologies like big data, cloud computing, and artificial intelligence are widely applied in cultural and tourism operations management, customer relations, and supply chain optimization [15]. This significantly improves resource allocation efficiency, enables precision marketing and personalized recommendations, and effectively lowers operational, marketing, and intermediary costs. The digital economy also powerfully empowers product innovation and experience upgrades. Technologies like virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) are deeply integrated into cultural and tourism product content, enabling personalized, immersive, and interactive experiences [16]. This drives continuous innovation and upgrades in the forms of cultural and tourism products and service models. Finally, digital technologies have facilitated the restructuring of industrial factors and the formation of new industrial ecosystems. They have accelerated the cross-border flow and integration of diverse elements such as culture, tourism, technology, and finance, giving rise to new business models and industrial practices. Ultimately, this has fostered a more diverse and vibrant cultural and tourism industry ecosystem [17].

However, despite providing valuable insights, existing literature exhibits significant limitations. First, few studies specifically examine the mechanisms through which the digital economy drives the development of “ integration” particularly overlooking the mediating role of new infrastructure and employment quality as key variables [18]. Most existing literature remains at the macro-level correlation stage, failing to fully reveal the specific transmission chains. Second, while acknowledging the widespread application of digitalization in tourism and cultural industries, existing research lacks systematic exploration of how the digital economy holistically drives the deep integration of these two sectors—that is, cross-sectoral integration that transcends the digitalization of individual industries [19]. Furthermore, while international organizations like UNWTO, OECD, and the EU have released reports on the global relationship between the digital economy and integration—providing macro trends and policy recommendations on the digital economy’s impact on tourism—research examining the complex interplay among the digital economy, new infrastructure, employment quality, and integration within China’s unique provincial-level development model remains scarce [20]. This gap is particularly pronounced in the absence of consideration for China’s distinctive contextual variable: the “new quality productive forces.”

2.2. New Infrastructure: Concepts, Roles, and Linkages to Culture–Tourism Integration

New infrastructure (hereinafter referred to as “new infrastructure”) refers to an infrastructure system guided by new development concepts, driven by technological innovation, and oriented toward high-quality development needs [21]. It provides services for digital transformation, intelligent upgrading, and integrated innovation. Its core components include next-generation information network infrastructure (such as 5G base stations, fiber-optic networks, and satellite internet), integrated infrastructure (such as artificial intelligence platforms, big data centers, cloud computing, and industrial internet platforms), and innovation infrastructure (such as major scientific and technological infrastructure and scientific education infrastructure) [22]. This new infrastructure system serves as the “highway” and “nervous system” for the digital economy’s operation, with its development level directly determining the depth and breadth of the digital economy. The vigorous growth of the digital economy has dramatically increased demand for high-speed networks and massive computing power, which in turn drives and accelerates investment in and adoption of new infrastructure, creating a virtuous cycle between the two. From an infrastructure theory perspective, new infrastructure is not merely a passive support for industries but an active enabler that reshapes industrial development trajectories and spurs new business models [23].

New infrastructure plays an increasingly vital role in the integration of cultural and tourism industries. High-bandwidth, low-latency networks (such as 5G) provide robust foundational support for the digital transformation of cultural and tourism sectors, enabling smart scenic area development, online transmission of high-definition cultural content, and immersive virtual experiences. This significantly promotes the deep integration of online cultural and tourism resources with offline physical experience venues. Concurrently, big data centers and cloud computing platforms, as core components of data empowerment, aggregate and analyze vast amounts of visitor behavior data and cultural consumption preferences [24]. This provides scientific decision support for personalized product customization, precision marketing, and intelligent operational management in the cultural tourism sector. The deep integration of artificial intelligence (AI) and Internet of Things (IoT) technologies with new infrastructure accelerates the intelligent upgrade of the cultural and tourism industry. Applications such as smart navigation, multilingual translation, and intelligent ticketing systems not only enhance service efficiency but also enrich visitor experiences [25]. These advancements collectively promote more efficient coordination between cultural and tourism elements, thereby driving deeper integration within the cultural industry. Existing literature has preliminarily revealed the foundational support role of new infrastructure within the context of the digital economy’s influence on industrial development. However, its specific transmission mechanism as a driver of integration within the digital economy requires further empirical validation. This is particularly true regarding how it functions as an independent transmission pathway influencing integration, distinct from merely being a phenomenon within the broader digital economy [26].

2.3. Employment Quality: Concept, Dimensions, and Linkages to Culture–Tourism Integration

Employment quality is a multidimensional concept that transcends mere employment quantity. It encompasses key aspects such as optimizing employment structure, enhancing skill levels, improving compensation and benefits, optimizing working conditions, and creating new types of employment opportunities. It serves as a vital indicator for measuring workers’ well-being and the quality of economic development [27]. From the perspective of human capital theory, investment in human capital (skills and knowledge) can lead to increased productivity and economic value. The rapid development of the digital economy has driven structural transformation in the labor market, particularly within the service sector. It has not only altered traditional work patterns but also spawned numerous innovative job roles. The digital economy’s significant impact on employment quality indirectly facilitates the integration of the cultural and tourism industries [28].

The digital platform economy serves as a key driver in elevating employment quality within the cultural and tourism sector. It has spawned numerous high-tech, innovative new business models and formats, such as online tour guides, digital cultural creators, live-streaming hosts promoting intangible cultural heritage, and online cultural tourism marketers. These emerging employment forms not only provide regional labor forces with more diverse, flexible, and often higher-paying job options. More importantly, they accelerate the rapid enhancement of digital skills, specialized knowledge, and interdisciplinary capabilities among cultural and tourism practitioners. This optimizes the overall employment structure to better align with the demands of the digital economy era. Technology-driven efficiency gains and heightened specialization requirements also increase the skill premium for practitioners, thereby improving their compensation, benefits, and career prospects. As the skill levels and incomes of cultural and tourism practitioners generally rise, the industry’s appeal to high-caliber talent significantly increases, creating a talent clustering effect that further solidifies the human capital foundation for industrial integration. Consequently, the improvement in employment quality not only enhances workers’ conditions but also injects critical human capital support into the deep integration of the cultural and tourism industries through talent aggregation and skill benefits [29]. This demonstrates that employment quality serves as an independent and indispensable transmission mechanism through which the digital economy empowers integration.

2.4. New-Quality Productivity: Concept and Economic Driving Force

New Quality Productivity (NQP) is a strategically significant economic development concept proposed and consistently emphasized by China in recent years. It transcends the simple aggregation and accumulation of traditional production factors, representing an advanced form of productive forces primarily driven by technological innovation and characterized by high-quality development. Its core essence lies in achieving leapfrog development through technological breakthroughs, innovative allocation of production factors, and deep industrial transformation and upgrading—encompassing workers, means of production, objects of labor, and their optimized integration [30]. New Quality Productivity is characterized by high technology, high efficiency, and high quality. It not only emphasizes enhanced production efficiency but also prioritizes green, intelligent, and integrated development, reflecting a fundamental shift in economic growth from traditional resource-driven models to higher-level innovation-driven approaches. At the national level, cultivating new-quality productive forces is regarded as crucial for seizing the high ground in international competition, achieving sustainable development, and building a modern economic system. Consequently, it plays a pivotal role in resource allocation, industrial structure optimization, and regional economic model transformation, shaping the overall direction and capacity of regional economic development [31].

From different perspectives, understanding new-quality productive forces reveals that, in the theory of innovation ecosystems, highly developed new-quality productive forces herald a mature and vibrant innovation ecosystem. Within such ecosystems, regions typically prioritize developing cutting-edge technologies and strategic emerging industries. This implies scarce resources may be redirected toward these sectors, thereby influencing the marginal empowerment of the digital economy in specific industries (such as cultural tourism) [32]. From a resource-based view, as new-quality productive forces are cultivated and established, their valuable, scarce, difficult-to-imitate, and irreplaceable resources (such as top talent, advanced R&D laboratories, and venture capital) may be reallocated to areas most critical for achieving new competitive advantages. This strategic resource redistribution may implicitly shift the marginal focus of digital economy empowerment. Under dynamic capability theory, it posits that enterprises and regions develop the ability to sense, capture, and reconfigure resources. High-level new-quality productive forces imply that regions possess advanced dynamic capabilities, potentially prompting them to pursue complex, specialized, and “hard-tech”-intensive integration pathways in convergence [33]. This approach may diminish the relative importance of universal digital economic empowerment. Building on this foundation, existing research has begun exploring the positive impacts of new-quality productive forces on macroeconomic growth and industrial upgrading. However, its role as a moderating variable influencing the enabling effects of the digital economy within specific industries—particularly its potential negative moderating effects—remains an under-explored academic frontier. This study aims to precisely fill this gap and delve into the theoretical logic underlying this counterintuitive phenomenon [34].

2.5. Research Outlook and Summary of Academic Gaps

Based on the literature review presented, while existing research has preliminarily explored the impact of the digital economy on industrial development, significant gaps remain in understanding how the digital economy profoundly drives the integration of cultural and tourism industries. First, there is a lack of systematic analysis regarding the mechanisms through which the digital economy facilitates the complex process of integration, particularly concerning in-depth examinations of micro-level transmission pathways, such as new infrastructure and employment quality. Most existing literature remains at the macro-level correlation stage, failing to fully elucidate specific transmission chains [35].

While international reports from organizations such as the UNWTO, OECD, and the EU have outlined macro trends and policy recommendations regarding the digital economy’s impact on tourism, the role of the digital economy in promoting integration—especially within the unique developmental context of China—remains insufficiently explored. The effects of new-quality productive forces as a key moderating variable, particularly its negative moderating role, have yet to be theoretically constructed or empirically tested. This represents a significant and counterintuitive gap in our understanding of the boundary conditions and contextual nuances of the effects of the digital economy, particularly regarding how advanced innovation-driven development alters the logic of digital empowerment [36].

Third, existing research rarely focuses on the complex interactions between the digital economy and the integration of cultural tourism at the provincial level in China, particularly in light of the impact of regional heterogeneity. Furthermore, current studies predominantly rely on official statistics, which may underestimate the contribution of informal cultural and tourism activities to this integration, thus presenting a limitation in data representativeness [37].

This study aims to address existing gaps by constructing and empirically testing an integrated theoretical framework that incorporates both mediation and moderation mechanisms. It seeks to elucidate the pathways, intensity, and boundary conditions through which digital economic development influences the integration of cultural and tourism industries across various provinces in China. By synthesizing international research with local Chinese experiences, this study provides refined theoretical guidance and policy recommendations for the high-quality development of cultural and tourism industries in the digital era, while also exploring its applicability to other developing countries [38].

3. Theoretical Mechanism Analysis and Research Hypotheses

3.1. Theoretical Framework Construction

Based on a comprehensive literature review of concepts including the digital economy, integration, new infrastructure, employment quality, and new-quality productive forces, this study constructs a theoretical framework integrating mediation and moderation effects [39]. This framework aims to comprehensively and deeply reveal the mechanisms through which the development of the digital economy influences the integration of the industry across Chinese provinces. Within this framework, the level of digital economy development (DigitalEconomy) serves as the core independent variable, while the level of cultural and tourism industry integration (Integration) functions as the dependent variable. The proposed levels of new infrastructure (NewInfra) and employment quality (EmploymentQuality) represent two key mediating pathways through which the digital economy influences integration. These pathways, respectively, embody the construction of physical and digital infrastructure during digital transformation, as well as the enhancement of human capital. These pathways were selected because they correspond to the hard conditions (infrastructure) and soft conditions (talent quality) required for industrial transformation, representing two fundamental and independent transmission dimensions through which the digital economy empowers industrial development, with clearly defined conceptual boundaries [40]. Furthermore, we introduce New Quality Productivity (NQP) as a moderating variable to examine whether the intensity of the digital economy’s promotional effect on integration varies with regional economic development stages and industrial upgrading directions. This framework aims to transcend simple correlation analysis, delve into the “black box” of how the digital economy empowers integration, and reveal the boundary conditions of its effects. The following Figure 1 outlines the theoretical mechanism roadmap of this paper.

3.2. Mechanism Analysis

3.2.1. Direct Promotional Effect of Digital Economy on Culture–Tourism Industrial Integration

The digital economy has a crucial and pronounced impact on the creation of integration models in culture and tourism by enabling the swift and effective flow of information and data, as well as fostering innovative integration of these elements. This enhancement arises from the extensive implementation of digital information and data, which has accelerated the speed at which information is shared within the cultural and tourism sectors, optimizing the timely execution of collaborative transactions and partnerships. Consequently, this has improved the distribution of resources in culture and tourism and stimulated collaborative innovation. In more detail, within the cultural and tourism marketplace, platforms driven by the digital economy can apply digital marketing strategies to deliver cultural assets and tourism offerings directly to those in demand, thereby significantly reducing the length of the information chain between supply and demand [41]. This improvement fosters market development and collaboration, allowing for a more direct alignment between information supply and value delivery. It is through the seamless linkage of demand factors between providers and consumers, enabled by digital platforms, that the transformation and integration of cultural and tourism components become better aligned with market dynamics, thus increasing the effective conversion of innovative efficiency into tangible market value.

3.2.2. Intermediary Pathway 1: The Conduction Effect of New Infrastructure

The promotional effect of the digital economy on the integration of the cultural and tourism industries is effectively transmitted through the development and improvement of new infrastructure. The rapid growth of the digital economy has triggered explosive demand for high-speed network connectivity, massive data storage, and advanced analytical capabilities. This has directly driven extensive investment and deployment in new infrastructure such as 5G base stations, big data centers, and artificial intelligence platforms. These advanced hardware and platforms provide indispensable physical infrastructure and technological support for the digital transformation, intelligent upgrading, and online offline integration of the cultural and tourism industry. This aligns closely with the foundational role of infrastructure in industrial development, as revealed by infrastructure theory [42].

Specifically, in terms of connectivity empowerment, high-bandwidth, low-latency networks like 5G provide the essential low-latency, high-reliability connections required for smart scenic area development, high-definition online cultural content transmission, and immersive virtual reality (VR)/augmented reality (AR) experiences. This significantly promotes the deep integration of online cultural and tourism resources with offline physical experiences. In data empowerment, big data centers aggregate and analyze vast amounts of visitor behavior data and cultural consumption preferences [43]. This provides scientific decision support for personalized customization of cultural and tourism products, precision marketing, and intelligent operational management, thereby optimizing the efficiency of supply demand matching. In terms of intelligent empowerment, the deep integration of artificial intelligence and IoT technologies with new infrastructure accelerates the intelligent upgrade of the cultural tourism industry. Applications such as smart navigation, multilingual translation, and intelligent ticketing systems not only enhance the intelligence level and operational efficiency of cultural tourism services but, more importantly, reduce the cost of factor circulation. This promotes the cross-border flow and integration of diverse elements including culture, tourism, and technology. This ultimately accelerates the transformation of the cultural and tourism industry from single-sector operations to composite models, and from superficial collaboration to deep integration. Therefore, new infrastructure serves as the critical bridge between digital economic development and cultural tourism integration. Its construction and refinement are pivotal for converting digital economy dividends into driving forces for cultural tourism convergence [44]. As the material foundation empowering the digital economy, it possesses an independent and distinct transmission mechanism.

3.2.3. Mediating Pathway 2: The Transmission Effect of Employment Quality

The promotional effect of digital economic development on the integration of the cultural and tourism industries can also be achieved through the pathway of enhancing employment quality. Employment quality is a multidimensional concept encompassing aspects such as optimization of employment structure, improvement in skill levels, enhancement of compensation and benefits, and creation of new employment opportunities. It serves as a key indicator for measuring workers’ well-being and the quality of economic development. This aligns with the emphasis in human capital theory [45] on the role of human capital investment in boosting productivity and economic value. The vigorous development of the digital economy, particularly the rise of platform-based business models, has brought profound structural changes to the labor market, significantly diversifying employment forms within the cultural and tourism sector.

Specifically, the digital economy has spawned numerous high-tech, innovative new business formats and models. Examples include online tour guides, digital cultural creators, live-streaming hosts promoting intangible cultural heritage, and online cultural tourism marketers [Research on Digital Platform Economy, 2020; Research on the Gig Economy, 2021] [46]. These emerging employment forms not only provide regional labor with more diversified, flexible, and often higher-income job options. More importantly, they drive rapid improvements in cultural and tourism practitioners’ digital skills, professional knowledge, and interdisciplinary capabilities, thereby optimizing the overall employment structure to better adapt to the demands of the digital economy era. Technology-driven efficiency gains and specialization requirements have also increased the skill premium for practitioners, improving their compensation, benefits, and career prospects. As the skill levels and incomes of cultural and tourism workers generally rise, the industry’s attractiveness to high-quality talent has significantly increased, creating a talent aggregation effect that further strengthens the human capital foundation for industrial integration. Therefore, the improvement in employment quality not only directly enhances workers’ conditions but also injects critical human capital support into the deep integration of the cultural and tourism industries through talent aggregation and skill spillovers. This demonstrates that employment quality, as the human capital foundation empowered by the digital economy, possesses clear conceptual boundaries and independent mechanisms.

3.2.4. Moderating Effect: Boundary Conditions of New Quality Productivity

This study reveals a core finding: New Quality Productivity (NQP) exerts a significant negative moderating effect on the integration between the digital economy and the cultural tourism industry. This discovery challenges the conventional understanding of linear growth in digital empowerment effects, revealing the complexity and conditionality of its operational mechanisms. To further elucidate this phenomenon, this study constructs a theoretical framework incorporating three major mechanisms from an integrated perspective of the Resource-Based View, Industrial Evolution Theory, and Development Economics.

One of the core findings of this study is that New Quality Productivity (NQP) exerts a significant negative moderating effect on the relationship between the digital economy and the integration of cultural and tourism industries. This implies that as a province’s NQP level increases, the promotional role of the digital economy in cultural tourism integration will relatively diminish [47]. This empirical result may seem counterintuitive, yet it embodies profound theoretical logic and practical considerations, challenging simplistic linear interpretations of digital empowerment. Its underlying mechanisms can be explained through the following aspects:

First, resource dilution and competitive crowding-out effects form the core micro-level mechanism. Based on the resource-based view, regions with high NQP serve as “innovation poles” within regional economies, deeply locking their development strategies into knowledge-intensive frontier industries such as artificial intelligence, biopharmaceuticals, and new energy. From the Resource-Based View (RBV) perspective [48,49], these regions possess valuable and often scarce digital economy resources, including policy support, fiscal investment, high-end technical talent, and venture capital [50]. These resources are more likely to be prioritized for strategic emerging industries perceived as having greater future potential and “new-quality” attributes, such as AI chip R&D, biopharmaceuticals, quantum computing, high-end equipment manufacturing, or new materials. These industries often feature higher technological barriers, longer R&D cycles, stronger potential marginal returns, and greater international competitiveness, making them more attractive to high-quality resources within the digital economy. This implies that even provinces with large overall digital economies may allocate relatively less marginal investment and attention to cultural tourism integration. Under this “lighthouse effect” of resource allocation, cultural tourism integration may experience some ‘dilution’ or “crowding out” in benefiting from digital economy dividends, thereby weakening the digital economy’s promotional role in this sector. Consequently, the overall development of the digital economy (X) has structurally constrained its promotional effect on cultural tourism integration (Y) in the competition for resources.

Second, there is the effect of differentiated integration pathways or maturity levels. In provinces driven by high NQP, the integration of cultural and tourism industries may have already reached a relatively high level of maturity. Their integration development path may no longer primarily rely on the universal empowerment of the digital economy, such as basic online platform construction or digital marketing. Instead, cultural and tourism integration in these regions may have shifted toward more refined, specialized, or even “hardcore technology” integration models deeply intertwined with core high-tech industries. This aligns with insights from innovation ecosystem theory [51] and disruptive innovation theory [52], which suggest that within advanced innovation ecosystems, mature industries transcend basic digital applications to pursue more complex, specialized, and disruptive forms of integration. For instance, applying cutting-edge virtual reality (VR)/augmented reality (AR) technologies to create immersive digital museums and virtual tourism experiences; leveraging blockchain to secure cultural copyright transactions and traceability; or utilizing big data analytics and artificial intelligence for intelligent destination management and precision services. In such scenarios, the marginal incremental effect of universal digital economy empowerment becomes limited. This is because the drivers of convergence shift from simply increasing the level of digital economic development to deeper technological innovation capabilities, more complex innovation ecosystem coordination, and vertical applications of specific cutting-edge technologies. Consequently, once regional innovation capacity and industrial upgrading reach a certain stage, the higher the overall level of the digital economy, the less pronounced its universal effect on promoting cultural tourism integration becomes—potentially even exhibiting diminishing marginal returns.

Finally, differences in development stages and priorities come into play. Economic development stages dictate strategic priorities across industries. In provinces with underdeveloped NQP, the cultural and tourism industry may be in the early stages of digital transformation. Any investment in the digital economy can deliver significant momentum, achieving rapid progress from “nothing to something” or “little to more.” This marginal effect is particularly critical and pronounced. However, in provinces that have already established NQP advantages, the economic focus may have successfully shifted toward strategic emerging industries with greater future growth potential and international competitiveness. In overall resource allocation considerations, the integrated development of the cultural and tourism industry may now serve more as a complementary or extended outcome of economic development rather than a priority area for concentrated digital economy resources. This difference in development priorities leads to diminishing marginal effects of the digital economy in promoting cultural tourism integration. This implies that in provinces pursuing higher-quality development and innovation leadership, the additional catalytic role of the digital economy in cultural tourism integration will be relatively weakened.

3.3. Research Hypotheses

Drawing from the theoretical framework concerning the digital economy, the integration of culture and tourism industries, the development of new infrastructure, the quality of employment, and the notion of new-quality productivity, this research puts forth the following hypotheses:

H1. 

The advancement of the digital economy significantly enhances the integration of culture and tourism industries across various Chinese provinces.

H2. 

The level of new infrastructure serves a positive mediating function in the connection between the advancement of the digital economy and the integration of culture and tourism industries.

H3. 

The quality of employment positively mediates the relationship between the progression of the digital economy and the integration of culture and tourism sectors.

H4. 

New-quality productivity has a negative moderating effect on the association between the progression of the digital economy and the culture tourism industry integration; in particular, an increased level of new-quality productivity diminishes the beneficial impact that the digital economy development has on the integration of culture and tourism industries.

4. Model Design and Indicator Selection

4.1. Data Sources and Definitions

This study utilizes annual balanced panel data from 2011 to 2023, encompassing 13 consecutive years and covering 31 provinces, autonomous regions, and municipalities directly under the central government in China, (excluding Hong Kong China, Macao China, and Taiwan China). The primary data sources include the China Statistical Yearbook, the China Cultural and Tourism Statistical Yearbook, the China Digital Economy Development White Paper, the China Labor Statistical Yearbook, as well as provincial statistical yearbooks and statistical bulletins on national economic and social development from each province, autonomous region, and municipality. The indicator system employed in this study incorporates control variables such as the integration of the cultural and tourism industry, digital economy development, employment quality, new infrastructure construction, new quality productive forces, economic development, fiscal investment, openness index, urbanization level, and resident consumption index [53]. The dependent variable is the level of cultural and tourism industry integration, while the independent variable is the level of economic development. The mediating variables—employment quality and new infrastructure construction—and the moderating variable—new quality productive forces—are quantified using entropy indices. The cultural and tourism integration indicator system comprises one primary indicator (cultural and tourism industry integration), two tertiary indicators (industry integration resource foundation, industry integration support, industry integration scale), and 28 corresponding tertiary indicators. The new infrastructure indicator system comprises one primary indicator (new infrastructure), three secondary indicators (information infrastructure, convergence infrastructure, innovation infrastructure), and 32 corresponding tertiary indicators. The employment quality indicator system similarly consists of one primary indicator (employment quality), four secondary indicators (employment environment, employment compensation, employment capability, employment protection), and 18 tertiary indicators [54]. The New Quality Productivity indicator system comprises 3 first-level indicators (New Quality Workforce, New Quality Labor Objects, New Quality Labor Resources), 7 second-level indicators (New Quality Human Capital Input, New Quality Human Capital Output, Informatization Level, Ecological Environment, Technology R&D and Innovation, Infrastructure Construction), and 20 corresponding indicators (Please refer to Appendix A for details). Specific indicator quantities are shown in

Table 1. Definition and measurement of core variables.

Variable	Type	Definition	Measurement	Symbol	Source
Culture Tourism Integration	Dependent Variable	Measuring the level of deep integration between the cultural industry and the tourism industry	The index was calculated using the entropy method.	Integration	China Cultural and Tourism Statistical Yearbook
Digital Economy Development	Independent Variable	Measuring the degree of regional digital economic development	The index was calculated using the entropy method.	Digital Economy	China Statistical Yearbook
Employment Quality	Mediator	Measuring the quality of regional employment	The index was calculated using the entropy method.	Employment Quality	China Statistical Yearbook
New Infrastructure	Mediator	Measuring the level of new infrastructure construction in the region	The index was calculated using the entropy method.	New Infra	China Statistical Yearbook
New-Quality Productivity	Moderator	Measuring the level of innovation-driven development and technological content in regional economic development	The index was calculated using the entropy method.	NQP	China Statistical Yearbook
Economic Development	Control Variables	Measuring the overall level of regional economic development	Log of Per Capita Regional GDP	GDP	China Statistical Yearbook
Fiscal Support		Measuring the extent of government support for economic and social development	Local government expenditure as a percentage of GDP	FS
Openness		Measuring the degree of internationalization of regional economies	Proportion of Total Investment by Foreign-Funded Enterprises in GDP	Open
Urbanization Level)		Measuring the concentration of population and economic activity	Urbanization Rate	Urban
Consumption Capacity		Measuring the consumption potential and market vitality of regional residents	Log of Per Capita Disposable Income of All Residents	CC

.

4.2. Empirical Model Specifications

This study predominantly uses panel data regression models to analyze the ways in which the digital economy affects the integrated development of the cultural and tourism sectors. Taking into account the features of panel data and aiming to maintain the rigor and internal consistency of model estimates, all essential regression models (which include the benchmark regression model, mediation model, and moderation model) are consistently estimated via a two-way fixed effects framework. This approach effectively removes individual heterogeneity at the provincial level that remains constant over time (μ_i) and common shocks at the time level impacting all provinces (λ_t), thus providing better control over biases from omitted variables and increasing the reliability of causal inferences. Within Stata software (stata18), the implementation of high-dimensional fixed effects estimation is mainly carried out using the reghdfe command.

4.2.1. Baseline Model: Two-Way Fixed Effects Panel Model

To evaluate the essential influence of the development of the digital economy on the integration of the culture and tourism industries, we establish the following baseline regression model:

$${\mathrm{Integration}}_{it}=\alpha +\beta ·\mathrm{DigitalEconom}{\mathbf{y}}_{it}+\gamma {\mathbf{X}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(1)

where: ${\mathrm{Integration}}_{it}$ is the culture–tourism industrial integration indicator for province $i$ in year $t$; $\mathrm{DigitalEconom}{\mathbf{y}}_{it}$ is the digital economy development level, serving as the core independent variable; ${\mathbf{X}}_{it}$ is a vector of control variables, including GDP, FS, Open, Urban, CC, and Edu; ${\mu }_i$ represents province-specific fixed effects, controlling for time-invariant characteristics unique to each province; ${\lambda }_t$ represents year-specific fixed effects, controlling for common time trends such as macroeconomic conditions or policy changes; and ${\epsilon }_{it}$ is the idiosyncratic error term. The coefficient $\beta$ is our primary focus, capturing the net effect of digital economy development on culture–tourism industrial integration.

4.2.2. Mediation Effect Model

To investigate the influence of digital economy development on the convergence of cultural and tourism sectors via the establishment of novel infrastructure and the quality of employment, this research will employ the three-step approach suggested by Baron and Kenny (1986) [55] to evaluate the mediating effect. To ensure the rigor and consistency of the model configurations, all regression analyses in the mediating effect pathways will be conducted utilizing two-way fixed effects [56].

Mediation Path 1: Digital Economy → New Infrastructure → Culture–Tourism Integration.

Initially, assess how advancements in the digital economy affect the intermediary variable of new infrastructure:

$${\mathrm{N}\mathrm{e}\mathrm{w}\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}}_{it}={\alpha }_1+a_1·{\mathrm{DigitalEconomy}}_{it}+{\gamma }_1{\mathbf{X}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{1,it}$$

(2)

Step 2: Test the total effect of digital economy development on the dependent variable, culture–tourism industrial integration (same as Equation (2)).

$${\mathrm{Integration}}_{it}=\alpha +c·{\mathrm{DigitalEconomy}}_{it}+\gamma {\mathbf{X}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(3)

The third step is to simultaneously examine the impact of digital economic development and the new infrastructure as a mediating variable on the integration of the cultural and tourism industries:

$${\mathrm{Integration}}_{it}={\alpha }_2+c_1^{\prime }·{\mathrm{DigitalEconomy}}_{it}+b_1·{\mathrm{N}\mathrm{e}\mathrm{w}\mathrm{I}\mathrm{n}\mathrm{f}\mathrm{r}\mathrm{a}}_{it}+{\gamma }_2{\mathbf{X}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{2,it}$$

(4)

Mediation Path 2: Digital Economy → Employment Quality → Culture–Tourism Integration

First, examine the impact of digital economic development on the quality of employment as an intermediate variable:

$${\mathrm{EmploymentQuality}}_{it}={\alpha }_3+a_2·{\mathrm{DigitalEconomy}}_{it}+{\gamma }_3{\mathbf{X}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{3,it}$$

(5)

Step 2: Test the total effect of digital economy development on the dependent variable, culture–tourism industrial integration (same as Equation (5)).

$${\mathrm{Integration}}_{it}=\alpha +c·{\mathrm{DigitalEconomy}}_{it}+\gamma {\mathbf{X}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(6)

Step 3: Simultaneously examine the impact of digital economic development and the mediating variable of employment quality on the integration of the cultural and tourism industries.

$${\mathrm{Integration}}_{\mathrm{i}\mathrm{t}}={\alpha }_4+c_2^{\prime }·{\mathrm{DigitalEconomy}}_{it}+b_2·{\mathrm{EmploymentQuality}}_{it}+{\gamma }_4{\mathbf{X}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{4,it}$$

(7)

4.2.3. Mediation Effect Testing Method

In order to overcome the constraints associated with the Sobel test and the significance testing of indirect path coefficients, this research also incorporates the Bootstrap technique for a conclusive significance assessment of indirect effects. The traditional Sobel test (Z-test) assumes that the sampling distribution of the mediating effect adheres to a normal distribution. Nevertheless, this assumption is frequently violated in scenarios involving small sample sizes and nonlinear paths, which results in diminished test power. The Bootstrap method is a non-parametric resampling approach that involves repeatedly drawing samples from the initial dataset several times (for instance, 1000 or 5000 repetitions), evaluating each sampled dataset, and constructing an empirical distribution based on these evaluations [57].

4.2.4. Moderation Effect Model

To investigate how new-type productive forces influence the integration between the digital economy and the cultural and tourism sector, we will incorporate a moderating variable (new-type productive forces, NQP) along with its interaction term related to the primary independent variable (digital economic development level, Digital Economy), using the foundational two-way fixed effects model [58].

$${\mathrm{Integration}}_{it}={\alpha }_5+{\beta }_1·{\mathrm{DigitalEconomy}}_{it}+{\beta }_2·{\mathrm{NQP}}_{it}+{\beta }_3·\left({\mathrm{DigitalEconomy}}_{it}\times {\mathrm{NQP}}_{it}\right)+{\gamma }_5{\mathbf{X}}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{5it}$$

(8)

where: (${\mathrm{NQP}}_{it}$) is the level of new-quality productivity for province ($i$) in year ($t$); (${\mathrm{DigitalEconomy}}_{it}\times {\mathrm{NQP}}_{it}$) is their interaction term. Our focus is on the interaction term coefficient (${\beta }_3$).

4.3. Robustness Checks and Endogeneity Treatment

To ensure the reliability and stability of the core findings, this study will adopt multi-angle robustness testing methods and address potential endogeneity issues [59].

4.3.1. Addressing Endogeneity Issues

There may exist a “common origin” (bidirectional causality) between the integration of the digital economy and the cultural tourism industry, as well as shared influence from other unobserved time-varying variables. Although all core models employ bidirectional fixed effects (implemented via the reghdfe command), which largely eliminate omitted variable bias caused by province-level variables that do not vary over time, further testing is required to address potential bidirectional causality and time-varying omitted variables.

(1) Hausman Test: This study will conduct a Hausman test on the benchmark model to assess the suitability of the fixed-effects model relative to the random-effects model, thereby providing statistical justification for selecting the bidirectional fixed-effects model.

(2) VIF Test: Subsequently, a variance inflation factor (VIF) test will be performed to evaluate whether severe multicollinearity exists among the explanatory variables in the model, ensuring the stability of the estimation results.

(3) Instrumental Variables (IV) Regression (2SLS): To address potential reverse causality and time-varying omitted variable issues, this study employs instrumental variables (IV) regression using two-stage least squares (2SLS) estimation. We select the lagged two-period value of digital economy development (L2.Digital_Economy_w) as the instrumental variable (iv_DE). The theoretical rationale for selecting a lagged variable as the instrument lies in the fact that prior digital economic development influences the current level of the digital economy (correlation), but its direct impact on the current integration of culture and tourism (beyond the path through the current digital economy) is weak (exogeneity). We will report the identification tests for the instrumental variables, including the Weak Identification Test, the Underidentification Test, and the Overidentification Test (if applicable).

4.3.2. Stability Testing

In addition to the previously mentioned endogenous treatment, this paper will perform several multi-faceted robustness tests to further validate the core findings:

(1) Heterogeneity Analysis: The research will apply grouped regression methods based on the geographic regions (e.g., eastern, central, and western provinces) or the level of economic development in Chinese provinces. It will also incorporate interaction terms between dummy variables representing geographic locations and the digital economy to assess whether notable regional disparities exist concerning the influence of the digital economy on the merging of culture and tourism.

(2) Sub-sample Analysis: To eliminate potential confounding effects stemming from distinct historical occurrences (such as the significant influence of the COVID-19 crisis on the cultural and tourism sectors and the digital economy between 2019 and 2022), the study will exclude data from this time frame and re-evaluate the core model using the remaining subsample to test the robustness of the findings.

(3) Cluster-based Robust Standard Error Test: This paper utilizes a cluster-based methodology to address possible intra-group correlation issues present in panel data and to bolster the accuracy of estimates and the trustworthiness of conclusions. The error terms will be clustered as much as feasible based on identity (ID), thereby mitigating omitted variable bias linked to time-invariant provincial characteristics.

(4) Alternative Regression Models: Beyond the standard two-way fixed effects model, this research will evaluate additional panel data models (for example, implementing a random effects model when the Hausman test results indicate its superiority) or employ various estimation techniques for further validation. All robustness tests will be conducted independently for the benchmark model, the mediation model, and the moderation model.

(5) Core independent variable lagged term: To further validate the robustness of core findings and account for the dynamic nature of digital economy impacts, we will replace the core independent variable measuring digital economy development with its lagged term (L1.Digital_Economy_w). We will rerun the baseline regression and mediation effect models to examine the long-term or delayed effects of the digital economy.

(6) Moderation Effect Robustness Test—NQP Grouping: To further validate the moderating effect of new-quality productivity, we will divide provincial samples into high-NQP and low-NQP groups based on NQP levels (e.g., using NQP mean or median) and run separate regressions of the digital economy on cultural tourism integration. If the promotional effect of the digital economy is significantly weaker in the high-NQP group compared to the low-NQP group (or even insignificant or negative in the high-NQP group), this will provide additional robust support for Hypothesis 4.

5. Empirical Result Analysis

5.1. Descriptive Statistics and Diagnostic Tests

Among these variables, Integration denotes the cultural tourism integration index, while DigitalEconomy signifies the index for digital economy development. Additionally, GDP, FS, Open, Urban, and CC serve as control variables. Given that the indices derived through the entropy method for both the independent and dependent variables are generally standardized, exhibiting means close to 0 or a fixed value, and that their standard deviations reflect the variability of the variables, we conducted a truncation of 1% and 99% on the primary continuous variables before the analysis in order to reduce the influence of outliers. Additionally, this section will present the results of the instrumental variables (IV) regression and robustness results.

5.1.1. Descriptive Statistics

Table 2. Descriptive Statistics of Variables.

Variable	Obs	Mean	Sd	min	Max
Integration	403	9.88 × 10−16	2.522974	−5.600797	7.858335
Digital Economy	403	0.1333607	0.1108026	0.0166211	0.583659
GDP	403	−6.74 × 10−17	1.134816	−2.531262	4.205738
FS	403	6.75 × 10−16	0.9619905	−1.731291	3.482343
Open	403	−5.68 × 10−16	0.755694	−1.725186	2.773047
Urban	403	−8.10 × 10−16	0.6414286	−1.509751	2.790069
CC	403	−1.24 × 10−15	0.4370264	−1.126694	1.746663

presents the descriptive statistics for the main variables used in this study.

As presented in Table 2, there are 403 observations within the sample size, confirming the dataset’s comprehensiveness. The average values and standard deviations of each variable illustrate their distribution throughout the sampling period. For instance, the average of the Digital Economy Development Index is noted as 0.133, while the standard deviation stands at 0.111. This indicates that there exist notable variances in the extent of digital economic development across different provinces, thereby laying the groundwork for further exploration of its effects on the synergy between culture and tourism.

5.1.2. Diagnostic Tests

Prior to executing the panel regression analysis, tests for multicollinearity and Hausman were conducted to verify the suitability of the model specification and the dependability of the estimation outcomes.

(1)

Multicollinearity Assessment (VIF Examination)

An examination of the variance inflation factor (VIF) was carried out on both the explanatory and control variables in the model. Every explanatory variable registered a VIF below 10 (where 10 serves as a common benchmark for assessing multicollinearity, although some scholars advocate for a stricter threshold of 5) [60]. The mean VIF was found to be 1.10. Therefore, the model did not exhibit significant multicollinearity problems and was capable of effectively mitigating the instability of regression coefficients that may arise due to high intercorrelation among the regression variables.

(2)

Hausman Assessment

To further examine the suitability of the approaches implemented in the primary regression model, this study conducts a Hausman assessment comparing the fixed-effects model (Fixed Effects Model) with the random-effects model (Random Effects Model) of the primary regression framework. The findings indicate that Prob&chi2 = 0.0000, with the p-value of the Hausman test being <0.05. This suggests the rejection of the null hypothesis (H0: the random effects model is both consistent and valid), indicating that the fixed effects regression model (including the two-way fixed effects model) is a superior option, as it can account for unobservable provincial heterogeneity and deliver more reliable estimation outcomes. This further supports the reasoning that the two-way fixed effects model outlined in the previous section is more justified.

(3)

Instrumental Variables (IV) Regression (2SLS)

To further address potential endogeneity issues, this study employs instrumental variable (IV) regression, specifically the two-stage least squares (2SLS) method. We select the lagged two-period value of the digital economy development level (L2.Digital_Economy_w, denoted as iv_DE) as the instrumental variable for the core independent variable (Digital_Economy_w).

Table 3. Instrumental Variables (IV-2SLS) Regression Results.

VARIABLES	Digital Economy (Phase One)	Integration (Phase Two)
L2. Digital Economy (iv_DE)	1.1257 * (0.0328)
Digital Economy		0.6368 * (0.0449)
Contorls	Yes	Yes
Constant	0.0111 *** (0.0018)	0.1092 *** (0.0055)
Observations	341	341
R-squared	0.9901	0.9360
Cragg Donald Wald F	7045.061 *
Kleibergen Paap rk Wald F	1177.858 *
Stock Yogo 10% IV Size	16.38

Robust standard errors in parentheses. *** p < 0.01,* p < 0.1.

presents the main results of the 2SLS regression, including the instrumental variable diagnostic statistics from the first-stage regression and the results from the second-stage regression.

The instrumental variable (IV-2SLS) regression results significantly strengthen the causal inference power of this study regarding the promotion of cultural and tourism integration through digital economic development. First, regarding the validity of the instrumental variables, all metrics indicate the selection was appropriate: the unidentifiability test (Kleibergen Paap rk LM statistic of 12.09, p-value of 0.0005) strongly rejects the null hypothesis of model unidentifiability, confirming significant correlation between the instrumental variables and the endogenous variables. In the weak identification test, both the Cragg Donald Wald F-statistic (7045.061) and the Kleibergen Paap rk Wald F-statistic (1177.858) far exceeded the 10% maximum IV size threshold of 16.38 for the Stock Yogo weak identification test, ruling out weak identification issues and indicating sufficient explanatory power of the instrumental variables. Although the Hansen J statistic yields a p-value of 0.000, since this model is perfectly identified (one endogenous variable corresponds to one excluded instrumental variable), the Hansen J statistic is typically not used to test the over-identification constraint in this context, rendering its p-value non-interpretable. The core rationale lies in selecting two-period lagged values of the digital economy as instrumental variables, which theoretically ensures reasonable exogeneity: prior digital economic development can influence the current level of the digital economy, but its direct impact on the current cultural tourism integration (beyond the path through the current digital economy) is relatively weak.

5.2. Baseline Regression Analysis: Direct Impact of Digital Economy on Culture Tourism Integration (H1)

This study employs a sophisticated regression model (two-way fixed effects) to perform a benchmark analysis aimed at exploring the direct influence of the digital economy on the convergence of culture and tourism (H1). The outcomes of the regression analysis are presented in

Table 4. Baseline Regression Results: Digital Economy’s Direct Impact on Culture Tourism Integration.

	Model1	Model2
VARIABLES	Integration	Integration
Digital Economy	4.885 ***	6.193 ***
	(0.935)	(1.844)
GDP		0.382 **
		(0.146)
FS		0.318
		(0.207)
Open		−0.234 *
		(0.132)
Urban		0.144 **
		(0.069)
CC		−0.429 ***
		(0.136)
Constant	−0.652 ***	−0.826 ***
	(0.125)	(0.246)
Observations	403	403
R-squared	0.991	0.994
yearfix	YES	YES
idfix	YES	YES

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

:

The R² value for Model 1 in the fundamental regression outcomes stands at 0.991, while the adjusted R² for Model 2 is recorded at 0.994. The F-value reaches a remarkable 1193.3, accompanied by a p-value of 0.000, which signifies a strong fit of the model and considerable explanatory power of the variables regarding the integration of the cultural tourism sector, thereby reinforcing hypothesis H1. Below, we provide a detailed explanation of the main explanatory variables as well as the control variables.

(1) The Digital Economy shows a significant positive correlation at the 1% significance level, suggesting that the advancement of the digital economy notably enhances the integration of the cultural tourism sector. In particular, the digital economy possesses essential attributes like penetrability, integration, and innovative capacity, which fundamentally transform the structural organization of cultural industry components and the mechanisms of organization, production, and operation. Digital platforms help to diminish information asymmetries and lower transaction costs, allowing cultural products and tourism services to align more closely with market demands and dismantling traditional industry barriers.

(2) FS exhibits a significant positive correlation at the 1% level, suggesting that the allocation of government fiscal expenditures towards socio-economic development plays a crucial role in enhancing the merger of the cultural and tourism sectors. Such fiscal backing can assist in financing the construction of infrastructure (including innovative infrastructure) within the cultural and tourism domains, support initiatives aimed at safeguarding and promoting cultural and tourism heritage, provide financial incentives and grants for industrial growth, and encourage cooperation among various departments. These overt and covert investments help lower production costs for businesses, stimulate innovation, and improve the business climate, thereby establishing a robust foundation and driving force for the successful integration of both cultural and tourism elements and the advancement of the cultural and tourism industry.

(3) The two variables, Open and CC, both exhibit significant negative effects at the 1% statistical significance level. When using the proportion of foreign investment as an indicator of openness, a rise in openness substantially influences the amalgamation of the cultural and tourism sectors. For instance, investing in extensive resorts, theme parks, or international-style cultural and tourism consumption venues, instead of emphasizing the profound integration of local culture with tourism, could cause resource allocation to become concentrated in a singular, non-integrated path. Additionally, the influence of variations in per capita disposable income (CC) on the integration of cultural and tourism industries appears to be more beneficial in regions with low income, while showing a negative effect in regions with high income. This could be attributed to the fact that certain facets of cultural and tourism integration are already relatively advanced in wealthier areas, meaning that further income growth would not correspondingly improve overall integration levels. Rather, the quest for highly specialized experiences may divert resources towards very specific niches, leading to adverse fluctuations in the integration index of the cultural and tourism sectors.

The benchmark regression findings mentioned above offer guidance for the following analyses of mediation and moderation effects. Additionally, they highlight that the digital economy acts as a key catalyst for the integration of the cultural and tourism sectors. Nonetheless, the results imply that macroeconomic elements, including levels of economic development, openness to global markets, and the consumption capacity of residents, not only facilitate the integration of culture and tourism but also demonstrate non-linear or indirect relationships with the advancement of this integration.

5.3. Mediation Effect Analysis: The Transmission Role of New Infrastructure and Employment Quality (H2, H3)

This section employs a mediation effect model to examine how the construction of new infrastructure and the quality of employment influence Integration. The detailed findings are presented in

Table 5. Presents the regression results for Employment Quality as a mediator test results.

	Model1	Model2	Model3	Model4
VARIABLES	Employment Quality	Integration	New Infra	Integration
Digital Economy	0.126 **	3.056 ***	0.466 ***	1.289 *
	(0.0518)	(0.820)	(0.103)	(0.704)
Employment Quality		24.91 ***
		(1.767)
New Infra				10.52 ***
				(0.854)
Control	Yes	Yes	Yes	Yes
Constant	0.202 ***	−5.855 ***	0.0774 ***	−1.640 ***
	(0.00690)	(0.361)	(0.0137)	(0.102)
fixyear	Yes	Yes	Yes	Yes
fixid	Yes	Yes	Yes	Yes
Observations	403	403	403	403
R-squared	0.994	0.998	0.992	0.996

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

:

This research employs the three-step approach proposed by Baron and Kenny, along with the Bootstrap technique, to examine how new infrastructure and the quality of employment contribute to the mediating pathway [61]. Specifically, as shown in

Table 6. Bootstrap test results.

Path	Type	Observed Coefficient	Bootstrap Std. Err.	z	P > |z|	Normal-Based [95% Conf. Interval]
Employment Quality	_bs_1	19.85419	0.718504	27.63	0.000	18.44595	21.26243
	_bs_2	5.338341	0.7539851	7.08	0.000	3.860557	6.816124
New Infra	_bs_1	13.09078	0.9700158	13.50	0.000	11.18959	14.99198
	_bs_2	12.10175	1.053138	11.49	0.000	10.03764	14.16586

Note: _bs_1(ind_eff)_bs_2(dir_eff).

:

5.3.1. Mediation Path 1: Digital Economy → New Infrastructure → Culture Tourism Integration (H2)

The findings from the mediation effect analysis displayed in Table 4, along with the results from the Bootstrap method presented in Table 5, indicate that in Model 3, the coefficient representing the impact of the Digital Economy on New Infra is 0.466, which achieves significance at the 1% level. This suggests that advancements in the digital economy substantially enhance the levels of new infrastructure development. Furthermore, when examining the joint effects of Digital Economy and New Infra on Integration in Model 4, the coefficient for New Infra is recorded at 10.52, also significant at the 1% level. This implies that enhancements in new infrastructure have considerably stimulated the cultural and tourism sectors. Moreover, the direct effect coefficient (c’ path) reflecting the influence of Digital Economy on Integration stands at 1.289, significant at the 10% level. The results from the Bootstrap test in Table 5 reveal an indirect effect coefficient (_bs_1) of 13.09078, significant at the 1% level (p = 0.000), with the 95% confidence interval not encompassing 0. The direct effect coefficient (_bs_2) is 12.10175, significant at the 1% level (p = 0.000), with the 95% confidence interval also excluding 0.

Based on the aforementioned conclusions, we can deduce that hypothesis H2, which posits that the advancement of the digital economy significantly positively mediates the integration of the cultural and tourism sectors through the enhancement of new infrastructure development, finds partial support. The growth of the digital economy necessitates elevated standards for the establishment of new infrastructure, including broadband networks and data centers, thus catalyzing the development of such infrastructure. This new infrastructure functions as the “highway” and “nervous system” essential for the digital economy’s operation, offering a robust foundation for the digitization, intelligence, and integration of online and offline components within the cultural and tourism sectors, subsequently fostering a deeper integration between these industries. For instance, during the 5G era, innovative smart tourism destinations and immersive virtual/augmented reality experiences are beginning to flourish.

5.3.2. Mediation Path 2: Digital Economy → Employment Quality → Culture Tourism Integration (H3)

Based on the findings from model1, it can be observed that the coefficient reflecting the effect of the Digital Economy on Employment Quality is 0.126. This value is significant at the 5% level, suggesting that advancements in the digital economy play a crucial role in enhancing employment quality. In model2, which examines the combined influence of Digital Economy and Employment Quality on Integration, the coefficient for Employment Quality is 24.91, exhibiting significance at the 1% level. This implies that improvements in employment quality substantially aid in boosting the development of the cultural and tourism sector. The direct effect of the Digital Economy on Integration has a coefficient of 3.056, also significant at the 1% level; Table 5 presents the results from the Bootstrap tests. The coefficient for the indirect effect (_bs_1) is 19.85419, which is significant at the 1% level, with p = 0.000, and the 95% confidence interval does not include 0. For the direct effect (_bs_2), the coefficient stands at 5.33841, also significant at the 1% level, with p = 0.000, and the 95% confidence interval excludes 0.

The conclusions drawn above validate H3, which posits that the quality of employment serves as a significant and positive mediating factor in how the digital economy impacts the integration of the cultural and tourism sectors. The mechanism by which the digital economy facilitates a deeper integration of these industries is outlined as follows: innovations in business models propelled by the digital economy’s growth, alongside improvements in workforce skills, affect changes in the employment structure. This transformation enhances the capabilities and quality of personnel within the cultural and tourism fields. Consequently, this allows for a more active involvement in the integration process, thereby furthering the profound integration and advancement of the cultural and tourism industries. For instance, new positions of a digital nature, such as online tour guides and professionals in digital culture and creative sectors—characterized by high skills and added value—are generated by the growth of platform-based digital economies, thereby boosting employment quality and facilitating the integration of cultural and tourism domains.

5.4. Moderation Effect Analysis: Boundary Conditions of New-Quality Productivity (H4)

In this section, we present the concept of new quality productivity (NQP) along with an interaction variable (Mix) that relates to the digital economy. We aim to analyze their moderating influence on the relationship between the digital economy and the cultural and tourism sectors. The findings from our regression analysis can be found in

Table 7. Moderation Effect Test Results for New-Quality Productivity.

VARIABLES	Integration
Digital Economy	7.714 ***
	(0.729)
NQP	12.78 ***
	(0.600)
Mix	−2.478 **
	(1.023)
Controls	Yes
Constant	−3.036 ***
	(0.121)
Observations	403
R-squared	0.998

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

:

As illustrated in Table 6, the coefficient for the primary explanatory variable, the digital economy, is 7.713785, which signifies a notably positive correlation at a 1% significance level. Similarly, the coefficient for the moderating variable NQP is 12.78158, demonstrating a significant positive correlation at the 1% level. The coefficient associated with the core interaction term Mix (DigitalEconomy * NQP) is −2.477726, which reflects a significant negative relationship at the 5% statistical level (p = 0.016). This notable negative coefficient suggests that the influence of new quality productivity adversely affects the relationship between the digital economy and the integrated development of the cultural and tourism sectors. Specifically, it implies that as the level of new quality productivity in a province or city increases, the digital economy’s capacity to drive the integration of these industries diminishes. This finding supports hypothesis H4 of this research. The integration of the cultural and tourism industry, influenced by new quality productivity within provinces and cities, may have reached an advanced stage, whereby their collaborative development no longer depends on the digital economy as a primary means of production for fostering inclusive integration of the cultural and tourism sectors. Instead, there is a shift toward integration with leading high-tech industries, aiming for a path characterized by refinement, specialization, and even “hardcore technology” integration. This includes initiatives such as utilizing VR/AR technology to enhance experiences in digital museums, adopting blockchain for securing transactions and tracking cultural copyrights, and applying data mining and intelligent systems for effective management and personalized services at tourist sites, among other strategies. At this point, the incremental benefits of the digital economy’s facilitative role appear to be constrained. The elements that foster integration and growth encompass enhanced innovation-driven capabilities, more intricate collaboration within innovation ecosystems, and a more focused vertical integration of advanced technologies, rather than merely amplifying the scale of digital economic growth. The phase of economic advancement influences the corresponding phase of industrial evolution. In regions lacking new productive forces, the cultural and tourism sector remains in the initial phases of growth and transformation. Investments in the digital economy that transition from “nothing to something” or “something to more” can serve as a “catalyst,” with their marginal utility being significantly pronounced. In areas where new-quality productive forces have been established, strategic emerging industries prevail in the economic development arena due to their hierarchical benefits, substantial potential, and robust international competitiveness. When viewed from a holistic economic resource distribution perspective, the integration of the cultural and tourism sector may transform into an auxiliary or expanded facet of economic growth, rather than a focal point for high-density allocation of digital economy resources. These varying developmental phases result in a reduced marginal impact of the digital economy on enhancing cultural and tourism integration. In regions striving for advanced development and increased competitive edges, the incremental advantages of the digital economy in advancing cultural and tourism integration will likely wane. Beyond a certain threshold of new-quality productive forces, the digital economy might even present adverse effects (although this may not be evident within the current dataset), leading to diverse policy interpretations among provinces at different levels of new-quality productive forces.

5.5. Robustness Checks

To ensure the reliability and stability of the above research conclusions, this study conducted multi-faceted robustness tests.

5.5.1. Heterogeneity Analysis: Regional Differences

Table 8. Regional Heterogeneity Analysis Results.

	Center	Western	East
VARIABLES	Integration	Integration	Integration
Digital Economy	3.455	18.900 ***	3.156 ***
	(3.588)	(2.109)	(0.770)
Constant	−1.312 **	−3.094 ***	1.802 ***
	(0.416)	(0.155)	(0.186)
Controls	Yes	Yes	Yes
Observations	78	156	117
R-squared	0.996	0.997	0.998
yearfix	YES	YES	YES
idfix	YES	YES	YES

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

displays the regression findings for the subsamples categorized into eastern, central, and western regions to investigate the regional differences in how the digital economy influences the merging of culture and tourism. The analysis of heterogeneity indicates that the extent to which the digital economy promotes the integration of culture and tourism significantly differs among the regions:

Table 8 displays the results of the regression analysis conducted after categorizing the subsamples into eastern, central, and western regions, aiming to investigate the regional disparities regarding the influence of digital economic development on the integration of culture and tourism. The findings from the heterogeneity tests reveal differing positive impacts of digital economic advancements on cultural and tourism integration across various regions. Specifically, for the central region, the coefficient for the Digital Economy is recorded at 3.455, which is found to be statistically insignificant. This indicates that, once other variables are accounted for, the effect of the digital economy on the integration of cultural and tourism sectors in the central region is minimal. In contrast, the western region exhibits a Digital Economy coefficient of 18.900, which is significant at the 1% level. This implies that the digital economy has a notably positive influence on the integration of culture and tourism in the western area, likely due to its relatively nascent state; both digital infrastructure and digital applications are still developing, leading to stronger growth potential, greater returns on digital economy investments, and more substantial incremental advantages. Meanwhile, in the eastern region, the Digital Economy coefficient is 3.156, representing significance at the 1% level. Although significant, this coefficient is substantially lower than that observed in the western region, highlighting that, even though the digital economy in the eastern regions contributes to cultural and tourism integration, its effects may be diminishing as development matures, potentially transitioning towards more nuanced and high-quality forms of integration. This observation supports the previous analysis regarding the counterproductive regulatory role of emerging productive forces, indicating that the enabling influence of the digital economy differs across regions at various stages of development.

5.5.2. Sub-Sample Analysis Excluding Specific Years

To eliminate the unique influence that the COVID-19 pandemic might have exerted on the cultural tourism sector and the digital economy during the exceptional years of 2019 to 2022, this study omitted the sample data from these four years, restructured the core model, and conducted a subsample regression analysis. The findings are presented in

Table 9. Sub-sample Regression Results Excluding 2019–2022.

VARIABLES	Integration
Digital Economy	26.04 ***
	(1.110)
	(0.196)
Controls	Yes
Constant	−3.44 ***
	(0.121)
Observations	310
R-squared	0.952
yearfix	YES
idfix	YES

Robust standard errors in parentheses. *** p < 0.01.

:

Excluding data from the pandemic period, the coefficient for the Digital Economy (w) is 26.04169, which remains highly significant at the 1% level. The level of significance and magnitude is notably consistent with the baseline regression result from the full sample (25.19253). This supports the core conclusion of this research—that the digital economy significantly enhances the integration of culture and tourism—is not solely influenced by the pandemic’s unique effects, but rather reflects a long-term, generalizable trend.

5.5.3. Cluster Robust Standard Errors

As highlighted in the section on model design, every primary regression model employed standard errors that are robust to clustering (via vce(cluster id)), organized by province ID. This approach adequately tackles potential intra-group correlation problems within panel data, thereby securing the validity of the estimates and the dependability of the inferences made. Furthermore, the baseline and sub-sample regression outcomes clearly indicate these cluster-robust standard errors, further reinforcing the robustness of all models.

5.5.4. Replacement Regression Model

To enhance the robustness of the model discussed in this paper, this section substitutes the original model (which was chosen following the Hausman test and implemented using a two-way fixed effects approach) with an ordinary least squares (OLS) model for the regression analysis. The regression model encompasses the benchmark regression, mediating effects model, and moderating effects model, as illustrated in

Table 10. Clustering and replacement model regression results.

	Model1	Model2	Model3	Model4	Model5	Model6	Model7
VARIABLES	Integration	Integration	Employment Quality	Integration	New Infra	Integration	Integration
Digital Economy	4.885 ***	6.193 ***	0.589 ***	5.338 ***	0.956 ***	12.10 ***	15.87 ***
	(0.935)	(1.844)	(0.0238)	(1.614)	(0.0510)	(1.983)	(2.798)
Employment Quality				33.70 ***
				(2.599)
New Infra						13.69 ***
						(1.824)
NQP							13.93 ***
							(2.132)
Mix							−5.385
							(4.078)
Contorls	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Constant	−0.652 ***	−0.826 ***	0.140 ***	−8.083 ***	0.0120 *	−3.524 ***	−4.217 ***
	(0.125)	(0.246)	(0.00292)	(0.353)	(0.00602)	(0.0673)	(0.155)
Observations	403	403	403	403	403	403	403
R-squared	0.991	0.994	0.972	0.986	0.935	0.975	0.973
yearfix	No	No	No	No	No	No	No
idfix	No	No	No	No	No	No	No
Clusterid	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Robust standard errors in parentheses. *** p < 0.01, * p < 0.1.

:

To enhance the robustness of the model discussed in this paper, this section substitutes the original model (which was chosen following the Hausman test and implemented using a two-way fixed effects approach) with an ordinary least squares (OLS) model for the regression analysis. The regression model encompasses the benchmark regression, mediating effects model, and moderating effects model, as illustrated in Table 10.

5.5.5. Core Independent Variable Lagged Term

To further visualize the dynamic characteristics of the digital economy’s impact, this paper plots the time trends of the current and lagged digital economic development levels. As shown in Figure 2, the two curves exhibit highly synchronized fluctuations, indicating the robust persistence of digital economic growth. More critically, the lagged curve demonstrates a pronounced systematic rightward shift relative to the current curve. This rightward shift provides intuitive visual evidence for the regression results presented in this paper: it vividly demonstrates that the current level of digital economic development (the leading curve) is a powerful predictor of the next period’s level (the lagging curve), but its influence naturally diminishes over time. This finding is fully consistent with the regression analysis results, which showed significant but smaller coefficients for the lagged terms.

5.5.6. Moderation Effect Robustness Test—NQP Grouping

To further validate the negative moderating effect of new quality productivity on the promotion of cultural tourism integration through the digital economy, this study categorizes the sample provinces into a high NQP group (R = 1) and a low NQP group (R = 0) based on their NQP levels (see

Table 11. Moderation Effect Robustness Test: Regression Results for NQP Group Subsamples.

VARIABLES	Low NQP Group Cultural and Tourism Industry Integration	High NQP Group: Cultural and Tourism Industry Integration
Digital Economy	0.4418 *** (0.1147)	0.1512 ** (0.0587)
Contorls	Yes	Yes
Constant	0.1168 *** (0.0087)	0.1982 *** (0.0115)
Observations	201	201
R-squared	0.9831	0.9900

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05.

). Separate regression analyses are conducted for the impact of digital economic development on cultural tourism integration within each group.

The results of the NQP subgroup sample analysis strongly support the negative moderating effect of new-quality productive forces on the promotion of cultural tourism integration by the digital economy. In the low NQP group (R = 0), the level of digital economic development significantly and substantially promotes integration, with a coefficient of 0.4418, significant at the 1% level (p = 0.001). This indicates that in regions with relatively underdeveloped NQP, investments in the digital economy yield stronger marginal effects and more pronounced promotional impacts. In the high NQP group (R = 1), the digital economy’s level significantly promotes integration (coefficient 0.1512, significant at the 5% level, p = 0.015), though its coefficient magnitude is notably smaller than that of the low NQP group (0.1512 < 0.4418). Inter-group coefficient difference calculations reveal that the promotion effect in the high NQP group is approximately 0.2906 smaller than that in the low NQP group (0.4418 − 0.1512 = 0.2906). Given the significant disparity in coefficient magnitude and respective statistical significance, this difference holds both economic relevance and statistical indication. This result provides intuitive and robust validation of the negative moderating effect of new-quality productivity on the promotion of integration by the digital economy. Specifically, higher NQP levels weaken the digital economy’s promotional effect on integration. This further reinforces the theoretical interpretation of this study: digital empowerment exhibits contextual dependency, potentially facing resource dilution, transformation of integration pathways, or adjustments in development priorities under high NQP conditions.

6. Discussion

This section will engage in an in-depth discussion of the empirical findings from Section 5, exploring their theoretical contributions and practical implications while comparing them with existing literature.

6.1. Summary of Key Findings

This study employs a two-way fixed effects model analysis of panel data from China’s 31 provinces between 2011 and 2023 to reveal the complex relationship between the digital economy and the integration of the cultural and tourism industries. Key findings are summarized as follows:

(1) The digital economy serves as the core driver of cultural tourism integration. This study confirms the significant positive impact of digital economic development on cultural and tourism integration. This aligns with existing literature on the positive effects of the digital economy on industrial development (e.g., UNCTAD, 2023; Schwab, 2016) [62,63], while focusing specifically on the complex domain of cultural and tourism integration.

(2) New infrastructure and employment quality serve as key transmission mechanisms. The study further reveals the crucial mediating role of new infrastructure and employment quality in the digital economy’s promotion of integration. By driving the construction of new infrastructure and enhancing employment quality, the digital economy effectively converts its own dividends into momentum for integration. This “unlocks the black box of digital economy empowerment,” elucidating the transmission pathways from both material foundation and human capital dimensions, thereby filling a gap in existing literature regarding the exploration of this refined mechanism.

(3) New-type productive forces exert a negative moderating effect on the digital economy’s enabling role. This constitutes a unique and significant finding. Results indicate that in provinces with higher levels of new-type productive forces, the digital economy’s promotional effect on integration is relatively diminished. This challenges the linear assumption that “higher digital economy development yields stronger enabling effects,” revealing the boundary conditions and complexity of digital economy empowerment.

(4) Complex influences of control variables. The study further reveals that economic development level, openness to the outside world, and residents’ consumption capacity exert significant negative impacts on integration, while fiscal support and urbanization level demonstrate significant positive effects. These findings suggest that the drivers of integration are multifaceted and complex. Some macroeconomic indicators may exhibit non-intuitive or diminishing marginal effects after controlling for factors like the digital economy, necessitating deeper contextual understanding.

6.2. Theoretical Contributions

The findings of this study contribute to existing theory and literature in multiple dimensions:

(1) This study deepens the understanding of how the digital economy empowers industrial development. While prior research primarily focuses on the macro-level impact of the digital economy on industrial development, this study specifically examines how the digital economy channels its promotional effects on integration through two dimensions: new infrastructure as a physical carrier and employment quality as a dimension of human capital. This research provides empirical evidence for understanding the micro-level transmission mechanisms, thereby aiding in the development of more refined theories regarding the impact pathways of the digital economy.

(2) This study innovatively introduces and validates the moderating role of new-quality productivity (NQP) in the context of integration. It is the first to empirically test the moderating effect of NQP, revealing a counterintuitive negative moderation phenomenon. This finding enhances our understanding of the boundary conditions influencing the driving effects of the digital economy, suggesting that the enabling logic of the digital economy may vary across different developmental stages and industrial structures. Furthermore, it provides new perspectives and empirical support for the application of resource endowment theory [48], innovation ecosystem theory [51], and dynamic capability theory [64] within the digital economy framework.

(3) This study expands the research scope of cultural tourism integration by incorporating contemporary concepts such as the digital economy, new infrastructure, employment quality, and new-quality productive forces into the analytical framework. As a result, a more comprehensive and explanatory theoretical model is constructed. This approach broadens the boundaries of the influencing factors in integration and offers a novel analytical paradigm for future research in this field.

(4) This paper offers unique insights within the Chinese context while enhancing global perspectives. By focusing on the provincial level in China, the study accounts for regional heterogeneity and integrates analysis with China’s distinctive development concept of ‘new quality productivity.’ This approach provides empirical evidence for understanding the practices and complexities of the digital economy within China’s socialist market economy system. Furthermore, by referencing and contrasting international literature, this study aids the global academic community in comprehending the patterns and challenges of China’s digital economic development, as well as its similarities and differences with global trends.

6.3. Practical Implications and Policy Recommendations

The findings of this study provide targeted and differentiated policy recommendations for Chinese provinces to advance the deep integration of the digital economy and cultural tourism. These insights also offer valuable references for other developing countries in their digital tourism development:

(1) To leverage the intermediary role of “digital infrastructure” and “employment quality,” specific measures have been proposed: “strengthening the integration ‘highway’” and “cultivating the integration ‘human capacity’” (such as building smart tourist attractions and nurturing digital cultural tourism creative professionals). Continuously deepen the development of the digital economy to solidify the foundation for integration. Given the significant overall positive impact of the digital economy on integration, governments at all levels should continue to increase investment in digital infrastructure development, digital technology R&D and application, and data element market cultivation, providing robust support for the comprehensive digital transformation of the industry.

(2) Focusing on the “regulatory effect of new-quality productive forces,” a revolutionary differentiated strategy is proposed: For regions with low NQP, implement “digital economy empowerment initiatives” (e.g., digital marketing training); For regions with high NQP, advocate for “targeted investment and integration with cutting-edge technologies” (e.g., promoting the convergence of cultural tourism with artificial intelligence and industrial internet platforms). Adopt a dual-pronged approach to optimize intermediary transmission mechanisms. Increase investment in new infrastructure to build an “integration highway.” Particular emphasis should be placed on applying new infrastructure such as 5G, big data centers, cloud computing, and artificial intelligence platforms within the cultural and tourism sectors. Through intelligent upgrades, enhance scenic area management efficiency, enrich visitor experiences, and optimize cultural content transmission. Examples include promoting smart scenic areas and developing VR/AR immersive cultural experience projects. Enhance employment quality by strengthening integrated “human capital.” Encourage deep integration between vocational education and lifelong learning systems with digital technologies to cultivate versatile cultural and tourism professionals suited to the digital economy. Support emerging sectors like online tour guiding, digital cultural creativity, and live-streamed cultural tourism marketing. Conduct targeted digital skills training to provide practitioners with more diversified, high-value-added employment opportunities, thereby strengthening their digital competencies and cross-sector integration capabilities.

(3) Adopting a dialectical approach to new-quality productive forces necessitates the implementation of differentiated strategies. For regions characterized by lower levels of new-quality productive forces, it is essential to fully leverage the inclusive empowerment of the digital economy. This can be achieved through foundational measures such as the introduction of digital platforms and the promotion of digital marketing, which can rapidly elevate the digitalization and integration of the cultural and tourism industries, facilitating significant breakthroughs from ‘nothing to something.’ Conversely, for regions with higher levels of new-quality productive forces, the policy focus should transition from breadth to depth and precision. It is crucial to avoid dispersing digital economy resources thinly across the cultural and tourism sectors. Instead, efforts should be directed towards guiding a deeper integration between the cultural and tourism industries and local strategic emerging industries through the application of ‘hardcore technology.’ For example, promoting the integration of digital cultural content production with AI technology, the digital preservation of cultural heritage through blockchain applications, and the design of cultural and creative products utilizing industrial internet platforms can achieve higher levels of innovation-driven convergence. Additionally, fostering cross-departmental coordination is vital. Acknowledging the risk that resources may be ‘crowded out’ by industries exhibiting stronger ‘new quality’ attributes, cultural and tourism departments should proactively enhance communication and collaboration with the science and technology, industry, and other sectors. This approach will secure greater cross-sectoral support within the digital economy, thereby forming a synergistic force for integrated development across industries.

(4) Based on the discovery of regional heterogeneity, distinct development strategies have been formulated for the eastern, central, and western regions. For instance, the western region pursues an incremental leapfrog development approach, while the eastern region explores innovation-driven growth. Tailored strategies are essential to advance coordinated regional development. Given the regional heterogeneity of the digital economy’s impact on integration, provinces should develop differentiated strategies based on their digital economic foundations, levels of new-quality productive forces, and resource endowments. The western regions should seize the substantial incremental opportunities presented by the digital economy to achieve leapfrog development, while the eastern regions should pursue high-quality, innovation-driven pathways for deep integration. Central regions must identify and address specific bottlenecks in their integration efforts by exploring digital economy empowerment models tailored to their unique circumstances.

(5) To optimize macroeconomic policies for fostering a favorable environment, it is essential to consider the complex and sometimes adverse effects of economic development levels, openness to the global market, and residents’ consumption capacity on the integration of culture and tourism. Policymakers must conduct nuanced assessments of how macroeconomic policies specifically impact the sector. For example, while pursuing overall economic growth, resources should be directed towards sectors that facilitate industrial integration. In the context of expanding openness, the focus should be on attracting high-quality foreign investment that enhances local integration, alongside strengthening support for domestic enterprises to enhance their competitiveness in the international arena. Furthermore, as residents’ incomes increase, consumption upgrades should be strategically guided to foster demand for high-quality, deeply integrated products.

7. Conclusions, Limitations, and Future Research Directions

7.1. Conclusions

This study investigates the impact mechanism of the digital economy on the integration of cultural and tourism industries at the provincial level in China. By introducing new infrastructure and employment quality as mediating variables, along with new-type productive forces as a moderating variable, a moderated mediation model is developed. Utilizing panel data from 31 provinces in China covering the years 2011 to 2023, empirical analysis is conducted through a two-way fixed effects model and Bootstrap testing, leading to the following key conclusions:

First, the development of the digital economy has a significant direct promotional effect on the integration of the cultural and tourism industries, serving as a crucial driving force for high-quality industrial development. Second, both new infrastructure and employment quality play significant positive mediating roles in the process by which digital economic development promotes cultural and tourism integration. This indicates that the digital economy does not merely exert a direct influence on integration but achieves empowerment through two key pathways: optimizing infrastructure and enhancing human capital quality. Finally, the study uncovers that new-type productive forces exert a significant negative moderating effect on the relationship between digital economic development and integration. This finding highlights that the enabling effects of the digital economy are not linearly increasing; rather, they may encounter challenges such as resource dilution, differentiated integration pathways, and adjustments in development priorities across various developmental stages and industrial structures. The findings of this study also offer significant insights for the global sustainable development agenda. Empirical results confirm that the digital economy promotes integration through two pathways: new infrastructure and high-quality employment. This aligns closely with the emphasis on infrastructure innovation (SDG 9) and inclusive growth (SDG 8) within sustainable development. However, the negative moderating effect exhibited by new-quality productive forces serves as a clear warning: without strategic guidance, regional focus on cutting-edge technology industries may inadvertently weaken the digital economy’s enabling effects on sustainable development dimensions within the cultural and tourism sector—such as heritage preservation and community well-being. Therefore, the policy recommendations of this study constitute a differentiated governance framework for promoting sustainable development at a deeper level. Its core lies in ensuring that the benefits of digitalization are shared equitably across regions at different stages of development, fostering a virtuous cycle where economic growth, cultural prosperity, and social inclusion mutually reinforce each other within a sustainable development paradigm. The relationship between digital culture tourism integration and sustainable development is not one-way but a mutually reinforcing system: digital culture tourism integration relies on the optimization of employment quality (ensuring social sustainability), the improvement of infrastructure (ensuring inclusive sustainability), and the upgrading of new-quality productivity (ensuring environmental and cultural sustainability) to achieve long-term development; in turn, the pursuit of sustainable development goals provides a directional guidance for digital culture tourism integration, preventing it from falling into the trap of short-term economic interests while ignoring ecological, social, and cultural costs. This moderated mediation mechanism not only enriches the theoretical framework of culture tourism development but also provides practical insights for China to promote the high-quality and sustainable development of the culture tourism industry in the digital era.

The contribution of this study lies not only in confirming the overall positive effect of the digital economy on integration but, more importantly, in “unlocking the black box of digital economy empowerment.” It reveals the crucial transmission roles of new infrastructure and employment quality while innovatively identifying new-type productive forces as a significant, counterintuitive boundary condition. These findings offer new perspectives for theoretical research and provide crucial empirical evidence for China’s provinces to formulate more refined and differentiated policies for integration.

7.2. Limitations

Despite rigorous methodology and analytical depth, this study has certain limitations that warrant future refinement:

(1) Limitations in Index Construction: This study employed the entropy method to construct composite indices for integration, digital economic development, new infrastructure, employment quality, and new-quality productive forces. While the entropy method enables objective weighting, these composite indices—particularly for complex concepts such as “ integration” and “new-quality productive forces”—may not fully capture all nuanced dimensions. For instance, qualitative aspects of cultural experiences and specific technological components of new-quality productive forces may be overlooked. Furthermore, the data, primarily sourced from official statistical yearbooks, may underestimate the contribution of informal cultural and tourism activities to integration. Informal sectors, including individual artisans and unregistered cultural events, potentially play vital roles in regional ecosystems. Their exclusion may compromise the representativeness of certain dimensions.

(2) Challenges in Causal Inference: Although this study employs a robust two-way fixed-effects model and conducts multiple robustness tests to mitigate potential endogeneity issues, such as omitted variable bias due to time-invariant factors, completely eliminating interference from all unobserved time-varying factors remains challenging. Specifically, potential reverse causality, where highly integrated industries may more actively drive digital economic development, or unmeasured time-varying confounding factors persist. Future research could explore stronger identification strategies, such as quasi-natural experiments resulting from policy shocks or instrumental variable methods, to further enhance the rigor of causal inference.

(3) In-depth Analysis of Negative Moderation Mechanisms: This paper theoretically elucidates the negative moderating effects on new quality productivity through the mechanisms of resource dilution, integration path maturity, and development priorities. However, the intrinsic operational logic and relative importance of these proposed mechanisms necessitate more granular empirical validation. For instance, quantifying the extent of resource reallocation or specifying the characteristics of ‘differentiated integration paths’ at elevated levels of new quality productivity would significantly enhance the analysis.

(4) Data granularity limitations: This study relies on provincial-level aggregated data, which while reflecting macro-regional characteristics and general trends, may obscure heterogeneity at sub-provincial, city, or enterprise levels. Specific manifestations of integration and digital economy empowerment may exhibit significant variations across finer geographic or organizational scales.

(5) Limitations of Model Generalizability: This study explores the complex relationship between the digital economy and integration at the provincial level. However, its findings—particularly the negative moderating effect of new-quality productivity—suggest potential limitations in the model’s generalizability across different developmental stages and industrial structures. In provinces with high levels of new-quality productivity, the development trajectory of integration may indeed follow a markedly different logic, driven more by deep innovation and specific technological applications than by the inclusive empowerment of the digital economy. This context-dependence implies that caution is warranted when directly extrapolating the model’s specific findings to other countries or regions, especially those with vastly different levels of new-quality productivity and industrial structures.

7.3. Future Research Directions

Given the aforementioned limitations, this study suggests the following avenues for future academic exploration:

(1) Expanding the Mediating and Moderating Mechanisms: In addition to new infrastructure and employment quality, future research should consider exploring other potential mediating pathways, including innovation ecosystems, evolving governmental regulatory environments, and shifts in market competition intensity. Furthermore, beyond the quality of new productivity, the moderating effects of additional macro-level factors—such as regional innovation capacity, institutional quality, and the presence of specific industrial clusters—on the enabling effects of the digital economy warrant examination.

(2) Micro-level and Case Studies: Future research should aim to conduct more granular empirical analyses utilizing city-level or even enterprise-level data. Such analyses would facilitate a deeper understanding of the specific behaviors and strategies driving the integration of cultural tourism. Furthermore, in-depth qualitative case studies should be conducted in representative provinces or cities with varying levels of NQP development to validate and refine the quantitative findings of this study, particularly regarding the specific patterns of ‘hard-core technology’ integration within the cultural and tourism sectors of high-NQP regions.

(3) Dynamic Effects and Long-Term Impacts: This study examines the dynamic relationship between the development of the digital economy and the integration of cultural tourism, focusing on factors such as lag effects, threshold effects, and nonlinear relationships. By utilizing extensive time-series data, we aim to explore the long-term impacts and evolving trends of the digital economy on integration.

(4) Refined NQP Measurement and Impact Decomposition: As our understanding of ‘New Quality Productivity’ (NQP) deepens, future research can develop more granular and decomposed measurement indicators. This will facilitate the investigation of how various dimensions of NQP—such as core technological breakthroughs, the concentration of high-end talent, green development, and specialized industrial clusters—differentially moderate the enabling effects of the digital economy on the integration of culture and tourism.

(5) International Comparative Studies: This analytical framework should be extended to examine the cultural tourism industries of other countries or regions through cross-national comparisons. Such an approach will validate the universality of the findings and identify unique patterns across diverse institutional and developmental contexts. In particular, it is essential to conduct in-depth comparative analyses of representative developing regions, such as Europe and Southeast Asia, to explore how these insights can inform the development of digital tourism in these areas.