Under the background of high-level scientific and technological self-reliance and high-quality development strategy, strengthening the dominant position of enterprise scientific and technological innovation has become the core issue of promoting Chinese modernization. Based on the perspective of the dynamic integration of “evolutionary logic-theoretical logic-practical logic”, this paper systematically analyzed the logical mechanism and realization path of strengthening the dominant position of enterprise science and technology innovation. The research showed that: (1) Strengthening the dominant position of enterprise’s scientific and technological innovation is the dynamic evolution result of the synergy of national strategic needs, external environmental pressure and enterprise’s own ability improvement. Its role has gradually upgraded from a passive application of technology to a leader in technology development and innovation, until the new era is given the core subject to lead the whole chain of scientific and technological innovation and undertake the important task of basic research. (2)The core is to promote the transformation of enterprises from “heavy technology and light science” to “science and technology”, undertake the dual mission of basic research “0 → 1” breakthrough and frontier leading, and open up the whole process from scientific discovery to industrial application by leading the four-dimensional link of “major scientific and technological decision-making-R & D investment-scientific research organization-achievement transformation”. (3) The core path to strengthen the dominant position of China’s enterprises in scientific and technological innovation is to build a three-dimensional promotion pattern of “dual drive of state-owned enterprises and private enterprises, ecological empowerment of basic research, and coordinated operation of four-wheel system”, and systematically build the micro foundation of scientific and technological self-reliance and self-improvement. This paper broadened the theoretical research on strengthening the dominant position of enterprise technology development, and will provide theoretical reference and countermeasures for China to achieve high-level scientific and technological self-reliance.

In the current critical period of China’s comprehensive advancement of rural revitalization and common prosperity strategies, the development of new quality productive forces in agriculture has emerged as the core endogenous driver for promoting urban-rural common prosperity. Given that agricultural technological innovation serves as the concrete manifestation of new quality productive forces in the agricultural sector, this study empirically examined both the overall impact of agricultural technological innovation on urban-rural common prosperity and its multidimensional nonlinear effects using panel regression models, mediation effect models, and nonlinear regression models, based on 1820 county-level samples spanning 2000-2020. The findings revealed that agricultural technological innovation significantly enhances urban-rural common prosperity, a conclusion that remains robust after addressing endogeneity concerns and conducting multiple robustness tests. Mechanism analysis demonstrated that agricultural technological innovation primarily drives common prosperity through enhancing agricultural labor productivity and improving agricultural technological conditions. Heterogeneity analysis indicated more pronounced effects in central regions, counties with more advanced digital rural development, higher economic development levels, and abundant water resources. Further investigation identified an inverted N-shaped nonlinear relationship between agricultural technological innovation and the “commonality” dimension, while an inverted U-shaped nonlinear relationship emerges with the “affluence” dimension. This research will provide crucial theoretical references and empirical evidence for understanding how agricultural technological innovation facilitates phased improvements in urban-rural income growth and distribution amidst the new wave of technological revolution.

Technological innovation is the core driving force for cultivating new quality productive forces. Under high economic policy uncertainty, enterprises’ perception of economic policy uncertainty significantly influences their innovation. Investigating the role of economic policy uncertainty perception in corporate innovation strategy choice is important for enhancing enterprises’ innovation capabilities. Based on data from Chinese A-share listed companies from 2007 to 2022, this study employed text mining methods to construct an index of economic policy uncertainty perception and utilized a two-way fixed effects model to empirically examine its impact and mechanisms on corporate innovation strategies. The findings indicated that: (1) an increase in economic policy uncertainty perception prompts enterprises to choose collaborative innovation strategies; (2) among informal institutions, clan culture and board networks negatively moderate this relationship, while financial connection positively moderates it; (3) this impact is more pronounced in state-owned enterprises, mature enterprises, and high-tech enterprises. The research will provide theoretical and practical foundations for enterprises to leverage informal institutions to mitigate economic policy uncertainty and optimize innovation governance.

In the current era of emphasizing science-driven innovation, science-based enterprises have frequently been found in violation of regulations. Identifying the factors that can predict such violations is a critical issue. This study took science-based publicly listed companies from 2008 to 2019 as its sample, employing machine learning methods to empirically investigate the predictive factors of violations by these companies and analyzing their governance processes. The findings revealed that: firm-level factors are more effective in predicting violations by such firms, as the underlying mechanism of these violations primarily stems from firm heterogeneity, which leads to firm-specific effects. As the most significant predictive factors, the shareholding ratio of the largest shareholder and ROA decrease the probability of violations by science-based enterprises, while the leverage ratio increases it. Due to the differing attention focus of scientists and external investors, the governance of such firms faces a trade-off between “science” and “capital”, which may lead to a paradox between “scientific logic” and “market logic”. Three possible pathways can help alleviate this paradox, strengthen the orientation toward science, focus on long-term interests, and reduce the tendency toward violations. This study expands the research on the internal governance mechanisms of science-based enterprises, will provide practical insights for improving their governance standards and offer theoretical support for investors in selecting investment projects and participating in corporate governance.

Under the background of global digitalization and greening, how collaborative development of digitalization and greening empowers high-quality innovation of SRDI enterprises is a critical problem to be solved urgently. Based on the data of A-share SRDI listed enterprises from 2011 to 2022, this paper empirically tested the impact of coupling coordination of digitalization and greening on high-quality innovation of SRDI enterprises. The study found that coupling coordination of digitalization and greening can promote the high-quality innovation of SRDI enterprises. The mechanism test found that coupling coordination of digitalization and greening promotes the high-quality innovation of SRDI enterprises by promoting the improvement of absorption capacity, the upgrading of human capital and the improvement of investor attention. Heterogeneity analysis showed that at the firm level, the heterogeneity of cooperation culture has no significant impact on the relationship between the coupling coordination of digitalization and greening and the high-quality innovation of SRDI enterprise. At the regional level, geographical heterogeneity has a significant impact on the relationship between the coupling coordination of digitalization and greening and the high-quality innovation of SRDI enterprises. The economic impact test showed that coupling coordination of digitalization and greening improves the total factor productivity of SRDI enterprises by promoting the high-quality innovation of SRDI enterprises. The research conclusion is helpful to reveal the relationship between the coupling coordination of digitalization and greening and the high-quality innovation of SRDI enterprises, and will provide empirical evidence and practical basis for promoting the collaborative development of digitalization and greening and empowering high-quality innovation of SRDI enterprises.

Small and medium-sized enterprises (SMEs) are constrained by financial and scale limitations, and it is often difficult for them to “do it alone” on the road of innovation. The status quo of SMEs’ weak innovative capacity has been highlighted in recent years. It is of great significance to discuss how to use policy tools to promote innovation in SMEs. This paper constructed a DID model based on the data of listed companies on SEM and GEM boards from 2013 to 2020, and examined the micro-mechanism of the impact of pretax additional deduction policy for R&D expenditure on SMEs’ innovation ability from the perspective of cooperative innovation. The study found that: (1) The implementation of pretax additional deduction policy for R&D expenditure can significantly enhance the innovation capacity of SMEs, which is reflected in the improvement of innovation continuity, innovation quality and total factor productivity. (2) Mechanism analysis shows that the mechanism by which pretax additional deduction policy for R&D expenditure affects SMEs’ innovation capacity is to promote collaborative innovation among firms. (3) The policy reduces the search costs for collaborative partners and risk costs for knowledge leakage by adding the deduction of costs related to collaborative innovation, thus enhancing the willingness of SMEs to collaborate and innovate. (4) The promotional effect of pretax additional deduction policy for R&D expenditure on SMEs’ collaborative innovation is more pronounced in highly digitized firms, and firms in highly technology-intensive industries and in regions with better science and technology intermediation and innovation platforms have benefited more from the policy benefits. This paper enriches the theoretical and empirical research on SMEs’ innovation policies, and will provide important insights into the promotion of innovative capacity through collaborative innovation in SMEs.

Clarifying the maturity of industrial technology ecosystems is essential for understanding the current state of industries and providing a scientific basis for formulating industrial strategies. However, existing research predominantly focuses on evaluating single-technology synergies or element similarities, often neglecting a systematic examination of multi-layer network synergies. This paper constructed judgment rules for assessing the stage and level of technology ecosystem maturity from two perspectives: mutual promotion-driven synergy and mapping overlap synergy. It proposed a comprehensive evaluation method that integrates hypernetwork models, transfer entropy, and two-mode network mapping relationships. Using patents from the nanomaterials industry between 2013 and 2022 as a foundation, an empirical analysis was conducted to assess the maturity of this industrial technology ecosystem. The results indicated that through normative and quantitative analyses of multi-layer network synergies, it is possible to effectively overcome the subjectivity and one-dimensional limitations inherent in traditional evaluations, thereby providing an objective basis for determining maturity. From the perspective of driving synergy, R&D cooperation along with patent development within the nanomaterials industry significantly propels market applications; however, there exists insufficient reverse driving effects—suggesting that this sector is currently in its formative stage regarding technological systems. In terms of mapping synergy, there is a notable overlap degree (90%) between core technology patents and market application fields; nevertheless, the level of synergy between R&D entities and their market environment remains relatively low overall—resulting in a medium to high maturity level. Based on these findings, strategies such as enhancing the establishment of market-oriented technology consortia are recommended. These strategies will provide valuable decision support for governments and enterprises aiming to optimize their technological ecosystem layouts while bolstering industrial competitiveness.

With the rise of artificial intelligence-generated content (AIGC), AI has moved from decision-making 1.0 to generative 2.0. Machine intelligence (“machine intelligence”), exemplified by next-generation cognitive computing capabilities, demonstrated significant potential in generating both incremental solutions for iterative optimization and exploratory solutions for value reconstruction. However, whether innovative solutions can be adopted and achieve dual synergy still depends heavily on prudent decision-making by human intelligence (“human intelligence”), which is centered on the cognitive evaluation of the senior management team. In this context, integrating the perspectives of “machine” and “human” intelligence held significant theoretical value in analyzing the synergy mechanisms of enterprise dual innovation. This study developed a process model of “generation of dual innovation solutions - adoption - synergy realization of dual innovation”. Based on survey data from 343 enterprises in the Yangtze River Delta region that are implementing AI innovation, this model was empirically tested using hierarchical regressionand bootstrap methods. The results indicated that: (1) Cognitive computing capability has a significant positive impact on exploratory innovation, exploitative innovation, and dual innovation synergy, and has a stronger driving effect on exploratory innovation. (2) The relationship between cognitive computing capability and dual innovation synergy is only positively moderated by fit cognition, while the moderating effects of risk cognition and complexity cognition are not significant. (3) The three micro-processes of data-driven dynamic capabilities, namely, sense and response, integrated and utilization, and reconstruction and transformation, exhibit a chain-mediated impact on the relationship between cognitive computing capability and dual innovation synergy. The independent mediating effect of digital integration and utilization capability is insignificant. This study revealed the complementary boundaries of human-machine collaboration from the perspective of cognitive division of labor, deepened the theoretical mechanism of AI empowering digital innovation, and will provide practical insights for enterprises to optimize human-machine configuration and coordinate dual innovation with strategic decision-making.

As a key action to realize the benefits of science and technology, address major social issues, and empower vulnerable and special groups in society, driving science and technology for social good holds significant importance in responding to the demands of stakeholder groups and improving people’s well-being. This study constructed an evolutionary game model of consumers, enterprises and the government in collaborative driving science and technology for social good, analyzed the strategic choices of each game subject and the stability of the equilibrium point of the game system, and carried out the numerical simulation analysis by using Matlab 2024a, to investigate the evolutionary path of behavioral strategy of each subject in the context of changes of key parameters. As results shown, in the process of collaborative driving science and technology for social good, the final evolution of the three-party game subjects’ stable strategy is consumers’ participation in science and technology for social good, enterprises’ active implementation of science and technology for social good, and the government’s rigorous governance of science and technology for social good; different parameter values of the three-party subjects’ initial strategy selection probability, the government’s level of rewards and penalties, enterprises’ commercial and reputational gains, consumers’ gains, and the three-party subjects’ loss can significantly affect the behavioral strategy evolution. That is, the increase in the three-party subjects’ initial strategy selection probability, the reinforcement of the government’s level of rewards and penalties, the improvement of enterprises’ commercial and reputational gains, the enhancement of consumers’ gains, and the increase in the losses caused by the three-party subjects’ negative strategies all contribute to the evolutionary system reaching an ideal stable state. The study has enriched the exploration of mechanism for collaborative driving science and technology for social good and will provide theoretical and practical reference to the evolution of strategic choices of consumers, enterprises and the government in positive directions.

Taking effective policy measures to promote green technology innovation is a key lever for China’s green and high-quality economic development. Considering the policy practice of piloting the construction of big data comprehensive experimental zones as a quasi natural experiment, this paper constructed a multi temporal double difference model based on panel data from 265 cities from 2008 to 2022, and studied the impact and internal mechanisms of the construction of big data comprehensive experimental zones on green technology innovation. The results showed that: (1) The construction of a big data comprehensive experimental zone can effectively promote green technology innovation, and the results are robust. (2) Heterogeneity analysis reveals that the impact of the pilot policy is more pronounced in eastern cities, provincial capitals, and non-resource-dependent cities. (3) Mechanism testing shows that improving labor quality, enhancing public environmental awareness, and improving industrial innovation capabilities are important mechanisms for promoting green technology innovation through the construction of experimental zones. (4) Further analysis shows that there is a significant demonstration and driving effect of green technology innovation activities between cities, and the construction of big data comprehensive experimental zones will have a positive spatial spillover effect on green technology innovation in surrounding cities. This study not only fills the theoretical gap in explaining the mechanism of policy action in the construction of experimental zones in existing research, but also has important reference significance and value for promoting the pilot construction of big data comprehensive experimental zones and formulating relevant policies to promote green technology innovation in China.

Enterprise innovation has become a core engine for cultivating new quality productive forces and achieving high-quality economic development. To effectively implement the innovation-driven development strategy, the Chinese government has utilized tax preferential policies as a crucial measure to incentivize enterprise R&D and innovation, with annual increases in tax reductions. However, issues such as an imbalanced R&D investment structure and inadequate policy implementation have hindered the full realization of China’s enterprise innovation potential. Therefore, based on data from Chinese listed companies, this paper innovatively introduced a machine learning-based heterogeneity analysis method, building upon the traditional PSM-DID method, to deeply explore the heterogeneous impact of tax preferential policies on enterprise innovation. The research found that, on one hand, tax preferential policies demonstrate a certain overall positive incentive effect on stimulating enterprise innovation investment activities and serve as an important policy tool for promoting the development of new quality productive forces in China; but on the other hand, some issues remain to be optimized in policy implementation. Specifically, many enterprises exploit tax incentives through policy arbitrage, significantly weakening the positive effects of the policy and even causing adverse consequences in some cases. Through machine learning analysis to identify the heterogeneous features of policy effects, it is found that the positive impact of tax preferential policies on patent quantity and quality is mainly concentrated in approximately 15% of enterprises. This indicated that many enterprises have not yet been able to fully and effectively utilize policy incentives to unleash their innovation potential. These under-benefited enterprises generally exhibit characteristics such as high R&D risk, R&D manipulation, and strong entrepreneurial control, warranting attention in subsequent policy optimizations and adjustments. Furthermore, the machine learning-based heterogeneity analysis method introduced in this study overcomes the limitations of traditional linear models, enabling a more accurate identification of heterogeneous policy effects, and will provide a new theoretical perspective and methodological reference for precise policy implementation and promoting the vigorous development of new quality productive forces.

Public policies increasingly promote IUR (Industry-University-Research) collaborations but are often criticized for their suboptimal resource allocation. This study employed textual analysis to extract detailed information on government subsidies for corporate postdoctoral programs from the notes to financial statements of A-share listed companies, thereby examining their actual impact on the national innovation system. The findings revealed that postdoctoral subsidies effectively increase firms’ number of patent applications in the following year, and this effect persisted even after excluding design patents. Additional evidence was provided by distinguishing between specific patent types and accounting treatments. After a series of robustness tests, the above conclusions remain valid. This study further examined the moderating effects of firm-, regional-, and industrial-level factors. The results showed that the positive effect of postdoctoral subsidies on innovation is relatively weaker in contexts where internal controls are ineffective, in regions with preexisting high levels of university resources, or when the firm operates in a labor-intensive industry. Moreover, postdoctoral subsidies enhance a firm’s innovation quality, as reflected by a higher number of patent citations, greater patent generality, and a higher share of exploitative innovations, indicating that the subsidies not only bolster firms’ innovation willingness but also improve innovation quality. Significant spillover effects across the supply chain are also been identified. Overall, these findings offer empirical evidence that government subsidies for corporate postdoctoral programs can effectively stimulate the economy’s innovation vitality, thus will provide a reference for evaluating and continuously optimizing the postdoctoral system.

As digital and intelligent transformation advances and the 15th Five-Year Plan progresses, innovation has emerged as a core driver propelling enterprises toward high-quality development. A critical challenge facing enterprises today is how to optimize leadership styles to stimulate employees’ innovative behaviors. Inclusive leadership, a novel leadership approach rooted in traditional Chinese notions of inclusivity, shapes employees’ innovative behaviors through motivational enhancement and cognitive restructuring. Drawing on Self-Determination Theory, this study developed a chain mediation model incorporating employees’ sense of career calling and work reflection to explore the mechanisms underlying the impact of inclusive leadership on employees’ innovative behaviors. An online questionnaire survey was conducted among 519 corporate employees using a three-wave longitudinal design, and hypotheses were tested via hierarchical regression and bootstrapping methods. The results indicated that: (1) Inclusive leadership exerts a significant positive impact on employees’ innovative behaviors. (2) Both employees’ sense of career calling and work reflection play a mediating role in the relationship between inclusive leadership and employees’ innovative behaviors. (3) Inclusive leadership shapes innovative behaviors through the serial mediation chain of “career calling-work reflection”. This study advances the literature on inclusive leadership within the Chinese context, clarifies the mechanism through which inclusive leadership drives employees’ innovative behaviors, and offers important practical insights for fostering inclusive leadership and enhancing employees’ innovative behaviors in the new era.

Against the backdrop of the booming gig economy, how to improve gig workers’ service performance has become a key concern for both the industry and academia. This paper employed a mixed-methods approach, combining qualitative and quantitative approaches, to systematically examine the influence mechanism of online labor platform technology feature set on gig workers’ service performance. Through a procedural grounded theory analysis of 97 articles about Upwork and interviews with 21 gig workers on it, this study revealed that the social interaction feature set and the job control feature set are two core types of online labor platforms’ technology feature sets that influence gig workers’ service performance. Grounded in this finding and in self-determination theory, this study developed a mechanism model for how online labor platforms’ technology feature set influence gig workers’ service performance. We collected data from a large-sample survey of 298 gig workers on Upwork and performed analysis using the PLS method. The results showed that: (1) Both social interaction feature set and job control feature set positively influence the satisfaction of gig workers’ basic psychological needs. (2) The satisfaction of gig workers’ competence needs mediates the influence of social interaction feature set and job control feature set on their service performance. (3) Career centrality positively moderates the mediating effect of job control feature set on gig workers’ service performance through the satisfaction of both autonomy and competence needs. This study innovatively identified two types of technology feature sets of online labor platforms and validated their influence mechanisms and boundary conditions on gig workers’ service performance, enriching the body of research on how online labor platforms shape the service performance of gig workers within the broader gig economy literature, and will provide effective guidance for online labor platforms to rationally design their technology features to enhance gig workers’ service performance.

Innovation is the core driving force behind corporate development. Although the notion that “meeting customer needs is the key to successful corporate innovation” is widely accepted, there remains a lack of in-depth research on how customers influence corporate innovation. To address this gap, this paper examined the impact of customer-firm relationships on corporate innovation and its underlying mechanisms using data from A-share listed companies in China from 2015 to 2022. The study matched customer data with company data and employed empirical analysis to explore this relationship. The findings were as follows: (1) Customer-company relationships significantly promote corporate innovation by increasing expected market returns, reducing innovation risks, and alleviating financing constraints. (2) Heterogeneity analysis reveals that the effect is stronger in firms with high customer dependency, state-owned enterprises, high-tech industries, and regions with strong intellectual property protection. (3) Further investigation into the “effectiveness” of the relationship shows that customer-company relationships can be sustainably transformed into a driving force for corporate innovation when they interact with the firm’s innovation capabilities. The overall synergistic effect is positive, and the dynamic marginal effect follows a trend of first increasing, then decreasing, and finally increasing again. The conclusions of this study will provide important insights for company to be demand-oriented and leverage the inexhaustible resource of customer relationships to achieve high-quality innovation.

The cross-border merger and acquisition(M&A) strategies of Chinese enterprises have shifted from resource seeking to technology seeking, but the practice of cross-border technology M&As faces significant challenges. Considering that the existing studies on the net effect of single factors fail to reveal the complex causal mechanism of high value creation in cross-border technology M&As, this paper, based on the theoretical perspective method and the literature induction method, took the cross-border technology M&As of 22 Chinese listed enterprises as examples, and employed the fsQCA research method to analyze the corresponding configurational paths for high value creation in cross-border technology M&As and the corresponding typical cases. The study found that: (1) Institutional distance, enterprise characteristics, absorptive capacity, and transaction characteristics are not necessary conditions for high value creation in cross-border technology M&As. (2) There are four types of paths that promote high value creation in cross-border technology M&As, namely, enterprises with weak experience and low actual absorptive capacity conduct complete technology-related M&A on targets with high institutional distance, enterprises with both strong experience and strong strength conduct technology-diversified M&A on targets with low institutional distance, enterprises with both strong experience and strong absorptive capacity conduct technology M&A on targets with low formal institutional distance, enterprises with strong actual absorptive capacity conduct technology-complementary M&A on targets with high institutional distance. On this basis, we summarized six M&A strategies that match the five M&A backgrounds. The research conclusions help to deepen the theoretical understanding of high value creation in cross-border technology M&As, and will provide a key answer to the practical problem of “how to achieve high value creation in cross-border technology M&As”.

In the context of the current global pursuit of green and sustainable development, industrial intelligence, as a new driving force to promote industrial upgrading and economic growth, has an increasingly prominent impact on regional green innovation and development. Utilizing panel data from 30 provincial-level regions across China, covering the period from 2013 to 2021, this study empirically investigated the effects of industrial intelligence on regional green innovation performance, and combined the panel threshold model to explore the impact mechanism of industrial intelligence on regional green innovation performance under different levels of intellectual property protection. The results showed that industrial intelligence has a significant promoting effect on the improvement of regional green innovation performance, and with the continuous improvement of the level of industrial intelligence, its promoting effect on regional green innovation performance will gradually increase, showing a non-linear characteristics of increasing marginal effect. Intellectual property protection not only plays a positive regulatory role in the process of industrial intelligence improving regional green innovation performance, but also shows a marginal increasing trend as the intensity of intellectual property protection increases. The analysis of regional heterogeneity shows that compared with the eastern and western regions, the impact of industrial intelligence on regional green innovation performance in the central region is more obvious. The research results clarify the mechanism and influence path between industrial intelligence, intellectual property protection and regional green innovation performance at the theoretical level. At the practical level, it will provide theoretical reference and decision-making support for regional innovation entities to leverage the advantages of industrial intelligence and achieve green innovative development under reasonable intellectual property protection levels.

In the era of knowledge economy, the deep integration of intellectual property (IP) with financial resources is of great significance in stimulating corporate innovation vitality and accelerating the transformation of enterprises’ scientific and technological (S&T) achievements. Based on the panel data of Chinese listed enterprises from 2015 to 2021, this paper empirically examined the impact of IP financing on the transformation of enterprises’ S&T achievements by employing a two-way fixed effects model alongside theoretical analysis. The results showed that: (1) IP financing could significantly promote enterprises’ capacity to transform S&T achievements, supported by various robustness tests. (2) The mechanism analysis revealed that IP financing can exert an incentive effect on the transformation of S&T achievements in enterprises by alleviating financing constraints and increasing innovation investments. (3) Further analysis revealed that IP financing for high-tech and large-scale enterprises has a more prominent role in promoting the transformation of S&T achievements. This paper has enriched the literature on factors promoting the transformation of S&T achievements from a micro-perspective and clarifies the mechanism through which IP financing could affect the transformation of enterprises’ S&T achievements. The conclusions are conducive to optimizing the landscape of IP financing and enhancing the efficacy of the technology transfer market.