Evaluating the dynamics of digital technology in enhancing overall effectiveness of China's national innovation systems: A study based on VHSD and EM approach

Evaluating the dynamics of digital technology in enhancing overall effectiveness of China's national innovation systems: A study based on VHSD and EM approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the dynamics of digital technology in enhancing overall effectiveness of China's national innovation systems: A study based on VHSD and EM approach Wei Chen, Hong-Ti Song This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3681653/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jun, 2025 Read the published version in Soft Computing → Version 1 posted 4 You are reading this latest preprint version Abstract Along with the gradual entry of the world into the digital era, digital technologies have flourished and have been silently integrated into the innovation processes of technology research and development, transformation, application, and diffusion. In the countries' efforts to establish and strengthen national innovation systems (NIS), the development of digital technologies has received increasing attention. It has become a key driving force for the optimal growth and effective operation of national innovation systems. This study quantitatively assesses the overall effectiveness of China's national innovation system (NIS) using data from 30 provinces in China from 2012 to 2022, employing the Vertical and Horizontal Scatter Degree Method (VHSD), Entropy Method (EM), and coupled coordination models, and examines the external impact, internal mechanism and spatial heterogeneity of the development of digital technologies on the overall effectiveness of national innovation systems in the light of the characteristics of the digital era. The study results show spatial aggregation in the overall effectiveness of national innovation systems, with regions with high overall effectiveness clustering and areas with low overall effectiveness clustering. Second, the development of digital technology improves the overall effectiveness of national innovation systems, which is confirmed by endogeneity treatment and various robustness tests. Third, digital technology improves the overall effectiveness of national innovation systems by promoting the development of a service-oriented industrial structure and active labor market. Fourth, the impact of digital technologies on the overall effectiveness of national innovation systems is spatially heterogeneous. It is less pronounced in the Northeast and East but very significant in the Central and West, and the main reasons for this counterfactual result can perhaps be explained in terms of both diminishing marginal effects and policy tilting effects. Finally, this study not only gives corresponding policy recommendations but also further discusses the dilemmas and challenges that may be encountered in implementing these policies. Digital technology National innovation system Overall effectiveness Service-oriented industrial structure Labor market Figures Figure 1 Figure 2 Figure 3 1. Introduction Against the backdrop of the global wave of digitization, the development of digital technologies has changed the nature and path of innovation. While the traditional innovation model relies on gradual scientific discovery and technological advancement, the convergence and evolution of digital technologies, represented by artificial intelligence and big data, provide a more efficient, flexible, and interconnected innovation pathway (Acciarini et al., 2023 ). Digital technologies have rapidly become a key driver of national innovation strength compared to traditional innovation pathways. Since adopting its innovation-driven strategy in 2012, China has markedly enhanced its scientific and technological innovation, exemplified by a surge in effective invention patents—from 470,000 in 2012 to 2.28 million in 2020, a nearly fivefold increase. Nevertheless, challenges in intellectual property protection (McGaughey et al., 2000 ; Awokuse and Hong, 2010) and core technologies like semiconductor chips have become apparent, raising concerns about China's S&T innovation sustainability. In response, aiming for innovation leadership by 2030 and global S&T preeminence by 2050, China is pivoting from quantity-focused to quality-centric innovation. This strategic shift is particularly evident in restructuring China’s national innovation system (NIS). The government's focus has evolved from enhancing the system's overall structure to boosting its overall effectiveness. Defining 'overall efficiency,' measuring it, and identifying influencing factors are vital for effective policy-making and NIS management. This paper seeks to demystify these aspects, thereby contributing to the understanding and advancing China's NIS. This study initially aims to define the "overall effectiveness of the national innovation system" through a systems theory lens to address these issues. It then seeks to construct a comprehensive evaluation framework for this effectiveness based on its definition and to measure it quantitatively. Concurrently, in recognition of the prevailing digital era, the study conducts empirical analyses of the factors influencing the national innovation system's overall effectiveness, internal mechanisms, and geospatial correlations. This investigation also explores potential avenues for optimization and enhancement of the system. This paper's novelties are threefold. Firstly, it uniquely defines the "overall effectiveness of the national innovation system" using systems theory and develops a comprehensive evaluation framework encompassing the dimensions of entities, functions, and environment and applying dynamic (vertical and horizontal dispersion methods) and static (entropy value empowerment method) empowerment models alongside a coupled coordination model to quantitatively assess China's national innovation system's overall effectiveness from 2012 to 2022. Secondly, amidst digitalization, the study investigates digital technology's role in influencing the NIS's overall effectiveness using spatial regression analysis. Thirdly, the paper goes beyond merely assessing digital technology's impact on NIS's overall effectiveness; it also empirically analyzes its intrinsic transmission mechanisms and spatial heterogeneity. Theoretically, this research extends the scope of NIS theory and introduces a novel digital-era perspective. Practically, the findings offer policy insights and recommendations for countries developing or refining their national innovation systems. 2. Relevant theories and literature review 2.1 NIS and NIS’s overall effectiveness In 1987, British scholar Freeman formally used the term "national innovation system" in "Technology, policy, and Economic Performance: lessons from Japan," which started the research on NIS by scholars in various fields. Since then, academic research on NIS can be roughly organized into four areas. First, most articles on the concept and definition of NIS focused on the 1990s. In other words, the period when the idea and purpose of NIS were explored was shortly after the term "national innovation system" emerged. With the proliferation and deepening of related research, the connotation of NIS has become more apparent. In general, NIS is a complex system of systemic (Freeman, 1987 ), interactive (Patel and Pavitt, 1994 ; Niosi and Bellon, 1994 ), and institutional characteristics (Wijnberg, 1994 ) that is composed of multiple actors to promote the development and diffusion of innovation. Second, many scholars have conducted a comprehensive inquiry into the composition and structure of NIS (Attia, 2015 ), for example, on universities and research institutions (Niu, 2014 ), government (Băzăvan, 2019 ), industry (Wong, 2011 ), enterprise (Lundvall and Rikap, 2022 ), associations (Watkins et al., 2015 ), regional innovation systems (Doloreux and Turkina, 2023 ) and other constituent subjects have been explored in depth. A systematic analysis of the composition and structure of the NIS has always been a prerequisite for constructing a sound NIS (Niosi et al., 1993 ). Third, the functions and roles of NISs are explored. For example, they are investigating the role of NIS building on economic growth (Wu et al., 2017 ; Lee and Lee, 2020 ) and its function as a technology amplifier (Petraite et al., 2022 ; Li et al., 2023 ) among others. Fourth, NISs are measured and compared. For example, the comparison of NIS innovation performance (Samara et al., 2021), innovation capacity (Castellacci and Natera, 2013 ), or comparing the national innovation systems of different countries using some technical indicators (Lee et al., 2021 ) to analyze their strengths and weaknesses and what can be learned from them. In summary, abundant studies on NIS's definition, structure, function, measurement, and comparison provide solid theoretical support and reference for our research. However, our analysis does not focus on "how to build a NIS" or "what kind of NIS to build," as previous studies have done. Still, it takes a relatively sound NIS (China) as a sample and explores its system's overall effectiveness. The systems theory perspective provides a valuable starting point for defining the NIS's overall effectiveness. According to the theory of system science, a system is a set or unity of several elements that are interconnected and interacting with each other, a whole that is a combination of components in a specific interrelationship and relationship with the environment, and a whole that has functions different from those of each element independently. Similarly, NIS can be regarded as a complex system with multiple subjects multi-functional and interrelated processes, whose most significant role is to enhance the innovation power of a country. The "overall effectiveness" of the NIS is the comprehensive capability of building the NIS to achieve innovation development goals, and the concept of overall effectiveness emphasizes the synergy of the whole system rather than focusing on specific parts of the system. Unlike the "innovation capability" or "innovation performance" in traditional research, NIS's overall effectiveness is a systematic reflection of the synergistic interaction of innovation participants, the effective performance of innovation functions, the rational use of innovation resources, and the creation of a friendly innovation environment. Compared with innovation capability, which emphasizes innovation results and transformation ability, NIS's overall effectiveness is more concerned with each subsystem's benign operation and synergistic development. Compared with innovation performance, which emphasizes the input-output ratio, NIS's overall effectiveness is more concerned with the function and coordination of the innovation process. Therefore, NIS's overall effectiveness has a broader connotation and more concerns than innovation capability or performance and reflects the operation status and construction effect of NIS more globally and scientifically. 2.2 Technological advances in the digital wave and related research In the 21st century, the swift progression of digital technology is radically altering all facets of society. This shift is primarily attributed to advancements in key technologies such as artificial intelligence (AI), big data, the Internet of Things (IoT), cloud computing, and machine learning. These innovations catalyze new industries' emergence and foster transformative changes and innovations in established sectors. For instance, rapid AI and machine learning developments are spurring advancements across various industries (Miikkulainen and Forrest, 2021 ; Prunkl et al., 2021 ). Conversely, big data has revolutionized data processing methods. IoT technologies are diminishing the divide between physical and digital realms, paving the way for more efficient and sustainable living. Meanwhile, cloud computing introduces novel approaches to data storage and computing resources, reducing costs and enhancing business flexibility. Research on digital technology encompasses a broad spectrum of topics, ranging from its impact on nature conservation and entrepreneurship to its roles in university teaching, psychological treatments, pandemic response, business networks, and aging in place. Numerous studies have shed light on the multifaceted ways digital technologies intersect with various societal and industrial aspects. (1) Impact on Nature and Health: Digital technology has profoundly influenced nature conservation, ushering in the concept of 'digital conservation.' This includes data on nature and human interaction, data integration, and participatory governance (Arts et al., 2015 ). Furthermore, implementing digital technology is transforming mental health treatment, emphasizing online clinics and digital training (Mitchell and Kan, 2019 ). Additionally, digital technology facilitates independence and quality of life for aging individuals in their homes, introducing innovative care models (Kim et al., 2017 ). (2) Role in Education and Business: In universities, digital technologies have become crucial to the student experience, offering flexibility and streamlined study management. However, their impact on the fundamental nature of university teaching remains limited (Selwyn, 2016 ). Concurrently, digitalization is reshaping B2B exchanges, highlighting companies' need to adopt Internet-connected digital technologies and applications (Pagani and Pardo, 2017 ). (3) Globalization and Digital Adoption: Certain studies emphasize how globalization influences digital technology adoption, focusing on technology transfers and converging patterns of digital technology adoption (Skare and Soriano, 2021 ). Collectively, existing research underscores the diverse impacts of digital technologies across numerous domains, accentuating both their transformative potential and the challenges they introduce. From personal health and aging to global business networks and environmental conservation, each piece of research contributes significantly to a more profound comprehension of how digital technologies are reshaping our world. 2.3 Relationship between digital technology development and the NIS’s overall effectiveness The influence of digital technologies on national innovation systems (NIS) and their overall effectiveness is intricate and multifaceted. Primarily, technologies like artificial intelligence, big data analytics, and cloud computing substantially impact the structure and functionality of NIS. Digital transformation is pivotal for the success of NIS, particularly amid escalating global competition. However, Mirtsch et al. ( 2021 ) note that while digital technologies open new avenues for innovation, they also present challenges such as data security and privacy concerns. This necessitates that NIS embrace new technologies and develop suitable management strategies and regulations. Additionally, Guerra et al. ( 2023 ) underscore the need for countries to focus on education and talent development to align with digitalization trends, thereby promoting innovation and sustainable technology growth. Conversely, Hannibal and Knight et al. (2018) observe that rapid digital technology advancements have disrupted traditional industries, compelling NIS to adjust both technologically and in terms of policy, legal frameworks, and market structures. The research by Lopez-Sintas et al. ( 2020 ) revisits the impact of digital technologies on the economy and social structure, particularly in the ICT sector, suggesting that NIS must integrate these technologies for holistic socio-economic progress. Cheng et al. ( 2023 ) further examine the role of digitalization in technology transfer and localized innovation. Regarding the link between digital technology and environmental sustainability, Wu et al. ( 2023 ) advocate for a digital transformation approach that considers the ecological ramifications of technological advancements and integrates sustainability into innovation strategies. Overall, current research demonstrates that digital technologies' impact on national innovation systems (NIS) is multidimensional, encompassing efficiency improvements, managerial challenges, talent cultivation, industrial adjustments, and extensive socio-economic and environmental effects. To attain comprehensive and enduring innovation, NIS must persistently adapt to digitalization trends while concurrently addressing the challenges and responsibilities emerging from technological advancements. 3. Research hypotheses 3.1 Direct impact of digital technology development on the NIS’s overall effectiveness Paul Romer's endogenous growth theory posits technological progress as a crucial driver of long-term economic growth. Within this framework, digital technology, a vital component of contemporary technological advancement, bolsters production efficiency and expedites innovation. The contribution of digital technology to enhancing the national innovation system's overall effectiveness can be distilled into several key aspects: First, it strengthens R&D capabilities. Popular digital technologies like big data, artificial intelligence, and cloud computing elevate R&D efficiency and effectiveness (Koronen et al., 2020 ). They enable rapid processing of vast data quantities and complex computations and simulations, thus fostering scientific discovery and technological innovation. Second, digital technology streamlines knowledge sharing and collaboration. It facilitates information exchange via web-based platforms and collaborative tools (Yang et al., 2022 ), bolstering cooperation between researchers, institutions, and firms. This seamless information flow aids in integrating external inputs (from consumers, suppliers, and partners) via Internet platforms, spurring interdisciplinary and cross-sectoral innovation (Kowalczuk et al., 2021 ). Third, it enhances decision-making and management. Digital technologies offer precise, real-time data analysis, aiding policymakers and managers in making informed decisions. This is instrumental in optimizing resource allocation, steering new technology R&D, and boosting management efficiency (Niu et al., 2021 ). Fourth, digital technology accelerates technology commercialization and application. It facilitates transforming research into marketable products, enabling faster market access through digital marketing and e-commerce platforms. This quickens the application and dissemination of innovations (Dao et al., 2023). Fifth, it improves education and training quality. Digital technologies in education, like online platforms and virtual labs, enhance educational quality and accessibility (Regan and Jesse, 2019 ), cultivating talent crucial for the innovation system. In conclusion, digital technologies significantly enhance the national innovation system's overall effectiveness by optimizing R&D efficiency, enabling knowledge sharing, improving decision-making, expediting technology commercialization, enhancing educational quality, and supporting open innovation in numerous ways. It is essential to recognize that spatial economics considers geographic proximity as a crucial element in facilitating the flow of innovation factors like knowledge, skills, and technology (Hung et al., 2021 ). Digital technology advancement has expedited these flows within local regions and broader areas, simplifying the sharing and dissemination of information and knowledge (Tajvidi et al., 2020 ). Concurrently, the evolution of digital technology often brings pronounced network effects and spillover effects, implying that its value increases with more users, thereby fostering the diffusion of knowledge and technology (Zheng et al., 2019 ). Such spillover effects enable neighboring regions to benefit from a central region's technological progress and knowledge innovation, thereby boosting not only their innovation capacity but also that of adjacent areas (Abramo et al., 2020 ). Furthermore, digital technology enhances collaboration and interaction among regional innovation actors (e.g., firms, research institutions, and government agencies), promoting optimal resource allocation and utilization of innovation capabilities across a wider area. This, in turn, improves the efficacy of the regional innovation system. Notably, the growth of digital technology acts as a new magnet for regional development, drawing significant external investment and talent A region with advanced digital infrastructure and a robust innovation environment attracts more enterprises and research activities (Ritter and Pedersen, 2020 ), creating a virtuous cycle that leads to more balanced and sustainable regional development. In summary, digital technology's development not only bolsters the NIS’s overall effectiveness within a region but also positively impacts neighboring regions' innovation systems. This occurs through the flow of innovation factors, network, and spillover effects, among other mechanisms. Consequently, this paper proposes the following hypothesis: Hypothesis 1 The development of digital technology will contribute to the NIS’s overall effectiveness in the local and neighboring areas. 3.2 Indirect impacts of digital technology development on the NIS’s overall effectiveness Rapid advancements in digital technology are increasingly recognized as a primary catalyst for global innovation and industrial modernization (Matthess and Kunkel, 2020 ). Traditional industries worldwide are transitioning towards more service- and knowledge-centric economic activities in this context. Digital technology plays a pivotal role in this shift, opening new pathways for enhancing the NIS’s overall effectiveness through the servitization of the industrial structure. Specifically, digital technology first facilitates the optimization and transformation of the industrial system. It spurs the digitalization of traditional service sectors and the emergence of new service industries, such as information technology and financial technology (Ali et al., 2020 ). Secondly, it increases the share of high-value-added services. The widespread use of digital technology has led to the rapid development of high-technology and high-value sectors in the service industry, like software development, data analysis, and digital marketing, which rely heavily on knowledge and technology. Thirdly, digital technology boosts production and service efficiency. Technologies like cloud computing and big data enable firms to process information more efficiently and make better decisions, while e-commerce and online services significantly reduce transaction costs and time (Qi et al., 2020 ). Fourthly, it fosters new business models and innovation opportunities. The rise of service industry models such as the sharing economy, online platforms, and remote services expands consumer choices and opens new avenues for corporate innovation and entrepreneurship (Hossain, 2020 ). Fifth, it enhances the dissemination and application of knowledge and technology. A service-oriented industrial structure facilitates broader knowledge and technology dissemination, promotes cross-industry knowledge flow, and accelerates innovation diffusion and technology commercialization. In conclusion, digital technology substantially augments the NIS's overall effectiveness by driving the industry towards a more service-oriented model. Consequently, this paper proposes the following second hypothesis: Hypothesis 2 The development of digital technology can enhance the NIS’s overall effectiveness by promoting the service-oriented industrial structure. The surge in digital technology has transformed not only the nature of occupations and labor skills in traditional industries. Still, it has also spawned a multitude of new employment patterns and job categories (Ferreira, 2022 ). By enhancing labor productivity, broadening employment diversity, and unleashing human resource potential, digital technologies are effectively redefining the labor market and, in turn, revitalizing the national innovation system. The interplay between digital technology development, labor markets, and the NIS’s overall effectiveness can be outlined as follows: First, digital technology is instrumental in fostering new skills and occupations. The advancement of digital technology has led to various new roles and required skills, such as data scientists, cloud computing engineers, and artificial intelligence specialists. This evolution generates fresh employment opportunities and drives the restructuring and enhancement of the labor market (Zuo and Feng, 2023 ). Second, it amplifies labor productivity. The boost in productivity due to digital technology (Kokina and Blanchette, 2019 ) not only empowers workers to perform more efficiently but also creates room for innovative activities. Third, digital technology fosters innovation in education and training. Its growth has revolutionized education and vocational training approaches, exemplified by online learning and virtual training. This transformation enables the workforce to acquire new knowledge and skills more efficiently, adapting to the rapidly evolving technological landscape (Singh and Thurman, 2019 ). Fourth, it encourages job market diversification. The proliferation of digital technology has diversified employment models, expanding the range of new job types such as remote work and freelancing. These flexible work arrangements attract and retain talent with innovative potential. In conclusion, digital technology dynamically stimulates the labor market by generating new job opportunities, enhancing labor productivity, innovating in education and training, and promoting job market diversification. Consequently, this fosters a more effective national innovation system. Therefore, this paper proposes the following third hypothesis: Hypothesis 3 Digital technology development can enhance the NIS’s overall effectiveness by stimulating the labor market. Furthermore, the role of digital technologies in augmenting the NIS’s overall effectiveness through the servitization of industrial structures and the invigoration of the labor market is visually represented in Fig. 1 . 3.3 Spatial heterogeneity of the impact of digital technology development on NIS’s overall effectiveness In a country characterized by vast regional disparities in economic development, natural geography, and resource allocation, there is a corresponding divergence in the development of digital technology and the strength of scientific and technological innovation across regions. This variation can be analyzed from several perspectives: From the vantage point of infrastructure and resources, regions with higher economic development typically boast superior digital infrastructure, such as high-speed Internet, advanced communication technologies, and abundant technological resources, all crucial for digital technology development and application (Ahmed et al., 2021 ). In contrast, economically less-developed areas might lag in these aspects. Regarding talent development and skill levels, economically prosperous regions often have a more extensive pool of highly skilled labor and professionals, which is crucial for innovating and applying digital technologies. However, regions with lesser economic development may struggle with talent cultivation and attraction challenges. From the perspective of industrial agglomeration, economically advanced regions are usually hubs for high-tech firms and research institutes, fostering an innovation ecosystem conducive to knowledge sharing and technology transfer (Song et al., 2022 ). Such an environment might be absent in less economically developed regions. Regarding market size and consumer behavior, regions with higher economic development often have larger markets and consumers more open to new technologies (Fernandes and Oliveira, 2021 ). This receptiveness aids the rapid growth and broad adoption of digital technologies, a scenario that might not hold for economically backward areas. Concerning policy support, economically affluent regions generally have a greater capacity to offer policy and financial backing for digital technology development. In contrast, economically weaker parts may face constraints in this area. To summarize, disparities in infrastructure, human resources, innovation environment, market dynamics, and regional policy support could lead to heterogeneous effects of digital technology on the NIS’s overall effectiveness. Consequently, this paper proposes the following fourth hypothesis: Hypothesis 4 The role of digital technology development in enhancing the NIS’s overall effectiveness will be characterized by heterogeneity across regions. 4. Empirical study design 4.1 Modeling of spatial measurements This study employs spatial econometric models that account for spatial factors. The models commonly utilized in spatial econometrics include the spatial lag model, the spatial error model, and the spatial Durbin model. The spatial lag model primarily assesses whether there are spatial spillover effects of the dependent variable across regions. In contrast, spatial error models are predominantly used to investigate the spatial impacts of omitted variables not encompassed in the explanatory variables or to analyze unobservable random shocks. Given that scenarios involving both spatial lag and spatial error can occur concurrently, spatial Durbin models are often employed in empirical research. The panel data format of this model is structured as follows: $${y}_{it}=\rho {\sum }_{j=1}^{N}{w}_{ij}{y}_{jt}+{X}_{it}^{{\prime }}\beta +{\sum }_{j=1}^{N}{w}_{ij}{X}_{jt}^{{\prime }}\theta +{\mu }_{i}+{\lambda }_{t}+{\epsilon }_{it}$$ 1 In Eq. ( 1 ), if \(\theta =0\) , the model degenerates into a spatial lag model; if \(\theta +\delta \beta =0\) , the model degenerates into a spatial error model. Combined with the variables that are the main focus of this study, a spatial panel model that fits this study can be constructed as follows: $${NIS}_{it}=\rho {\sum }_{j=1}^{N}{w}_{ij}{NIS}_{jt}+{\beta }_{1}{Dtd}_{it}+{\sigma }_{1}{\sum }_{j=1}^{N}{w}_{ij}{Dtd}_{jt}+{\gamma }_{1}{X}_{it}+{\mu }_{i}+{\lambda }_{t}+{\epsilon }_{it}$$ 2 In Eq. ( 2 ), \(\rho\) denotes the spatial autoregressive coefficient; \({w}_{ij}\) denotes the spatial weight matrix element; \({\mu }_{i}\) denotes the region-fixed effect, \({\lambda }_{t}\) denotes the year fixed effect, and \({\epsilon }_{it}\) denotes the random perturbation term. In addition, \({NIS}_{it}\) denotes the NIS’s overall effectiveness of province i in year t; \({Dtd}_{it}\) denotes the level of digital technology development of province i in year t; and \({X}_{it}\) is a control variable containing network interconnectivity (Net), mobile interconnectivity (Mob), foreign trade dependence (Tra), foreign investment dependence (Fdi), science and technology investment intensity (Sti), and education investment intensity (Eti). In the subsequent empirical analysis, this paper chooses the most commonly used geographic proximity matrix \({W}_{1}\) , i.e., when region i and region j are geographically adjacent, \({w}_{ij}\) is 1, otherwise it is 0. It is worth noting that there are various types of spatial weight matrices, and the regression results may be diametrically opposed if different spatial weight matrices are set in the same spatial regression model. In view of this, this paper chooses to use the geographic distance matrix \({W}_{2}\) (the inverse of the Euclidean distance \({d}_{ij}\) between region i and region j's capital city, i.e., when \(i\ne j\) , \({w}_{ij}\) = \({1/d}_{ij}\) ; when \(i=j\) , \({w}_{ij}=0\) ), and the economic distance matrix \({W}_{3}\) (the inverse distance between region i's per capita real GDP and region j's per capita real GDP, i.e., when \(i\ne j\) , \({w}_{ij}\) = \(1/({PGDP}_{i}-{PGDP}_{j})\) ; when \(i=j\) , \({w}_{ij}=0\) ) replaces the geographic adjacency matrix in the spatial regression model, a robustness test for the benchmark regression results. 4.2 Description of variables 4.2.1 Explained Variables The overall effectiveness of national innovation systems (Nis): According to the definition of the NIS’s overall effectiveness in the previous article, it can be seen that the quantification of the NIS’s overall effectiveness should focus on the synergistic interaction of the innovation subjects within it, the effective play of innovation functions, the rational utilization of innovation resources, and the friendly creation of the innovation environment. This is because the main body of innovation is the main body of invention. Because innovation subjects such as enterprises, higher education institutions, scientific research institutes, and governmental organizations are the executors of innovation activities, their ability determines the overall innovation ability of the national innovation system. At the same time, highly skilled researchers and innovative enterprises can transform inputs into creative outputs more effectively, which also determines that the quality, ability, and activity level of innovation subjects directly affect innovation efficiency. Secondly, innovation functions cover various aspects such as R&D, technology transfer, and marketization. The effective performance of these functions enhances the capacity of the innovation system and ensures the smooth implementation of innovation activities. At the same time, optimizing and coordinating these functions also directly improve innovation efficiency, for example, by enhancing the R&D process, promoting industry-university-research cooperation, and accelerating the process of technology transfer and commercialization. Finally, a good innovation environment can incentivize and protect innovation, thus enhancing the overall capacity of the national innovation system. To summarize, based on the three dimensions of "subject-function-environment," this paper divides the comprehensive effectiveness evaluation system of the national innovation system into a composite system consisting of the innovation subject subsystem, the innovation function subsystem, and the innovation environment subsystem (Table 1 ). Table 1 The comprehensive evaluation system of NIS's overall effectiveness Primary Indicators Secondary Indicators Tertiary Indicators Indicator content Innovation Subjects Universities Number of universities per 10,000 people Assessing the contribution of universities to local innovation development through their teaching capacity, research capabilities, and innovation output levels Number of full-time university faculty per 10,000 people Number of R&D projects in universities Number of papers published by universities R&D institutions Number of R&D institutions per 10,000 people They are assessing the contribution of R&D institutions to local innovation development through their innovation capacity and output levels. The full-time equivalent of R&D personnel per 10,000 people Number of R&D subjects in R&D institutions Number of R&D institutions publishing papers Enterprise Internal expenditure on R&D of enterprises above the scale Assessing the contribution of enterprises to local innovation development through their R&D and innovation input-output status. The full-time equivalent amount of R&D personnel of enterprises above the scale Number of R&D projects of enterprises above the scale New product development investment Government R&D government funds per capita expenditure of enterprises above the scale Assessing the government's support for local innovation development through its financial investment in science, technology, innovation, and education. R&D institutions government funds per capita expenditure Per capita financial expenditure on education Per capita financial expenditure on science and technology Intermediaries Patent ownership transfer (item) The contribution of intermediaries to local innovation development is assessed through their involvement in technology transfer and introduction activities. Patent ownership transfer (10,000 yuan) Foreign technology introduction (item) Foreign technology introduction (10,000 dollars) Financial Institutions Local and foreign currency loans from financial institutions Assessing the contribution of financial institutions to local innovation and development through their support and service levels in science, technology innovation, and enterprise development. Premium income of insurance companies Financial Industry Employees Number of Bank Branches Innovative Functions Innovative creation Number of Invention Patents Granted They assess the functionality of intellectual property creation, and innovation results in transformation for each innovation subject through their performance and outcomes in technological innovation, new product development, and sales. Number of utility model patents granted Number of Appearance Patents Granted New Product Sales Revenue Innovative Applications R&D funding to domestic research institutions' spending Assessing the functionality of transformation and application of scientific and technological achievements through the level of investment in R&D activities and the allocation of science and technology resources. R&D expenditure to domestic universities Number of Technology Market Transactions Technology Market Turnover Innovative Services Expenditure on the introduction of technology The quality and efficiency of innovation services are assessed through the input and impact of technology introduction, absorption, digestion, and innovation development. Expenditure on digestion and absorption Expenditure on purchase of domestic technology Technology renovation expenditure Innovation Environment Economic Environment GDP per capita They assess the environment through local economic development levels, per capita consumption levels, and income status. Disposable income per capita Per capita consumption expenditure Facility Environment Internet penetration rate They are assessing the infrastructure development environment through local telecommunications and transportation infrastructure. The average number of cell phone subscribers per 100 people Road miles per 10,000 people Market Environment Level of Marketization They are assessing the market development environment through the level of local marketization and openness to external engagement. Foreign trade dependency (total import/export/GDP) Foreign investment dependency (foreign investment/GDP) Institutional Environment Level of intellectual property protection They assess the institutional development environment through the strength of local intellectual property protection and government support for science and education. Science and technology investment/fiscal expenditure Education expenditure/fiscal expenditure Humanistic environment Number of library collections per capita They are assessing the human environment through the number of local libraries, book collections, and museums. Number of libraries per 10,000 people Number of museums per 10,000 people The comprehensive evaluation system of NIS’s overall effectiveness has a multi-dimensional and multi-annual comprehensive character. In the composite indicator comprehensive evaluation system, determining the scientific value of the weights is the crucial factor in determining the level of the complete evaluation results. At present, the commonly used weighting methods are the Principal Component Analysis (PCA), Factor Analysis (FA) and Entropy method (EM). For time-series three-dimensional data with multiple indicators and multiple years, incorporating the influence of time factors on the weight values can make the evaluation results have dynamic comparability, and focusing on the examination of the information content of the indicators can well clarify the importance of each hand to the evaluation object in each year. The Vertical and Horizontal Scatter Degree Method (VHSD) is a dynamic evaluation method that incorporates the time factor into determining the weight values, which can maximize the differences among the evaluated objects in different years. The time-series three-dimensional data arrangement matrix of the comprehensive evaluation system of the NIS’s overall effectiveness is: $$X={x}_{ij}\left({t}_{k}\right), (i=\text{1,2},3\dots m; j=\text{1,2},3\dots n; k=\text{1,2},3\dots k)$$ 3 where \({x}_{ij}\left({t}_{k}\right)\) denotes the value of the j indicator of the i sample in year k . The other \({u}_{i}\) denotes the specific name of the i sample, whose time-series stereo data table is shown in Table 2 . Table 2 Timing Stereo Data Sheet \({t}_{1}\) \({t}_{2}\) … \({t}_{k}\) \({x}_{1}{x}_{2}\dots {x}_{n}\) \({x}_{1}{x}_{2}\dots {x}_{n}\) … \({x}_{1}{x}_{2}\dots {x}_{n}\) \({u}_{1}\) \({x}_{11}\left({t}_{1}\right){x}_{12}\left({t}_{1}\right)\dots {x}_{1n}\left({t}_{1}\right)\) \({x}_{11}\left({t}_{2}\right){x}_{12}\left({t}_{2}\right)\dots {x}_{1n}\left({t}_{2}\right)\) … \({x}_{11}\left({t}_{k}\right){x}_{12}\left({t}_{k}\right)\dots {x}_{1n}\left({t}_{k}\right)\) \({u}_{2}\) \({x}_{21}\left({t}_{1}\right){x}_{22}\left({t}_{1}\right)\dots {x2}_{n}\left({t}_{1}\right)\) \({x}_{21}\left({t}_{2}\right){x}_{22}\left({t}_{2}\right)\dots {x2}_{n}\left({t}_{2}\right)\) … \({x}_{21}\left({t}_{k}\right){x}_{22}\left({t}_{k}\right)\dots {x2}_{n}\left({t}_{k}\right)\) … … … … … \({u}_{m}\) \({x}_{m1}\left({t}_{1}\right){x}_{m2}\left({t}_{1}\right)\dots {x}_{mn}\left({t}_{1}\right)\) \({x}_{m1}\left({t}_{2}\right){x}_{m2}\left({t}_{2}\right)\dots {x}_{mn}\left({t}_{2}\right)\) … \({x}_{m1}\left({t}_{k}\right){x}_{m2}\left({t}_{k}\right)\dots {x}_{mn}\left({t}_{k}\right)\) To ensure comparability of the data, the data were Z-score standardized and processed as follows: $${Y}_{ijk}={(x}_{ijk}-{\overline{x}}_{ijk})/{\sigma }_{ijk}, i=\text{1,2},3\dots m; j=\text{1,2},3\dots n; k=\text{1,2},3\dots k$$ 4 In Eq. ( 4 ), \({Y}_{ijk}\) denotes the indicator value of indicator j in year k of the i sample after standardization, \({\overline{x}}_{ijk}\) is the mean value of indicator j in year k , and \({\sigma }_{ijk}\) denotes the standard deviation of indicator j in year k . Subsequently, the indicator weights were determined and the comprehensive evaluation function was set as: $${z}_{i}\left({t}_{k}\right)=\sum _{j=1}^{n}{\delta }_{i}{y}_{ij}\left({t}_{k}\right)$$ 5 Where \({\delta }_{i}\) is the indicator weight and \({z}_{i}\left({t}_{k}\right)\) is the composite evaluation value of sample i in year k of the comprehensive evaluation system of the NIS’s overall effectiveness. For the determination of indicator weights, the total sum of squares of deviations can be used to maximize the differences among samples by: $${\sigma }^{2}=\sum _{k=1}^{K}\sum _{i=1}^{m}{({z}_{i}\left({t}_{k}\right)-\overline{z})}^{2}={\delta }^{T}\sum _{k=1}^{K}{H}_{k}\delta ={\delta }^{T}H\delta$$ 6 In Eq. ( 6 ), \(\delta ={({\delta }_{1},{\beta }_{2},\dots {\delta }_{n})}^{T}\) , \(H=\sum _{k=1}^{K}{H}_{k}\) are symmetric matrices, \({H}_{k}={A}_{k}^{T}{A}_{k}\) , \(k=\text{1,2},3\dots K\) , \({\sigma }^{2}\) gets the maximum value when taking the eigenvector corresponding to the maximum eigenvalue of the matrix H with the restriction \({\delta }^{T}\delta =1\) . At this point, the normalization of this eigenvector is to obtain the determined weight \({\delta }_{j}\) . However, one of the limitations of the VHSD method is that its determination of index weights only depends on the evaluation matrix, which cannot reflect the size of the information in each evaluation index. As a critical static evaluation method, the advantage of the Entropy method (EM) is that it can determine the weights based on the amount of information contained in each evaluation index so that the differences among the indicators can be well reflected. Based on the indicator data after Z-score normalization in the previous section, the degree of variation was first calculated: $${v}_{ijk}=\frac{{y}_{ijk}}{\sum _{i=1}^{m}{y}_{ijk}}$$ 7 where \({v}_{ijk}\) denotes the characteristic weight of the i -evaluation object under the j indicator in the k year. Calculate the EM value of the j indicator, denoted as \({E}_{jk}\) : $${E}_{jk}=-\frac{1}{\text{l}\text{n}\left(m\right)}\sum _{i=1}^{m}{v}_{ijk}\text{l}\text{n}\left({v}_{ijk}\right)$$ 8 When \({v}_{ijk}=0\) or 1, let \({v}_{ijk}\text{l}\text{n}({v}_{ijk)}=0\) . Let the coefficient of variation of the indicator be \({D}_{jk}\) At this point: $${D}_{jk}=1-{E}_{jk}$$ 9 The larger \({D}_{jk}\) indicates that the greater the amount of information of the evaluated object contained in indicator j , the greater the weight should be given to it, from which the EM weight of the indicator can be determined as: $${\omega }_{jk}=\frac{{D}_{jk}}{\sum _{j=1}^{n}{D}_{jk}}$$ 10 It's worth noting that the EM method cannot achieve dynamic comparability of evaluation results. Considering the measurement errors that may exist in a single evaluation method, using a combined evaluation method is beneficial to optimize the weight values. This paper combines VHSD with EM to unify the advantages of the two evaluation methods and to assign weights to the innovation subject subsystem, innovation function subsystem, and innovation environment subsystem in the comprehensive evaluation system of the NIS’s overall effectiveness. Based on the weights \({\delta }_{j}\) determined by the VHSD model and the weights \({\omega }_{jk}\) of each indicator in each year determined by the EM method, they are formed into a matrix \({C}_{jk}\) by year: $${C}_{jk}={\left[\begin{array}{cc}{\delta }_{1}& {\omega }_{1k}\\ \dots & \dots \\ {\delta }_{n}& {\omega }_{nk}\end{array}\right]}_{n\times 2}$$ 11 The final weight \({W}_{jk}\) of each indicator is obtained by summing up the elements in each row of \({C}_{jk}\) and taking the arithmetic mean of them, while the comprehensive evaluation value \({P}_{mk}\) can be obtained after the linear weighting method for the comprehensive evaluation value of each evaluation object in each year is weighted and aggregated layer by layer. Finally, to practically reflect the interrelated and coordinated development relationship of innovation subject, innovation function, and innovation environment in the complex system of NIS, this paper adopts the physical coupling model to calculate the coupling of innovation subject, innovation function, and innovation environment, and the final coupling value is the actual level of NIS’s overall effectiveness. The basic functional form of its coupling degree is as follows: $$C={\left[\frac{{U}_{1}\times {U}_{2}\times \bullet \bullet \bullet \times {U}_{n}}{\prod _{i\ne j}({U}_{i}+{U}_{j})}\right]}^{\frac{1}{n}}$$ 12 In Eq. ( 12 ), C denotes the coupling degree; U denotes the comprehensive development score of each system. In this paper, three systems of innovation subject, innovation function, and innovation environment are involved, so Eq. ( 12 ) can be changed as follows: $$C={\left[\frac{{U}_{1}\times {U}_{2}\times {U}_{3}}{{({U}_{1}+{U}_{2}+{U}_{3})}^{3}}\right]}^{\frac{1}{3}}$$ 13 In Eq. ( 13 ), C indicates the coupling degree of the three systems; \({U}_{1}{U}_{2}\) and \({U}_{3}\) are the comprehensive development scores of the three systems of innovation subject, innovation function, and innovation environment, respectively. The larger the coupling degree C value, the better the coupling and coordination of the three systems. After the coupling degree is calculated, the total comprehensive evaluation score of the three systems of innovation subject, innovation function, and innovation environment can be further calculated by the following formula: $$T={\beta }_{1}{U}_{1}+{\beta }_{2}{U}_{2}+{\beta }_{3}{U}_{3}$$ 14 In Eq. ( 14 ), T is the total comprehensive evaluation score of the three systems; \(\beta\) is the pending coefficient, which aims to measure the weight of the comprehensive evaluation score of each subsystem. In this paper, the three subsystems of innovation subject, innovation function, and innovation environment are considered to be equally important in the comprehensive evaluation system of the NIS’s overall effectiveness, so the three pending coefficients are set to 1/3. It is worth noting that although the coupling degree can calculate the strength of the role of each subsystem, it cannot reflect the overall coordination of the system, so it is necessary to construct the coupling coordination degree model further: $$D=\sqrt{C\times T}$$ 15 In Eq. ( 15 ), D is the coupling coordination degree, which reflects the interactive and coordinated development of the three systems of innovation subject, innovation function, and innovation environment (the level of NIS’s overall effectiveness) 4.2.2 Explanatory variables Level of digital technology development (DigT): This study addresses the challenge encountered in previous research of differentiating the digital economy component from pure digital technology, often leading to inaccuracies in assessing digital technology levels. It posits that the evolution of digital technology, notably supported as a strategic focus by the Chinese government, is best represented by specific informational elements in each provincial government's annual work report. These reports are delivered by provincial governors at the regional yearly people's congresses, usually held at the beginning of each year. They review the previous year's achievements and set goals for the upcoming year, thus serving as a comprehensive summary of the province's key activities and a blueprint for future endeavors. The vocabulary used in these reports can substantially reflect the region's development philosophy, trajectory, and status. Consequently, this paper adopts an innovative approach by quantifying the level of digital technology development in each region based on the frequency of digital technology-related terms in these government work reports, a method both scientific and reasonable. In support of this approach, Zhu et al. ( 2023 ) similarly assessed the degree of digital transformation in enterprises by analyzing the frequency of relevant terms in the annual reports of listed companies. This method is proposed as a proxy variable for measuring digital technology levels in each province, utilizing the relative frequency of pertinent keywords in provincial government work reports. The keyword extraction, collection, and computation are primarily executed using Python. However, given the individual differences among local officials and the varying lengths of government work reports, a direct comparison of keyword frequency could introduce bias. To circumvent this issue, the study uses the proportion of keyword frequency to total word frequency to indicate the province's digital technology level. The selection of digital technology keywords is informed by prior national government work reports from the State Council of China, the China Digital Economy Development Report (2022) by the China ICT Institute, and the Statistical Classification of the Digital Economy and Its Core Industries (2021) by the National Bureau of Statistics. Ultimately, 48 keywords are identified and listed in Table 3 . Table 3 Summary of keywords for digital technology levels Digital technology keywords Digital Information, Modern Information Network, Information and Communication Technology, ICT, Communication Infrastructure, Internet, Cloud Computing, Blockchain, Internet of Things, Digitization, Digital Countryside, Digital Industry, E-Commerce, 5G, Digital Infrastructure, Artificial Intelligence, Big Data, Digitization, Industrial Digitization, Digital Industrialization, Data Association, Smart Cities, Cloud Services, Cloud Technology, Cloud, E-Government, Mobile Payment, Online, Information Industry, Software, Information Infrastructure, Information Technology, Digital Life, Smart Manufacturing, Intelligent, Smart Cities, Cloud Computing, Going to the Cloud, Cloud Platform, Cloud Services, Data Security, Data Services, Data Governance, Data Sharing, Industrial Internet, Blockchain, Robotics, Digital Technology 4.2.3 Mechanism variables Continuing the analysis of how digital technology bolsters the NIS’s overall effectiveness, this paper identifies two intermediary mechanism variables for empirical research. First, the servitization of the industrial structure (Sind) is characterized by the local tertiary industry's output value as a proportion of the total output value. This servitization reflects the economy's transformation from agriculture or manufacturing to a more service-based industry. This shift in the digital era necessitates a higher level of knowledge and technological support, increasingly reliant on innovation and information technology. Therefore, a growing share of services in GDP typically indicates a country or region's innovation propensity and reliance on knowledge and technology application (Chang et al., 2020 ). Examining the influence of digital technology on NIS through servitization provides a more comprehensive understanding of digitization's deeper economic impacts and elucidates how these changes enhance the NIS’s overall effectiveness. Second, to gauge the stimulation of the labor market (Lab), this study uses the proportion of the local working population in the total population as a proxy indicator. The state of the labor market indicates the local economy's dynamism and the job market's health. Firstly, as a critical driver of innovation, the labor market significantly influences creation. A vibrant and healthy labor market supplies necessary technical and innovative talents, facilitating knowledge and skill flow, thereby supporting the NIS's overall effectiveness. Secondly, digital technology's role in creating new jobs, altering the nature and requirements of existing occupations, and enhancing labor mobility and flexibility has been extensively discussed (Orel, 2019 ). Understanding how digital technologies indirectly boost the NIS's overall effectiveness via the job market is vital for a comprehensive assessment of digital technology's impact. Thus, using labor market conditions as a mediating mechanism variable allows for a more precise and in-depth evaluation of digital technology's indirect contribution to enhancing the NIS's overall effectiveness, offering a richer analytical framework. 4.2.4 Control variables In this research, several control variables were selected for inclusion in the regression model to enhance the analysis. First, the network interconnectedness (Net) degree is measured using local Internet penetration rates as a proxy. Network interconnectivity is often a prerequisite for implementing and utilizing digital technologies in today's digital age. Moreover, a region's Internet penetration facilitates rapid information dissemination, knowledge sharing, and collaboration, which is essential for bolstering innovation (Yang et al., 2022 ). Second, the mobile connectivity (Mob) level is gauged using local cell phone penetration rates. Mobile Internet is increasingly pivotal in modern society, underpinning telework, online education, e-commerce, and social media. Accounting for this factor allows for a more precise evaluation of the mobile Internet's role as a critical aspect of digital technology in enhancing innovation system effectiveness. Additionally, mobile technology's prevalence significantly influences the speed and extent of information dissemination, communication methods, and the way individuals and firms access and utilize information, fostering innovative thinking and collaboration (Wang et al., 2021 ). Third, foreign trade dependence (Tra) is indicated by the proportion of local imports and exports in GDP. This metric reflects a region's economic openness and integration with global markets. Areas with active foreign trade often engage in technological exchanges and knowledge diffusion, with high foreign trade dependence suggesting a more pronounced role in global technology and knowledge flows (Papanastassiou et al., 2020 ), thereby impacting innovation system development. Fourth, foreign investment dependence (Fdi) is represented by the ratio of local foreign direct investment (FDI) to GDP. This measure indicates the reliance on foreign capital for economic and technological progress. Inflows of foreign capital, often accompanied by advanced technology and managerial expertise, are crucial for enhancing local technology levels and innovation capabilities (Song and Chen, 2023 ). Fifth, science and technology investment intensity (Sti) are measured by the proportion of local government science and technology spending to total fiscal expenditures. This ratio signifies a region's commitment to SandT activities and resource allocation, directly influencing its SandT innovation capacity and efficiency. The intensity of SandT investment underlines a region's foundational support for digital technology development, which is pivotal in assessing the impact of digital technology on NIS’s overall effectiveness. Sixth, education investment intensity (Eti) is quantified by the share of local government education spending in total fiscal expenditures. Education lays the groundwork for developing innovators and technologists, with the level of educational investment directly affecting workforce skills and quality, thereby influencing a country's SandT innovation ability, essential for an efficient NIS. To conclude, in analyzing the influence of digital technology on NIS' overall effectiveness, it is crucial to consider several vital confounding variables. These include network interconnectivity, mobile connectivity, foreign trade and investment dependence, science and technology, and education investment intensity. These factors may be intricately linked with the level of digital technology development and NIS’s overall effectiveness. By incorporating these variables as controls, the analysis can more precisely isolate and assess the specific impact of digital technology on NIS’s overall effectiveness. 4.3 Description of data sources Due to data availability, this study selects the period from 2012 to 2022 and focuses on 30 provinces in China, excluding Hong Kong, Macao, Taiwan, and Tibet. The assessment of digital technology levels is conducted using Python to extract and collect keywords related to digital technology in each province. This is quantified by the proportion of the total frequency of these keywords relative to the overall word frequency, serving as a proxy indicator. The remaining data are sourced primarily from the China Statistical Yearbook, China Electronic Information Industry Statistical Yearbook, China Science and Technology Statistical Yearbook, China High-Tech Industry Statistical Yearbook, and China Industrial Statistical Yearbook. The NIS’s overall effectiveness is evaluated using the VHSD-EM model, with the specific methodology detailed in a previous section of this paper. Descriptive statistics for the variables in this study are presented in Table 4 . Table 4 Descriptive statistics for variables Variable N Mean Std Min Max Nis 330 1.779 0.654 0.622 4.153 DigT 330 0.481 0.311 0.000 1.761 Net 330 3.889 0.220 3.384 4.367 Mob 330 4.630 0.339 0.000 5.249 Tra 330 0.257 0.271 0.007 1.408 Fdi 330 0.725 2.879 0.053 30.790 Sti 330 0.022 0.015 0.005 0.068 Eti 330 0.163 0.027 0.089 0.222 Sind 330 1.007 0.014 0.953 1.081 5. Analysis of empirical results 5.1 Spatial correlation analysis of the NIS’s overall effectiveness In this study, the Moran's I index value of NIS’s overall effectiveness was calculated for the whole region from 2012 to 2022 to determine whether there is a spatial correlation between the NIS’s overall effectiveness in different regions. The analysis employs three spatial weight matrices: geographic adjacency matrix, geographic distance matrix, and economic geography matrix. According to the results presented in Table 5 , regardless of the spatial weight matrix used, Moran's I index values for NIS’s overall effectiveness consistently passed the significance level tests (at 1% and 5%) and were positive from 2012 to 2022. This indicates a significant positive spatial autocorrelation in NIS’s overall effectiveness across provinces, suggesting a spatial clustering pattern. While Moran's I index values for NIS’s overall effectiveness exhibit a decreasing trend over time across all three spatial weighting matrices, they consistently indicate significant spatial autocorrelation, underlining a persistent positive relationship in the region. Table 5 Entire domain of Moran's index for the three matrices geographic adjacency matrix Geographic distance matrix Economic Geography Matrix 2012 0.313*** 0.220*** 0.388*** 2013 0.376*** 0.264*** 0.412*** 2014 0.299*** 0.228*** 0.413*** 2015 0.306*** 0.236*** 0.374*** 2016 0.318*** 0.185** 0.342*** 2017 0.296*** 0.199** 0.351*** 2018 0.257** 0.170** 0.315*** 2019 0.307*** 0.213*** 0.319*** 2020 0.242** 0.172** 0.332*** 2021 0.214** 0.158** 0.331*** 2022 0.197** 0.143** 0.318*** Note: *, **, and *** in brackets represent the significance levels of 10%, 5%, and 1%, respectively. This paper further explores the local spatial autocorrelation using the LISA index (Fig. 4).The number of locally significant provinces for the overall effectiveness of the NIS in 2012, 2015, 2019 and 2022 does not show an obvious trend of change, which indicates to a certain extent that the overall effectiveness of the NIS has a strong spatial dependence on the local geography. Specifically: (1) High-value and high-value clusters (H-H) are concentrated in the eastern coastal region, and the H-H clusters in 2022 have changed from 2012 (shifted from Jiangsu Province to Guangdong Province). (2) Most of the low-value and low-value-type agglomerations (L-L) are concentrated in the western region, and there is a change in the L-H type agglomerations in 2022 compared with 2012 (shifting from Sichuan and Gansu Provinces to Inner Mongolia Autonomous Region). (3) High-value and low-value type agglomerations (H-L) are concentrated in the eastern coastal region, and unlike other agglomerations, their agglomerations are gradually expanding, which to a certain extent reflects the siphoning effect of the eastern coastal region pulling apart the development gap between regions. Overall, from 2012 to 2022, the overall effectiveness of the NIS mainly shows polarization of H-H type and L-L type agglomeration. 5.2 Benchmark regression Based on the spatial econometric model selection criteria established by Anselin et al. (1996), the outcomes of the Lagrange Multiplier (LM) test and the robust LM test, as presented in the last column of Table 6 , suggest that the spatial lag model is preferable to the spatial error model. Consequently, this study adopts the spatial lag model for conducting regression analysis. The baseline estimation results utilizing this model are detailed in Table 6 : Table 6 Benchmark regression results Variables Model(1) Model(2) Model (3) Model (4) Model(5) Model (6) Model(7) DigT 0.208*** (0.039) 0.177*** (0.042) 0.174*** (0.042) 0.123*** (0.042) 0.123*** (0.042) 0.080** (0.040) 0.080** (0.040) Net 0.320** (0.148) 0.297** (0.148) 0.281* (0.144) 0.281* (0.144) 0.287** (0.134) 0.264* (0.143) Mob 0.060* (0.034) 0.060* (0.033) 0.060* (0.033) 0.054* (0.031) 0.052** (0.031) Tra -0.445*** (0.098) -0.444*** (0.099) -0.431*** (0.092) -0.426*** (0.092) Fdi 0.001 (0.004) -0.002 (0.003) -0.002 (0.003) Sti 13.962*** (1.888) 14.069*** (1.902) Eti -0.393 (0.841) LM-lag 15.909*** LM-error 0.040 Robust-LM-lag 18.053*** Robust-LM-Error 2.183 \(\rho\) 0.335*** (0.065) 0.306*** (0.067) 0.284*** (0.069) 0.251*** (0.069) 0.251*** (0.069) 0.183*** (0.067) 0.178*** (0.068) N 330 330 330 330 330 330 330 R 0.291 0.289 0.306 0.330 0.330 0.612 0.613 Note: The numbers in parenthesis are robust standard errors; *, **, and *** in brackets represent the significance levels of 10%, 5%, and 1%, respectively. The following table is the same. Table 6 presents models 1 to 7, which depict the influence of digital technology development on NIS’s overall effectiveness, progressively incorporating control variables. The results reveal that while the estimated coefficient for digital technology diminishes with the addition of control variables, it consistently remains positive and statistically significant. This outcome suggests that digital technology positively impacts NIS’s overall effectiveness. Specifically, a 1% increase in digital technology development correlates with a 0.08% improvement in NIS’s overall effectiveness. This finding aligns with the first theoretical hypothesis posited earlier in the study. Additionally, the spatial spillover coefficient ( \(\rho\) ), valued at 0.178 and significant at the 1% level, indicates that the benefits of digital and technological development extend beyond the immediate location. A positive spatial spillover effect enhances NIS’s overall effectiveness in neighboring regions. This underscores the broader impact of digital technology advancement within a specific area and across adjacent regions. Using Model (7) as a reference point, the analysis of control variables yields the following insights: The regression coefficients for the degree of network interconnectivity and mobile connectivity are 0.264 and 0.056, respectively. Both coefficients are statistically significant, passing the 10% and 5% significance level tests, respectively. This suggests that enhancements in regional network and mobile connectivity can positively impact NIS’s overall effectiveness. Regarding foreign trade dependence, the regression coefficient is -0.426, significant at the 1% level. In contrast, the coefficient for foreign investment dependence is -0.002. These findings imply that China's traditional approach of leveraging import-export trade and foreign investment for technological advancement, colloquially known as "exchange market for technology," is not yielding the anticipated benefits for NIS’s overall effectiveness. This result challenges the efficacy of this long-standing strategy. The coefficient for science and technology investment intensity is significantly high at 18.053, passing the 1% significance level test. In contrast, the coefficient for education investment intensity is -0.393. This indicates that, in the short term, specific science and technology investments yield better returns than long-term education investments in enhancing NIS’s overall effectiveness. However, it is essential to consider that educational impacts often have a delayed effect (Wang et al., 2021 ). Therefore, investment in education should still be a key component of local governments' long-term plans for innovation development. 5.3 Endogenous treatment This study adopts a methodological adjustment to address the potential issues of omitted variables and endogeneity arising from possible reverse causality between the level of digital technology and NIS’s overall effectiveness. It utilizes explanatory variables with a one-period lag as instrumental variables. It employs the two-step generalized system moments estimation (SYS-GMM) approach for retesting the impact of digital technology level on NIS’s overall effectiveness. The estimation results are presented in Table 7 . The test outcomes for AR 2 and Sargan do not refute the null hypothesis, suggesting the absence of second-order autocorrelation in the model's residuals and confirming the instrumental variables' validity. The regression coefficient for digital technology, at 0.025, is statistically significant at the 5% level. This reinforces the pivotal role of digital technology level in augmenting NIS’s overall effectiveness, thereby substantiating the study's initial findings. Table 7 Generalized System Estimated Regression Results Variables SYS-GMM Nis − 1 0.766*** (0.042) DigT 0.025** (0.014) Net 0.062 (0.103) Mob -0.030 (0.020) Tra 0.022 (0.043) Fdi -0.006*** (0.001) Sti 6.499*** (2.283) Eti -1.198* (0.624) AR 1 -2.989*** AR 2 1.378 Sargan 22.540 N 300 5.4 Robustness Tests To ensure the robustness of the study's findings, this paper implements a series of robustness tests based on the following strategies: Exclusion of Municipalities. The sample excludes the municipalities directly under the central government, such as Beijing, Shanghai, Tianjin, and Chongqing. Due to their unique administrative status, these municipalities exhibit distinct characteristics in digital technology development, government governance, population density, and urban area compared to other provinces. To achieve more general and comparable research samples, the analysis focuses solely on the data from ordinary regions for regression testing (Robustness 1). Data Truncation. To mitigate the potential bias in regression outcomes due to outliers, the tails of each variable are truncated at the 1% and 99% levels, and the regression is re-conducted (Robustness 2). Alternative Spatial Weight Matrices. Recognizing that different spatial weight matrices can yield varied regression results, the study substitutes the baseline regression's geographic adjacency matrices with an economic distance matrix and an Euclidean geographic distance matrix. The spatial lag model is then reapplied for regression analysis (Robustness 3 and 4). The final regression outcomes are detailed in Table 8 . Regardless of the robustness test applied, the influence of digital technology development on the NIS’s overall effectiveness remains positive and statistically significant. This consistency across different tests reinforces the reliability and robustness of the baseline regression results. Table 8 Robustness test results Variables Robustness test 1 Robustness test 2 Robustness test 3 Robustness test 4 DigT 0.130*** (0.038) 0.039* (0.043) 0.088** (0.039) 0.085** (0.039) Net 0.194 (0.133) 0.208 (0.142) 0.265* (0.144) 0.279* (0.142) Mob 0.064** (0.028) 0.272*** (0.091) 0.058* (0.031) 0.054* (0.031) Tra -0.880*** (0.144) -0.392*** (0.096) -0.421*** (0.093) -0.427*** (0.092) Fdi -0.007** (0.003) 0.019 (0.040) -0.003 (0.003) -0.004 (0.003) Sti 11.656*** (1.830) 12.995*** (1.915) 14.330*** (1.899) 14.137*** (1.904) Eti -0.902 (0.832) -0.051 (0.884) -0.520 (0.839) -0.380 (0.844) \(\rho\) 0.131*** (0.057) 0.128* (0.073) 0.150** (0.067) 0.188** (0.076) LM-lag 8.415*** 22.356*** 2.984* 2.559** LM-error 0.064 0.008 0.330 0.020 Robust-LM-lag 8.885*** 24.942*** 3.622* 4.051** Robust-LM-Error 0.533 2.594 0.969 1.512 N 286 330 330 330 R 0.545 0.441 0.434 0.434 5.5 Mechanism testing In the preceding section, this paper theoretically analyzed the contribution of digital technology development to NIS’s overall effectiveness. This analysis was grounded in two key aspects: the industrial structure's servitization and the labor market's stimulation. To empirically validate these hypotheses, the study, drawing inspiration from Baron and Kenny (1986), constructs a mediation effect model that incorporates geospatial elements: $${NIS}_{it}=\rho {\sum }_{j=1}^{N}{w}_{ij}{NIS}_{jt}+{\beta }_{1}{Dtd}_{it}+{\beta }_{i}{X}_{it}+{\mu }_{i}+{\lambda }_{t}+{\epsilon }_{it}$$ 16 $${Med}_{it}=\rho {\sum }_{j=1}^{N}{w}_{ij}{SInd}_{jt}+{\gamma }_{1}{Dtd}_{it}+{\gamma }_{i}{X}_{it}+{\mu }_{i}+{\lambda }_{t}+{\epsilon }_{it}$$ 17 $${NIS}_{it}=\rho {\sum }_{j=1}^{N}{w}_{ij}{NIS}_{jt}+{\delta }_{1}{Dtd}_{it}+{\delta }_{2}{Med}_{it}+{\delta }_{i}{X}_{it}+{\mu }_{i}+{\lambda }_{t}+{\epsilon }_{it}$$ 18 In Eq. ( 17 ), \({Med}_{it}\) Is the mediating mechanism variables to be tested, which are industrial structure servicing (SInd) and labor market stimulation (Lab), respectively, and β and δ are the coefficient values to be focused on in the regression analysis, and the estimation results of the mediation effect model including geospatial elements are shown in Table 9 : Table 9 Results of stepwise mediation effects test Variables Group 1 Group 2 Nis Sind Nis Nis Lab Nis DigT 0.080** (0.040) 0.008** (0.005) 0.076* 0.040 0.080** (0.040) 0.004*** (0.001) 0.071** (0.041) Sind 0.277** 0.369 Lab 0.117*** (0.110) Net 0.279* (0.142) 0.074*** (0.020) 0.226 0.151 0.279* (0.142) 0.193*** (0.066) 0.222 (0.148) Mob 0.054* (0.031) 0.004 (0.004) 0.051* 0.031 0.054* (0.031) 0.001 (0.000) 0.052* (0.031) Tra -0.427*** (0.092) -0.020 (0.012) -0.410*** 0.095 -0.427*** (0.092) -0.124*** (0.041) -0.414*** (0.093) Fdi -0.004 (0.003) 0.002*** (0.001) -0.003 0.003 -0.004 (0.003) 0.004** (0.002) -0.003 (0.003) Sti 14.137*** (1.904) 0.604** (0.256) 13.718*** 1.958 14.137*** (1.904) 3.375*** (0.843) 13.575*** (1.954) Eti -0.380 (0.844) -0.691*** (0.109) -0.167 0.893 -0.380 (0.844) 0.704* (0.373) -0.465 (0.842) \(\rho\) 0.178*** (0.068) 0.539*** (0.046) 0.162** 0.072 0.178*** (0.068) 0.467*** (0.056) 0.183*** (0.068) N 330 330 330 330 330 330 R 0.613 0.666 0.632 0.613 0.324 0.433 Table 9 presents the analysis of the spatial autocorrelation coefficient ( \(\rho\) ), which consistently remains positive and statistically significant. The table is divided into two groups, each illustrating the mediating effects of different variables on the relationship between digital technology level and NIS’s overall effectiveness. Group 1 assesses the role of industrial structure servitization as a mediating variable. Here, the regression coefficient of the digital technology level on industrial system servitization is 0.008, significant at the 5% level. Additionally, the regression coefficients of digital technology level and industrial structure servitization on NIS's overall effectiveness are 0.076 and 0.277, respectively, passing the 10% and 5% significance level tests. Notably, after accounting for the mediating role of industrial structure servitization, the impact of digital technology level on NIS's overall effectiveness decreases from 0.080 to 0.076. This implies that the total effect of digital technology on NIS's overall effectiveness exceeds its direct result, thereby confirming the positive mediating influence of industrial structure servitization. Group 2 examines the impact of stimulating the labor market as a mediating variable. The regression coefficient of the digital technology level on promoting the labor market is 0.004, significant at the 1% level. Moreover, the coefficients of digital technology level and stimulation of the labor market on NIS’s overall effectiveness are 0.071 and 0.117, respectively, both significant at the 5% and 1% levels. This analysis corroborates the positive mediating effect of stimulating the labor market on the relationship between digital technology level and NIS's overall effectiveness, thereby validating Hypothesis 3 . 5.6 Heterogeneity analysis The relationship between the level of digital technology and NIS’s overall effectiveness in China's various regions may exhibit spatial heterogeneity due to historical disparities in policy support, resource endowments, and geographic locations. To delve deeper into these regional differences, this study adopts the regional classification criteria set by the State Council of China. Accordingly, China's 30 provinces are categorized into four distinct regions: northeastern, eastern, central, and western (as depicted in Fig. 3 ). The analysis then applies the spatial lag model for conducting regression analysis within these defined regions. Table 10 provides a regional analysis of the impact of digital technology on NIS’s overall effectiveness. The findings reveal distinct regional variations: Northeast and East Regions. In these areas, the regression coefficients of digital technology level on NIS's overall effectiveness are not statistically significant, nor is the spatial autocorrelation coefficient ( \(\rho\) ). This suggests that in the northeastern and eastern regions of China, the level of digital technology does not significantly impact NIS’s overall effectiveness—central and Western Regions. Conversely, in the west and major regions, the regression coefficients are 0.001 and 0.126, respectively, and these results are statistically significant at the 10% and 1% levels. Additionally, the spatial autocorrelation coefficient (ρ) is positive and significant at the 1% level. This indicates a substantial and positive impact of digital technology level on NIS’s overall effectiveness in these regions. In summary, there is a marked disparity in the influence of digital technology level on NIS’s overall effectiveness across different areas of China. These findings affirm Hypothesis 4 , highlighting the varied effects of digital technology development on NIS’s overall effectiveness in other geographical regions. The empirical results surprisingly show a lack of positive correlation between the advanced level of digital technology in developed regions, such as the eastern region of China, and NIS’s overall effectiveness. This counterintuitive finding can be attributed to the Diminishing Marginal Effect in Advanced Regions. China's eastern and northeastern regions typically boast advanced digital infrastructures, including high-speed Internet, data centers, and high-tech industrial parks (Pan et al., 2022 ). Digital technologies are extensively applied across various industries in these regions, leading to optimization of operations, productivity improvement, and new business model creation. However, due to the principle of diminishing marginal returns, the incremental contribution of further digital enhancements in these already highly developed areas is relatively limited. The existing digital infrastructure and technological applications are nearing saturation (Ma and Zhu, 2022 ). Hence, in regions like the East and Northeast, where digital technology is already mature, innovation is likely shifting towards new business models, managerial innovations, or deeper technological integration rather than solely relying on essential technical upgrades—Government Policies and Investments in Less Developed Regions. Furthermore, The Chinese government has historically implemented policies to reduce disparities between the eastern coastal regions and the central and western regions, such as the "Western Development" policy and the "Rise of Central China" strategy. These initiatives have facilitated digital technology and innovation projects in the central and western regions through tax incentives, capital subsidies, and government procurement (Shao and Chen, 2022 ). This support has significantly enhanced the digital technology appeal in these areas and focused on Digital Infrastructure Development in Central and Western Regions. The central government's focus on improving digital infrastructure in the West and significant regions includes expanding and upgrading Internet and mobile networks and constructing data centers and cloud computing platforms (Luo et al., 2022 ). Such infrastructural developments are essential for elevating the region's digital technology capabilities. Consequently, these initiatives have played a crucial role in advancing digital technologies in these regions, thus bolstering NIS’s overall effectiveness. In summary, some developed areas may already experience diminishing returns from further digital advancements, the central and western parts of China benefit significantly from governmental policies and infrastructural investments, leading to a more pronounced impact of digital technology on the overall effectiveness of their NIS. Table 10 Results of the Regional Heterogeneity Test Variables Northeastern regions Eastern regions Central regions Western regions DigT 0.100 (0.113) -0.146 (0.074) 0.001* (0.059) 0.126*** (0.063) Net -1.022*** (0.282) 1.138*** (0.351) -0.058 (0.147) 0.271 (0.221) Mob 0.137 (0.282) 0.018 (0.033) 0.112*** (0.134) 0.490*** (0.188) Tra 0.092 (0.821) 0.584*** (0.113) 3.040*** (0.813) -0.167 (0.515) Fdi -0.345** (0.136) -0.006 (0.004) 0.405 (0.152) 0.032 (0.084) Sti 17.152* (9.074) 26.430*** (3.165) 4.517 (2.135) -1.418 (6.109) Eti -7.060*** (0.077) 1.658 (1.572) -2.316 (1.058) − .081 (1.864) \(\rho\) 0.462 (0.116) 0.014 (0.070) 0.304*** (0.109) 0.394*** (0.137) N 33 110 66 121 R 0.396 0.602 0.812 0.395 6. Conclusions and discussion This study examines data from 30 provinces, municipalities directly governed by the central government, and autonomous regions across China from 2012 to 2022. Employing the VHSD-EM empowerment model, it assesses the NIS’s overall effectiveness in China in each area, incorporating aspects of digitalization. The research focuses on how the progression of digital technology influences NIS’s overall effectiveness, taking into account its intrinsic mechanisms and spatial heterogeneity. Results indicate that NIS’s overall effectiveness demonstrates spatial clustering, with highly and less effective regions forming distinct groupings. Further, the development of digital technology bolsters NIS’s overall effectiveness, a finding that remains robust even after considering endogeneity and through various robustness checks. Notably, digital technology enhances NIS’s overall effectiveness by promoting a service-oriented industrial structure and revitalizing the labor market. Nonetheless, the influence of digital technology on NIS’s overall effectiveness varies by region; it is less marked in the northeastern and eastern areas but significantly more pronounced in the central and western regions. Based on the findings, this paper offers several policy recommendations for policymakers and relevant stakeholders: Firstly, it is essential to intensify the development of digital infrastructure. A key strategy to enhance digital growth in central and western China involves augmenting Internet access speed and quality. This includes investing in high-speed broadband and 5G networks in these areas, boosting Internet speed and reliability. Such advancements are vital for digital foundations and information flow and technology applications. Additionally, establishing data centers and cloud computing facilities will bolster big data and cloud technology development, providing essential data processing and storage capacities for local businesses and innovation efforts. Moreover, fostering smart cities and digital villages with intelligent transportation systems, telemedicine services, and e-government initiatives can significantly elevate urban management efficiency and rural development levels, facilitating comprehensive economic and social enhancement in these regions. Secondly, industrial structure transformation should prioritize service-orientation and digitalization. Encouraging traditional manufacturing, agriculture, and retail sectors to adopt digital technologies (e.g., IoT, Big Data, AI) can enhance production efficiency and product quality. This includes integrating innovative manufacturing systems and employing precision agricultural technologies (Mohamed et al., 2021 ). Concurrently, it is crucial to support the growth of digital service industries such as IT services, e-commerce, online education, and telemedicine. These industries create new job opportunities and significantly improve service efficiency and quality. Thirdly, revitalizing the labor market through digital resources is imperative. Establishing vocational training centers in technologically lagging regions, focusing on digital and modern industrial skills, is essential. This initiative should include programming, data analysis, and digital marketing courses to equip residents with skills for emerging industries. Leveraging Internet technology for online education and distance learning platforms will enable broader access to quality educational resources, particularly in remote areas. Furthermore, encouraging collaboration between enterprises and educational institutions in developing market-relevant training programs can help upgrade skills and enhance employment competitiveness. Fourthly, strengthening inter-regional collaborative innovation is crucial. Formulating inter-regional cooperation frameworks and guidelines can promote technology exchange and R&D resource sharing between less developed and advanced regions, thereby improving NIS’s overall effectiveness. Establishing technology transfer platforms to facilitate knowledge and technology flow significantly to assist less developed areas in accessing advanced technology and management expertise is essential. Additionally, organizing seminars, training programs, and exchange initiatives can foster expert and talent exchanges across regions, enhancing innovation and development capabilities. Finally, implementing differentiated policy support is essential for balanced regional development. Tailored policy support based on regional specifics, such as increased financial assistance and technical support in less developed areas and enhanced market expansion and cooperation opportunities in advanced regions, can achieve coordinated development across all sites. This paper highlights that the policy recommendations proposed may encounter several challenges during implementation. Firstly, establishing high-speed broadband networks and 5G base stations in central and western China involves substantial investments and confronts geographical and environmental hurdles, such as complex terrain and inadequate transportation, making infrastructure development difficult and expensive. Moreover, these regions often lack the expertise and technical support to construct and maintain high-tech infrastructure. Secondly, firms satisfied with their current operations may resist training employees for new technologies due to increased burdens on production time and costs (Horváth and Szabó, 2019 ), hindering the digital transformation of traditional industries. Thirdly, the success of digital education and training centers hinges on the quality of training and market demand for these skills (Chinoracký and Čorejova, 2019 ). Challenges such as limited Internet access and equipment availability can impede the implementation of online education and distance learning in remote areas. Lastly, the establishment and maintenance of cooperation mechanisms are crucial. Regional governments have varying development priorities and policy approaches, affecting the efficiency of interregional collaboration. Therefore, considering different regions' unique needs and strengths is essential for formulating effective cooperation frameworks ensuring equitable resource distribution and technology sharing. Technology transfer involves complexities beyond the technology itself, including intellectual property rights and market adaptability. This article may have the following limitations: Geographical Scope. The study focuses exclusively on China, which may limit the generalizability of its findings to other national contexts with different economic, political, and technological landscapes—temporal Scope. The data spans 2012 to 2022. Changes in technology and innovation systems post-2022 are not considered. Methodological Limitations. While innovative, the reliance on spatial econometric models may have inherent limitations in capturing the complex and dynamic nature of innovation systems and digital technology's impact. External Validity. The findings might be influenced by specific policies and economic conditions in China, which may not apply universally. In addition, this paper has the following considerations for future research on digital technologies and NIS’s overall effectiveness: Global Comparison. Future studies could compare the NIS’s overall effectiveness in different countries, considering varying levels of digital technology integration—Longitudinal Studies. Continued research post-2022 could provide insights into the evolving impact of digital technology on innovation systems—cross-disciplinary Approaches. Integrating insights from sociology, economics, and political science could enrich the understanding NIS’s overall effectiveness in digital technology. In-Depth Qualitative Research. Exploring individual case studies or conducting interviews with key stakeholders in the innovation system could provide deeper insights into digital technology's causal mechanisms and real-world implications on NIS. Declarations Acknowledgments The authors acknowledge funding from the National Social Science Foundation of China (Project No. 23BGL063), as well as the contributions from all partners of the mentioned project. Compliance with Ethical Standards Funding: The National Social Science Foundation of China (Project No. 23BGL063). Author Chen Wei declares that he has no conflict of interest. The corresponding author Song Hong-ti declares that he has no conflict of interest. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. 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Sustainable Cities and Society, 88, 104266. https://doi.org/10.1016/j.scs.2022.104266 Wu J, Zhuo S, Wu Z (2017) National innovation system, social entrepreneurship, and rural economic growth in China. Technological Forecasting and Social Change, 121, 238-250. https://doi.org/10.1016/j.techfore.2016.10.014 Yang L, Holtz D, Jaffe S, Suri S, Sinha S, Weston J, Teevan J (2022) The effects of remote work on collaboration among information workers. Nature human behaviour, 6(1), 43-54. https://doi.org/10.1038/s41562-021-01196-4 Yang M, Zheng S, Zhou L (2022) Broadband internet and enterprise innovation. China Economic Review, 74, 101802. https://doi.org/10.1016/j.chieco.2022.101802 Zheng W, Pan H, Sun C (2019) A friendship-based altruistic incentive knowledge diffusion model in social networks. 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Finance Research Letters, 104688. https://doi.org/10.1016/j.frl.2023.104688 Zuo S, Feng J (2023) Institutional Segmentation, Inequality in Work Opportunities, and Income in Digital Labor Markets: Evidence Based on Witkey Transactions, Emerging Markets Finance and Trade, 59:15, 4125-4137, https://doi.org/10.1080/1540496X.2023.2179874 Cite Share Download PDF Status: Published Journal Publication published 21 Jun, 2025 Read the published version in Soft Computing → Version 1 posted Reviewers agreed at journal 06 Jan, 2024 Reviewers invited by journal 02 Dec, 2023 Editor assigned by journal 30 Nov, 2023 First submitted to journal 27 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3681653","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":255756602,"identity":"2eb40df0-f107-4dc3-b5a4-9f3d66dc70be","order_by":0,"name":"Wei Chen","email":"","orcid":"","institution":"Chongqing Technology and Business University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Chen","suffix":""},{"id":255756603,"identity":"0afe3c36-de0e-48da-bd0f-906411f573c1","order_by":1,"name":"Hong-Ti Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYJACZgjJfICBsQHESiBSCw8zWwLDQdK0MPAYEKdFvr338OuCijt2+9l5Pn7+uOMwAz97jgHDzx24tRicOZdmPePMs+QeZt7NEgfPHGaQ7HljwNh7Bo8WiRwzY962w8k8zLzbGA62HWYwuJFjwMzYhsdhM+BaeJ6BtdgT0sJwI8f4MVCLHVALG8QWCQJaDM6cMWPmOXM4gecwm7HE2TPpPBJnnhUc7MXnsPYe4888FYft2fsPP/xQucNajr89eeODn/gcxsDAJgEkEhugPB4QcQCvBmBMfgAS9gQUjYJRMApGwUgGAKq6UMRule5oAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1103-7744","institution":"Chongqing Technology and Business University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hong-Ti","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2023-11-29 12:31:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3681653/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3681653/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00500-025-10613-z","type":"published","date":"2025-06-21T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":47713039,"identity":"21415345-9f20-49fb-98ac-9dcace9d1837","added_by":"auto","created_at":"2023-12-06 13:31:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3681653/v1/effce4cb4dafa43d69dd818e.png"},{"id":47713041,"identity":"e9e061cb-8e03-4a8d-ba7d-5accf0566856","added_by":"auto","created_at":"2023-12-06 13:31:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":179896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLISA clustering map of NIS’ overall effectiveness\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3681653/v1/491db25731a7491ff1556ba6.png"},{"id":47713040,"identity":"12b094b5-ba49-4d6e-b45f-83d2279fcb3d","added_by":"auto","created_at":"2023-12-06 13:31:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":276907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChina's four regional divisions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3681653/v1/1af672f33930079bf638a5cd.png"},{"id":85231513,"identity":"ae82ede5-7f8f-480b-8a46-473dc179d7c3","added_by":"auto","created_at":"2025-06-23 16:09:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2180000,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3681653/v1/16dacd3a-66e9-4fca-9a3c-64b9f8188cc5.pdf"}],"financialInterests":"","formattedTitle":"Evaluating the dynamics of digital technology in enhancing overall effectiveness of China's national innovation systems: A study based on VHSD and EM approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgainst the backdrop of the global wave of digitization, the development of digital technologies has changed the nature and path of innovation. While the traditional innovation model relies on gradual scientific discovery and technological advancement, the convergence and evolution of digital technologies, represented by artificial intelligence and big data, provide a more efficient, flexible, and interconnected innovation pathway (Acciarini et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Digital technologies have rapidly become a key driver of national innovation strength compared to traditional innovation pathways. Since adopting its innovation-driven strategy in 2012, China has markedly enhanced its scientific and technological innovation, exemplified by a surge in effective invention patents\u0026mdash;from 470,000 in 2012 to 2.28\u0026nbsp;million in 2020, a nearly fivefold increase. Nevertheless, challenges in intellectual property protection (McGaughey et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Awokuse and Hong, 2010) and core technologies like semiconductor chips have become apparent, raising concerns about China's S\u0026amp;T innovation sustainability. In response, aiming for innovation leadership by 2030 and global S\u0026amp;T preeminence by 2050, China is pivoting from quantity-focused to quality-centric innovation. This strategic shift is particularly evident in restructuring China\u0026rsquo;s national innovation system (NIS). The government's focus has evolved from enhancing the system's overall structure to boosting its overall effectiveness. Defining 'overall efficiency,' measuring it, and identifying influencing factors are vital for effective policy-making and NIS management. This paper seeks to demystify these aspects, thereby contributing to the understanding and advancing China's NIS. This study initially aims to define the \"overall effectiveness of the national innovation system\" through a systems theory lens to address these issues. It then seeks to construct a comprehensive evaluation framework for this effectiveness based on its definition and to measure it quantitatively. Concurrently, in recognition of the prevailing digital era, the study conducts empirical analyses of the factors influencing the national innovation system's overall effectiveness, internal mechanisms, and geospatial correlations. This investigation also explores potential avenues for optimization and enhancement of the system.\u003c/p\u003e \u003cp\u003eThis paper's novelties are threefold. Firstly, it uniquely defines the \"overall effectiveness of the national innovation system\" using systems theory and develops a comprehensive evaluation framework encompassing the dimensions of entities, functions, and environment and applying dynamic (vertical and horizontal dispersion methods) and static (entropy value empowerment method) empowerment models alongside a coupled coordination model to quantitatively assess China's national innovation system's overall effectiveness from 2012 to 2022. Secondly, amidst digitalization, the study investigates digital technology's role in influencing the NIS's overall effectiveness using spatial regression analysis. Thirdly, the paper goes beyond merely assessing digital technology's impact on NIS's overall effectiveness; it also empirically analyzes its intrinsic transmission mechanisms and spatial heterogeneity. Theoretically, this research extends the scope of NIS theory and introduces a novel digital-era perspective. Practically, the findings offer policy insights and recommendations for countries developing or refining their national innovation systems.\u003c/p\u003e"},{"header":"2. Relevant theories and literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 NIS and NIS\u0026rsquo;s overall effectiveness\u003c/h2\u003e \u003cp\u003eIn 1987, British scholar Freeman formally used the term \"national innovation system\" in \"Technology, policy, and Economic Performance: lessons from Japan,\" which started the research on NIS by scholars in various fields. Since then, academic research on NIS can be roughly organized into four areas. First, most articles on the concept and definition of NIS focused on the 1990s. In other words, the period when the idea and purpose of NIS were explored was shortly after the term \"national innovation system\" emerged. With the proliferation and deepening of related research, the connotation of NIS has become more apparent. In general, NIS is a complex system of systemic (Freeman, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), interactive (Patel and Pavitt, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Niosi and Bellon, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), and institutional characteristics (Wijnberg, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) that is composed of multiple actors to promote the development and diffusion of innovation. Second, many scholars have conducted a comprehensive inquiry into the composition and structure of NIS (Attia, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), for example, on universities and research institutions (Niu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), government (Băzăvan, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), industry (Wong, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), enterprise (Lundvall and Rikap, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), associations (Watkins et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), regional innovation systems (Doloreux and Turkina, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and other constituent subjects have been explored in depth. A systematic analysis of the composition and structure of the NIS has always been a prerequisite for constructing a sound NIS (Niosi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Third, the functions and roles of NISs are explored. For example, they are investigating the role of NIS building on economic growth (Wu et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lee and Lee, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and its function as a technology amplifier (Petraite et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) among others. Fourth, NISs are measured and compared. For example, the comparison of NIS innovation performance (Samara et al., 2021), innovation capacity (Castellacci and Natera, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), or comparing the national innovation systems of different countries using some technical indicators (Lee et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to analyze their strengths and weaknesses and what can be learned from them.\u003c/p\u003e \u003cp\u003eIn summary, abundant studies on NIS's definition, structure, function, measurement, and comparison provide solid theoretical support and reference for our research. However, our analysis does not focus on \"how to build a NIS\" or \"what kind of NIS to build,\" as previous studies have done. Still, it takes a relatively sound NIS (China) as a sample and explores its system's overall effectiveness. The systems theory perspective provides a valuable starting point for defining the NIS's overall effectiveness. According to the theory of system science, a system is a set or unity of several elements that are interconnected and interacting with each other, a whole that is a combination of components in a specific interrelationship and relationship with the environment, and a whole that has functions different from those of each element independently. Similarly, NIS can be regarded as a complex system with multiple subjects multi-functional and interrelated processes, whose most significant role is to enhance the innovation power of a country. The \"overall effectiveness\" of the NIS is the comprehensive capability of building the NIS to achieve innovation development goals, and the concept of overall effectiveness emphasizes the synergy of the whole system rather than focusing on specific parts of the system. Unlike the \"innovation capability\" or \"innovation performance\" in traditional research, NIS's overall effectiveness is a systematic reflection of the synergistic interaction of innovation participants, the effective performance of innovation functions, the rational use of innovation resources, and the creation of a friendly innovation environment. Compared with innovation capability, which emphasizes innovation results and transformation ability, NIS's overall effectiveness is more concerned with each subsystem's benign operation and synergistic development. Compared with innovation performance, which emphasizes the input-output ratio, NIS's overall effectiveness is more concerned with the function and coordination of the innovation process. Therefore, NIS's overall effectiveness has a broader connotation and more concerns than innovation capability or performance and reflects the operation status and construction effect of NIS more globally and scientifically.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Technological advances in the digital wave and related research\u003c/h2\u003e \u003cp\u003eIn the 21st century, the swift progression of digital technology is radically altering all facets of society. This shift is primarily attributed to advancements in key technologies such as artificial intelligence (AI), big data, the Internet of Things (IoT), cloud computing, and machine learning. These innovations catalyze new industries' emergence and foster transformative changes and innovations in established sectors. For instance, rapid AI and machine learning developments are spurring advancements across various industries (Miikkulainen and Forrest, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Prunkl et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, big data has revolutionized data processing methods. IoT technologies are diminishing the divide between physical and digital realms, paving the way for more efficient and sustainable living. Meanwhile, cloud computing introduces novel approaches to data storage and computing resources, reducing costs and enhancing business flexibility. Research on digital technology encompasses a broad spectrum of topics, ranging from its impact on nature conservation and entrepreneurship to its roles in university teaching, psychological treatments, pandemic response, business networks, and aging in place. Numerous studies have shed light on the multifaceted ways digital technologies intersect with various societal and industrial aspects. (1) Impact on Nature and Health: Digital technology has profoundly influenced nature conservation, ushering in the concept of 'digital conservation.' This includes data on nature and human interaction, data integration, and participatory governance (Arts et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, implementing digital technology is transforming mental health treatment, emphasizing online clinics and digital training (Mitchell and Kan, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, digital technology facilitates independence and quality of life for aging individuals in their homes, introducing innovative care models (Kim et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). (2) Role in Education and Business: In universities, digital technologies have become crucial to the student experience, offering flexibility and streamlined study management. However, their impact on the fundamental nature of university teaching remains limited (Selwyn, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Concurrently, digitalization is reshaping B2B exchanges, highlighting companies' need to adopt Internet-connected digital technologies and applications (Pagani and Pardo, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). (3) Globalization and Digital Adoption: Certain studies emphasize how globalization influences digital technology adoption, focusing on technology transfers and converging patterns of digital technology adoption (Skare and Soriano, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCollectively, existing research underscores the diverse impacts of digital technologies across numerous domains, accentuating both their transformative potential and the challenges they introduce. From personal health and aging to global business networks and environmental conservation, each piece of research contributes significantly to a more profound comprehension of how digital technologies are reshaping our world.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Relationship between digital technology development and the NIS\u0026rsquo;s overall effectiveness\u003c/h2\u003e \u003cp\u003eThe influence of digital technologies on national innovation systems (NIS) and their overall effectiveness is intricate and multifaceted. Primarily, technologies like artificial intelligence, big data analytics, and cloud computing substantially impact the structure and functionality of NIS. Digital transformation is pivotal for the success of NIS, particularly amid escalating global competition. However, Mirtsch et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) note that while digital technologies open new avenues for innovation, they also present challenges such as data security and privacy concerns. This necessitates that NIS embrace new technologies and develop suitable management strategies and regulations. Additionally, Guerra et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) underscore the need for countries to focus on education and talent development to align with digitalization trends, thereby promoting innovation and sustainable technology growth. Conversely, Hannibal and Knight et al. (2018) observe that rapid digital technology advancements have disrupted traditional industries, compelling NIS to adjust both technologically and in terms of policy, legal frameworks, and market structures. The research by Lopez-Sintas et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) revisits the impact of digital technologies on the economy and social structure, particularly in the ICT sector, suggesting that NIS must integrate these technologies for holistic socio-economic progress. Cheng et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further examine the role of digitalization in technology transfer and localized innovation. Regarding the link between digital technology and environmental sustainability, Wu et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) advocate for a digital transformation approach that considers the ecological ramifications of technological advancements and integrates sustainability into innovation strategies. Overall, current research demonstrates that digital technologies' impact on national innovation systems (NIS) is multidimensional, encompassing efficiency improvements, managerial challenges, talent cultivation, industrial adjustments, and extensive socio-economic and environmental effects. To attain comprehensive and enduring innovation, NIS must persistently adapt to digitalization trends while concurrently addressing the challenges and responsibilities emerging from technological advancements.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research hypotheses","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Direct impact of digital technology development on the NIS\u0026rsquo;s overall effectiveness\u003c/h2\u003e \u003cp\u003ePaul Romer's endogenous growth theory posits technological progress as a crucial driver of long-term economic growth. Within this framework, digital technology, a vital component of contemporary technological advancement, bolsters production efficiency and expedites innovation. The contribution of digital technology to enhancing the national innovation system's overall effectiveness can be distilled into several key aspects: First, it strengthens R\u0026amp;D capabilities. Popular digital technologies like big data, artificial intelligence, and cloud computing elevate R\u0026amp;D efficiency and effectiveness (Koronen et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They enable rapid processing of vast data quantities and complex computations and simulations, thus fostering scientific discovery and technological innovation. Second, digital technology streamlines knowledge sharing and collaboration. It facilitates information exchange via web-based platforms and collaborative tools (Yang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), bolstering cooperation between researchers, institutions, and firms. This seamless information flow aids in integrating external inputs (from consumers, suppliers, and partners) via Internet platforms, spurring interdisciplinary and cross-sectoral innovation (Kowalczuk et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Third, it enhances decision-making and management. Digital technologies offer precise, real-time data analysis, aiding policymakers and managers in making informed decisions. This is instrumental in optimizing resource allocation, steering new technology R\u0026amp;D, and boosting management efficiency (Niu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fourth, digital technology accelerates technology commercialization and application. It facilitates transforming research into marketable products, enabling faster market access through digital marketing and e-commerce platforms. This quickens the application and dissemination of innovations (Dao et al., 2023). Fifth, it improves education and training quality. Digital technologies in education, like online platforms and virtual labs, enhance educational quality and accessibility (Regan and Jesse, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), cultivating talent crucial for the innovation system. In conclusion, digital technologies significantly enhance the national innovation system's overall effectiveness by optimizing R\u0026amp;D efficiency, enabling knowledge sharing, improving decision-making, expediting technology commercialization, enhancing educational quality, and supporting open innovation in numerous ways.\u003c/p\u003e \u003cp\u003eIt is essential to recognize that spatial economics considers geographic proximity as a crucial element in facilitating the flow of innovation factors like knowledge, skills, and technology (Hung et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Digital technology advancement has expedited these flows within local regions and broader areas, simplifying the sharing and dissemination of information and knowledge (Tajvidi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Concurrently, the evolution of digital technology often brings pronounced network effects and spillover effects, implying that its value increases with more users, thereby fostering the diffusion of knowledge and technology (Zheng et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such spillover effects enable neighboring regions to benefit from a central region's technological progress and knowledge innovation, thereby boosting not only their innovation capacity but also that of adjacent areas (Abramo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, digital technology enhances collaboration and interaction among regional innovation actors (e.g., firms, research institutions, and government agencies), promoting optimal resource allocation and utilization of innovation capabilities across a wider area. This, in turn, improves the efficacy of the regional innovation system. Notably, the growth of digital technology acts as a new magnet for regional development, drawing significant external investment and talent A region with advanced digital infrastructure and a robust innovation environment attracts more enterprises and research activities (Ritter and Pedersen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), creating a virtuous cycle that leads to more balanced and sustainable regional development. In summary, digital technology's development not only bolsters the NIS\u0026rsquo;s overall effectiveness within a region but also positively impacts neighboring regions' innovation systems. This occurs through the flow of innovation factors, network, and spillover effects, among other mechanisms. Consequently, this paper proposes the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eThe development of digital technology will contribute to the NIS\u0026rsquo;s overall effectiveness in the local and neighboring areas.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Indirect impacts of digital technology development on the NIS\u0026rsquo;s overall effectiveness\u003c/h2\u003e \u003cp\u003eRapid advancements in digital technology are increasingly recognized as a primary catalyst for global innovation and industrial modernization (Matthess and Kunkel, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Traditional industries worldwide are transitioning towards more service- and knowledge-centric economic activities in this context. Digital technology plays a pivotal role in this shift, opening new pathways for enhancing the NIS\u0026rsquo;s overall effectiveness through the servitization of the industrial structure. Specifically, digital technology first facilitates the optimization and transformation of the industrial system. It spurs the digitalization of traditional service sectors and the emergence of new service industries, such as information technology and financial technology (Ali et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Secondly, it increases the share of high-value-added services. The widespread use of digital technology has led to the rapid development of high-technology and high-value sectors in the service industry, like software development, data analysis, and digital marketing, which rely heavily on knowledge and technology. Thirdly, digital technology boosts production and service efficiency. Technologies like cloud computing and big data enable firms to process information more efficiently and make better decisions, while e-commerce and online services significantly reduce transaction costs and time (Qi et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fourthly, it fosters new business models and innovation opportunities. The rise of service industry models such as the sharing economy, online platforms, and remote services expands consumer choices and opens new avenues for corporate innovation and entrepreneurship (Hossain, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fifth, it enhances the dissemination and application of knowledge and technology. A service-oriented industrial structure facilitates broader knowledge and technology dissemination, promotes cross-industry knowledge flow, and accelerates innovation diffusion and technology commercialization. In conclusion, digital technology substantially augments the NIS's overall effectiveness by driving the industry towards a more service-oriented model. Consequently, this paper proposes the following second hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eThe development of digital technology can enhance the NIS\u0026rsquo;s overall effectiveness by promoting the service-oriented industrial structure.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe surge in digital technology has transformed not only the nature of occupations and labor skills in traditional industries. Still, it has also spawned a multitude of new employment patterns and job categories (Ferreira, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By enhancing labor productivity, broadening employment diversity, and unleashing human resource potential, digital technologies are effectively redefining the labor market and, in turn, revitalizing the national innovation system. The interplay between digital technology development, labor markets, and the NIS\u0026rsquo;s overall effectiveness can be outlined as follows: First, digital technology is instrumental in fostering new skills and occupations. The advancement of digital technology has led to various new roles and required skills, such as data scientists, cloud computing engineers, and artificial intelligence specialists. This evolution generates fresh employment opportunities and drives the restructuring and enhancement of the labor market (Zuo and Feng, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, it amplifies labor productivity. The boost in productivity due to digital technology (Kokina and Blanchette, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) not only empowers workers to perform more efficiently but also creates room for innovative activities. Third, digital technology fosters innovation in education and training. Its growth has revolutionized education and vocational training approaches, exemplified by online learning and virtual training. This transformation enables the workforce to acquire new knowledge and skills more efficiently, adapting to the rapidly evolving technological landscape (Singh and Thurman, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Fourth, it encourages job market diversification. The proliferation of digital technology has diversified employment models, expanding the range of new job types such as remote work and freelancing. These flexible work arrangements attract and retain talent with innovative potential. In conclusion, digital technology dynamically stimulates the labor market by generating new job opportunities, enhancing labor productivity, innovating in education and training, and promoting job market diversification. Consequently, this fosters a more effective national innovation system. Therefore, this paper proposes the following third hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eDigital technology development can enhance the NIS\u0026rsquo;s overall effectiveness by stimulating the labor market.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the role of digital technologies in augmenting the NIS\u0026rsquo;s overall effectiveness through the servitization of industrial structures and the invigoration of the labor market is visually represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spatial heterogeneity of the impact of digital technology development on NIS\u0026rsquo;s overall effectiveness\u003c/h2\u003e \u003cp\u003eIn a country characterized by vast regional disparities in economic development, natural geography, and resource allocation, there is a corresponding divergence in the development of digital technology and the strength of scientific and technological innovation across regions. This variation can be analyzed from several perspectives: From the vantage point of infrastructure and resources, regions with higher economic development typically boast superior digital infrastructure, such as high-speed Internet, advanced communication technologies, and abundant technological resources, all crucial for digital technology development and application (Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, economically less-developed areas might lag in these aspects. Regarding talent development and skill levels, economically prosperous regions often have a more extensive pool of highly skilled labor and professionals, which is crucial for innovating and applying digital technologies. However, regions with lesser economic development may struggle with talent cultivation and attraction challenges. From the perspective of industrial agglomeration, economically advanced regions are usually hubs for high-tech firms and research institutes, fostering an innovation ecosystem conducive to knowledge sharing and technology transfer (Song et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Such an environment might be absent in less economically developed regions. Regarding market size and consumer behavior, regions with higher economic development often have larger markets and consumers more open to new technologies (Fernandes and Oliveira, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This receptiveness aids the rapid growth and broad adoption of digital technologies, a scenario that might not hold for economically backward areas. Concerning policy support, economically affluent regions generally have a greater capacity to offer policy and financial backing for digital technology development. In contrast, economically weaker parts may face constraints in this area. To summarize, disparities in infrastructure, human resources, innovation environment, market dynamics, and regional policy support could lead to heterogeneous effects of digital technology on the NIS\u0026rsquo;s overall effectiveness. Consequently, this paper proposes the following fourth hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003eThe role of digital technology development in enhancing the NIS\u0026rsquo;s overall effectiveness will be characterized by heterogeneity across regions.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical study design","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Modeling of spatial measurements\u003c/h2\u003e \u003cp\u003eThis study employs spatial econometric models that account for spatial factors. The models commonly utilized in spatial econometrics include the spatial lag model, the spatial error model, and the spatial Durbin model. The spatial lag model primarily assesses whether there are spatial spillover effects of the dependent variable across regions. In contrast, spatial error models are predominantly used to investigate the spatial impacts of omitted variables not encompassed in the explanatory variables or to analyze unobservable random shocks. Given that scenarios involving both spatial lag and spatial error can occur concurrently, spatial Durbin models are often employed in empirical research. The panel data format of this model is structured as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${y}_{it}=\\rho {\\sum }_{j=1}^{N}{w}_{ij}{y}_{jt}+{X}_{it}^{{\\prime }}\\beta +{\\sum }_{j=1}^{N}{w}_{ij}{X}_{jt}^{{\\prime }}\\theta +{\\mu }_{i}+{\\lambda }_{t}+{\\epsilon }_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\theta =0\\)\u003c/span\u003e\u003c/span\u003e, the model degenerates into a spatial lag model; if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\theta +\\delta \\beta =0\\)\u003c/span\u003e\u003c/span\u003e, the model degenerates into a spatial error model. Combined with the variables that are the main focus of this study, a spatial panel model that fits this study can be constructed as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${NIS}_{it}=\\rho {\\sum }_{j=1}^{N}{w}_{ij}{NIS}_{jt}+{\\beta }_{1}{Dtd}_{it}+{\\sigma }_{1}{\\sum }_{j=1}^{N}{w}_{ij}{Dtd}_{jt}+{\\gamma }_{1}{X}_{it}+{\\mu }_{i}+{\\lambda }_{t}+{\\epsilon }_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e denotes the spatial autoregressive coefficient; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{ij}\\)\u003c/span\u003e\u003c/span\u003e denotes the spatial weight matrix element; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{i}\\)\u003c/span\u003e\u003c/span\u003e denotes the region-fixed effect, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\lambda }_{t}\\)\u003c/span\u003e\u003c/span\u003e denotes the year fixed effect, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{it}\\)\u003c/span\u003e\u003c/span\u003e denotes the random perturbation term. In addition, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({NIS}_{it}\\)\u003c/span\u003e\u003c/span\u003e denotes the NIS\u0026rsquo;s overall effectiveness of province i in year t; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Dtd}_{it}\\)\u003c/span\u003e\u003c/span\u003e denotes the level of digital technology development of province i in year t; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a control variable containing network interconnectivity (Net), mobile interconnectivity (Mob), foreign trade dependence (Tra), foreign investment dependence (Fdi), science and technology investment intensity (Sti), and education investment intensity (Eti). In the subsequent empirical analysis, this paper chooses the most commonly used geographic proximity matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{1}\\)\u003c/span\u003e\u003c/span\u003e, i.e., when region i and region j are geographically adjacent, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is 1, otherwise it is 0. It is worth noting that there are various types of spatial weight matrices, and the regression results may be diametrically opposed if different spatial weight matrices are set in the same spatial regression model. In view of this, this paper chooses to use the geographic distance matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{2}\\)\u003c/span\u003e\u003c/span\u003e (the inverse of the Euclidean distance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({d}_{ij}\\)\u003c/span\u003e\u003c/span\u003e between region i and region j's capital city, i.e., when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\ne j\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{ij}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({1/d}_{ij}\\)\u003c/span\u003e\u003c/span\u003e; when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i=j\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{ij}=0\\)\u003c/span\u003e\u003c/span\u003e), and the economic distance matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{3}\\)\u003c/span\u003e\u003c/span\u003e (the inverse distance between region i's per capita real GDP and region j's per capita real GDP, i.e., when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\ne j\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{ij}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(1/({PGDP}_{i}-{PGDP}_{j})\\)\u003c/span\u003e\u003c/span\u003e; when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i=j\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{ij}=0\\)\u003c/span\u003e\u003c/span\u003e) replaces the geographic adjacency matrix in the spatial regression model, a robustness test for the benchmark regression results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Description of variables\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Explained Variables\u003c/h2\u003e \u003cp\u003eThe overall effectiveness of national innovation systems (Nis): According to the definition of the NIS\u0026rsquo;s overall effectiveness in the previous article, it can be seen that the quantification of the NIS\u0026rsquo;s overall effectiveness should focus on the synergistic interaction of the innovation subjects within it, the effective play of innovation functions, the rational utilization of innovation resources, and the friendly creation of the innovation environment. This is because the main body of innovation is the main body of invention. Because innovation subjects such as enterprises, higher education institutions, scientific research institutes, and governmental organizations are the executors of innovation activities, their ability determines the overall innovation ability of the national innovation system. At the same time, highly skilled researchers and innovative enterprises can transform inputs into creative outputs more effectively, which also determines that the quality, ability, and activity level of innovation subjects directly affect innovation efficiency. Secondly, innovation functions cover various aspects such as R\u0026amp;D, technology transfer, and marketization. The effective performance of these functions enhances the capacity of the innovation system and ensures the smooth implementation of innovation activities. At the same time, optimizing and coordinating these functions also directly improve innovation efficiency, for example, by enhancing the R\u0026amp;D process, promoting industry-university-research cooperation, and accelerating the process of technology transfer and commercialization. Finally, a good innovation environment can incentivize and protect innovation, thus enhancing the overall capacity of the national innovation system. To summarize, based on the three dimensions of \"subject-function-environment,\" this paper divides the comprehensive effectiveness evaluation system of the national innovation system into a composite system consisting of the innovation subject subsystem, the innovation function subsystem, and the innovation environment subsystem (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe comprehensive evaluation system of NIS's overall effectiveness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTertiary Indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicator content\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"23\" rowspan=\"24\"\u003e \u003cp\u003eInnovation Subjects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUniversities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of universities per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAssessing the contribution of universities to local innovation development through their teaching capacity, research capabilities, and innovation output levels\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of full-time university faculty per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of R\u0026amp;D projects in universities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of papers published by universities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eR\u0026amp;D institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of R\u0026amp;D institutions per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eThey are assessing the contribution of R\u0026amp;D institutions to local innovation development through their innovation capacity and output levels.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe full-time equivalent of R\u0026amp;D personnel per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of R\u0026amp;D subjects in R\u0026amp;D institutions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of R\u0026amp;D institutions publishing papers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEnterprise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternal expenditure on R\u0026amp;D of enterprises above the scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAssessing the contribution of enterprises to local innovation development through their R\u0026amp;D and innovation input-output status.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe full-time equivalent amount of R\u0026amp;D personnel of enterprises above the scale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of R\u0026amp;D projects of enterprises above the scale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNew product development investment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eGovernment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026amp;D government funds per capita expenditure of enterprises above the scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAssessing the government's support for local innovation development through its financial investment in science, technology, innovation, and education.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026amp;D institutions government funds per capita expenditure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita financial expenditure on education\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita financial expenditure on science and technology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIntermediaries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatent ownership transfer (item)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eThe contribution of intermediaries to local innovation development is assessed through their involvement in technology transfer and introduction activities.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatent ownership transfer (10,000 yuan)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForeign technology introduction (item)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForeign technology introduction (10,000 dollars)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFinancial Institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocal and foreign currency loans from financial institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAssessing the contribution of financial institutions to local innovation and development through their support and service levels in science, technology innovation, and enterprise development.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePremium income of insurance companies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinancial Industry Employees\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Bank Branches\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eInnovative Functions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eInnovative creation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Invention Patents Granted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eThey assess the functionality of intellectual property creation, and innovation results in transformation for each innovation subject through their performance and outcomes in technological innovation, new product development, and sales.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of utility model patents granted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Appearance Patents Granted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNew Product Sales Revenue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eInnovative Applications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026amp;D funding to domestic research institutions' spending\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAssessing the functionality of transformation and application of scientific and technological achievements through the level of investment in R\u0026amp;D activities and the allocation of science and technology resources.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026amp;D expenditure to domestic universities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Technology Market Transactions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnology Market Turnover\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eInnovative Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpenditure on the introduction of technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eThe quality and efficiency of innovation services are assessed through the input and impact of technology introduction, absorption, digestion, and innovation development.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpenditure on digestion and absorption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpenditure on purchase of domestic technology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnology renovation expenditure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eInnovation Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEconomic Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThey assess the environment through local economic development levels, per capita consumption levels, and income status.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisposable income per capita\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita consumption expenditure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFacility Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternet penetration rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThey are assessing the infrastructure development environment through local telecommunications and transportation infrastructure.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe average number of cell phone subscribers per 100 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoad miles per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMarket Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel of Marketization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThey are assessing the market development environment through the level of local marketization and openness to external engagement.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForeign trade dependency (total import/export/GDP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForeign investment dependency (foreign investment/GDP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInstitutional Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel of intellectual property protection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThey assess the institutional development environment through the strength of local intellectual property protection and government support for science and education.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScience and technology investment/fiscal expenditure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducation expenditure/fiscal expenditure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHumanistic environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of library collections per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThey are assessing the human environment through the number of local libraries, book collections, and museums.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of libraries per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of museums per 10,000 people\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe comprehensive evaluation system of NIS\u0026rsquo;s overall effectiveness has a multi-dimensional and multi-annual comprehensive character. In the composite indicator comprehensive evaluation system, determining the scientific value of the weights is the crucial factor in determining the level of the complete evaluation results. At present, the commonly used weighting methods are the Principal Component Analysis (PCA), Factor Analysis (FA) and Entropy method (EM). For time-series three-dimensional data with multiple indicators and multiple years, incorporating the influence of time factors on the weight values can make the evaluation results have dynamic comparability, and focusing on the examination of the information content of the indicators can well clarify the importance of each hand to the evaluation object in each year. The Vertical and Horizontal Scatter Degree Method (VHSD) is a dynamic evaluation method that incorporates the time factor into determining the weight values, which can maximize the differences among the evaluated objects in different years. The time-series three-dimensional data arrangement matrix of the comprehensive evaluation system of the NIS\u0026rsquo;s overall effectiveness is:\u003c/p\u003e \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$X={x}_{ij}\\left({t}_{k}\\right), (i=\\text{1,2},3\\dots m; j=\\text{1,2},3\\dots n; k=\\text{1,2},3\\dots k)$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{ij}\\left({t}_{k}\\right)\\)\u003c/span\u003e\u003c/span\u003e denotes the value of the \u003cem\u003ej\u003c/em\u003e indicator of the \u003cem\u003ei\u003c/em\u003e sample in year \u003cem\u003ek\u003c/em\u003e. The other \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{i}\\)\u003c/span\u003e\u003c/span\u003e denotes the specific name of the \u003cem\u003ei\u003c/em\u003e sample, whose time-series stereo data table is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTiming Stereo Data Sheet\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({t}_{k}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{1}{x}_{2}\\dots {x}_{n}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{1}{x}_{2}\\dots {x}_{n}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{1}{x}_{2}\\dots {x}_{n}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{11}\\left({t}_{1}\\right){x}_{12}\\left({t}_{1}\\right)\\dots {x}_{1n}\\left({t}_{1}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{11}\\left({t}_{2}\\right){x}_{12}\\left({t}_{2}\\right)\\dots {x}_{1n}\\left({t}_{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{11}\\left({t}_{k}\\right){x}_{12}\\left({t}_{k}\\right)\\dots {x}_{1n}\\left({t}_{k}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{21}\\left({t}_{1}\\right){x}_{22}\\left({t}_{1}\\right)\\dots {x2}_{n}\\left({t}_{1}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{21}\\left({t}_{2}\\right){x}_{22}\\left({t}_{2}\\right)\\dots {x2}_{n}\\left({t}_{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{21}\\left({t}_{k}\\right){x}_{22}\\left({t}_{k}\\right)\\dots {x2}_{n}\\left({t}_{k}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{m}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{m1}\\left({t}_{1}\\right){x}_{m2}\\left({t}_{1}\\right)\\dots {x}_{mn}\\left({t}_{1}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{m1}\\left({t}_{2}\\right){x}_{m2}\\left({t}_{2}\\right)\\dots {x}_{mn}\\left({t}_{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{m1}\\left({t}_{k}\\right){x}_{m2}\\left({t}_{k}\\right)\\dots {x}_{mn}\\left({t}_{k}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTo ensure comparability of the data, the data were Z-score standardized and processed as follows:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${Y}_{ijk}={(x}_{ijk}-{\\overline{x}}_{ijk})/{\\sigma }_{ijk}, i=\\text{1,2},3\\dots m; j=\\text{1,2},3\\dots n; k=\\text{1,2},3\\dots k$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e denotes the indicator value of indicator \u003cem\u003ej\u003c/em\u003e in year \u003cem\u003ek\u003c/em\u003e of the \u003cem\u003ei\u003c/em\u003e sample after standardization, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\overline{x}}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e is the mean value of indicator \u003cem\u003ej\u003c/em\u003e in year \u003cem\u003ek\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{ijk}\\)\u003c/span\u003e\u003c/span\u003e denotes the standard deviation of indicator \u003cem\u003ej\u003c/em\u003e in year \u003cem\u003ek\u003c/em\u003e. Subsequently, the indicator weights were determined and the comprehensive evaluation function was set as:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${z}_{i}\\left({t}_{k}\\right)=\\sum _{j=1}^{n}{\\delta }_{i}{y}_{ij}\\left({t}_{k}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\delta }_{i}\\)\u003c/span\u003e\u003c/span\u003e is the indicator weight and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({z}_{i}\\left({t}_{k}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the composite evaluation value of sample i in year k of the comprehensive evaluation system of the NIS\u0026rsquo;s overall effectiveness. For the determination of indicator weights, the total sum of squares of deviations can be used to maximize the differences among samples by:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${\\sigma }^{2}=\\sum _{k=1}^{K}\\sum _{i=1}^{m}{({z}_{i}\\left({t}_{k}\\right)-\\overline{z})}^{2}={\\delta }^{T}\\sum _{k=1}^{K}{H}_{k}\\delta ={\\delta }^{T}H\\delta$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\delta ={({\\delta }_{1},{\\beta }_{2},\\dots {\\delta }_{n})}^{T}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(H=\\sum _{k=1}^{K}{H}_{k}\\)\u003c/span\u003e\u003c/span\u003e are symmetric matrices, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{k}={A}_{k}^{T}{A}_{k}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k=\\text{1,2},3\\dots K\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }^{2}\\)\u003c/span\u003e\u003c/span\u003e gets the maximum value when taking the eigenvector corresponding to the maximum eigenvalue of the matrix H with the restriction \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\delta }^{T}\\delta =1\\)\u003c/span\u003e\u003c/span\u003e. At this point, the normalization of this eigenvector is to obtain the determined weight \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\delta }_{j}\\)\u003c/span\u003e\u003c/span\u003e. However, one of the limitations of the VHSD method is that its determination of index weights only depends on the evaluation matrix, which cannot reflect the size of the information in each evaluation index. As a critical static evaluation method, the advantage of the Entropy method (EM) is that it can determine the weights based on the amount of information contained in each evaluation index so that the differences among the indicators can be well reflected. Based on the indicator data after Z-score normalization in the previous section, the degree of variation was first calculated:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$${v}_{ijk}=\\frac{{y}_{ijk}}{\\sum _{i=1}^{m}{y}_{ijk}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e denotes the characteristic weight of the \u003cem\u003ei\u003c/em\u003e-evaluation object under the \u003cem\u003ej\u003c/em\u003e indicator in the \u003cem\u003ek\u003c/em\u003e year. Calculate the EM value of the \u003cem\u003ej\u003c/em\u003e indicator, denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{jk}\\)\u003c/span\u003e\u003c/span\u003e:\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${E}_{jk}=-\\frac{1}{\\text{l}\\text{n}\\left(m\\right)}\\sum _{i=1}^{m}{v}_{ijk}\\text{l}\\text{n}\\left({v}_{ijk}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhen \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{ijk}=0\\)\u003c/span\u003e\u003c/span\u003e or 1, let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{ijk}\\text{l}\\text{n}({v}_{ijk)}=0\\)\u003c/span\u003e\u003c/span\u003e. Let the coefficient of variation of the indicator be \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{jk}\\)\u003c/span\u003e\u003c/span\u003e At this point:\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$${D}_{jk}=1-{E}_{jk}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe larger \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({D}_{jk}\\)\u003c/span\u003e\u003c/span\u003e indicates that the greater the amount of information of the evaluated object contained in indicator \u003cem\u003ej\u003c/em\u003e, the greater the weight should be given to it, from which the EM weight of the indicator can be determined as:\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$${\\omega }_{jk}=\\frac{{D}_{jk}}{\\sum _{j=1}^{n}{D}_{jk}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIt's worth noting that the EM method cannot achieve dynamic comparability of evaluation results. Considering the measurement errors that may exist in a single evaluation method, using a combined evaluation method is beneficial to optimize the weight values. This paper combines VHSD with EM to unify the advantages of the two evaluation methods and to assign weights to the innovation subject subsystem, innovation function subsystem, and innovation environment subsystem in the comprehensive evaluation system of the NIS\u0026rsquo;s overall effectiveness. Based on the weights \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\delta }_{j}\\)\u003c/span\u003e\u003c/span\u003e determined by the VHSD model and the weights \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega }_{jk}\\)\u003c/span\u003e\u003c/span\u003e of each indicator in each year determined by the EM method, they are formed into a matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{jk}\\)\u003c/span\u003e\u003c/span\u003e by year:\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$${C}_{jk}={\\left[\\begin{array}{cc}{\\delta }_{1}\u0026amp; {\\omega }_{1k}\\\\ \\dots \u0026amp; \\dots \\\\ {\\delta }_{n}\u0026amp; {\\omega }_{nk}\\end{array}\\right]}_{n\\times 2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe final weight \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{jk}\\)\u003c/span\u003e\u003c/span\u003e of each indicator is obtained by summing up the elements in each row of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{jk}\\)\u003c/span\u003e\u003c/span\u003e and taking the arithmetic mean of them, while the comprehensive evaluation value \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{mk}\\)\u003c/span\u003e\u003c/span\u003e can be obtained after the linear weighting method for the comprehensive evaluation value of each evaluation object in each year is weighted and aggregated layer by layer. Finally, to practically reflect the interrelated and coordinated development relationship of innovation subject, innovation function, and innovation environment in the complex system of NIS, this paper adopts the physical coupling model to calculate the coupling of innovation subject, innovation function, and innovation environment, and the final coupling value is the actual level of NIS\u0026rsquo;s overall effectiveness. The basic functional form of its coupling degree is as follows:\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$$C={\\left[\\frac{{U}_{1}\\times {U}_{2}\\times \\bullet \\bullet \\bullet \\times {U}_{n}}{\\prod _{i\\ne j}({U}_{i}+{U}_{j})}\\right]}^{\\frac{1}{n}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ12\" class=\"InternalRef\"\u003e12\u003c/span\u003e), \u003cem\u003eC\u003c/em\u003e denotes the coupling degree; \u003cem\u003eU\u003c/em\u003e denotes the comprehensive development score of each system. In this paper, three systems of innovation subject, innovation function, and innovation environment are involved, so Eq.\u0026nbsp;(\u003cspan refid=\"Equ12\" class=\"InternalRef\"\u003e12\u003c/span\u003e) can be changed as follows:\u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e\n$$C={\\left[\\frac{{U}_{1}\\times {U}_{2}\\times {U}_{3}}{{({U}_{1}+{U}_{2}+{U}_{3})}^{3}}\\right]}^{\\frac{1}{3}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ13\" class=\"InternalRef\"\u003e13\u003c/span\u003e), \u003cem\u003eC\u003c/em\u003e indicates the coupling degree of the three systems; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({U}_{1}{U}_{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({U}_{3}\\)\u003c/span\u003e\u003c/span\u003e are the comprehensive development scores of the three systems of innovation subject, innovation function, and innovation environment, respectively. The larger the coupling degree C value, the better the coupling and coordination of the three systems. After the coupling degree is calculated, the total comprehensive evaluation score of the three systems of innovation subject, innovation function, and innovation environment can be further calculated by the following formula:\u003cdiv id=\"Equ14\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ14\" name=\"EquationSource\"\u003e\n$$T={\\beta }_{1}{U}_{1}+{\\beta }_{2}{U}_{2}+{\\beta }_{3}{U}_{3}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e14\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ14\" class=\"InternalRef\"\u003e14\u003c/span\u003e), \u003cem\u003eT\u003c/em\u003e is the total comprehensive evaluation score of the three systems; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e is the pending coefficient, which aims to measure the weight of the comprehensive evaluation score of each subsystem. In this paper, the three subsystems of innovation subject, innovation function, and innovation environment are considered to be equally important in the comprehensive evaluation system of the NIS\u0026rsquo;s overall effectiveness, so the three pending coefficients are set to 1/3. It is worth noting that although the coupling degree can calculate the strength of the role of each subsystem, it cannot reflect the overall coordination of the system, so it is necessary to construct the coupling coordination degree model further:\u003cdiv id=\"Equ15\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ15\" name=\"EquationSource\"\u003e\n$$D=\\sqrt{C\\times T}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e15\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ15\" class=\"InternalRef\"\u003e15\u003c/span\u003e), \u003cem\u003eD\u003c/em\u003e is the coupling coordination degree, which reflects the interactive and coordinated development of the three systems of innovation subject, innovation function, and innovation environment (the level of NIS\u0026rsquo;s overall effectiveness)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Explanatory variables\u003c/h2\u003e \u003cp\u003eLevel of digital technology development (DigT): This study addresses the challenge encountered in previous research of differentiating the digital economy component from pure digital technology, often leading to inaccuracies in assessing digital technology levels. It posits that the evolution of digital technology, notably supported as a strategic focus by the Chinese government, is best represented by specific informational elements in each provincial government's annual work report. These reports are delivered by provincial governors at the regional yearly people's congresses, usually held at the beginning of each year. They review the previous year's achievements and set goals for the upcoming year, thus serving as a comprehensive summary of the province's key activities and a blueprint for future endeavors. The vocabulary used in these reports can substantially reflect the region's development philosophy, trajectory, and status. Consequently, this paper adopts an innovative approach by quantifying the level of digital technology development in each region based on the frequency of digital technology-related terms in these government work reports, a method both scientific and reasonable. In support of this approach, Zhu et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) similarly assessed the degree of digital transformation in enterprises by analyzing the frequency of relevant terms in the annual reports of listed companies. This method is proposed as a proxy variable for measuring digital technology levels in each province, utilizing the relative frequency of pertinent keywords in provincial government work reports. The keyword extraction, collection, and computation are primarily executed using Python. However, given the individual differences among local officials and the varying lengths of government work reports, a direct comparison of keyword frequency could introduce bias. To circumvent this issue, the study uses the proportion of keyword frequency to total word frequency to indicate the province's digital technology level. The selection of digital technology keywords is informed by prior national government work reports from the State Council of China, the China Digital Economy Development Report (2022) by the China ICT Institute, and the Statistical Classification of the Digital Economy and Its Core Industries (2021) by the National Bureau of Statistics. Ultimately, 48 keywords are identified and listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of keywords for digital technology levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital technology keywords\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Information, Modern Information Network, Information and Communication Technology, ICT, Communication Infrastructure, Internet, Cloud Computing, Blockchain, Internet of Things, Digitization, Digital Countryside, Digital Industry, E-Commerce, 5G, Digital Infrastructure, Artificial Intelligence, Big Data, Digitization, Industrial Digitization, Digital Industrialization, Data Association, Smart Cities, Cloud Services, Cloud Technology, Cloud, E-Government, Mobile Payment, Online, Information Industry, Software, Information Infrastructure, Information Technology, Digital Life, Smart Manufacturing, Intelligent, Smart Cities, Cloud Computing, Going to the Cloud, Cloud Platform, Cloud Services, Data Security, Data Services, Data Governance, Data Sharing, Industrial Internet, Blockchain, Robotics, Digital Technology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Mechanism variables\u003c/h2\u003e \u003cp\u003eContinuing the analysis of how digital technology bolsters the NIS\u0026rsquo;s overall effectiveness, this paper identifies two intermediary mechanism variables for empirical research. First, the servitization of the industrial structure (Sind) is characterized by the local tertiary industry's output value as a proportion of the total output value. This servitization reflects the economy's transformation from agriculture or manufacturing to a more service-based industry. This shift in the digital era necessitates a higher level of knowledge and technological support, increasingly reliant on innovation and information technology. Therefore, a growing share of services in GDP typically indicates a country or region's innovation propensity and reliance on knowledge and technology application (Chang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Examining the influence of digital technology on NIS through servitization provides a more comprehensive understanding of digitization's deeper economic impacts and elucidates how these changes enhance the NIS\u0026rsquo;s overall effectiveness. Second, to gauge the stimulation of the labor market (Lab), this study uses the proportion of the local working population in the total population as a proxy indicator. The state of the labor market indicates the local economy's dynamism and the job market's health. Firstly, as a critical driver of innovation, the labor market significantly influences creation. A vibrant and healthy labor market supplies necessary technical and innovative talents, facilitating knowledge and skill flow, thereby supporting the NIS's overall effectiveness. Secondly, digital technology's role in creating new jobs, altering the nature and requirements of existing occupations, and enhancing labor mobility and flexibility has been extensively discussed (Orel, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Understanding how digital technologies indirectly boost the NIS's overall effectiveness via the job market is vital for a comprehensive assessment of digital technology's impact. Thus, using labor market conditions as a mediating mechanism variable allows for a more precise and in-depth evaluation of digital technology's indirect contribution to enhancing the NIS's overall effectiveness, offering a richer analytical framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.2.4 Control variables\u003c/h2\u003e \u003cp\u003eIn this research, several control variables were selected for inclusion in the regression model to enhance the analysis. First, the network interconnectedness (Net) degree is measured using local Internet penetration rates as a proxy. Network interconnectivity is often a prerequisite for implementing and utilizing digital technologies in today's digital age. Moreover, a region's Internet penetration facilitates rapid information dissemination, knowledge sharing, and collaboration, which is essential for bolstering innovation (Yang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, the mobile connectivity (Mob) level is gauged using local cell phone penetration rates. Mobile Internet is increasingly pivotal in modern society, underpinning telework, online education, e-commerce, and social media. Accounting for this factor allows for a more precise evaluation of the mobile Internet's role as a critical aspect of digital technology in enhancing innovation system effectiveness. Additionally, mobile technology's prevalence significantly influences the speed and extent of information dissemination, communication methods, and the way individuals and firms access and utilize information, fostering innovative thinking and collaboration (Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Third, foreign trade dependence (Tra) is indicated by the proportion of local imports and exports in GDP. This metric reflects a region's economic openness and integration with global markets. Areas with active foreign trade often engage in technological exchanges and knowledge diffusion, with high foreign trade dependence suggesting a more pronounced role in global technology and knowledge flows (Papanastassiou et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), thereby impacting innovation system development. Fourth, foreign investment dependence (Fdi) is represented by the ratio of local foreign direct investment (FDI) to GDP. This measure indicates the reliance on foreign capital for economic and technological progress. Inflows of foreign capital, often accompanied by advanced technology and managerial expertise, are crucial for enhancing local technology levels and innovation capabilities (Song and Chen, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Fifth, science and technology investment intensity (Sti) are measured by the proportion of local government science and technology spending to total fiscal expenditures. This ratio signifies a region's commitment to SandT activities and resource allocation, directly influencing its SandT innovation capacity and efficiency. The intensity of SandT investment underlines a region's foundational support for digital technology development, which is pivotal in assessing the impact of digital technology on NIS\u0026rsquo;s overall effectiveness. Sixth, education investment intensity (Eti) is quantified by the share of local government education spending in total fiscal expenditures. Education lays the groundwork for developing innovators and technologists, with the level of educational investment directly affecting workforce skills and quality, thereby influencing a country's SandT innovation ability, essential for an efficient NIS.\u003c/p\u003e \u003cp\u003eTo conclude, in analyzing the influence of digital technology on NIS' overall effectiveness, it is crucial to consider several vital confounding variables. These include network interconnectivity, mobile connectivity, foreign trade and investment dependence, science and technology, and education investment intensity. These factors may be intricately linked with the level of digital technology development and NIS\u0026rsquo;s overall effectiveness. By incorporating these variables as controls, the analysis can more precisely isolate and assess the specific impact of digital technology on NIS\u0026rsquo;s overall effectiveness.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Description of data sources\u003c/h2\u003e \u003cp\u003eDue to data availability, this study selects the period from 2012 to 2022 and focuses on 30 provinces in China, excluding Hong Kong, Macao, Taiwan, and Tibet. The assessment of digital technology levels is conducted using Python to extract and collect keywords related to digital technology in each province. This is quantified by the proportion of the total frequency of these keywords relative to the overall word frequency, serving as a proxy indicator. The remaining data are sourced primarily from the China Statistical Yearbook, China Electronic Information Industry Statistical Yearbook, China Science and Technology Statistical Yearbook, China High-Tech Industry Statistical Yearbook, and China Industrial Statistical Yearbook. The NIS\u0026rsquo;s overall effectiveness is evaluated using the VHSD-EM model, with the specific methodology detailed in a previous section of this paper. Descriptive statistics for the variables in this study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Analysis of empirical results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Spatial correlation analysis of the NIS\u0026rsquo;s overall effectiveness\u003c/h2\u003e \u003cp\u003eIn this study, the Moran's I index value of NIS\u0026rsquo;s overall effectiveness was calculated for the whole region from 2012 to 2022 to determine whether there is a spatial correlation between the NIS\u0026rsquo;s overall effectiveness in different regions. The analysis employs three spatial weight matrices: geographic adjacency matrix, geographic distance matrix, and economic geography matrix. According to the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, regardless of the spatial weight matrix used, Moran's I index values for NIS\u0026rsquo;s overall effectiveness consistently passed the significance level tests (at 1% and 5%) and were positive from 2012 to 2022. This indicates a significant positive spatial autocorrelation in NIS\u0026rsquo;s overall effectiveness across provinces, suggesting a spatial clustering pattern. While Moran's I index values for NIS\u0026rsquo;s overall effectiveness exhibit a decreasing trend over time across all three spatial weighting matrices, they consistently indicate significant spatial autocorrelation, underlining a persistent positive relationship in the region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEntire domain of Moran's index for the three matrices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003egeographic adjacency matrix\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeographic distance matrix\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEconomic Geography Matrix\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.313***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.220***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.376***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.264***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.412***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.228***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.413***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.306***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.236***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.374***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.318***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.185**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.342***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.296***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.199**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.351***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.257**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.170**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.315***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.307***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.213***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.319***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.242**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.172**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.332***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.214**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.158**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.331***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.197**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.143**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.318***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *, **, and *** in brackets represent the significance levels of 10%, 5%, and 1%, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis paper further explores the local spatial autocorrelation using the LISA index (Fig.\u0026nbsp;4).The number of locally significant provinces for the overall effectiveness of the NIS in 2012, 2015, 2019 and 2022 does not show an obvious trend of change, which indicates to a certain extent that the overall effectiveness of the NIS has a strong spatial dependence on the local geography. Specifically: (1) High-value and high-value clusters (H-H) are concentrated in the eastern coastal region, and the H-H clusters in 2022 have changed from 2012 (shifted from Jiangsu Province to Guangdong Province). (2) Most of the low-value and low-value-type agglomerations (L-L) are concentrated in the western region, and there is a change in the L-H type agglomerations in 2022 compared with 2012 (shifting from Sichuan and Gansu Provinces to Inner Mongolia Autonomous Region). (3) High-value and low-value type agglomerations (H-L) are concentrated in the eastern coastal region, and unlike other agglomerations, their agglomerations are gradually expanding, which to a certain extent reflects the siphoning effect of the eastern coastal region pulling apart the development gap between regions. Overall, from 2012 to 2022, the overall effectiveness of the NIS mainly shows polarization of H-H type and L-L type agglomeration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Benchmark regression\u003c/h2\u003e \u003cp\u003eBased on the spatial econometric model selection criteria established by Anselin et al. (1996), the outcomes of the Lagrange Multiplier (LM) test and the robust LM test, as presented in the last column of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, suggest that the spatial lag model is preferable to the spatial error model. Consequently, this study adopts the spatial lag model for conducting regression analysis. The baseline estimation results utilizing this model are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBenchmark regression results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel(7)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.208***\u003c/p\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177***\u003c/p\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.174***\u003c/p\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123***\u003c/p\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.123***\u003c/p\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.080**\u003c/p\u003e \u003cp\u003e(0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.080**\u003c/p\u003e \u003cp\u003e(0.040)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.320**\u003c/p\u003e \u003cp\u003e(0.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297**\u003c/p\u003e \u003cp\u003e(0.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.281*\u003c/p\u003e \u003cp\u003e(0.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.281*\u003c/p\u003e \u003cp\u003e(0.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.287**\u003c/p\u003e \u003cp\u003e(0.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.264*\u003c/p\u003e \u003cp\u003e(0.143)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060*\u003c/p\u003e \u003cp\u003e(0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060*\u003c/p\u003e \u003cp\u003e(0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.060*\u003c/p\u003e \u003cp\u003e(0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.052**\u003c/p\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.445***\u003c/p\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.444***\u003c/p\u003e \u003cp\u003e(0.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.431***\u003c/p\u003e \u003cp\u003e(0.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.426***\u003c/p\u003e \u003cp\u003e(0.092)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.962***\u003c/p\u003e \u003cp\u003e(1.888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.069***\u003c/p\u003e \u003cp\u003e(1.902)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.393\u003c/p\u003e \u003cp\u003e(0.841)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM-lag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.909***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM-error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust-LM-lag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.053***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust-LM-Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.335***\u003c/p\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.306***\u003c/p\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.284***\u003c/p\u003e \u003cp\u003e(0.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.251***\u003c/p\u003e \u003cp\u003e(0.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.251***\u003c/p\u003e \u003cp\u003e(0.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.183***\u003c/p\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.178***\u003c/p\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: The numbers in parenthesis are robust standard errors; *, **, and *** in brackets represent the significance levels of 10%, 5%, and 1%, respectively. The following table is the same.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents models 1 to 7, which depict the influence of digital technology development on NIS\u0026rsquo;s overall effectiveness, progressively incorporating control variables. The results reveal that while the estimated coefficient for digital technology diminishes with the addition of control variables, it consistently remains positive and statistically significant. This outcome suggests that digital technology positively impacts NIS\u0026rsquo;s overall effectiveness. Specifically, a 1% increase in digital technology development correlates with a 0.08% improvement in NIS\u0026rsquo;s overall effectiveness. This finding aligns with the first theoretical hypothesis posited earlier in the study.\u003c/p\u003e \u003cp\u003eAdditionally, the spatial spillover coefficient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e), valued at 0.178 and significant at the 1% level, indicates that the benefits of digital and technological development extend beyond the immediate location. A positive spatial spillover effect enhances NIS\u0026rsquo;s overall effectiveness in neighboring regions. This underscores the broader impact of digital technology advancement within a specific area and across adjacent regions.\u003c/p\u003e \u003cp\u003eUsing Model (7) as a reference point, the analysis of control variables yields the following insights: The regression coefficients for the degree of network interconnectivity and mobile connectivity are 0.264 and 0.056, respectively. Both coefficients are statistically significant, passing the 10% and 5% significance level tests, respectively. This suggests that enhancements in regional network and mobile connectivity can positively impact NIS\u0026rsquo;s overall effectiveness. Regarding foreign trade dependence, the regression coefficient is -0.426, significant at the 1% level. In contrast, the coefficient for foreign investment dependence is -0.002. These findings imply that China's traditional approach of leveraging import-export trade and foreign investment for technological advancement, colloquially known as \"exchange market for technology,\" is not yielding the anticipated benefits for NIS\u0026rsquo;s overall effectiveness. This result challenges the efficacy of this long-standing strategy. The coefficient for science and technology investment intensity is significantly high at 18.053, passing the 1% significance level test.\u003c/p\u003e \u003cp\u003eIn contrast, the coefficient for education investment intensity is -0.393. This indicates that, in the short term, specific science and technology investments yield better returns than long-term education investments in enhancing NIS\u0026rsquo;s overall effectiveness. However, it is essential to consider that educational impacts often have a delayed effect (Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, investment in education should still be a key component of local governments' long-term plans for innovation development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Endogenous treatment\u003c/h2\u003e \u003cp\u003eThis study adopts a methodological adjustment to address the potential issues of omitted variables and endogeneity arising from possible reverse causality between the level of digital technology and NIS\u0026rsquo;s overall effectiveness. It utilizes explanatory variables with a one-period lag as instrumental variables. It employs the two-step generalized system moments estimation (SYS-GMM) approach for retesting the impact of digital technology level on NIS\u0026rsquo;s overall effectiveness. The estimation results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The test outcomes for AR 2 and Sargan do not refute the null hypothesis, suggesting the absence of second-order autocorrelation in the model's residuals and confirming the instrumental variables' validity. The regression coefficient for digital technology, at 0.025, is statistically significant at the 5% level. This reinforces the pivotal role of digital technology level in augmenting NIS\u0026rsquo;s overall effectiveness, thereby substantiating the study's initial findings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized System Estimated Regression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSYS-GMM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNis\u003csub\u003e\u0026minus;\u0026thinsp;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.766***\u003c/p\u003e \u003cp\u003e(0.042)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025**\u003c/p\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003cp\u003e(0.103)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.006***\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.499***\u003c/p\u003e \u003cp\u003e(2.283)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.198*\u003c/p\u003e \u003cp\u003e(0.624)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.989***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSargan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Robustness Tests\u003c/h2\u003e \u003cp\u003eTo ensure the robustness of the study's findings, this paper implements a series of robustness tests based on the following strategies: Exclusion of Municipalities. The sample excludes the municipalities directly under the central government, such as Beijing, Shanghai, Tianjin, and Chongqing. Due to their unique administrative status, these municipalities exhibit distinct characteristics in digital technology development, government governance, population density, and urban area compared to other provinces. To achieve more general and comparable research samples, the analysis focuses solely on the data from ordinary regions for regression testing (Robustness 1). Data Truncation. To mitigate the potential bias in regression outcomes due to outliers, the tails of each variable are truncated at the 1% and 99% levels, and the regression is re-conducted (Robustness 2). Alternative Spatial Weight Matrices. Recognizing that different spatial weight matrices can yield varied regression results, the study substitutes the baseline regression's geographic adjacency matrices with an economic distance matrix and an Euclidean geographic distance matrix. The spatial lag model is then reapplied for regression analysis (Robustness 3 and 4). The final regression outcomes are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Regardless of the robustness test applied, the influence of digital technology development on the NIS\u0026rsquo;s overall effectiveness remains positive and statistically significant. This consistency across different tests reinforces the reliability and robustness of the baseline regression results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobustness test 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobustness test 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRobustness test 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobustness test 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.130***\u003c/p\u003e \u003cp\u003e(0.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088**\u003c/p\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085**\u003c/p\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003cp\u003e(0.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003cp\u003e(0.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.265*\u003c/p\u003e \u003cp\u003e(0.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.279*\u003c/p\u003e \u003cp\u003e(0.142)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.064**\u003c/p\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.272***\u003c/p\u003e \u003cp\u003e(0.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058*\u003c/p\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.880***\u003c/p\u003e \u003cp\u003e(0.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.392***\u003c/p\u003e \u003cp\u003e(0.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.421***\u003c/p\u003e \u003cp\u003e(0.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.427***\u003c/p\u003e \u003cp\u003e(0.092)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.007**\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003cp\u003e(0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.656***\u003c/p\u003e \u003cp\u003e(1.830)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.995***\u003c/p\u003e \u003cp\u003e(1.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.330***\u003c/p\u003e \u003cp\u003e(1.899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.137***\u003c/p\u003e \u003cp\u003e(1.904)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.902\u003c/p\u003e \u003cp\u003e(0.832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003cp\u003e(0.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.520\u003c/p\u003e \u003cp\u003e(0.839)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003cp\u003e(0.844)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.131***\u003c/p\u003e \u003cp\u003e(0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.128*\u003c/p\u003e \u003cp\u003e(0.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.150**\u003c/p\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.188**\u003c/p\u003e \u003cp\u003e(0.076)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM-lag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.415***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.356***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.984*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.559**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLM-error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust-LM-lag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.885***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.942***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.622*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.051**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust-LM-Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Mechanism testing\u003c/h2\u003e \u003cp\u003eIn the preceding section, this paper theoretically analyzed the contribution of digital technology development to NIS\u0026rsquo;s overall effectiveness. This analysis was grounded in two key aspects: the industrial structure's servitization and the labor market's stimulation. To empirically validate these hypotheses, the study, drawing inspiration from Baron and Kenny (1986), constructs a mediation effect model that incorporates geospatial elements:\u003cdiv id=\"Equ16\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ16\" name=\"EquationSource\"\u003e\n$${NIS}_{it}=\\rho {\\sum }_{j=1}^{N}{w}_{ij}{NIS}_{jt}+{\\beta }_{1}{Dtd}_{it}+{\\beta }_{i}{X}_{it}+{\\mu }_{i}+{\\lambda }_{t}+{\\epsilon }_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e16\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ17\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ17\" name=\"EquationSource\"\u003e\n$${Med}_{it}=\\rho {\\sum }_{j=1}^{N}{w}_{ij}{SInd}_{jt}+{\\gamma }_{1}{Dtd}_{it}+{\\gamma }_{i}{X}_{it}+{\\mu }_{i}+{\\lambda }_{t}+{\\epsilon }_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e17\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ18\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ18\" name=\"EquationSource\"\u003e\n$${NIS}_{it}=\\rho {\\sum }_{j=1}^{N}{w}_{ij}{NIS}_{jt}+{\\delta }_{1}{Dtd}_{it}+{\\delta }_{2}{Med}_{it}+{\\delta }_{i}{X}_{it}+{\\mu }_{i}+{\\lambda }_{t}+{\\epsilon }_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e18\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ17\" class=\"InternalRef\"\u003e17\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Med}_{it}\\)\u003c/span\u003e\u003c/span\u003e Is the mediating mechanism variables to be tested, which are industrial structure servicing (SInd) and labor market stimulation (Lab), respectively, and β and δ are the coefficient values to be focused on in the regression analysis, and the estimation results of the mediation effect model including geospatial elements are shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of stepwise mediation effects test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSind\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLab\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.080**\u003c/p\u003e \u003cp\u003e(0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008**\u003c/p\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076*\u003c/p\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.080**\u003c/p\u003e \u003cp\u003e(0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.071**\u003c/p\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.277**\u003c/p\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.117***\u003c/p\u003e \u003cp\u003e(0.110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.279*\u003c/p\u003e \u003cp\u003e(0.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074***\u003c/p\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.279*\u003c/p\u003e \u003cp\u003e(0.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.193***\u003c/p\u003e \u003cp\u003e(0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003cp\u003e(0.148)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051*\u003c/p\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.052*\u003c/p\u003e \u003cp\u003e(0.031)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.427***\u003c/p\u003e \u003cp\u003e(0.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.410***\u003c/p\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.427***\u003c/p\u003e \u003cp\u003e(0.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.124***\u003c/p\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.414***\u003c/p\u003e \u003cp\u003e(0.093)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002***\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.137***\u003c/p\u003e \u003cp\u003e(1.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.604**\u003c/p\u003e \u003cp\u003e(0.256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.718***\u003c/p\u003e \u003cp\u003e1.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.137***\u003c/p\u003e \u003cp\u003e(1.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.375***\u003c/p\u003e \u003cp\u003e(0.843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.575***\u003c/p\u003e \u003cp\u003e(1.954)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003cp\u003e(0.844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.691***\u003c/p\u003e \u003cp\u003e(0.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.167\u003c/p\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003cp\u003e(0.844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.704*\u003c/p\u003e \u003cp\u003e(0.373)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.465\u003c/p\u003e \u003cp\u003e(0.842)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.178***\u003c/p\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.539***\u003c/p\u003e \u003cp\u003e(0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162**\u003c/p\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.178***\u003c/p\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.467***\u003c/p\u003e \u003cp\u003e(0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.183***\u003c/p\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents the analysis of the spatial autocorrelation coefficient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e), which consistently remains positive and statistically significant. The table is divided into two groups, each illustrating the mediating effects of different variables on the relationship between digital technology level and NIS\u0026rsquo;s overall effectiveness. Group 1 assesses the role of industrial structure servitization as a mediating variable. Here, the regression coefficient of the digital technology level on industrial system servitization is 0.008, significant at the 5% level. Additionally, the regression coefficients of digital technology level and industrial structure servitization on NIS's overall effectiveness are 0.076 and 0.277, respectively, passing the 10% and 5% significance level tests. Notably, after accounting for the mediating role of industrial structure servitization, the impact of digital technology level on NIS's overall effectiveness decreases from 0.080 to 0.076. This implies that the total effect of digital technology on NIS's overall effectiveness exceeds its direct result, thereby confirming the positive mediating influence of industrial structure servitization. Group 2 examines the impact of stimulating the labor market as a mediating variable. The regression coefficient of the digital technology level on promoting the labor market is 0.004, significant at the 1% level. Moreover, the coefficients of digital technology level and stimulation of the labor market on NIS\u0026rsquo;s overall effectiveness are 0.071 and 0.117, respectively, both significant at the 5% and 1% levels. This analysis corroborates the positive mediating effect of stimulating the labor market on the relationship between digital technology level and NIS's overall effectiveness, thereby validating Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Heterogeneity analysis\u003c/h2\u003e \u003cp\u003eThe relationship between the level of digital technology and NIS\u0026rsquo;s overall effectiveness in China's various regions may exhibit spatial heterogeneity due to historical disparities in policy support, resource endowments, and geographic locations. To delve deeper into these regional differences, this study adopts the regional classification criteria set by the State Council of China. Accordingly, China's 30 provinces are categorized into four distinct regions: northeastern, eastern, central, and western (as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis then applies the spatial lag model for conducting regression analysis within these defined regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e provides a regional analysis of the impact of digital technology on NIS\u0026rsquo;s overall effectiveness. The findings reveal distinct regional variations: Northeast and East Regions. In these areas, the regression coefficients of digital technology level on NIS's overall effectiveness are not statistically significant, nor is the spatial autocorrelation coefficient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e). This suggests that in the northeastern and eastern regions of China, the level of digital technology does not significantly impact NIS\u0026rsquo;s overall effectiveness\u0026mdash;central and Western Regions. Conversely, in the west and major regions, the regression coefficients are 0.001 and 0.126, respectively, and these results are statistically significant at the 10% and 1% levels.\u003c/p\u003e \u003cp\u003eAdditionally, the spatial autocorrelation coefficient (ρ) is positive and significant at the 1% level. This indicates a substantial and positive impact of digital technology level on NIS\u0026rsquo;s overall effectiveness in these regions. In summary, there is a marked disparity in the influence of digital technology level on NIS\u0026rsquo;s overall effectiveness across different areas of China. These findings affirm Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, highlighting the varied effects of digital technology development on NIS\u0026rsquo;s overall effectiveness in other geographical regions.\u003c/p\u003e \u003cp\u003eThe empirical results surprisingly show a lack of positive correlation between the advanced level of digital technology in developed regions, such as the eastern region of China, and NIS\u0026rsquo;s overall effectiveness. This counterintuitive finding can be attributed to the Diminishing Marginal Effect in Advanced Regions. China's eastern and northeastern regions typically boast advanced digital infrastructures, including high-speed Internet, data centers, and high-tech industrial parks (Pan et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Digital technologies are extensively applied across various industries in these regions, leading to optimization of operations, productivity improvement, and new business model creation. However, due to the principle of diminishing marginal returns, the incremental contribution of further digital enhancements in these already highly developed areas is relatively limited. The existing digital infrastructure and technological applications are nearing saturation (Ma and Zhu, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hence, in regions like the East and Northeast, where digital technology is already mature, innovation is likely shifting towards new business models, managerial innovations, or deeper technological integration rather than solely relying on essential technical upgrades\u0026mdash;Government Policies and Investments in Less Developed Regions. Furthermore, The Chinese government has historically implemented policies to reduce disparities between the eastern coastal regions and the central and western regions, such as the \"Western Development\" policy and the \"Rise of Central China\" strategy. These initiatives have facilitated digital technology and innovation projects in the central and western regions through tax incentives, capital subsidies, and government procurement (Shao and Chen, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This support has significantly enhanced the digital technology appeal in these areas and focused on Digital Infrastructure Development in Central and Western Regions. The central government's focus on improving digital infrastructure in the West and significant regions includes expanding and upgrading Internet and mobile networks and constructing data centers and cloud computing platforms (Luo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Such infrastructural developments are essential for elevating the region's digital technology capabilities. Consequently, these initiatives have played a crucial role in advancing digital technologies in these regions, thus bolstering NIS\u0026rsquo;s overall effectiveness. In summary, some developed areas may already experience diminishing returns from further digital advancements, the central and western parts of China benefit significantly from governmental policies and infrastructural investments, leading to a more pronounced impact of digital technology on the overall effectiveness of their NIS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the Regional Heterogeneity Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNortheastern regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEastern regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentral regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWestern regions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003cp\u003e(0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003cp\u003e(0.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.126***\u003c/p\u003e \u003cp\u003e(0.063)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.022***\u003c/p\u003e \u003cp\u003e(0.282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.138***\u003c/p\u003e \u003cp\u003e(0.351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003cp\u003e(0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003cp\u003e(0.221)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003cp\u003e(0.282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003cp\u003e(0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112***\u003c/p\u003e \u003cp\u003e(0.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.490***\u003c/p\u003e \u003cp\u003e(0.188)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003cp\u003e(0.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.584***\u003c/p\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.040***\u003c/p\u003e \u003cp\u003e(0.813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.167\u003c/p\u003e \u003cp\u003e(0.515)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.345**\u003c/p\u003e \u003cp\u003e(0.136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003cp\u003e(0.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003cp\u003e(0.084)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.152*\u003c/p\u003e \u003cp\u003e(9.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.430***\u003c/p\u003e \u003cp\u003e(3.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.517\u003c/p\u003e \u003cp\u003e(2.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.418\u003c/p\u003e \u003cp\u003e(6.109)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.060***\u003c/p\u003e \u003cp\u003e(0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.658\u003c/p\u003e \u003cp\u003e(1.572)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.316\u003c/p\u003e \u003cp\u003e(1.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.081\u003c/p\u003e \u003cp\u003e(1.864)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003cp\u003e(0.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003cp\u003e(0.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.304***\u003c/p\u003e \u003cp\u003e(0.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.394***\u003c/p\u003e \u003cp\u003e(0.137)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions and discussion","content":"\u003cp\u003eThis study examines data from 30 provinces, municipalities directly governed by the central government, and autonomous regions across China from 2012 to 2022. Employing the VHSD-EM empowerment model, it assesses the NIS\u0026rsquo;s overall effectiveness in China in each area, incorporating aspects of digitalization. The research focuses on how the progression of digital technology influences NIS\u0026rsquo;s overall effectiveness, taking into account its intrinsic mechanisms and spatial heterogeneity. Results indicate that NIS\u0026rsquo;s overall effectiveness demonstrates spatial clustering, with highly and less effective regions forming distinct groupings. Further, the development of digital technology bolsters NIS\u0026rsquo;s overall effectiveness, a finding that remains robust even after considering endogeneity and through various robustness checks. Notably, digital technology enhances NIS\u0026rsquo;s overall effectiveness by promoting a service-oriented industrial structure and revitalizing the labor market. Nonetheless, the influence of digital technology on NIS\u0026rsquo;s overall effectiveness varies by region; it is less marked in the northeastern and eastern areas but significantly more pronounced in the central and western regions.\u003c/p\u003e \u003cp\u003eBased on the findings, this paper offers several policy recommendations for policymakers and relevant stakeholders: Firstly, it is essential to intensify the development of digital infrastructure. A key strategy to enhance digital growth in central and western China involves augmenting Internet access speed and quality. This includes investing in high-speed broadband and 5G networks in these areas, boosting Internet speed and reliability. Such advancements are vital for digital foundations and information flow and technology applications. Additionally, establishing data centers and cloud computing facilities will bolster big data and cloud technology development, providing essential data processing and storage capacities for local businesses and innovation efforts.\u003c/p\u003e \u003cp\u003eMoreover, fostering smart cities and digital villages with intelligent transportation systems, telemedicine services, and e-government initiatives can significantly elevate urban management efficiency and rural development levels, facilitating comprehensive economic and social enhancement in these regions. Secondly, industrial structure transformation should prioritize service-orientation and digitalization. Encouraging traditional manufacturing, agriculture, and retail sectors to adopt digital technologies (e.g., IoT, Big Data, AI) can enhance production efficiency and product quality. This includes integrating innovative manufacturing systems and employing precision agricultural technologies (Mohamed et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Concurrently, it is crucial to support the growth of digital service industries such as IT services, e-commerce, online education, and telemedicine. These industries create new job opportunities and significantly improve service efficiency and quality. Thirdly, revitalizing the labor market through digital resources is imperative. Establishing vocational training centers in technologically lagging regions, focusing on digital and modern industrial skills, is essential. This initiative should include programming, data analysis, and digital marketing courses to equip residents with skills for emerging industries. Leveraging Internet technology for online education and distance learning platforms will enable broader access to quality educational resources, particularly in remote areas.\u003c/p\u003e \u003cp\u003eFurthermore, encouraging collaboration between enterprises and educational institutions in developing market-relevant training programs can help upgrade skills and enhance employment competitiveness. Fourthly, strengthening inter-regional collaborative innovation is crucial. Formulating inter-regional cooperation frameworks and guidelines can promote technology exchange and R\u0026amp;D resource sharing between less developed and advanced regions, thereby improving NIS\u0026rsquo;s overall effectiveness. Establishing technology transfer platforms to facilitate knowledge and technology flow significantly to assist less developed areas in accessing advanced technology and management expertise is essential. Additionally, organizing seminars, training programs, and exchange initiatives can foster expert and talent exchanges across regions, enhancing innovation and development capabilities. Finally, implementing differentiated policy support is essential for balanced regional development. Tailored policy support based on regional specifics, such as increased financial assistance and technical support in less developed areas and enhanced market expansion and cooperation opportunities in advanced regions, can achieve coordinated development across all sites.\u003c/p\u003e \u003cp\u003eThis paper highlights that the policy recommendations proposed may encounter several challenges during implementation. Firstly, establishing high-speed broadband networks and 5G base stations in central and western China involves substantial investments and confronts geographical and environmental hurdles, such as complex terrain and inadequate transportation, making infrastructure development difficult and expensive. Moreover, these regions often lack the expertise and technical support to construct and maintain high-tech infrastructure. Secondly, firms satisfied with their current operations may resist training employees for new technologies due to increased burdens on production time and costs (Horv\u0026aacute;th and Szab\u0026oacute;, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), hindering the digital transformation of traditional industries. Thirdly, the success of digital education and training centers hinges on the quality of training and market demand for these skills (Chinorack\u0026yacute; and Čorejova, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Challenges such as limited Internet access and equipment availability can impede the implementation of online education and distance learning in remote areas. Lastly, the establishment and maintenance of cooperation mechanisms are crucial. Regional governments have varying development priorities and policy approaches, affecting the efficiency of interregional collaboration. Therefore, considering different regions' unique needs and strengths is essential for formulating effective cooperation frameworks ensuring equitable resource distribution and technology sharing. Technology transfer involves complexities beyond the technology itself, including intellectual property rights and market adaptability.\u003c/p\u003e \u003cp\u003eThis article may have the following limitations: Geographical Scope. The study focuses exclusively on China, which may limit the generalizability of its findings to other national contexts with different economic, political, and technological landscapes\u0026mdash;temporal Scope. The data spans 2012 to 2022. Changes in technology and innovation systems post-2022 are not considered. Methodological Limitations. While innovative, the reliance on spatial econometric models may have inherent limitations in capturing the complex and dynamic nature of innovation systems and digital technology's impact. External Validity. The findings might be influenced by specific policies and economic conditions in China, which may not apply universally. In addition, this paper has the following considerations for future research on digital technologies and NIS\u0026rsquo;s overall effectiveness: Global Comparison. Future studies could compare the NIS\u0026rsquo;s overall effectiveness in different countries, considering varying levels of digital technology integration\u0026mdash;Longitudinal Studies. Continued research post-2022 could provide insights into the evolving impact of digital technology on innovation systems\u0026mdash;cross-disciplinary Approaches. Integrating insights from sociology, economics, and political science could enrich the understanding NIS\u0026rsquo;s overall effectiveness in digital technology. In-Depth Qualitative Research. Exploring individual case studies or conducting interviews with key stakeholders in the innovation system could provide deeper insights into digital technology's causal mechanisms and real-world implications on NIS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge funding from the National Social Science Foundation of China (Project No. 23BGL063), as well as the contributions from all partners of the mentioned project.\u003c/p\u003e\n\u003cp\u003eCompliance with Ethical Standards\u003c/p\u003e\n\u003cp\u003eFunding: The National Social Science Foundation of China (Project No.\u0026nbsp;23BGL063).\u003c/p\u003e\n\u003cp\u003eAuthor Chen Wei declares that he has no conflict of interest. The corresponding author Song Hong-ti declares that he has no conflict of interest.\u003c/p\u003e\n\u003cp\u003eEthical approval: This article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbramo G, D\u0026rsquo;Angelo CA, Di Costa F (2020) The role of geographical proximity in knowledge diffusion, measured by citations to scientific literature. Journal of Informetrics, 14(1), 101010. https://doi.org/10.1016/j.joi.2020.101010 \u003c/li\u003e\n\u003cli\u003eAcciarini C, Cappa F, Boccardelli P, Oriani R (2023) How can organizations leverage big data to innovate their business models? A systematic literature review. 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In the countries' efforts to establish and strengthen national innovation systems (NIS), the development of digital technologies has received increasing attention. It has become a key driving force for the optimal growth and effective operation of national innovation systems. This study quantitatively assesses the overall effectiveness of China's national innovation system (NIS) using data from 30 provinces in China from 2012 to 2022, employing the Vertical and Horizontal Scatter Degree Method (VHSD), Entropy Method (EM), and coupled coordination models, and examines the external impact, internal mechanism and spatial heterogeneity of the development of digital technologies on the overall effectiveness of national innovation systems in the light of the characteristics of the digital era. The study results show spatial aggregation in the overall effectiveness of national innovation systems, with regions with high overall effectiveness clustering and areas with low overall effectiveness clustering. Second, the development of digital technology improves the overall effectiveness of national innovation systems, which is confirmed by endogeneity treatment and various robustness tests. Third, digital technology improves the overall effectiveness of national innovation systems by promoting the development of a service-oriented industrial structure and active labor market. Fourth, the impact of digital technologies on the overall effectiveness of national innovation systems is spatially heterogeneous. It is less pronounced in the Northeast and East but very significant in the Central and West, and the main reasons for this counterfactual result can perhaps be explained in terms of both diminishing marginal effects and policy tilting effects. Finally, this study not only gives corresponding policy recommendations but also further discusses the dilemmas and challenges that may be encountered in implementing these policies.\u003c/p\u003e","manuscriptTitle":"Evaluating the dynamics of digital technology in enhancing overall effectiveness of China's national innovation systems: A study based on VHSD and EM approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-06 13:31:50","doi":"10.21203/rs.3.rs-3681653/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-06T07:35:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-02T19:03:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-11-30T13:13:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Soft Computing","date":"2023-11-28T02:29:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4e5a518f-6ba4-4e63-9fc9-b4418870c8aa","owner":[],"postedDate":"December 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T16:05:52+00:00","versionOfRecord":{"articleIdentity":"rs-3681653","link":"https://doi.org/10.1007/s00500-025-10613-z","journal":{"identity":"soft-computing","isVorOnly":false,"title":"Soft Computing"},"publishedOn":"2025-06-21 15:57:15","publishedOnDateReadable":"June 21st, 2025"},"versionCreatedAt":"2023-12-06 13:31:50","video":"","vorDoi":"10.1007/s00500-025-10613-z","vorDoiUrl":"https://doi.org/10.1007/s00500-025-10613-z","workflowStages":[]},"version":"v1","identity":"rs-3681653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3681653","identity":"rs-3681653","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}