The Driving Effect of Strategic Emerging Industries on New Quality Productivity from the Perspective of Industry–Human Coupling Coordination: The Mediating Role of Digitalization Level

Abstract

In the context of fostering new quality productivity (NQP), this study explores how coupling coordination between strategic emerging industries (SEIs) and human resources (HR) affects the mechanisms behind NQP, with a particular focus on the mediating role of digitalization level (DL). Based on panel data from 30 provinces in China from 2014 to 2023, we used the entropy weight TOPSIS method to measure the development of NQP and the coupling coordination model to quantify the synergy between SEIs and HR. A mediating effect model and heterogeneity analysis were constructed to test the research hypotheses. The findings revealed three key findings: (1) The coupling coordination between SEIs and HR significantly enhanced DL and NQP, where DL played a robust mediating role in promoting NQP through the coupling coordination between SEIs and HR. (2) There is heterogeneity in the mediating effect of DL in regions with higher R&D investment and advanced high-tech industries; the positive impact of SEIs–HR coupling coordination on NQP is more significant compared with regions with lower R&D expenditure and underdeveloped high-tech industries. Conversely, in regions with lower R&D spending and underdeveloped high-tech industries, the promoting effect of DL on NQP is stronger. (3) This study enriches the literature on the interaction between SEIs, HR, and NQP and provides theoretical insights and practical implications for improving SEIs–HR coupling coordination, enhancing DL, and advancing NQP. From the perspective of the intersection of industrial economics and behavioral science, this study also supplements the research on human capital allocation and technological innovation behavior in the context of digital transformation.

1. Introduction

In 2023, China put forward the important concept of “new quality productivity” (NQP) for the first time, clearly stating that it is an advanced form of productivity quality state that is innovation-driven, free from the constraints of traditional growth paths, and features high technology, high efficiency, and high quality, in line with the new development philosophy. As the core carriers and important realization paths towards the achievement of NQP, strategic emerging industries (SEIs) are not only a key means of promoting self-reliance and self-strengthening in science and technology and solving the “bottleneck” problems in industrial chains and supply chains but also a strategic component of reshaping China’s international competitive advantage and achieving high-quality economic development. In this context, a systematic study of the coupling and coordination between SEIs and human resources and their impact on the development of NQP can not only deepen the theoretical understanding of the intrinsic connections between the three and accurately identify the structural contradictions and bottlenecks in coordinated development but also provide a scientific basis for optimizing the policy system of “investing in people” and improving the cultivation mechanism of SEIs to inject lasting impetus into the accelerated formation of NQP.

Based on existing research gaps and national strategic demands, this paper uses China’s provincial panel data from 2014 to 2023 as research samples to construct a multi-dimensional and quantifiable comprehensive evaluation index system for NQP, with a focus on two core aspects: First, a systematic analysis of the level of coupling and coordination between SEIs and human resources and an exploration of the direct impact of their coordinated development on the improvements to NQP. Second, empirical testing of the mediating role played by digitalization level in the above-mentioned influence process and clarification of the mechanism enabling three-dimensional interaction of “industry–talent–digital” interaction. The research results of this paper aim to achieve three goals: To fill, at the theoretical level, the gap in the cross-research on SEIs, human resources, and NQP, and to deepen the understanding of the coupling and coordination mechanism of the three. At the practical level, it seeks to provide precise countermeasures to enhance the compatibility of regional SEIs and human resources to accelerate the process of digital transformation. At the policy level, the aim is to provide theoretical support and practical references, enabling improvements to the “invest in people” policy system, optimizing the cultivation mechanism of SEIs, and thereby promoting high-quality development of new quality productive forces.

The marginal contribution of this paper lies in the following: First, from multiple perspectives, comprehensively constructing an evaluation index system for China’s SEIs and new quality productive forces, as well as comprehensively measuring the level of coupling coordination between China’s SEIs, human resources, and the level of development of new quality productive forces. Second, unlike previous studies that only explored the co-development status of SEIs and human resources from a theoretical perspective, this paper quantitatively measures the coupling coordination degree of SEIs and human resources in China. A model of the influence mechanism between the coupling coordination degree of SEIs and human resources, digitalization level, and NQP was constructed, and a relatively in-depth study was conducted on the logical influence relationship between the coupling coordination degree of SEIs and human resources, digitalization level, and NQP in China. Finally, from a new heterogeneity analysis perspective, differences in the mediating effect of digitalization level were comprehensively analyzed, which is conducive to further improving the mechanisms of action of underpinning how the coupled and coordinated development of SEIs and human resources impact the development of NQP. Due to space limitations, this paper only studies the influence mechanism of a single mediating variable, with the influence mechanisms of other mediating variables to be analyzed and verified in future.

2. Theoretical Model and Research Hypotheses

2.1. Coupling and Coordination of SEIs and Human Resources

The coupling and coordination mechanism between SEIs and human resources can be explained systematically by relying on system coupling theory and the coupling coordination degree model. The core of system coupling theory is a focus on the coordination mechanism, feedback path, and evolution law of the coupling relationship between two or more system elements or subsystems within the same system. The theory originated in the natural sciences, such as physics and biology, and then gradually expanded across disciplines to the analysis of complex systems in the socio-economic field. The theory suggests that when multiple subsystems interact and permeate each other to form dynamic associations and meet specific synergy conditions, they can break through the limitations of a single system, integrate with one another, and form new functional structural bodies with greater synergic efficiency.

In the study of the synergy between industry and human resources, some scholars have conducted quantitative and qualitative analyses of the coupling mechanisms between the two. Among them, researchers used the gray relational analysis method to precisely measure the coupling strength and correlation characteristics between human capital stock and the evolution of industrial structure, providing a methodological reference for empirical research on the coupling relationship between industry and human resources [1]. Regarding coupling coordination between SEIs and human resources, this is essentially a dynamic adaptation process between the demand side of industry and the supply side of talent, and this adaptation is manifested via three core dimensions: scale coupling, structural coupling, and quality coupling. Structural coupling is when the educational, professional, and skill structure of human resources matches the industrial layout and the division of labor in the industrial chain. Quality coupling is the ability level of human resources to meet the core demands generated by the high-end and intelligent development of industry. The level of coupling coordination between the two directly determines the allocation efficiency and transformation effect of industrial resources and is a key variable affecting high-quality industrial development [1].

From the perspective of synergistic logic, coupling coordination between SEIs and human resources is a dynamic relationship between bidirectional drive and circular empowerment. On the one hand, the high-quality development of SEIs constitutes a rigid demand for high-end and specialized human resources through paths such as technological upgrading, business model innovation, and industrial chain extension, thereby driving precise upgrades in the quantity, structure, and quality of the supply of human resources A. On the other hand, the supply of high-quality human resources, through knowledge spillover, technological breakthroughs, and innovation empowerment, provides critical support for enhancements to the core competitiveness of SEIs and drives industries to leap to the high end of the value chain. This two-way synergy ultimately results in a dynamic and cyclical interaction process of “employment expansion–labor force quality improvement–product innovation–consumption upgrade–industry iteration”, driving deep integration and efficiency multiplication [1].

2.2. Strategic Emerging Industries, Human Resource Coupling and Digitalization Level

The coupling coordination mechanism between SEIs and human resources can be explained systematically with the aid of system coupling theory and the coupling coordination model. The core of system coupling theory is a focus on the coordination mechanism, feedback path, and evolution law of the coupling relationship between two or more system elements or subsystems within the same system. The theory originated in the natural sciences, such as physics and biology, and then gradually expanded across disciplines to the analysis of complex systems in the socio-economic field. The theory suggests that when multiple subsystems interact and permeate each other to form dynamic associations and meet specific synergy conditions, they can break through the limitations of a single system, integrate with one another, and form new functional structural bodies with higher synergic efficiency.

It is notable that coupling and coordination between SEIs and human resources can systematically drive a leap forward in regional digitalization through four core mechanisms: factor adaptation, efficiency improvement, innovation-driven development, and value transformation [2,3]. These four mechanisms are interrelated and progressive, forming a complete path from the transmission of coupling coordination to digital transformation.

Element adaptation mechanisms strengthen the resource foundation of digital development. Element fit is a core starting point from which coupling coordination influences the level of digitalization. The core logic is to enhance the synergy and fit between SEIs and human resources through the precise matching of industrial demand and talent supply. In the context of the digital economy, this fit is prominently reflected in the fact that SEIs, based on the demand generated by their digital and intelligent transformation, have expanded the demand gap for digital skills and intelligent technology talents, and the targeted response from the talent supply side has, in turn, forced advances in research and development and the popularization and application of digital technologies. A virtuous cycle of “talent demand–technology upgrade–level improvement” is formed [2,4].

Efficiency improvement mechanisms accelerate the full-chain penetration of digital technology. There is a two-way empowerment relationship between the synergy of SEIs and human resources and the popularization of digital technology applications. Digital technology, with its core advantages of high efficiency and high convenience, supports business model innovation and process optimization in SEIs, promoting industrial-scale expansion and quality improvement [5,6]. At the same time, the iterative upgrading of SEIs and improvements to human digital literacy and professional capabilities have created a rigid demand for digital technology, driving the improvement of digital infrastructure, the expansion of application scenarios, and the continuous improvements to digital development levels [7]. Among these, the popularization of digital human resource management models has reinforced the digital synergy chain between talent and industry, becoming an important boost to higher levels of digitalization.

Innovation-driven mechanisms activate the core drivers of digital transformation. The core demands for advanced digital transformation stem from continuous innovation, the coupling and coordination of SEIs and human resources, and the continuous impetus for digital innovation driven by an integrated system of “talent–industry–innovation” [8]. Talent, as the core carrier of innovation, provides key support for the digital transformation of SEIs. The synergy between the two can accelerate the aggregation of innovation elements and generate cross-border innovation results. High-end talents, with their professional strengths and resource integration capabilities, drive collaborative innovation across all links of the industrial chain, promoting the penetration of digital technology from single-point applications to the entire chain, building a closed-loop ecosystem of “innovation R&D–scenario application–iterative optimization”, and strengthening the sustainability of the digital transformation [9,10].

Value transformation mechanisms drive the leap from instrumental digitalization to digitalization with strategic attributes [11]. Deep synergy between SEIs and human resources can drive the efficient flow and value mining of data elements, completing their transformation from “data resources” to “value assets” [12]. This process is accompanied by a fundamental shift in digital positioning—from a mere technological tool to a core strategy that underpins industrial development [13]. Specifically, the coupling coordination relationship drives enterprises to shift from the “passive application of digital technology” to the “active reconstruction of strategic layout through digitalization”, relying on data-driven decision-making to achieve precise perceptions of market demand, scientific prediction of technological trends, and the optimal allocation of resources, further driving digital development towards maturity and systematization [14].

Based on the above theoretical derivations, Hypothesis 1 was derived:

Hypothesis 1 (H1): 

Coupling and coordination between SEIs and human resources have contributed to increased level of digitalization.

2.3. The Mechanism by Which Coupling and Coordination Between Strategic Emerging Industries and Human Resources Affects New Quality Productivity

In the existing domestic literature, many scholars have studied how SEIs promote the development of new quality productive forces and have achieved fruitful results. Specifically, their research has focused on several issues: Firstly, the internal mechanisms for developing SEIs and future industries accelerate the formation of new quality productive forces [15,16]. Secondly, the theoretical logic and practical pathways between innovation in SEIs and NQP [17]. Thirdly, the correlation between fostering the development of SEIs and accelerating the formation of NQP [18]. Fourthly, the impact of strategic emerging industrial agglomeration on the NQP level of enterprises [18]. Fifthly, it is clear that fostering and strengthening SEIs is the core approach to developing NQPs [19,20]. Sixthly, how allocation patterns for the development of high-quality new productive forces driven by technological innovation in SEIs result in a logical framework and implementation pathway for SEIs to empower NQP [15]. As can be seen from the above, SEIs have become a key variable influencing the development of NQP. At the same time, human resources, a core component of production factors, are also an important supporting factor for the development of new, quality productive forces. Coupling and coordination between SEIs and human resources mainly provide dynamic support for the development of new quality productive forces through four major mechanisms: technological innovation, factor upgrading, structural optimization, and institutional innovation [21].

Technological innovation mechanisms: Talent aggregation can accelerate knowledge spillover and the diffusion of technology, significantly enhancing innovation efficiency [22]. The demand-oriented role of SEIs can precisely anchor the direction of technological innovation and promote breakthroughs in key core technologies [23]. Building an industry–university–research collaborative innovation system can accelerate the transformation of scientific and technological achievements into real productive forces and achieve deep integration and connection between the innovation chain and the industrial chain [24].

Factor upgrading mechanism: The core of this mechanism is to drive the development of new quality productive forces through improvements to human resource quality and the optimization of industrial factor structure [25]. At the human resources level, measures such as enhancing the overall quality of the workforce, optimizing the talent echelon structure, and stimulating the innovative vitality of talent can promote the upgrading of factor quality. At the industrial development level, relying on technological progress, management innovation, and other means can achieve innovative allocation and the efficient utilization of production factors [26].

Structural optimization mechanism: This mechanism is characterized by the coordinated upgrading of industrial and employment structures. Vigorously developing SEIs can drive the overall industrial structure to shift towards high technology, high added value, and high growth. The coupled and coordinated development of SEIs and human resources further promotes the iteration of the employment structure towards being more knowledge-based, skill-based, and innovative [21]. Beneficial interaction between industrial and employment structures ultimately leads to the overall optimization of the economic structure and higher quality and more efficient economic development.

Institutional innovation mechanisms: These mechanisms aim to create an institutional environment that adapts to the development of new quality productive forces through institutional reform and policy innovation [23], removing institutional obstacles to the coupled and coordinated development of SEIs and human resources, and providing a solid guarantee for the cultivation of new quality productive forces.

Based on the above theoretical derivations, Hypothesis 2 was derived:

Hypothesis 2 (H2): 

Coupling and Coordination between Strategic Emerging Industries and Human Resources Promote the Development of New Quality Productive Forces.

2.4. Mediating Effects of Digitalization Levels

At present, the academic community has produced abundant research findings on how digitalization levels impact NQP. Varian constructed a logical framework for how digital transformation empowers the emergence of NQP and systematically analyzed the existing problems and optimization pathways of these processes [27]. Researchers used microdata on Chinese listed companies to empirically examine the enabling effect of enterprises undergoing digital transformation on the development of NQP [4]. Researchers focused on the intrinsic mechanisms, real challenges, and path choices of digital transformation empowering NQP [28]. In addition, other scholars have focused on manufacturing as the research scenario and further revealed, through empirical research, the specific paths by which digital transformation empowers NQP.

The level of digitalization plays a mediating role in the process of promoting the development of NQP through coupling and coordination between SEIs and human resources, mainly through three dimensions: the upgrading of human capital, the optimization of the innovation ecosystem, and innovation in resource allocation [29].

In the dimension of upgrading human capital, the level of digitalization promotes the precise matching of human resources with the demands of SEIs by reconfiguring the ways of human capital formation and accumulation, providing core support for the development of NQP [30]. From the perspective of talent development, digital technology breaks the temporal and spatial limitations of traditional education models, enabling workers to acquire knowledge and skills in cutting-edge fields such as quantum communication, new energy materials, and artificial intelligence algorithms in real time through carriers such as MOOC platforms, virtual simulation training systems, and digital twin teaching scenarios, quickly adapting to the technological demands of SEIs [31]. From the perspective of capability transformation, the prevalence of digital terminals and the application of cloud-based collaboration tools have driven the deep integration of human resources into distributed innovation networks. With the help of digital tools such as big data analytics and machine learning, the accuracy and efficiency of problem-solving have been significantly enhanced, and the value of human capital has been efficiently increased [32].

In the dimension of optimizing the innovation ecosystem, the level of digitalization amplifies the innovation spillover effect generated by the coupling and coordination between SEIs and human resources through the construction of element sharing and collaboration platforms, and injects core impetus into the development of new high-quality productive forces. Regarding the interaction between innovation entities, carriers such as industrial Internet platforms and digital platforms for industry-university-research collaborative innovation networks have effectively broken down the information barriers and organizational boundaries between enterprises, universities, research institutions and social organizations, enabling the intellectual achievements generated by human resources to quickly meet the technological demands of industries, accelerating the transformation and implementation of innovation outcomes [33]. From the perspective of innovation resource integration, technologies such as big data analysis and artificial intelligence-driven matching algorithms can precisely identify the core technical bottlenecks and areas advantageous to human resources in SEIs, build an intelligent “industry demand–talent supply” matching system, and achieve the efficient aggregation of innovation resources [34].

In the dimension of innovation in resource allocation, the level of digitalization, by optimizing the efficiency and precision of factor allocation, enhances the efficiency of economic value transformation in the coupling and coordination relationship between SEIs and human resources, thereby promoting the high-quality development of new quality productive forces. At the micro level of individual enterprises, the integrated application of digital management systems and Internet of Things technology can monitor the input-output ratios of human resources in real time, build job allocation optimization models in combination with industrial production data, and achieve the precision allocation of human resources [35]. At the macro level of entire industries, the inclusiveness and cross-regional nature of the digital economy enable the coupling and coordination of SEIs and human resources, breaking through geographical and spatial limitations, forming cross-regional and cross-industry factor allocation networks, and enhancing the overall efficiency of factor allocation [36].

In addition, the mediating role of digitalization is also reflected at the levels of risk management and control. By establishing a sound risk management system to ensure the stability, sustainability and resilience of the coupling and coordination relationship between SEIs and human resources, institutional guarantees are provided for the development of new quality productive forces [37]. At the risk identification and early warning stage, big data monitoring technology can track in real time the evolution of technological routes for SEIs and changes in market demand, as well as changes in the flow patterns and skill structures of human resources. By constructing a coupled and coordinated system of risk assessment indexes, it becomes possible to predict potential contradictions and risk points in the matching process [38]. In the risk response phase, the decentralization and immutability of blockchain technology can effectively clarify the ownership of innovation achievements, resolve disputes over the distribution of benefits between talents and enterprises, and maintain the sound operation of the coupled coordination relationship. It is notable that the mediating effect of the level of digitalization has significant threshold characteristics, and the ability to mediate transmission effects can only be fully released when regional digital infrastructure construction reaches a critical level and human resources attain basic digital literacy and application capabilities [2].

Based on the above theoretical derivations, Hypothesis 3 can be derived:

Hypothesis 3 (H3): 

Coupling and coordination between strategic emerging industries and human resources, through the improvements to the level of digitalization, promotes the development of new quality productive forces.

3. Methods

3.1. Methodology and Theoretical Model

The presented mediating effect model has frequently been used by scholars seeking to empirically analyze how the development of artificial intelligence could enhance NQP by promoting digital innovation. Within this context, this paper, drawing on previous research results, uses the “three-step method” to examine the mediating effect of the level of digitalization on how the coupling and coordinated development of SEIs and human resources promote NQPs [39,40].

First, to test H2, and with the NQP development level (XZSCL) as the explained variable and the coupling coordination degree of SEIs and human resources (XTD) as the independent variable, the following benchmark econometric model was obtained:

$$XZSC{L}_{\mathit{it}}=a_0+a_{1}XT{D}_{\mathit{it}}+a_2{Z}_{\mathit{it}}+u_{it}$$

(1)

In the formula, $a_1$ is the total effect of the coupling and coordination of SEIs and human resources on the development of NQP. $Z_{it}$ is the control variable. $a_0$ is the intercept term. $a_2$ is the regression coefficient of the control variable, and $u_{it}$ is a random perturbation term.

Next, to test H1, with DIGI as the explained variable and coupling coordination between SEIs and human resources (XTD) as the independent variable, the model was set as follows:

$$DIG{I}_{it}=b_0+b_{1}XT{D}_{it}+b_2{Z}_{it}+u_{it}$$

(2)

In the formula, $b_1$ represents the total effect of the coupling and coordination between SEIs and human resources on the improvement of the digitalization level, and $b_0$ is the intercept term. $b_2$ is the regression coefficient of the control variable.

Finally, to test H3, the model was set up as follows:

$$XZSC{L}_{it}=c_0+c_{1}XT{D}_{it}+c_{2}DIG{I}_{it}+c_3{Z}_{it}+u_{it}$$

(3)

In the formula, $c_1$ represents the direct effect of the coupling and coordination between SEIs and human resources on the development of NQP. $c_2$ is the direct effect of the improvement of digitalization on the development of NQP. $c_0$ is the intercept term, and $c_3$ is the regression coefficient of the control variable. If both $b_1$ and $c_2$ are significant and in line with expectations, there is a mediating effect, the size of which is $b_1{c}_2$ and the proportion of the mediating effect is $b_1{c}_2$/$a_1$.

3.2. Variables and Measurement

Dependent variable—NQP development level (XZSCL). We should vigorously cultivate NQP to provide new impetus for sustainable development. Cultivating and developing NQP is an important measure to comprehensively enhance the strength of China’s economy, science and technology, and national defense, as well as the country’s comprehensive national strength and international influence. It is also an important guarantee for achieving Chinese-style modernization. At present, academic research on NQP is already very rich and includes theoretical research and a great deal of empirical research findings. To measure the level of development of NQP, many scholars have used a variety of indicators such as laborers and technology and greenization. This paper measures NQP using the three modules of high-quality workforce, new labor resources, and new targets of labor, which are most commonly used by scholars.

The data for our study are sourced from the China Statistical Yearbook, the China High-Tech Industry Statistical Yearbook, the China Science and Technology Statistical Yearbook, the China Labor Statistical Yearbook, the China Population and Employment Statistical Yearbook, etc. Detailed information of the NQP measurement indicators considered in this study is listed in

Table 1. New quality productivity measurement standards.

Target Layer	Criteria Layer	Metrics Layer	Units
NQP development level indicator system	High-Quality Workforce	The average years of education per employee	years
		The proportion of people employed in the information transmission, computer and software industries to the employed population	% percent
		R&D personnel converted to full-time equivalent	person-years
		Average number of students enrolled in higher education per 100,000 people	people
	New Labor Resources	Integrated circuit production	billions of pieces
		Percentage of R&D Expenditures in Industrial Enterprises Above a Certain Scale	% percent
		The number of Internet broadband users	ten thousand households
		Number of patents granted	Items per 10,000 people
	New Target of Labor	Proportion of new energy generation	% percent
		The utilization efficiency of new energy	% percent
		Upgrading the industrial structure	% percent
		Number of AI companies	number

.

Independent variable—coupling coordination (XTD) between SEIs and human resources. The importance of the coordinated development of SEIs and human resources lies in the mutual promotion of industries and labor forces, which is conducive to cultivating more innovative talents adapted to the development of SEIs, and thereby promoting social progress and economic development. The entropy weight TOPSIS method was adopted to measure the development level of SEIs from three aspects: innovation input, scientific and technological output, and industrial scale and benefit; see

Table 2. Measurement index system for the development level of strategic emerging industries.

Target Level	Criteria Layer	Metrics Layer	Units
Strategic emerging Industrial development Level evaluation Index system	Innovation input	Expenditure on new product development in high-tech industries	ten thousand yuan
		Number of doctoral students on campus	people
		R&D personnel/local year-end population	% percent
		Internal expenditure on R&D	ten thousand yuan
		Local fiscal expenditure on science and technology	100 million yuan
	Technological output	Number of valid invention patents in high-tech industries	a
		Number of new product development projects	item
		Volume of transactions in the technology market	billions of yuan
	Industrial scale and Benefits	Number of high-tech industrial enterprises	a
		Investment growth in high-tech industries	% percent
		Revenue from high-tech industries	one billion yuan
		Total profits of high-tech industries	100 million yuan

for details. In this paper, human resources refer to group human resources, which are comprehensively measured by the number of employed people (scale indicator), education expenditure (education indicator), average working years (experience indicator), and medical and health care expenditure (health indicator). At present, academia has produced few research findings on the coupling coordination degree between SEIs and human resources in the academic circle. Based on the relevant theories, this paper attempts to measure the coupling coordination degree between SEIs and human resources by using the coupling coordination degree model. Details on the independent variable are as shown in Table 2.

Mediating variables—digitalization level (DIGI). There are many ways to measure digitalization, such as digital inclusion, digital infrastructure, digital talent, and digital application [41]. This paper adopts the entropy weight TOPSIS method and uses the three indicators—digital infrastructure, digital services, and digital application—to construct a comprehensive digitalization index. This can more comprehensively and objectively measure the level of digitalization of a region, as shown in

Table 3. Digital level measurement index system.

Target Layer	Criteria Layer	Metrics Layer	Units
Level of digital development Indicator system	Digital infrastructure	Internet broadband access ports	Ten thousand
		Number of domain names	Ten thousand
		Capacity of mobile phone switchboard	10,000 households
	Digital Services	Mobile phone penetration rate	Unit/100 people
		Mobile Internet access traffic	10,000 GB
		Web page count	Ten thousand
		The proportion of information technology workers	% percent
	Digital applications	Software business revenue	Ten thousand yuan
		Total volume of telecommunications services	Billions of yuan
		Revenue from the express delivery business	Ten thousand yuan

.

Control variables—the development of NQP is a long-term systematic project that is disturbed by many external factors. Drawing on the research findings of other scholars [42,43], this paper concludes that the main factors influencing the development of NQP include the following: (1) Economic development level X1 (per capita GDP)—for the development of NQP, economic strength is the foundation, and high-quality economic development will provide the resources and impetus needed for the cultivation of NQP. (2) Fintech level X2—improving the fintech level will provide financing support and risk management services to NQP, alleviating the financing difficulties faced by technology companies, especially smaller high-tech companies. (3) Transportation level X3 (freight volume)—the improvement of transportation capacity will inject strong impetus into the development of new quality productive forces [44]. (4) Urbanization level X4 (urban population/total population)—an increase in the urbanization level can provide a good working and living environment, better attract talent and capital and technology, and promote the development of NQPs. (5) Industrial structure X5—output value of the tertiary industry/output value of the secondary industry)—industrial transformation and upgrading will provide industrial chain support for the development of NQP and will also promote technological upgrading and development. (6) Industrial development level X6 (industrial added value/regional GDP)—an increase in industrial development level will strongly enhance the vitality of economic development; attract the participation of greater numbers of investors, innovators, and entrepreneurs; and promote the development of NQP [45]. (7) Foreign investment scale X7 (foreign direct investment/gross domestic product)—the greater the scale of foreign investment, the more international capital, talent and technology will flow into the system, promoting industrial and NQP development. The above seven indicators are the control variables studied in this paper.

3.3. Data Collection

The subjects of this study were 30 provinces (municipalities and autonomous regions) in China, excluding the provinces of Hong Kong, Macao and Taiwan. Xizang was also excluded due to the severe lack of relevant data. The data in this study are from the China Statistical Yearbook, the China Education Statistical Yearbook, the China Social Statistical Yearbook, the China Electric Power Yearbook and the statistical yearbooks of the studied provinces. To eliminate heteroscedasticity and reduce error terms, the relevant indicator data used in this study were logarithmically processed in this study. Stata 18 and SPSS 30 were used for data processing and analysis.

4. Empirical Testing and Results

4.1. Main Effects Analysis

Table 4. Benchmark regression results.

	Lnxzscl
	OLS		FE		RE
	(1)	(2)	(3)	(4)	(5)	(6)
lnxtd	0.875 **** (13.724)	1.174 **** (14.071)	1.864 **** (5.078)	0.215 (0.988)	1.363 **** (6.195)	0.596 **** (3.581)
Controls	No	Yes	No	Yes	No	Yes
Year	No	No	Yes	Yes	Yes	Yes
Province	No	No	Yes	Yes	Yes	Yes
_cons	−1.170 **** (−15.769)	−2.716 *** (−2.580)	−0.011 (−0.026)	−13.255 **** (−5.886)	−0.598 *** (−2.799)	−10.495 **** (−5.395)
N	300	300	300	300	300	300
R2	0.403	0.762	0.112	0.28	0.277	0.586

Notes: t-values in parentheses, *** p < 0.01, **** p < 0.001.

shows the benchmark regression results on how coupling and coordination between SEIs and human resources impact NQP. Based on Equation (1) and drawing on previous research results, the regression coefficients of the coupling and coordination degree between SEIs and human resources were estimated in the benchmark model using three methods—mixed regression (OLS), fixed effects (FEs), and random effects (REs)—to verify the overall impact coupling and coordination between SEIs and human resources on the development of NQP. The results of this analysis are shown in Table 4. Columns (1), (3), and (5) are regression results without control variables including economic development level (X1), fintech level (X2), transportation level (X3), urbanization level (X4), industrial structure (X5), industrial development level (X6), and foreign investment scale (X7). Columns (2), (4), and (6) are the regression results with control variables included.

The regression coefficients of columns (1)–(6) are 0.875, 1.174, 1.864, 0.215, 1.363, and 0.596, respectively, and all are significantly greater than 0 (with the exception of Model 4), indicating that the coupling and coordination between SEIs and human resources have a significant positive impact on the development of NQP, thus verifying Hypothesis 2. In all three models, the regression coefficients were significantly greater than 0 at the 1% significance level before and after the addition of the control variables, indicating to some extent that the models were robust.

4.2. Regression Results Analysis

To further verify the transmission mechanism behind how coordinated development between SEIs and human resources promotes the development of new high-quality productive forces, this paper takes digitalization as the mediating variable and, based on the established mediation effect model Formulas (2) and (3), uses the RE method through the Hausman test to evaluate the promoting effect of the coordinated development of SEIs and human resources on the development of new high-quality productive forces.

Table 5. Regression results.

	Lndigi
	OLS		FE		RE
	(1)	(2)	(3)	(4)	(5)	(6)
lnxtd	0.286 **** (30.969)	0.114 **** (7.570)	0.334 **** (3.417)	0.009 (0.294)	0.292 **** (14.289)	0.079 *** (2.947)
Controls	No	Yes	No	Yes	No	Yes
Year	No	No	Yes	Yes	Yes	Yes
Province	No	No	Yes	Yes	Yes	Yes
_cons	0.870 **** (78.752)	−1.553 **** (−7.780)	0.926 **** (8.085)	−1.819 **** (−4.927)	0.877 **** (35.699)	−1.986 **** (−5.701)
N	300	300	300	300	300	300
R2	0.707	0.898	0.688	0.046	0.707	0.875

Notes: t-values in parentheses, *** p < 0.01, **** p < 0.001.

presents the regression results showing how the coordinated development of SEIs and human resources impacts the level of digitalization. Columns (1)–(2) are OLS estimates, Columns (3)–(4) are FE model estimates, and Columns (5)–(6) are RE model estimates. Among them, Columns (1), (3), and (5) are the regression results without control variables, and Columns (2), (4), and (6) are the regression results with control variables added. Table 5 shows that the regression coefficient of the coordinated development between SEIs and human resources is positive at the 1% significance level (with the exception of Model (4)), and the regression coefficients in Columns (1)–(6) are 0.286, 0.114, 0.334, 0.009, 0.292, and 0.079, respectively. This indicates that the coordinated development of SEIs and human resources significantly promotes the improvement of the level of digitalization, and Hypothesis 1 is verified.

4.3. Endogeneity and Robustness Tests

Given the potential reverse causality between the coordination of SEIs and human resources and the development of new-quality productive forces, as well as the endogeneity issues in the estimation results caused by measurement errors and omitted variables, this study draws on the research findings of other scholars and employs the instrumental variables method to evaluate the model and address these endogeneity issues. Furthermore, since using only the OLS, FE, and RE models makes it difficult to eliminate historical behavior errors, this study employs terrain undulation as an instrumental variable and, based on this, evaluates the model using OLS, FE, RE, and the two-stage GMM estimation method. The evaluation results are shown in

Table 6. Results of the endogenous discussion.

	Lnxzscl
	OLS	FE	RE	GMM
	(1)	(2)	(3)	(4)
lnxtd	1.135 **** (13.640)	1.060 **** (3.954)	0.618 **** (3.815)	3.714 ** (2.406)
lndxqfd	−0.053 **** (−4.001)	−0.055 * (−1.810)	0.018 (0.446)
Controls	Yes	Yes	Yes	Yes
Year	No	Yes	Yes	Yes
Province	No	Yes	Yes	Yes
_cons	−2.240 ** (−2.170)	−2.734 (−1.011)	−10.445 **** (−5.315)	13.953 (1.363)
N	300	300	300	300
R2	0.772	0.771	0.593	0.133

Notes: t-values in parentheses, * p < 0.1, ** p < 0.05, **** p < 0.001.

, where Columns (1)–(4) represent the OLS, FE, RE, and GMM, respectively. The results indicate that the regression coefficients for the coupling coordination between SEIs and human resources are 1.135, 1.06, 0.618, and 3.714, respectively. All regression coefficients are significantly greater than 0, suggesting that the model remains robust and the results remain valid even after accounting for endogeneity. This further validates Hypothesis 2. The results are shown in Table 6.

This paper uses the coupling coordination model to measure the coupling coordination system between SEIs and human resources. To ensure the robustness of the benchmark regression results, considering that there may be a certain lag between SEIs and human resources coupling coordination and the development of new high-quality productive forces, the OLS, FE and RE methods were used for regression, and the model had a lag of one period. The regression results are shown in

Table 7. Robustness test results.

	Lnxzscl
	OLS		FE		RE
	(1)	(2)	(3)	(4)	(5)	(6)
lnxtdt-1	0.348 **** (5.095)	0.305 **** (5.985)	1.722 **** (4.154)	0.079 (0.398)	1.182 **** (4.266)	0.291 ** (2.062)
Controls	No	Yes	No	Yes	No	Yes
Year	No	No	Yes	Yes	Yes	Yes
Province	No	No	Yes	Yes	Yes	Yes
_cons	−1.791 **** (−21.641)	−9.648 **** (−9.306)	−0.181 (−0.373)	−14.129 **** (−6.724)	−0.809 ** (−2.450)	−13.208 **** (−7.916)
N	300	300	300	300	300	300
R2	0.064	0.66	0.937	0.178	0.305	0.391

Notes: t-values in parentheses, ** p < 0.05, **** p < 0.001.

.

The regression coefficients for columns (1)–(6) were 0.348, 0.305, 1.722, 0.079, and 1.182, 0.291, respectively. It can be seen that after replacing the explanatory variables, the regression coefficients before and after adding the control variables in all three methods are significantly greater than 0 (the exception of Model 4), indicating that the model construction is robust. Furthermore, the regression coefficients for the coupling and coordination between SEIs and human resources in Columns (1), (3), and (5) are greater than those in Columns (2), (4), and (6), indicating, without the addition of control variables, the positive impact of the coupling and coordination of SEIs and human resources on the development of new quality productive forces is overestimated without the addition of control variables. The robustness results are in good agreement with the baseline regression results, indicating that the model construction is reliable and further suggesting that the coupling and coordination between SEIs and human resources will promote the development of NQP.

4.4. Mediating Effects Analysis

To examine how the coupled and coordinated development of SEIs and human resources promotes the development of NQP through the improvements to the level of digitalization, the digitalization level was introduced as a mediating variable, and regression verification was conducted using the OLS, FE, and RE methods. The regression results are shown in

Table 8. Regression results of the mediating effect of digitalization levels.

	Lnxzscl
	OLS		FE		RE
	(1)	(2)	(3)	(4)	(5)	(6)
lnxtd	0.243 *** (2.654)	1.181 **** (13.167)	1.272 **** (4.323)	0.216 (0.990)	0.856 **** (4.096)	0.580 *** (3.270)
lndigi	2.207 **** (7.529)	−0.054 (−0.162)	1.770 **** (5.416)	−0.125 (−0.299)	1.810 **** (6.032)	0.170 (0.455)
Controls	No	Yes	No	Yes	No	Yes
Year	No	No	Yes	Yes	Yes	Yes
Province	No	No	Yes	Yes	Yes	Yes
_cons	−3.090 **** (−12.410)	−2.800 ** (−2.285)	−1.651 **** (−3.709)	−13.482 **** (−6.728)	−2.160 **** (−6.434)	−10.199 **** (−5.691)
N	300	300	300	300	300	300
R2	0.49	0.762	0.056	0.256	0.356	0.587

Notes: t-values in parentheses, ** p < 0.05, *** p < 0.01, **** p < 0.001.

.

Firstly, in Columns (1)–(6) of Table 8, the regression coefficients of the coupling and coordination between SEIs and human resources in the five models are all significantly positive at the 1% significance level, with the coefficients being 0.243, 1.181, 1.272, 0.216, 0.856, and 0.58, respectively.

Meanwhile, the regression coefficients of the level of digitalization in Models (1) and (3) are significantly positive at the 0.1% significance level, with the coefficients being 2.207, 1.77, and 1.81, respectively. It can be seen that both b1 and c1 in the econometric model are significant, which is consistent with expectations, and that there exists a mediating effect. This indicates that the coupling and coordinated development of SEIs and human resources promotes the development of new high-quality productive forces by enhancing the digitalization level.

Furthermore, the regression coefficients of the coupling and coordination degree between SEIs and human resources in Columns (1), (3), (5), and (6) are all smaller than those in the benchmark regression results of Columns (1), (3), (5), and (6), further demonstrating that the digitalization level is a mediating variable that boosts the development of new high-quality productive forces through the coupling and coordinated development of SEIs and human resources. Thus, Hypothesis 3 is verified. The regression results are basically consistent under the three methods, which to some extent confirms the robustness of how the model is set up.

To further verify the robustness of the mediating mechanism of digitalization level, this paper, drawing on previous research results, selects digi2 (digital infrastructure) instead of digitalization level and digi3 (digital application) instead of digitalization level as mediating variable for regression. The random effects (REs) method was used for the analysis, and the results are shown in

Table 9. Robustness test of the mediating effect of digitalization level.

	Lnxzscl
	(1)	(2)	(3)	(4)
lnxtd	0.996 **** (4.053)	0.624 *** (3.211)	1.772 **** (5.266)	0.584 **** (3.441)
lndigi2	1.147 **** (3.676)	−0.042 (−0.104)
lndigi3			0.707 **** (4.471)	0.157 (1.264)
Controls	No	Yes	No	Yes
Year	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes
_cons	−1.705 **** (−4.523)	−10.313 **** (−6.079)	−0.483 (−1.123)	−10.300 **** (−5.445)
N	300	300	300	300
R2	0.138	0.597	0.255	0.589

Notes: t-values in parentheses, *** p < 0.01, **** p < 0.001.

.

Columns (1) and (2) show the regression results using the digital infrastructure level. The results in Column (1) show that the regression coefficient for the coupling coordination between SEIs and human resources is significantly positive at the 1% significance level, and that the positive regression coefficient at the digital infrastructure level is significant (Model 1). Columns (3) and (4) present the regression results for digital applications. These results show that the regression coefficient of the coupling coordination between SEIs and human resources is significantly positive at the 0.1% significance level. The regression coefficients for digital applications were significantly positive at 0.1% significance levels (Model 3).

To sum up, after controlling for the mediating variable digitalization level, the regression results after substituting digitalization level into two digitalization-related variables, reflect to some extent that the coupled and coordinated development of SEIs and human resources has significantly promoted the development of new quality productive forces. The digitalization level mediates the development of NQP through the coupled and coordinated development of SEIs and human resources, indicating the robustness of the model constructed in this paper.

4.5. Heterogeneity Analysis

The level of R&D expenditure is an important factor influencing the development of NQP, given that R&D expenditure can provide certain sources of funds, innovative talents, technological demands and policy support for the development of NQP. This paper ranks the average R&D expenditure levels of 30 provinces (municipalities and autonomous regions) during the study period (2014–2023) and divides the top 15 into regions with high R&D expenditure and the bottom 15 into regions with low R&D expenditure. With reference to previous research results and in combination with Hausman test results, the random effects (REs) method was used for this analysis; the results are shown in

Table 10. Regression results of heterogeneity in R&D spending levels.

	Lnxzscl
	Regions with High R&D Spending		Regions with Low R&D Spending
	(1)	(2)	(3)	(4)
lnxtd	1.897 **** (8.959)	1.285 **** (9.429)	0.457 * (1.738)	0.092 (0.373)
lndigi	1.073 *** (2.942)	−0.351 (−1.563)	1.944 **** (4.830)	0.435 (0.700)
Controls	No	Yes	No	Yes
Year	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes
_cons	−0.988 *** (−2.626)	−8.834 *** (−3.077)	−2.644 **** (−5.894)	−9.576 **** (−3.610)
N	150	150	150	150
R2	0.476	0.891	0.172	0.073

Notes: t-values in parentheses, * p < 0.1, *** p < 0.01, **** p < 0.001.

.

As shown in Table 10, compared with regions with low R&D expenditure, the regression coefficient of the coupling and coordination between SEIs and human resources in regions with high R&D expenditure is significantly positive, indicating that compared with regions with low R&D expenditure, the coupling and coordination between SEIs and human resources in regions with high R&D expenditure has a more obvious promoting effect on the development of NQP in the region. This is in line with the reality on the ground. First, regions with high R&D spending will strongly promote the application of advanced technologies in SEIs, such as new energy vehicles and green environmental protection; facilitate the green transformation of the industrial structure; and promote the formation of green productivity in NQP. Secondly, there will be more technological innovation in research and development in regions with high R&D expenditure, which is conducive to promoting the development of new materials and new sources of energy, further promoting the development of SEIs. In this process, a group of outstanding scientific and technological talents will be cultivated, which will promote the deep integration between SEIs and human resources, and will thereby promote the development of NQP. In addition, regions with high R&D expenditure can make full use of the advantages of scientific and technological research and development to facilitate complementary resource advantages and coordinated development of industrial chains among regions, which will further promote the coordinated development between SEIs and human resources, thereby promoting the development of NQP. Finally, regions with high R&D spending will strongly promote the development between SEIs through means such as advanced manufacturing, new materials, new sources of energy and the formation of human resources in related industries, which will greatly promote the development of NQP.

Compared with regions with high R&D spending, regions with low R&D spending have significantly positive regression coefficients for digitalization levels (as shown in Model (3) in Table 10. This indicates that the improvements to digitalization levels in regions with low R&D expenditures have a more significant impact on the development of NQP. The main reasons for this are as follows: Firstly, in general, the development of digital technologies (such as artificial intelligence, machine learning, automation systems, etc.) in regions with low R&D spending is also lagging behind, and the government and enterprises are more motivated and willing to improve the local level of digital development. Digitalization, as a foundation, is bound to drive the development of new productivity. Secondly, to achieve economic transformation and coordinated regional development, the central government will provide financial and policy support to regions with low R&D spending and increase investment in digital infrastructure, which will promote the development of NQP. Thirdly, regions with low R&D spending have a weak scientific research foundation and face great pressure demanding technological upgrading and innovation. They need to promote industrial upgrading and transformation through digital transformation, thereby fostering and developing NQP. Fourthly, there may be untapped market potential in regions with low R&D spending. These market opportunities may be related to local resources and characteristics. By enhancing the digitalization level of the region, development opportunities can be better seized to promote the development of NQP.

Given that the varying levels of high-tech industrial development in different regions may have an impact on the development of NQP, regions with higher levels of high-tech industrial development can more easily obtain tax incentives, attract more capital and innovative talents, and better promote the development of NQP compared with regions with lower levels of high-tech industrial development. Therefore, this paper ranks the average number of high-tech enterprises in 30 provinces (municipalities and autonomous regions) during the study period (2014–2023), and divides the top 15 into regions with higher levels of high-tech industry development and the bottom 15 into regions with lower levels of high-tech industry development. With reference to previous research results, the random effects (RE) method was used for analysis, and the results are shown in

Table 11. Regression results of heterogeneity in the development level of high-tech industries.

	Lnxzscl
	Regions with a High Number of High-Tech Companies		Regions with Few High-Tech Companies
	(1)	(2)	(3)	(4)
lnxtd	1.912 **** (9.980)	1.282 **** (8.731)	0.400 (1.498)	0.049 (0.196)
lndigi	1.015 *** (2.771)	−0.295 (−1.199)	1.853 **** (4.784)	0.362 (0.591)
Controls	No	Yes	No	Yes
Year	Yes	Yes	Yes	Yes
Province	Yes	Yes	Yes	Yes
_cons	−0.941 ** (−2.517)	−8.619 *** (−2.894)	−2.693 **** (−6.005)	−9.467 **** (−3.610)
N	150	150	150	150
R2	0.491	0.892	0.157	0.022

Notes: t-values in parentheses, ** p < 0.05, *** p < 0.01, **** p < 0.001.

.

It is not difficult to see from Table 11 that in regions with a large number of high-tech industrial enterprises, the regression coefficient of the coupling coordination degree between SEIs and human resources is significantly positive at the 0.1% significance level, indicating that the coordinated development of SEIs and human resources in these regions has a more obvious promoting effect on NQP. This contrasts sharply with regions with a smaller number of high-tech enterprises. This may be because, first of all, regions with a high level of high-tech industrial development will provide a solid industrial foundation and strong technological support, which is conducive to promoting the development of SEIs and thereby creating NQP. Secondly, high-tech industries have excellent characteristics such as high level, high efficiency, high added value and high technological content. Regions with a high level of high-tech industry development are more conducive, therefore, to promoting the development of SEIs and new quality productive forces.

Finally, regions with a high level of high-tech industry development are more conducive to fostering the coordinated development of traditional industries and SEIs, which will also promote the development of new quality productive forces. The regression coefficients of digitalization levels in regions with fewer high-tech industrial enterprises are significantly positive (as shown in Model 3 in Table 11), indicating that the promotion effect of digitalization levels on NQP is more pronounced in these regions. The main reasons for this are as follows: Firstly, in regions with underdeveloped high-tech industries, the government will provide more financial and technical support, and also offer greater assistance to the digital transformation of enterprises, thereby promoting the development of NQP. Secondly, regions with a smaller number of high-tech industrial enterprises have a more urgent need to upgrade and optimize their industrial structure, compelling governments and enterprises to enhance their digitalization levels to cope with the pressure of industrial innovation, which is conducive to the cultivation and development of NQP.

5. Research Results and Conclusions

This paper uses provincial panel data from Chinese provinces recorded between 2014 and 2023 to measure the coupling and coordination degree of SEIs and human resources in China. On this basis, it discusses the mechanism by which the coupled and coordinated development of SEIs and human resources, along with improvements to the digitalization level, impact the development of NQP. Based on theoretical derivations and empirical analyses, the below three conclusions can be drawn.

The fundamental and guaranteeing nature of the coupling and coordination between SEIs and human resources can be better exerted if accompanied by improvements to digitalization levels. The coupled and coordinated development of SEIs and human resources can themselves significantly enhance the digitalization level of a region and promote the development of NQP in that region. The level of digitalization plays a significant mediating role in promoting the development of NQPs through the coupled and coordinated development of SEIs and human resources.

There is significant heterogeneity in the development level of the impact of the coupled and coordinated development of SEIs and human resources on the development of NQPs through the improvement of the digitalization level. Firstly, compared with regions with low R&D expenditure, regions with high R&D expenditure have a more obvious promoting effect on the development of NQP in regions through the coupling and coordinated development of SEIs and human resources. Compared with regions with high R&D expenditure, the improvements to digitalization levels in regions with low R&D expenditure have a more significant promoting effect on the development of NQP. Secondly, compared with regions with a lower level of high-tech industry development, in regions with a higher level of high-tech industry development, the coordinated development of SEIs and human resources in regions with a higher level of high-tech industry development has a more obvious promoting effect on the development of new high-quality productive forces. On the other hand, compared with regions with a higher level of high-tech industry development, the improvement of digitalization in regions with a lower level of high-tech industry development has a more significant promoting effect on the development of NQP.

Based on the above analysis, this paper puts forward the following suggestions: Firstly, the development and growth of SEIs cannot be achieved without the support of talents. We should promote cooperation among industry, academia and research; solve the problem of insufficient innovation of enterprises; and give full play to the important role of universities and research institutions in cultivating innovative talents. Secondly, establishing a reasonable employment mechanism is vital. SEIs are knowledge-intensive industries, and efforts should be made to strengthen the construction of talent teams in emerging industries. Efforts should be made to cultivate, attract, and retain talent to build a high-end talent team for the development of SEIs. Thirdly, support if needed for the development of SEIs regarding terms of institutional guarantees, legal guarantees, technological support guarantees and investment and financing guarantees. Fourthly, it is important to establish talent cultivation and incentive mechanisms to expand the stock of human capital, cultivate specialized talents based on the technological demands of our country’s industries, and improve the relevant policies and systems to increase the reserves of human capital in our country. Fifthly, digital technology should be used to continuously enhance the level of digitalization and promote the development of NQP in an environment of uncertainty and instability. Sixth, during the 15th Five-Year Plan period, the special plan for digital infrastructure construction should be upgraded to promote the construction of digital infrastructure and the development of digital economy application scenarios, as well as strive to enhance the level of digitalization.