Digital Economy, Industry–Academia–Research Collaborative Innovation, and the Development of New-Quality Productive Forces

Abstract

Under the continuous innovation and widespread application of digital technology, accelerating the formation of new-quality productive forces is an unavoidable theme of the times. Based on a systematic analysis of the logical relationships between the digital economy, industry–academia–research collaborative innovation, and new-quality productive forces, this article constructs a theoretical analysis framework for the impact of the digital economy on new-quality productive forces. This article also employs spatial econometric techniques and panel threshold models to empirically test the relationships among the three. This study finds that spatial correlation is a significant factor that cannot be ignored in the process of the digital economy and industry–academia–research collaborative innovation empowering the formation of new-quality productive forces. The empowering effects of the digital economy and industry–academia–research collaborative innovation on new-quality productive forces differ, with the former’s effect significantly higher than the latter. The combined effect of both shows a strong synergistic impact on accelerating the formation of new-quality productive forces. Further threshold model tests reveal that both the digital economy and industry–academia–research collaborative innovation have certain thresholds in promoting the development of new productive forces. Only when they achieve synergy and progress together can they maximize their respective roles in driving the development of new productive forces. This research is of great significance for fully leveraging the digital economy to empower the formation of new-quality productive forces, thereby promoting high-quality economic development in China.

1. Introduction

In the context of today’s global economic integration and rapid development of information technology, the digital economy has emerged as a new engine driving economic and social development. This has not only reshaped the operational models of traditional industries but also spawned a multitude of emerging business forms, injecting new vitality into economic growth. New-quality productivity represents a leap to a new and high-quality type of productivity and serves as a crucial new driver for achieving high-quality development. According to a Xinhua News Agency report, in September 2023, President Xi Jinping, during his inspection tour of Northeast China, first proposed the concept of “new-quality productivity”. New-quality productivity refers to an efficient and high-quality productivity driven by technological innovation and supported by breakthroughs in key disruptive technologies. This surpasses the traditional resource-intensive model and aligns with high-quality development, embodying the new integration and connotations of the digital era [1]. Its core lies in integrating technological innovation resources, promoting the development of strategic emerging industries and future industries, and accelerating the construction of a new productivity paradigm. This concept was further emphasized at the Political Bureau meeting of the CPC Central Committee in 2024, where it was stated that developing new-quality productivity is an inherent requirement and key focal point for advancing high-quality development. In the context of globalization, cross-domain and cross-industry collaborative innovation has risen as the core engine driving technological innovation and industrial upgrading, with new-quality productivity being the shining fruit of this trend. Leveraging the power of collaborative innovation, participants can integrate resources, knowledge, and technologies, significantly accelerating the conquest of key disruptive technologies and the commercialization of results, thereby injecting inexhaustible impetus into the continuous leap of new-quality productivity. So, how can we accelerate the formation of new-quality productivity in the new development stage? The Government Work Report of 2024 clearly states that the digital economy will be regarded as a key means to promote the development of new productivity, requiring in-depth promotion of innovative development in the digital economy. According to the “Research Report on the Development of China’s Digital Economy (2023),” the total size of China’s digital economy reached 50.2 trillion yuan in 2022, with a nominal growth rate of 10.3%. This growth rate significantly surpassed the nominal growth rate of the gross domestic product (GDP) during the same period, and the proportion of the digital economy in GDP rose to 41.5%. The fourth industrial revolution, led by the digital economy and artificial intelligence, is driving human social progress and technological innovation at an unprecedented speed, with the digital economy becoming a powerful driver for the formation of new-quality productivity [2,3]. Therefore, in the current development environment of the new era, it is of great academic value and practical significance to deeply study the practical applications and theoretical mechanisms of the digital economy in promoting collaborative innovation among industry, academia, and research institutions to accelerate the formation of new-quality productivity. This is particularly crucial for advancing China’s high-quality economic development and the process of Chinese-style modernization. In addition to the digital economy, industry–university–research collaboration and innovation are also significant driving forces for the development of new-quality productivity. They not only help to improve resource allocation efficiency, reduce technical exchange barriers among various entities, and enhance the level of collaboration and cooperation among industry, universities, and research institutions, but also powerfully promote the development of new-quality productivity. Therefore, can the digital economy and industry–university–research collaboration and innovation each promote the development of new-quality productivity? Is there a synergistic effect between them? Are there spatial effects? Do they have a nonlinear relationship with new-quality productivity? Clarifying and answering these questions are particularly crucial for accelerating the formation of new-quality productivity, promoting high-quality economic development in China, and advancing the process of Chinese-style modernization.

Extensive research and discussions have been conducted by academic circles both domestically and internationally on how the digital economy and collaborative innovation jointly drive productivity growth. In the realm of international research, scholars have widely explored the multifaceted positive impacts of the digital economy on productivity development. On the one hand, studies have shown that the digital economy has a significant positive effect on total factor productivity across Europe, highlighting its central role in enhancing regional economic efficiency [4]. Further in-depth analysis of the Spanish manufacturing sector reveals that the complementary application of digital technologies can continuously drive long-term productivity growth, providing ample evidence that the deep integration of digital technologies with traditional industries can stimulate new drivers of economic growth [5]. Additionally, research has confirmed that the early construction of digital economy infrastructure has far-reaching and lasting positive impacts on subsequent regional productivity enhancements, emphasizing the importance of proactive strategic planning for the digital economy [6]. Meanwhile, the digital economy has demonstrated its powerful catalytic effect in multiple domains. For instance, it can significantly promote the development of blockchain technology, providing robust support for emerging technological fields [7]. Across 39 African countries, the widespread development of the digital economy has played a tremendous role in driving economic growth [8]. The digital economy also serves as a catalyst for achieving a circular economy, facilitating the green transformation of economic models [9]. In the West African region, the digital economy has provided more employment opportunities for youth, alleviating employment pressures [10]. Furthermore, the digital economy has notably accelerated the pace of economic globalization and international labor division, expanding its scope and making global economic connections closer [11]. Lastly, the interaction between the digital economy and international trade has also had a significant positive impact on economic growth in regions such as Africa, injecting new vitality into global economic cooperation and development [12]. On the other hand, some scholars have unveiled the new opportunities presented by the combination of digital platforms and collaborative innovation. This integration not only accelerates the generation and dissemination of innovative outcomes but also increasingly becomes a core force driving the transformation of business operation models and efficiency enhancement [13]. Meanwhile, the synergistic effect of innovation and information technology (IT) capital has also been proven to be an effective way to boost productivity, with the two complementing each other and jointly promoting high-quality economic and social development [14].

In the domestic research field, studies on the relationship between the digital economy and new-quality productivity have primarily focused on two aspects: the connotation and identification of new-quality productivity’s characteristics, as well as the internal logic, implementation pathways, and influencing factors of how the digital economy accelerates the formation of new-quality productivity. On the one hand, regarding the connotation and characteristics of new-quality productivity, existing literature mainly elaborates on its connotation from theoretical perspectives such as Marx’s theory of productivity [15], the Sinicization of Marxism [16], and the three essential factors of productivity [17]. The connotation of new-quality productivity is primarily embodied in its “newness” and “quality”. The “newness” refers to its distinction from traditional productivity, manifesting as a productivity that integrates the coordinated development of new technologies, new economies, and new business forms. The “quality” highlights the crucial role of innovation in disruptive technology research and development, emphasizing a leap in productivity [1,18]. Therefore, new-quality productivity is a synthesis of innovation-led changes in production methods, reorganization of production processes, and significant improvements in production outcomes, inherently possessing high-quality attributes. This not only aligns with current requirements for high-quality development but also fully embodies the deep integration and permeability of the digital economy.

In terms of the internal logic and implementation pathways for the digital economy to accelerate the formation of new-quality productivity, the vigorous development of the digital economy provides powerful technological support and market space for the enhancement of new-quality productivity. The widespread application of digital technologies has reduced transaction costs [19], improved resource allocation efficiency [20], and promoted industrial upgrading and innovation capability enhancement [21,22]. Simultaneously, the continuous development of new-quality productivity also places higher demands on the digital economy, requiring more efficient, intelligent, and green digital services to support it [23]. Specifically, first, relying on the digital economy can alleviate the triple constraints of “inadequate demand, excess supply, and weak expectations,” proposing that the digital economy can empower new-quality productivity through three mechanisms: enhancing the level of disruptive technological innovation, driving the innovative development of strategic emerging industries, and aligning with the characteristics of new-quality productivity [24]. Second, the formation process of new-quality productivity is inseparable from the fertile soil cultivated by the development of the digital economy; conversely, the development of new-quality productivity is also a process of nurturing key breakthroughs in science and technology, providing space and technical support for the development of the digital economy. The bidirectional driving relationship between the two indicates that the digital economy can promote the development of new-quality productivity through technological innovation, institutional optimization, and factor coordination [3]. Finally, based on the qualitative reshaping function of digital technologies on the three essential factors of productivity, it is proposed that the digital economy can provide inexhaustible impetus for new-quality productivity by enhancing enterprise innovation capability, strengthening the integration and mutual promotion of industrial and innovation chains, and assisting in improving the quality and efficiency of the national innovation system at the micro, meso, and macro levels [25].

On the other hand, regarding the influencing factors of new-quality productivity, research mainly focuses on macro-financial policy aspects, such as financial agglomeration [26], digital inclusive finance [27], science and technology financial policies [28], and green finance [29,30]. These policies play a crucial role in cultivating and enhancing new-quality productivity. Meanwhile, some scholars have also delved into more specific factors, exploring the profound impact of effective allocation of data elements [31], the enabling role of ESG (Environmental, Social, and Governance) [32], and the enhancement of human capital [33] on the development of new-quality productivity.

The digital economy has exerted a certain impact on productivity both domestically and internationally, with some commonalities and differences observed. The commonality lies in the fact that the digital economy has become a key driver of economic growth and productivity enhancement in both developed and developing countries. The widespread application of digital technologies, such as cloud computing, big data, and artificial intelligence, has greatly promoted improvements in production efficiency and innovations in business models. The differences mainly manifest in the stages, speeds, and policy environments of digital economic development across countries. As a latecomer in the digital economy, China has achieved rapid rise through various efforts including government guidance, market-driven initiatives, and enterprise innovation, and has taken a leading position in certain fields. Other countries, such as those in Europe, although having started earlier, are also continuously exploring and making progress in the deep application and cross-sector integration of the digital economy.

Overall, existing research on the digital economy, industry–university–research collaboration and innovation, and the development of new-quality productivity mainly has the following limitations. Firstly, few studies systematically explore the relationships among the digital economy, industry–university–research collaboration and innovation, and new-quality productivity within a unified analytical framework. Scholars tend to focus separately on the impacts of the digital economy and industry–university–research collaboration and innovation, neglecting the synergistic effects between the two. Secondly, there is insufficient attention paid to the spatial spillover effects and spatial interactions of the digital economy and industry–university–research collaboration and innovation. Furthermore, existing research overlooks the nonlinear impacts of the digital economy and industry–university–research collaboration and innovation on new-quality productivity.

In light of this, to address these research gaps, the following supplementary research has been conducted: ① Innovative research perspective. This paper not only focuses on the enabling role of the digital economy on new-quality productivity but also considers the impact of industry–university–research collaboration and innovation on new-quality productivity in the context of the digital economy, as well as their resonant effects. This provides a reference for formulating appropriate related policies. Specifically, this paper attempts to construct a logical framework for how the digital economy influences new-quality productivity from a new perspective and systematically examines the “independent effects” and “matching effects” of the digital economy and industry–university–research collaboration and innovation on new-quality productivity. ② Diversity in research methods. On the one hand, this paper employs spatial econometric models to empirically test the spatial spillover effects of the digital economy and industry–university–research collaboration and innovation on new-quality productivity, aiming to analyze the spatial correlations in the formation process of new-quality productivity. On the other hand, it further constructs a panel threshold model to explore the possible nonlinear relationships between the digital economy, industry–university–research collaboration and innovation, and new-quality productivity. This research not only contributes to promoting the deep integration and accelerated development of industry–university–research collaboration, fostering the continuous emergence of new-quality productivity, and thus achieving the sustainable development goals of the digital economy; but also serves as a beneficial extension and supplement to productivity theory and synergy theory. Based on existing theoretical foundations, it provides new perspectives and paths for the integration and application of the two.

2. Theoretical Analysis and Research Assumptions

2.1. The Impact of Digital Economy on New-Quality Productivity

According to Marx’s theory of productive forces, productive forces are the fundamental driving force behind social development, and new-quality productive forces represent a leap in productivity that integrates the development of new technologies, new economies, and new business models, emphasizing the leading role of scientific and technological innovation and the outcome-oriented nature of disruptive technologies. Compared to traditional productive forces, they constitute a significant advancement. Consequently, the digital economy, rich in scientific and technological innovation, has become the most active element in productive forces and is bound to promote the development of new-quality productive forces [3]. Specifically, first, the digital economy, with its unique characteristics, has nurtured the vigorous development of new organizational forms, innovative business models, and emerging industries. This not only excels at efficiently integrating and optimizing traditional production factors [34] but also significantly promotes the deep integration and widespread penetration of new factors [35]. This characteristic not only alters the comparative advantage landscape of the labor force but also accelerates the flow of labor from traditional to emerging industries, achieving more precise and efficient resource allocation [36]. Second, the digital economy injects new vitality into economic development with its powerful innovative capabilities. Industries closely related to the digital economy are often highly knowledge-intensive and innovative, leveraging new technologies, methods, and products to deeply transform and upgrade traditional industries, significantly enhancing the innovative content and efficiency of total factor productivity [37]. This innovation is manifested not only in technological breakthroughs but also in multifaceted reforms such as business models and management philosophies, providing a continuous driving force for economic development. Third, the digital economy greatly expands the market boundaries of enterprises [38], enabling them to easily transcend geographical limitations and penetrate global markets through digital means like e-commerce to participate in international competition and cooperation [39]. This not only enhances the international competitiveness of enterprises but also promotes interactive exchange and resource sharing in global trade, injecting new vitality into the process of global economic integration. Fourth, the rise of the internet and digital platforms has brought unprecedented changes in business models to traditional industries. Take the sharing economy as an example, as an essential part of the digital economy, it not only significantly expands market scope and reduces transaction costs but also increases market activity and flexibility [40]. This new business model not only satisfies the diversified needs of consumers but also promotes efficient resource utilization and optimal allocation. Fifth, the development of the digital economy has also driven the rapid advancement of cutting-edge technologies such as information technology, artificial intelligence, and blockchain [41]. Through changes in quality, efficiency, and dynamics, these technologies provide strong support for innovation-driven sustainable development. They not only reshape production processes and value chains but also propel the entire society’s productivity innovation and transformation, laying a solid foundation for constructing a new development paradigm in the digital economy era. Based on the above discussion, this paper proposes the following research hypothesis:

H1: 

There is a positive correlation between the digital economy and new-quality productive forces.

2.2. The Influence of Industry–Academia–Research Collaborative Innovation on the Development of New-Quality Productivity

New-quality productive forces are the top priority for driving high-quality development in China, and industry–academia–research collaborative innovation is one of the key measures to bridge the gap between science and technology and the economy, as well as to promote the development of new-quality productive forces. According to synergy theory, various collaborative entities pursue self-organizing development to maximize their own benefits under a common goal. This process enables the system to evolve from disorder to order, from low to high levels, ultimately achieving a “1 + 1 + 1 > 3” effect. Industry–academia–research collaborative innovation is a concrete manifestation of this synergy theory. This represents an innovative model that integrates education, science and technology, talent, and industry to jointly engage in technology development, knowledge creation, and achievement transformation [42]. Industry–academia–research collaborative innovation promotes the development of new-quality productive forces through synergistic enhancement, risk-sharing, and talent stimulation effects: First, it effectively integrates the technological research and development capabilities of universities and research institutions with the market application capabilities of enterprises, accelerating the flow of heterogeneous resources among various innovative entities, promoting the development of new technologies and products, and enhancing the economic benefits of scientific and technological achievements [43]. This forms an organic whole among the innovative entities and maintains their mutual synergy, thereby achieving a benign interaction between the industrial chain and the innovation chain. Second, industry–academia–research collaborative innovation helps reduce innovation costs and risks, and achieves optimal resource allocation and efficient utilization [44]. Collaborative entities can share experimental equipment, research results, and market information, avoiding duplicated resource investment and waste. At the same time, it can lower research and development costs, improve efficiency, and enhance the sustainable competitive advantages of each entity [45]. Third, new-quality productive forces are driven by innovation and talent [46]. Industry–academia–research collaborative innovation often involves universities and high-tech enterprises. Universities optimize talent cultivation pathways through knowledge transfer, while enterprises promptly optimize their talent structure, address talent shortages, and promote high-quality industrial development [47]. Therefore, the implementation of industry–academia–research collaboration not only enhances innovation levels but also cultivates innovative technical talents, accelerates upfront research and development innovation, and drives the development of new-quality productive forces. Based on the above discussion, this paper proposes the following research hypothesis:

H2: 

Industry–academia–research collaborative innovation can promote the development of new-quality productive forces.

2.3. The Impact of the Matching Effect of Digital Economy and Industry–Academia–Research Collaborative Innovation on the Development of New-Quality Productivity

The digital economy, as a new economic form driven by both technological and institutional innovations, exerts amplifying, superimposing, and multiplying effects on economic development, while upgrading and reshaping the paradigms of technological innovation and traditional industry models [24]. The digital economy not only facilitates the establishment of a new digital ecosystem and the development of new models of cross-regional, cross-platform, and cross-industry collaborative development but also integrates innovative elements to promote the formation of a regional industry–academia–research collaborative innovation system featuring mutual synergy, complementary advantages, and supply–demand interaction [48]. Specifically, the role of the digital economy in promoting industry–academia–research collaborative innovation manifests in the “network effect” and “scale effect.” On the one hand, the widespread application scenarios of the digital economy accelerate the research and development of digital technologies, effectively addressing barriers to cross-regional technology exchange among enterprises and promoting technology spillovers across the industry [49]. At the same time, it provides a platform and foundation for the innovative development of various industry–academia–research entities, thereby laying the groundwork for the formation of an industry–academia–research collaborative innovation model [50], demonstrating a strong “network effect.” On the other hand, digital elements effectively reduce resource misallocation and accelerate the digital integration of various innovative elements and resources, leading to a continuous wave of innovation [51]. This not only provides financial support for enterprises to undertake digital transformation, for universities and research institutions to cultivate digital talents, and for tackling “bottleneck” technologies but also stimulates the participation of industry–academia–research collaborative innovation entities and enhances the level of collaborative innovation [52], exhibiting a pronounced “scale effect.” The development of the digital economy further stimulates the enthusiasm of innovative entities, strengthens the collaborative innovation capabilities of industries, schools, and research institutions, and provides high-quality skilled talents and disruptive technological innovations for the development of new-quality productive forces. Based on the above discussion, this paper proposes the following research hypothesis:

H3: 

The matching effect between the digital economy and collaborative innovation among industry, academia, and research can significantly promote the development of new productive forces.

3. Research Design

3.1. Model Setting

3.1.1. Selection and Setting of the Spatial Metrology Model

The SDM model not only integrates the respective advantages of the SAR and SEM spatial models but also takes into account the spatial correlation between dependent and independent variables. Furthermore, the SDM model can simultaneously capture the effects of spatial lag and spatial error terms, enabling a more accurate description of the relationships between spatial data. As an extended form of the spatial lag model and the spatial error model, the Spatial Durbin Model (SDM) considers the spatial correlation of both dependent and independent variables, further enhancing the model’s ability to handle spatial heterogeneity. Furthermore, the SDM can simultaneously capture the effects of spatial lag terms and spatial error terms, making the model more accurate in describing the relationships among spatial data. Therefore, drawing on the research characteristics of existing spatial econometric models, this paper employs the Spatial Durbin Model (SDM) to examine the relationship between the digital economy, industry–academia–research collaborative innovation, and new-quality productive forces. The equation is as follows:

$${NPRO}_{i,t}={\alpha }_0+\rho W{NPRO}_{i,t}+{\alpha }_1{DI}_{i,t}+{\alpha }_2{CXY}_{i,t}+{\alpha }_3{Z}_{i,t}+{\theta }_{1}W{DI}_{i,t}+{\theta }_2{WCXY}_{i,t}+{\theta }_3{WZ}_{i,t}+{\epsilon }_{i,t}$$

(1)

$${NPRO}_{i,t}={\alpha }_0+\rho W{NPRO}_{i,t}+{\alpha }_1{DI}_{i,t}\ast {CXY}_{i,t}+{\alpha }_2{Z}_{i,t}+{\theta }_{1}W{DI}_{i,t}\ast {CXY}_{i,t}+{\theta }_2{WZ}_{i,t}+{\epsilon }_{i,t}$$

(2)

In the equation, NPRO_i,t, DI_i,t, and CXY_i,t represent the new productive forces, digital economy, and collaborative innovation among industry, academia, and research, respectively, in region i in year t. Z_i,t denotes the set of control variables, ρ is the spatial autoregressive coefficient, W is the spatial weight matrix, and ε_i,t represents the random disturbance term.

3.1.2. Threshold Model Setting

The digital economy and industry–academia–research collaborative innovation complement and interact with each other, and the combined effect of the two on promoting the development of new-quality productive forces may encounter a “threshold” due to coordination issues between them. Therefore, this paper takes the digital economy and industry–academia–research collaborative innovation as threshold variables and constructs a panel threshold model with the following equation:

$${lnNPRO}_{i,t}={\beta }_0+{\beta }_1{DI}_{i,t}\ast I\left\{{CXY}_{i,t}\le \omega \right\}+{\beta }_2{DI}_{i,t}\ast I\left\{{CXY}_{i,t}>\omega \right\}+{\phi Z}_{i,t}+{\epsilon }_{i,t}$$

(3)

$${lnNPRO}_{i,t}={\gamma }_0+{\gamma }_1{CXY}_{i,t}\ast I\left\{{DI}_{i,t}\le \omega \right\}+{\gamma }_2{CXY}_{i,t}\ast I\left\{{DI}_{i,t}>\omega \right\}+{\phi Z}_{i,t}+{\epsilon }_{i,t}$$

(4)

3.2. Variable Definition

3.2.1. Explained Variable

Based on the three essential factors of productivity, namely labor, objects of labor, and means of labor, this paper constructs an index system for new-quality productive forces, drawing on the research methods of Liu J.H. et al. [53], as shown in

Table 1. New-quality productivity evaluation index system.

Primary Index	Secondary Index	Three-Level Index	Measurement Method	Stats
Labor	Human capital input	Scientific input	Annual government expenditure on science	+
		Educational input	Annual government expenditure on education	+
		Number of R&D personnel	Full-time equivalent of R&D personnel in industrial enterprises above designated size	+
	Labor output	Innovative R&D	Number of domestic patents granted	+
	Labor skill	Human capital structure	Average number of students enrolled in colleges and universities	+
	Labor productivity	Per capita output value	Per capita GDP	+
Object of labor	Emerging industry	emerging sectors of strategic importance	Output value of high-tech industries of industrial enterprises above designated size	+
		Activity of emerging industries	Total import and export trade	+
	Ecological environment	Intensity of environmental protection	Environmental protection expenditure/Government public finance expenditure	+
		Pollutant discharge	Sulfur dioxide emission	−
		Pollutant treatment	Industrial waste gas treatment facilities (set)	+
Means of labor	Level of infrastructure improvement	Traditional infrastructure	Highway mileage	+
			Railway mileage	+
		Digital infrastructure	Number of broadband Internet access ports	+
		Telecommunications service penetration	Total volume of telecommunication service	+
	Energy consumption level	Total energy consumption	Energy consumption	−
	Scientific and technological innovation	R&D investment	R&D expenditure	+

.

As the core component of new-quality productive forces, the characteristics and value of new-quality laborers can be comprehensively summarized from four aspects: human capital investment, labor output, laborer skills, and labor productivity, encompassing six indicators: scientific investment, educational investment, number of R&D personnel, innovative R&D, human capital structure, and output per capita. These four aspects not only deeply reflect the internal qualities and external performances of new-quality laborers but also closely relate to the overall efficiency and development potential of new-quality productive forces. Firstly, human capital investment is a crucial indicator for measuring the growth and development of new-quality laborers. This covers investments in education, training, and other aspects, directly determining laborers’ knowledge levels, skill proficiency, and innovative capabilities. In the context of new-quality productive forces, continuous human capital investment is key to enhancing laborers’ comprehensive qualities and adapting to technological changes and industrial upgrades. Secondly, labor output reflects the productivity and contribution of new-quality laborers. High-output laborers can create more value for enterprises and society, driving sustained economic development. The improvement of labor output not only depends on laborers’ skills and efforts but is also influenced by various factors such as the production environment and technological conditions, serving as a direct manifestation of new-quality laborers’ effectiveness. Furthermore, laborer skills are one of the core elements of new-quality productive forces. Laborers with advanced skills and innovative capabilities can more effectively utilize new-quality means of labor, improving production efficiency and driving industrial upgrades. The enhancement of skills is not only crucial for individual career development but also serves as a driving force for the continuous progress of new-quality productive forces. Lastly, labor productivity is a comprehensive indicator for measuring the productivity of new-quality laborers. This comprehensively considers the relationship between labor input and output, reflecting laborers’ production achievements per unit of time. Improving labor productivity is one of the core goals of new-quality productive force development and a key path to achieving high-quality economic development. In summary, summarizing new-quality laborers from the four aspects of human capital investment, labor output, laborer skills, and labor productivity provides a comprehensive and accurate revelation of their internal characteristics and external performances, offering strong support for the research and practice of new-quality productive forces.

As an important component of new-quality productive forces, the characteristics and value of new-quality objects of labor can be summarized from two aspects: emerging industries and ecological environment, mainly including five indicators: strategic emerging industries, emerging industry activity, environmental protection efforts, pollutant emissions, and pollutant treatment. This not only reveals the crucial role of new-quality objects of labor in the modern economy but also reflects their far-reaching impact on sustainable development. Firstly, from the perspective of emerging industries, new-quality objects of labor represent the latest achievements in technological innovation and industrial transformation. With technological advancements, more and more emerging technologies and products are being applied in production practices, becoming new objects of labor. For example, the development of new-generation information technology, biotechnology, new energy, and new materials has spawned numerous new-quality objects of labor with high technological content and added value. These objects of labor not only drive the rapid development of emerging industries but also provide strong support for the transformation and upgrading of traditional industries. By continuously expanding and optimizing objects of labor, new-quality productive forces can exert their effects in a wider range of fields, driving the optimization and upgrading of economic structures and the transformation of economic growth patterns. Secondly, from the perspective of the ecological environment, new-quality objects of labor embody the requirements of green development concepts and sustainable development strategies. Driven by new-quality productive forces, people are increasingly focusing on the environmental friendliness and resource efficiency of objects of labor. For example, by developing clean energy, promoting circular economy, and strengthening waste recycling and utilization, more green, low-carbon, and environmentally friendly new-quality objects of labor can be created. These objects of labor not only help reduce environmental pollution and ecological damage but also improve resource utilization efficiency, promoting sustainable economic and social development.

As a key element of new-quality productive forces, the characteristics and roles of new-quality means of labor can be deeply summarized from three aspects: infrastructure improvement, energy consumption levels, and technological innovation, including five indicators: traditional infrastructure, digital infrastructure, telecommunications penetration, overall energy consumption, and R&D investment. This not only comprehensively reflects the core position of new-quality means of labor in modern production but also reveals their important role in promoting high-quality economic and social development. Firstly, the improvement of infrastructure is the foundation for new-quality means of labor to exert their effectiveness. In modern production, efficient and convenient infrastructure such as transportation networks, information networks, and energy supply systems provide strong support for the efficient use of new-quality means of labor. The improvement of infrastructure not only enhances production efficiency but also promotes the optimal allocation of resource elements, laying a solid foundation for the rapid development of new-quality productive forces. Secondly, energy consumption levels are an important indicator for measuring the efficiency and environmental performance of new-quality means of labor. With the increasingly tense global energy situation and heightened environmental protection awareness, new-quality means of labor are increasingly focusing on controlling and optimizing energy consumption during their design and application processes. By adopting energy-saving technologies and improving energy utilization efficiency, new-quality means of labor can ensure production efficiency while reducing energy consumption, minimizing environmental pollution, and achieving a win-win situation for both economic and ecological benefits. Lastly, technological innovation is the core driving force for the continuous evolution of new-quality means of labor. With technological advancements, new-quality means of labor continue to emerge, with significant improvements in performance, functionality, and efficiency. Technological innovation not only drives the upgrading and replacement of new-quality means of labor but also spawns new production methods and business models, injecting strong momentum into the sustained development of new-quality productive forces.

After conducting the dimensionless processing of the data for various indicators, the entropy method is utilized to calculate the composite index of new productive forces, thereby deriving the NPRO variable. The specific calculation steps are as follows: As there are both positive and negative indicators in the indicator system, the data are first standardized:

Standardize the positive indicators:

$$Y_{ij}={\displaystyle \frac{x_{ij}-\min \left(x_{ij}\right)}{\max (x_{ij})-\min (x_{ij})}}$$

(5)

Standardize the negative indicators:

$$Y_{ij}={\displaystyle \frac{\max (x_{ij})-x_{ij}}{\max (x_{ij})-\min (x_{ij})}}$$

(6)

Secondly, calculate the entropy value of each indicator:

$$e_j=-k{\displaystyle \sum _{t=1}^T{\displaystyle \sum _{i=1}^{m}P_{ij}^t}}\ln P_{ij}^t$$

(7)

Once again, determine the weights of each indicator:

$$w_j=(1-e_j)/{\displaystyle \sum _{j=1}^n(1-e_j})$$

(8)

Finally, calculate the comprehensive index of new-quality productivity:

$$S_i={\displaystyle \sum _{j=1}^n{w}_j}{y}_{it}$$

(9)

3.2.2. Explanatory Variable

The core explanatory variables in this paper are the digital economy and collaborative innovation among industry, academia, and research. With the rapid development of information technology and the widespread application of intelligent technology, the formulation and implementation of digital economy policies have become increasingly important. Keyword frequency analysis of digital economy policies has emerged as a crucial tool for conducting in-depth interpretations of policy documents and declarations. By analyzing the frequency and distribution of relevant words in policy documents, we can gain deep insights into the main focuses and key areas of the policies, helping relevant departments better grasp the spirit and implementation direction of the policies. Therefore, conducting keyword frequency analysis of digital economy policies not only helps to grasp policy orientations but also guides practical actions and promotes the healthy development of the digital economy in various cities. Hence, this paper refers to existing methods for measuring keyword frequency in the digital economy [54,55] to extract the frequency of digital economy-related keywords from provincial government work reports, thereby comprehensively measuring the digital economy variable (DI).

Referring to the research ideas of relevant scholars [56], this paper examines the inputs and outputs of innovation from multiple dimensions, including elements of the innovation environment. This selects indicators such as scientific and technological funding investment, number of R&D personnel, number of patent grants, digital inclusive finance index, and internet access ports. Through scientific weighting and calculation, the variable for collaborative innovation among industry, academia, and research (CXY) is obtained.

3.2.3. Control Variable

In the selection of control variables, while considering the research objectives and drawing on existing studies [57,58,59], this paper chooses the following control variables: economic development level (EGdp), represented by per capita GDP; urbanization level (LU), measured by the urbanization rate; degree of openness (Open), indicated by the proportion of total import and export value to GDP; human capital level (HC), measured by the proportion of college students to the total population; financial development efficiency (FE), represented by the digital inclusive finance index; and government support (GS), indicated by the proportion of local fiscal budget revenue to GDP.

3.2.4. Data Sources and Descriptive Statistics

Around 2012, the digital economy began to enter a phase of rapid development globally. With the popularization of internet technology and the widespread use of mobile devices, new-generation information technologies such as big data, cloud computing, and artificial intelligence gradually matured and were applied in various fields, laying a solid foundation for the rise of the digital economy. Additionally, data from this period are relatively complete and easily accessible. As statistical systems continue to improve and information disclosure becomes more transparent, the data from this era can accurately reflect the development status of the digital economy and new forms of productivity. Therefore, this paper selects data from 30 provinces and regions in China from 2012 to 2022 (excluding regions with difficult-to-obtain data such as Tibet and Hong Kong) as samples. The research data mainly come from the “China Statistical Yearbook”, EPS database, and Wind database.

Table 2. Descriptive statistics.

Variable	N	Mean	Std. Dev.	Min	Max
NPRO	330	0.206	0.232	0.01	1.454
DI	330	0.127	0.1	0.017	0.59
CXY	330	0.146	0.152	0.009	0.945
Open	330	0.265	0.268	0.008	1.354
EGdp	330	6.069	3.084	1.895	19.031
GS	330	0.113	0.032	0.058	0.245
HC	330	0.021	0.006	0.009	0.044
LU	330	0.607	0.117	0.363	0.896
FE	330	330.416	87.255	107.07	475.26

reports the descriptive statistical results of the sample.

4. Empirical Results and Analysis

4.1. Empirical Analysis of the Spatial Econometric Model

4.1.1. Spatial Autocorrelation Analysis

To address the potential spatial heterogeneity in spatial econometric models, this paper employs Moran’s I index based on a geographical matrix to test the spatial correlation between the digital economy, industry–university–research collaborative innovation, and new forms of productivity.

Table 3. Results of the spatial autocorrelation test.

Year	DI	CXY	NPRO
	Moran’s I	Moran’s I	Moran’s I
2012	0.055 ** (2.493)	0.011 ** (2.011)	0.457 *** (4.000)
2013	0.061 *** (2.666)	0.019 *** (2.096)	0.443 *** (3.880)
2014	0.065 *** (2.786)	0.023 *** (2.136)	0.432 *** (3.796)
2015	0.069 *** (2.914)	0.036 *** (2.261)	0.450 *** (3.947)
2016	0.059 *** (2.650)	0.042 *** (2.313)	0.418 *** (3.699)
2017	0.055 *** (2.543)	0.049 *** (2.408)	0.416 *** (3.698)
2018	0.068 *** (2.913)	0.055 *** (2.488)	0.388 *** (3.508)
2019	0.042 ** (2.191)	0.058 ** (2.498)	0.391 *** (3.546)
2020	0.043 ** (2.228)	0.063 ** (2.506)	0.385 *** (3.504)
2021	0.055 ** (0.982)	0.067 ** (0.519)	0.396 *** (3.593)
2022	0.061 ** (1.585)	0.073 ** (1.626)	0.377 *** (3.435)

Note: Z-values in brackets, **, and *** are statistically significant at 5%, and 1%, respectively.

indicates that Moran’s I coefficients for the development levels of China’s digital economy, collaborative innovation among industry, academia, and research, and new-quality productivity from 2012 to 2022 are significantly positive at the 5% level. This suggests that there is a significant spatial autocorrelation between the digital economy and new-quality productivity across China’s provinces during the sample period, providing a feasible foundation for conducting spatial econometric tests in this paper.

4.1.2. Independent Effect Analysis of Digital Economy and Industry–Academic–Research Collaborative Innovation

This paper presents the estimation results of multiple spatial panel models to test the robustness of the empirical findings, and confirms that the SDM model has the best fit based on the Wald test and R² values. Therefore, subsequent analysis will focus on the SDM model. The results in

Table 4. The test results of the independent effects of the digital economy and collaborative innovation among industry–academia–research.

Variable	SEM		SAR		SDM
	Geographic Matrix	Economic Matrix	Geographic Matrix	Economic Matrix	Geographic Matrix	Economic Matrix
DI	0.2411 ** (2.660)	0.2033 ** (2.421)	0.2162 ** (1.963)	0.1372 *** (3.682)	1.490 *** (0.105)	1.466 *** (0.103)
CXY	0.1388 *** (3.665)	0.1372 *** (3.650)	0.0633 (0.1521)	0.1169 *** (3.015)	0.136 *** (4.633)	0.119 *** (4.681)
$\rho$	0.8147 *** (42.662)	0.8266 *** (44.660)	0.7199 *** (33.281)	0.8311 *** (54.669)	0.002 *** (19.365)	0.018 *** (33.361)
W × DI					0.393 * (2.339)	0.616 ** (4.382)
W × CXY					0.172 * (3.778)	0.153 * (3.023)
Wald lag					76.335 *** (15.013)	41.775 *** (7.991)
Wald error					88.286 *** (19.216)	44.336 *** (8.188)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes	Yes	Yes
LogL	468.119	461.331	483.398	487.038	501.223	503.201
Adj_R2	0.441	0.440	0.522	0.599	0.683	0.763

Note: Z-values in brackets, *, **, and *** are statistically significant at 10%, 5%, and 1%, respectively.

indicate that both the digital economy and industry–university–research collaborative innovation have significantly positive effects on the development of new forms of productivity, under both geographical and economic matrices. Moreover, the impact of the digital economy is notably higher than that of industry–university–research collaboration. This is because the digital economy, driven by technological innovation and data, directly promotes productivity growth, while industry–university–research collaboration, though effective, still needs to expand its depth and breadth to unleash greater potential. Additionally, the coefficients of the spatial lag terms for new forms of productivity are positive, and significantly higher in the economic matrix than in the geographical matrix, suggesting that the spatial correlation of regional economic development effectively facilitates the spillover of regional technological advancements, which is conducive to accelerating the formation of new forms of productivity. Thus, this paper validates Hypotheses 1 and 2.

4.1.3. Matching Effect Analysis of Digital Economy and Industry–Academic–Research Collaborative Innovation

In this paper, the mainstream research method of constructing interaction terms between variables is adopted to identify the matching effect of the digital economy and collaborative innovation among industry, academia, and research on new-quality productivity. According to the regression analysis results presented in

Table 5. The test results of the matching effect between the digital economy and collaborative innovation among industry, academia, and research.

Variable	SDM
	Geographic Matrix	Economic Matrix
DI × CXY	0.326 *** (4.903)	0.283 *** (4.551)
Controls	YES	YES
$\rho$	0.701 *** (29.033)	0.749 *** (33.912)
W × DI × CXY	0.439 (2.903)	0.791 * (4.773)
Wald lag	83.891 *** (16.772)	47.443 *** (8.656)
Wald error	90.286 *** (23.761)	48.668 *** (10.286)
LogL	491.224	503.556
R2	0.608	0.583

Note: Z-values in brackets, *, and *** are statistically significant at 10%, and 1%, respectively.

, the regression coefficient of the matching effect is significantly higher than that of the independent effects. This suggests that future development should focus on the effective matching between the digital economy and collaborative innovation among industry, academia, and research. Emphasizing the role of either the digital economy or collaborative innovation alone in promoting new-quality productivity cannot fully harness their potential. Only through the organic integration of both can new-quality productivity be maximized, achieving the benefit of “1 + 1 > 2”. Thus, Hypothesis H3 is verified.

4.1.4. Robustness Testing

The method of extracting keyword frequency related to the digital economy from provincial government work reports to comprehensively assess the digital economy variable may risk sample bias due to potential exaggerated representations of digital economy content in these reports. Therefore, following the measurement methods of the digital economy proposed by Zhong W et al. [2] and Liu J et al. [60], this paper selects a total of 11 indicators across three dimensions: digital information infrastructure, the degree of enterprise digitalization, and the level of digital industry development, to construct a comprehensive index for measuring the development level of the digital economy. Additionally, drawing on the research approach of Song Y [61], this paper divides the measurement of the digital economy into direct and indirect effects. The direct effects are measured through three indicators: software product sales, information technology service fees income, and the number of websites, while the indirect effects are assessed using three indicators: total e-commerce sales, the number of computers used, and total e-commerce purchases. The variation coefficient method is employed to evaluate the development level of the digital economy using these indicators. By replacing the original digital economy variable and conducting regression analysis again, the results are presented in

Table 6. Robustness test regression results.

Variable	SDM		SDM
	Geographic Matrix	Economic Matrix	Geographic Matrix	Economic Matrix
DI	0.248 *** (4.281)	0.217 *** (3.871)	1.501 *** (0.118)	1.513 *** (0.121)
CXY	0.154 *** (4.631)	0.159 *** (4.633)	0.133 *** (4.615)	0.111 *** (4.601)
Controls	YES	YES	YES	YES
$\rho$	0.692 *** (28.991)	0.723 *** (32.608)	0.003 *** (19.371)	0.016 *** (33.353)
W × CXY	0.163 * (3.681)	0.133 * (3.029)	0.179 * (3.782)	0.162 (3.043)
W × DI direct effect			0.306 (3.431)	0.501 * (3.686)
W × DI indirect effect			0.109 * (1.037)	0.133 ** (1.039)
W × DI direct effect × CXY			0.359 (3.203)	0.636 * (3.873)
W × DI indirect effect × CXY			0.131 ** (1.732)	0.179 ** (2.018)
Wald lag	76.332 * (15.001)	41.712 * (7.991)	77.556 *** (16.337)	42.651 *** (7.661)
Wald error	88.281 * (19.219)	44.321 * (8.691)	90.212 *** (20.236)	45.305 *** (8.307)
LogL	501.220	503.201	505.203	509.273
R2	0.6392	0.6331	0.686	0.665

Note: Z-values in brackets, *, **, and *** are statistically significant at 10%, 5%, and 1%, respectively.

. The regression results in Table 6 indicate that after replacing the measurement method of the digital economy, there are only slight changes in the coefficient sizes, while the significance and direction remain largely unchanged. This demonstrates that the research conclusions of this paper are relatively stable and persuasive.

4.2. Decomposition Testing of Spatial Correlation Between the Independent Effect and the Matching Effect

The regression results in

Table 7. The results of spatial correlation decomposition test of the independent effect and the matching effect.

Variable	Direct Effect		Indirect Effect		Total Effect
	Geographic Matrix	Economic Matrix	Geographic Matrix	Economic Matrix	Geographic Matrix	Economic Matrix
DI	0.243 ** (2.663)	0.201 ** (2.425)	0.138 * (1.363)	0.143 * (1.475)	0.381 *** (4.286)	0.344 *** (3.881)
CXY	0.147 * (3.117)	0.106 ** (2.705)	0.056 * (2.013)	0.069 * (2.271)	0.198 ** (3.806)	0.173 ** (3.627)
DI × CXY	0.293 ** (2.981)	0.306 ** (3.229)	0.152 ** (1.525)	0.173 ** (1.722)	0.4422 ** (4.771)	0.4743 ** (4.902)
Controls	YES	YES	YES	YES	YES	YES

Note: Z-values in brackets, *, **, and *** are statistically significant at 10%, 5%, and 1%, respectively.

show that, in terms of independent effects, the direct, indirect, and total effects of both the digital economy and industry–university–research collaborative innovation are significantly positive. The impact of the digital economy on new forms of productivity is greater than that of industry–university–research collaboration, and the direct effect is larger than the indirect effect. This may be attributed to several factors. First, the digital economy, with data as its key production factor, accelerates information flow and optimizes resource allocation, directly driving a leap in production efficiency, which aligns well with the high-efficiency and intelligence goals pursued by new forms of productivity. Second, the digital economy has nurtured emerging industries and promoted the digital transformation of traditional industries, exerting a far-reaching and profound impact beyond single-field industry–university–research collaboration and injecting new impetus into economic growth. Third, compared to the complexity of coordinating multiple parties in industry–university–research collaboration, the digital economy can more directly and swiftly respond to market demands through technological and model innovations, promoting the rapid development of new forms of productivity. Furthermore, to bridge the gap between the digital economy and industry–university–research collaborative innovation in promoting new forms of productivity, it is necessary to strengthen long-term cooperation mechanisms among industry, universities, and research institutions, clarify divisions of labor and responsibilities, increase policy and financial investments, establish special funds, optimize talent cultivation and recruitment strategies, foster and attract high-quality talents, promote the integration of digital technologies with industry–university–research collaboration, build digital platforms, and facilitate the commercialization and industrialization of scientific and technological achievements, shortening the cycle from research and development to the market.

Therefore, it can be observed that besides innovation, there are other important mechanisms yet to be identified that contribute to the impact of the digital economy on new forms of productivity. Meanwhile, the digital economy and industry–university–research collaborative innovation mainly affect the local area, with room for improvement in spatial spillovers to neighboring regions. In terms of matching effects, compared to the regression of independent effects, the direct, indirect, and total effects of matching effects have been significantly enhanced, primarily manifesting as spatial spillover effects under economic weights. This suggests that the matching effect of the digital economy and industry–university–research collaborative innovation is an effective way to accelerate the formation of new forms of productivity in the future.

4.3. Empirical Analysis of the Panel Threshold Model

In the examination of matching effects, a benign interactive relationship between the digital economy and industry–university–research collaborative innovation was found. To identify the impact of this interaction on new-quality productivity, this paper further selected the digital economy and industry–university–research collaborative innovation as threshold variables and constructed a threshold model for empirical testing. From the regression results in

Table 8. Threshold effect test results.

Variable		Industry–Academic–Research Collaborative Innovation Is the Threshold Variable			Digital Economy Is the Threshold Variable
		Single-Threshold	Double-Threshold	Three-Threshold	Single-Threshold	Double-Threshold	Three-Threshold
Single threshold estimate		1.633	1.499		0.672	0.671
Confidence interval		[0.731, 1.623]	[1.001, 1.596]		[0.481, 0.798]	[0.481, 0.821]
Double threshold estimates			0.628			0.916
Confidence interval			[0.667, 0.876]			[0.348, 1.239]
Three threshold estimates				0.978			0.513
Confidence interval				[0.621, 1.112]			[0.341, 0.973]
F statistic		16.171 *	14.991 **	5.777	14.771 **	8.013	7.779
p-value		0.033	0.010	0.301	0.051	0.315	0.162
BS degree		600	600	600	600	600	600
Threshold	1%	69.35	20.332	28.033	44.338	45.772	34.128
	5%	44.991	14.885	18.191	31.664	35.663	29.661
	10%	34.001	11.015	14.331	28.002	31.024	26.112

Note: P-values in brackets, *, and ** are statistically significant at 10%, and 5%, respectively.

, it can be observed that the digital economy exhibits a double-threshold effect with threshold values of 0.628 and 1.499, while industry–university–research collaborative innovation shows a single-threshold effect with a threshold value of 0.671.

This paper continues to conduct regression analysis on the model in conjunction with the threshold effect test results. The panel threshold regression results presented in

Table 9. Threshold regression result.

Variable	Digital Economy Is the Threshold Variable		Variable	Innovation Ability Cultivation Is the Threshold Variable
	Estimated Value	T-Value		Estimated Value	T-Value
Controls	YES		Controls	YES
CXY1	0.037	0.823	DL1	0.053	0.327
CXY2	0.091 **	2.661	DL2	0.131 **	2.013
			DL3	0.285 ***	3.171
R2	0.578		0.612

Note: T-values in brackets, **, and *** are statistically significant at 5%, and 1%, respectively.

indicate that as digital technology undergoes rapid updates and iterations, the digital economy has developed swiftly. The resulting innovative environment has enhanced the level of industry–university–research collaborative innovation across society and gradually exerted a positive impact on new forms of productivity, becoming a powerful driving force. This suggests that the digital economy is an effective means of unleashing the benefits of industry–university–research collaborative innovation. By strengthening such collaboration through the digital economy, total factor productivity can be improved, thereby sustaining the momentum for the development of new forms of productivity.

In the regression results with industry–university–research collaborative innovation as the threshold, it is shown that when the level of such collaboration is below 0.628, the coefficient of the digital economy’s impact on new forms of productivity is positive but insignificant. This is due to the low level of collaboration at this stage, resulting in inefficient technology conversion and inadequate resource integration capabilities, which prevent the digital economy from fully realizing its potential in driving the development of new forms of productivity. Additionally, an imperfect policy environment or inadequate implementation efforts may also constrain the improvement of industry–university–research collaborative innovation, thereby affecting the promoting role of the digital economy. When the level of industry–university–research collaborative innovation falls between 0.628 and 1.499, the coefficient of the digital economy’s impact on new forms of productivity shifts from being insignificant to significantly positive. At this stage, industry–university–research collaborative innovation enhances the conversion and application capabilities of scientific and technological achievements, creating a synergistic effect with the digital economy to jointly promote the rapid development of new forms of productivity. As the level of industry–university–research collaborative innovation continues to rise, exceeding 1.499, the regression coefficient of the digital economy’s impact on new forms of productivity remains significantly positive and increases in value. High-level industry–university–research collaborative innovation strengthens technological innovation capabilities, forming a stronger synergy with the digital economy to accelerate the development of new forms of productivity.

5. Conclusions and Policy Recommendations

5.1. Research Conclusion

Based on a systematic analysis of the logical relationships between the digital economy, industry–university–research collaborative innovation, and new-quality productivity, this paper selects 30 provinces in China from 2012 to 2022 as research samples and empirically investigates the relationships among the three using spatial econometric models and panel threshold models. The main conclusions are as follows: ① Both the digital economy and industry–university–research collaborative innovation alone, as well as their matching effect, have significant spatial effects on new-quality productivity. The impact of the digital economy on new-quality productivity is mainly observed in the local region, but it has considerable positive spillover potential to neighboring areas, whereas the impact of industry–university–research collaborative innovation on new-quality productivity is weaker than that of the digital economy, both in the local and neighboring regions, with the local effect being primary and spillover to neighboring areas being secondary. ② There is a close and benign interactive potential between the digital economy and industry–university–research collaborative innovation. The independent effects of either the digital economy or industry–university–research collaborative innovation on new-quality productivity are not as significant as their matching effect. Only when they are benignly matched do they have the greatest impact on new-quality productivity, achieving a “1 + 1 > 2” benefit. ③ There are certain thresholds for the digital economy and industry–university–research collaborative innovation in driving new-quality productivity. They are interdependent, with the accelerated development of the digital economy more effectively promoting industry–university–research collaborative innovation, thereby driving the formation of new-quality productivity. At the same time, industry–university–research collaborative innovation also supports the digital economy, making it an important driving force for accelerating the formation of new-quality productivity.

5.2. Policy Suggestion

The policy implications derived from the aforementioned conclusions are as follows:

Firstly, to effectively promote the deep integration and positive interaction between the digital economy and industry–university–research (IUR) collaborative innovation across various regions, the following strategies can be adopted to stimulate innovation vitality and drive the continuous upgrading of new productive forces: ① Policy makers should first assess the development status of the digital economy in each region, identify digital divides, and invest in infrastructure such as high-speed internet and data centers to bridge the technological gap and provide a solid material foundation for IUR cooperation. ② Establish regional digital resource sharing platforms to encourage data exchange, technology transfer, and knowledge sharing between developed and less developed regions, thereby reducing the costs of IUR cooperation and enhancing cooperation efficiency. ③ Customize IUR cooperation policies based on the digital economy maturity of different regions. In regions where the digital economy is relatively backward, priority can be given to supporting basic research and application demonstration projects, while in more developed digital economy regions, higher-level innovation cooperation, such as joint research and development centers and innovation alliances, should be encouraged. ④ Increase efforts to cultivate talents related to the digital economy and IUR cooperation, including establishing special funds, providing scholarships, and attracting external high-end talents, especially those experts with rich experience and innovation capabilities in the digital economy field. ⑤ Encourage the adoption of flexible and diverse cooperation modes, such as project cooperation, talent exchanges, and joint laboratory construction, to adapt to the actual needs of different regions, industries, and enterprises, thereby improving the pertinence and effectiveness of cooperation. ⑥ The government should play a guiding role by promoting IUR cooperation through policy incentives and financial support, while also respecting market laws and encouraging enterprises to independently choose cooperation partners and modes based on market demand. Regular assessments and dynamic adjustments should be conducted.

Secondly, the government should prioritize the spatial spillover effects of the digital economy on new-quality productivity. This should vigorously promote the upgrading of digital technologies, focusing on strategic emerging industries such as new-generation information technology. This should increase investments in scientific and technological research and development, strengthen attacks on the entire industrial chain, and establish a more open and benign innovation ecosystem, thereby elevating the level of new-quality productivity and reinforcing its supportive role in the digital economy. At the same time, the incentivizing effects of other policies on new-quality productivity, such as the “Broadband China” policy and comprehensive big data experimental zone policies, should not be overlooked. These policies are important means for promoting the development of new-quality productivity, providing new growth points and impetus for economic development.

5.3. Discussion

Through in-depth empirical analysis, this paper reveals the core role of the digital economy in driving the development of new-quality productivity. Firstly, this paper confirms that the development of the digital economy significantly enhances new-quality productivity, a finding that aligns with existing research on the role of the digital economy in promoting development both domestically and internationally, and further delves into the existence of its spatial spillover effects. Secondly, this paper meticulously explores the interaction mechanism between the digital economy and industry–university–research collaboration and innovation, providing a novel perspective on how the digital economy specifically influences the development of new-quality productivity in China. Furthermore, this paper discusses the threshold effect of the digital economy and industry–university–research collaboration and innovation on new-quality productivity, laying a theoretical foundation for the rational development of the digital economy and the scientific formulation of industry–university–research collaboration strategies. The main contribution of this paper lies in providing policymakers with a theoretical framework that integrates the digital economy and industry–university–research collaboration and innovation, and presenting empirical evidence from China, which is of great significance for guiding practice.

However, constrained by the difficulty in data acquisition, several directions remain worthy of further exploration and excavation. Specifically, they are as follows: ① This paper only analyzes the impact of the digital economy on new-quality productivity from a macro perspective. Future research can further focus on the micro-enterprise perspective to deeply explore the specific role and operation mechanism of the digital economy on new-quality productivity at the micro level. ② By specifying the research object to a particular industry, such as manufacturing or resource-based enterprises, a more in-depth analysis can be conducted on the specific role and impact mechanism of the digital economy on new-quality productivity within that industry, providing more targeted strategic suggestions for industry digital transformation and productivity enhancement. ③ Explore how companies or institutions leverage digital technologies (such as artificial intelligence, big data, and cloud computing) to optimize the process of industry–university–research collaborative innovation, including project selection, cooperation modes, and resource allocation, thereby constructing a collaborative innovation model based on digital technologies to improve cooperation efficiency and outcome quality. Additionally, quantify the impact of digital technology application in industry–university–research collaborative innovation on cooperation performance, including technological innovation achievements, economic benefits, and social benefits, to reveal the actual value of digital technology in such collaborations and provide data support for company or institutional decision making. Furthermore, investigate the influence of the current policy and regulatory environment on the application of digital technology in industry–university–research collaborative innovation, including laws and regulations related to intellectual property protection, data security, and privacy protection, to provide policy and regulatory suggestions for companies or institutions to promote industry–university–research collaborative innovation using digital technology in a legal and compliant manner. ④ Further analyze the specific differences in the impact of the digital economy and industry–university–research collaborative innovation on new-quality productivity across different geographical spaces (such as cities, city clusters, provinces, and regions), and explore the temporal changes in spatial effects, i.e., whether the spatial spillover effects of the digital economy and industry–university–research collaborative innovation increase or decrease over time.