Against the backdrop of China’s "twin transition" strategy integrating digitalization and green development, the mechanisms through which AI technology integration influences sustainable product innovation (SPI) have become a prominent research topic. However, existing studies predominantly examine this relationship at the macro or industry level, overlooking firm-level mechanisms. Furthermore, the intermediate pathways through which AI technology integration influences SPI remain underexplored. Based on resource orchestration theory, this study examines how AI technology integration influences different types of SPI using a sample of 298 firms. The findings reveal that AI technology integration positively affects both functional and systemic SPI, with a stronger impact on systemic SPI. AI technology integration also positively influences both consumer and business digital platform capabilities. Consumer digital platform capability positively affects both functional and systemic SPI, with a stronger impact on functional SPI. Business digital platform capability positively affects both functional and systemic SPI, with a stronger impact on systemic SPI. Furthermore, consumer digital platform capability mediates the relationship between AI technology integration and functional SPI, while business digital platform capability mediates the relationship between AI technology integration and systemic SPI.

This study makes several theoretical contributions. First, it reveals the mechanism through which AI technology integration affects SPI and advances research at the nexus of AI and corporate sustainability. Notably, existing studies often treat SPI as a whole and lack refined exploration of its types. This study deconstructs SPI into functional and systemic types, reveals how AI technology integration affects distinct innovation scenarios, and also provides theoretical insights for subsequent classification research on SPI.

Second, the study deeply explores the value creation process of AI technology integration in SPI and clarifies the internal link between digital platform capabilities in AI technology integration and SPI based on resource orchestration theory. This offers a more integrative framework for examining the connection between AI technology integration and SPI. It also extends resource orchestration theory to the emerging field of AI and sustainable innovation research.

Third, this paper clarifies how digital platform capabilities foster SPI and enriches the research on SPI antecedent variables. Prior empirical research on the role of digital platform capabilities in helping firms achieve SPI goals remains scarce. In particular, the distinct effects of consumer and business capabilities across SPI contexts have not been fully examined. This paper reveals their differentiated impacts on functional and systemic SPI. It specifies how digital platform capabilities align with SPI outcomes and also supplements existing research on SPI antecedent variables.

Practically, the study provides actionable guidance for firms to leverage AI technologies to promote SPI. Specifically, when focusing on functional SPI, firms should prioritize investment in AI-driven personalized recommendation and intelligent service systems to enhance consumer digital platform capability and identify functional requirements from user experience and preference data. When focusing on systemic SPI, firms should emphasize the development of business digital platform capability, applying machine learning algorithms to accurately predict customer needs and establishing flexible resource integration mechanisms and data-sharing systems to achieve collaborative innovation across the entire product lifecycle.

This paper undertakes a systematic review and theoretical synthesis of the mechanisms through which Pasteur-type scientists engage in and catalyze industry-university collaboration (IUC). Moving beyond theoretical exposition, the research is empirically grounded in the practical model of Industry-University Collaborative R&D Centers, a policy-driven initiative prominently adopted by small and medium-sized enterprises (SMEs) within Hubei Province, China. The methodological approach combines a review of extant literature with rich, qualitative data derived from semi-structured interviews conducted with administrators and principal investigators at twelve distinct such centers. This robust methodological foundation enables the construction of a novel, multi-level incentive theory that explicates the drivers of Pasteurian scientific engagement within the complex socio-economic framework of IUC.

The theoretical model pivots on two central pillars: the indispensable, agency-like role of the Pasteur-type scientist as a bridge between fundamental inquiry and applied commercial development, and the critical importance of institutional incentive structures designed to augment the innovative output of these partnerships. Quantitative analysis conducted as part of this study yields a compelling finding: collaborative ventures that actively involve Pasteur-type scientists demonstrate a statistically significant enhancement in patent output, with an average increase of 3.512 patents compared to those that do not feature such pivotal figures. This finding underscores the scientist's role not merely as a participant but as a fundamental multiplier of innovation productivity.

Delving deeper into the institutional enablers, the theory posits and validates several key moderating factors. Firstly, the organizational transformation of the academic institution itself is paramount. When the host university of a Pasteur-type scientist consciously evolves towards an "entrepreneurial university" model, it institutionalizes support structures that are critical for success. Crucially, this shift involves the implementation of equitable research assessment and evaluation systems that recognize and reward applied research, patent filings, and knowledge transfer activities on par with traditional academic publications. This institutional legitimacy removes a significant barrier to scientist participation and directly correlates with greater collaborative output.

Secondly, the micro-level design of the collaboration contract is identified as a direct motivational lever. Agreements that stipulate transparent, equitable, and forward-looking clauses regarding intellectual property (IP) ownership and the sharing of long-term profits/revenues serve as powerful ex-ante incentives. By ensuring that Pasteur-type scientists are not merely compensated for their time but are also granted a stake in the future commercial success stemming from their ingenuity, these contracts align the interests of the individual researcher with the broader objectives of the collaboration, thereby fostering deeper commitment and yielding superior innovation performance.

Finally, the macro-level role of government and external stakeholders is emphasized. The research finds that proactive support from local governments, manifested through higher direct subsidies for IUC projects, acts as a crucial risk-mitigating catalyst. Furthermore, government agencies can perform a vital brokerage function by facilitating introductions and attracting greater volumes of social capital and venture funding into these partnerships. This injection of financial and network resources expands the operational scale and ambition of the R&D centers, creating a more fertile environment for the generation of marketable technologies and thus leading to a higher volume of tangible outcomes.

The paper employ literature review to address theoretical questions related to user innovation under the paradigm of artificial intelligence (AI). It begins by defining AI and user innovation and then conducts a literature search, using specific keywords and inclusion criteria to identify relevant studies. The search strategy is designed to be comprehensive yet accurate, focusing on peer-reviewed studies published in English from 1991 to 2023. The authors use the Gioia methodology for data analysis. This methodology involves three stages: first-order categories, second-order themes, and overall dimensions, moving from a strict focus on empirical data to developing theoretical insights.

The purpose of this review is to bridge the gap between AI and user innovation research by identifying key themes and constructing an integrated framework that delineates the reciprocal impact of AI on user innovation and vice versa. The review aims to answer research questions such as "What impact does AI have on user innovation? Why and how can users improve or even innovate AI?" and to pave the way for future research focusing on the interplay of AI, enterprises, users, and multi-actor engagement. The study identifies eight key themes and constructs a comprehensive research framework that provides a systematic understanding of AI and user innovation.

The study found that the paths through which AI acts on user innovation include: ①Enterprises identify user innovations through AI; ② AI-driven innovation toolkits; ③AI as a medium reshaping enterprise-user co-creation relationships; ④AI empowering innovation communities; ⑤AI directly participating in innovation as an actor. The paths through which user innovation acts on AI include: ①Consumer users through participatory design. ②Optimizing AI systems through the "human-in-the-loop" framework; ③Enterprise users driving AI innovation through data assets, scenario applications, and multiple roles.

The paper enriches existing research results and promotes further research on the bidirectional impact of artificial intelligence and user innovation. It also identifies gaps in the literature and proposes a research agenda to advance innovation in AI users, contributing to the broader literature on user innovation management and providing a foundation for managing AI effectively in enterprises and user innovation processes.

We integrated these findings into an integrative framework that depicts AI and user innovation as a mutually constitutive system. Building on this synthesis, we propose a future research agenda that addresses five under-explored areas: At the firm level, future inquiry should shift from simply using machine learning to identify user insights toward investigating how retrieval-augmented generation and other generative-AI techniques can actively search for, synthesise and integrate user-generated knowledge and solutions. This includes clarifying the new boundaries and governance mechanisms that distinguish AI-native innovation communities from traditional digital ones, and critically examining data-acquisition M&A strategies—such as large firms buying hospitals to secure private health data—by unpacking their strategic motives and the attendant ethical risks of commodifying sensitive patient information. At the user level, in-depth case studies and experiments are needed to tease out the contextual contingencies of AI-driven innovation toolkits, moving AI from a passive “resource” to a co-evolving network actor. Particular attention should be paid to micro-level mechanisms through which (1) employees undergoing digital transformation tinker with or augment AI systems in situ, and (2) individual contributors in open AI communities iteratively refine algorithms via pull requests, issue tracking and human-in-the-loop feedback loops. At the multi-actor ecosystem level, research must go beyond marketing-oriented value co-creation to explore how AI infrastructures mobilise cross-domain collective intelligence in real time. Empirical studies should examine how heterogeneous actors dynamically orchestrate AI capabilities to sense, seize and scale transient digital opportunities within complex, rapidly changing environments.

As the main participants in regional innovation capacity building, local governments' fiscal science and technology expenditures support basic research, major technological research and the transformation of innovation achievements, playing a role in reducing innovation risks, making up for market failures and enhancing regional innovation capabilities. This paper analyzes the current situation of central and local fiscal science and technology expenditures and the current situation of vertical fiscal imbalance in the three major regions, and then conducts an empirical study and empirical analysis on the impact of fiscal science and technology expenditures on regional innovation capabilities. The research finds that the promoting effect of local fiscal science and technology expenditure on the innovation capacity of the eastern and central regions is significantly greater than that of the western region. Basic research expenditure JCYJ and applied research expenditure YYYJ have a positive incentive effect on the improvement of regional innovation capacity, while technology research and development expenditure JSYJ has a negative effect on the improvement of regional innovation capacity. The vertical fiscal imbalance in the western region plays a significant negative moderating role in the improvement of regional innovation capacity by fiscal science and technology expenditure. Put forward policy opinions on optimizing the structure and scale of local fiscal science and technology expenditure, enhancing regional innovation capabilities, and optimizing the division of fiscal powers and expenditure responsibilities, as well as moderately reducing the level of vertical fiscal imbalance.

Adversity stimulates transformation, deficiency cultivates diligence, and affluence requires strategic foresight. The combination of production and financing is not only a power source for enterprises to promote new quality productive forces, but also a strategic fulcrum for accelerating the construction of a modernized industrial system and realizing innovation-driven and high-quality development. Existing research focuses primarily on the impact of financial-industrial integration on corporate operating performance, investment efficiency, business risk, and credit availability. However, studies examining the relationship between financial-industrial integration and corporate innovation are relatively scarce. As a crucial mode of financial-industrial integration, enterprise investment in banks reshapes the bank-firm relationship through capital nexus, injecting new impetus into the development of new quality productive forces.

Based on the data of Chinese A-share listed companies from 2011 to 2023, this paper examines the impact and mechanisms of industry-finance integration on new quality productive forces of enterprises. It finds that financial-industrial integration significantly improves the new quality productive forces of enterprises, especially improving labor resources and realizing the leap of optimal combination. Mechanism analysis reveals that the combination of industry and finance mainly enhances new quality productive forces by alleviating financing constraints, curbing over-indebtedness and reducing agency costs. Heterogeneity analysis indicates that the effect of financial-industrial integration on firms’ new quality productive forces is more pronounced among firms in the central and western regions, where credit resources are scarce, as well as firms that receive fewer government subsidies, manufacturing firms and small-scale firms.

The potential contributions of this paper include the following aspects. First, existing literature focuses on the impact of corporate transformation and development as well as the external market environment on new quality productive forces. However, few studies analyze how to enhance enterprises’ new quality productive forces from the perspective of financial-industrial integration. This paper examines the relationship between financial-industrial integration and new quality productive forces of enterprises, thereby enriching the field of new quality productive studies. Second, this paper investigates the mechanisms through which financial-industrial integration promotes the development of enterprises’ new quality productive forces from the perspectives of alleviating financing constraints, curbing over-indebtedness, and reducing agency costs. It provides a theoretical basis for accelerating the cultivation of enterprises’ new quality productive forces. Third, this paper analyzes the heterogeneous effects of financial-industrial integration on the development of enterprises’ new quality productive forces under varying regional and enterprise-specific characteristics. By considering these differences, it provides valuable insights for enterprise strategic decision-making and policy formulation.

Based on the above findings, this study provides a theoretical foundation and policy implications to support enterprises in holding shares in non-listed banks and accelerate the development of new quality productive forces. The following recommendations are proposed in this paper. First, policy mechanisms should be optimized to actively promote financial-industrial integration among enterprises. This can be achieved through two key measures: gradually relaxing equity holding limits and developing differentiated policy frameworks. Second, industry supervision should be strengthened to regulate enterprise shareholding behaviors. Third, enterprises should optimize their strategic layout for financial-industrial integration to accelerate the cultivation of new quality productive forces.

The orderly operation of the science and technology (S&T) talent policy system constitutes a fundamental guarantee for the construction and governance of high-level talent hubs. However, the intrinsic interaction mechanisms among the constituent elements within this system remain inadequately explored and require further revelation. To address this gap, this study leverages panel data from the Mainland Chinese city cluster within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) spanning the period 2007-2021. Employing an integrated methodological approach that combines dynamic fuzzy-set Qualitative Comparative Analysis (fsQCA) and BERTopic machine learning, the research investigates the synergistic effects of key factors—namely innovation motivation, innovation capability, innovation emotion, and institutional environment—on enhancing the level of orderly operation within the S&T talent system. This investigation is grounded in an "individual-organizational" interaction perspective and systematically explores these dynamics across both temporal and spatial dimensions.

The analysis yields several significant findings. Firstly, the orderly operation of the S&T talent policy system manifests through multiple pathways underpinned by complex mechanisms. Five distinct configurations were identified: a "motivation"-dominated synergy-driven type; an "emotion"-dominated regulation-driven type; a "motivation-emotion"-dominated synergy-driven type; a "motivation-capability"-dominated synergy-regulation-driven type; and a multi-factor synergy regulation-driven type. Crucially, within these five pathways, none of the individual or organizational factors alone constitutes a necessary condition for achieving a high degree of system orderliness. Instead, the research reveals the existence of equifinality—equivalent substitutability—between specific condition combinations. Notably, substitutable relationships exist between "motivation-driven" mechanisms and "regulatory intensity," between "emotion-driven" mechanisms and "interdepartmental coordination," and between the combination of "capability cultivation + interdepartmental coordination + regulatory intensity" and "emotion-driven" mechanisms. Among these, motivation-driven mechanisms consistently emerge as a core condition across multiple pathways, playing a pivotal role in achieving high system orderliness.Secondly, weak innovation motivation coupled with low regulatory intensity is identified as a primary constraint negatively impacting the orderly operation of the S&T talent policy system. Thirdly, the development trajectory of this system within the GBA's Mainland city cluster exhibits significant time effects, with 2013 and 2017 serving as critical inflection points. Heterogeneous pathways observed prior to these nodes consistently converged towards states characterized by high consistency and stability thereafter. Fourthly, distinct regions demonstrate differentiated realization pathways for achieving system orderliness, highlighting the significant influence of specific contextual conditions on the effective configuration of factors.

From a theoretical perspective, this research offers novel contributions. By adopting the "individual-organizational" interaction lens, it focuses on micro-level elements influencing S&T talents—specifically individual innovation motivation, capability, and emotion—thereby revealing the synergistic mechanisms operating between conditions at the individual and organizational levels. Furthermore, grounded in the theory of policy attention allocation, the study clarifies the equifinal relationships among key elements, systematically delineating both the core conditions and the substitutable pathways essential for realizing the orderly operation of the S&T talent policy system.From a pactical perspective, the findings provide actionable insights for policymakers. Decision-makers should prioritize "motivation-driven" mechanisms while also flexibly utilizing the identified equivalent substitutability relationships to optimize the allocation of policy attention. Crucially, capability cultivation is not a universally applicable key mechanism, its effectiveness is contingent upon regional economic, social, and cultural environmental factors. During policy design and implementation, policymakers must simultaneously address individual talent needs and organizational synergy mechanisms to ensure the policy system operates sustainably and efficiently.Consequently, the conclusions provide robust theoretical support and concrete policy recommendations aimed at enhancing the operational effectiveness and governance of S&T talent policy systems.

The Science and Technology Backyard is a new exploration in the process of agricultural modernization with Chinese characteristics. The model of Science and Technology Backyard pioneered by universities in China recently has Reshaped the agricultural technology extension service system,providing effective assistance for small-scale farmers around the world to shake off poverty and increase income. The Science and Technology Backyard dates back to the rural construction movement during the Republic of China period. It originates from the establishment of the Quzhou Experimental Station of China Agricultural University and the construction of agricultural technology promotion network. Since its birth in 2009, the Science and Technology Backyard in China have gone through three stages, namely, exploration, growth, and development. In the new era, the Science and Technology Backyard has positive significance, becoming a new handle to promote comprehensive rural revitalization, a new tool to cultivate new agricultural productivity, a new practice to integrate education, science and technology, and talents as well as a new Platform to Implement key core technology research in agriculture. In the future, the development of Science and Technology Backyard will present five major trends, namely, diversified participation forces, clustered spatial layout, internationalized brand influence, professionalized service functions, and digitalized operation and management.

Future energy is a critical arena for national competition, where the growing energy demands driven by artificial intelligence and the need for energy security compel China to evaluate the evolution and competitive landscape of future energy technologies, optimize strategies to enhance global competitiveness, and ensure energy self-sufficiency. Utilizing the S-curve principle of TRIZ, the study analyzes and compares the technological evolution stages of future industries at the national level, constructing a competitiveness evaluation system that includes the Technology Impact Index, Technology Growth Index, Technology Maturity Index, Technology Attention Index, and Market Attraction Potential Index. The CRITIC-Entropy Weight Method is employed to quantify the competitiveness of future industries, with an empirical validation conducted using 13 technologies in the future energy sector across China, the United States, Japan, and the United Kingdom. Results indicate that the United States leads in high-barrier fields such as controlled nuclear fusion and CCUS, with a maturity lead of 10-69 years over China; Japan and the United Kingdom hold localized advantages in virtual power plants and nuclear fission; China ranks first in competitiveness for six technologies, including deep-sea wind energy and high-efficiency photovoltaic cells, but its overall technological influence and maturity remain relatively low. The study provides a new approach for analyzing international competition in future industries and offers strategic recommendations for enhancing the competitiveness of Chinese future energy technologies.

The future industry is driven by breakthrough technological innovation as its core driving force, and its development combines multiple characteristics such as strategic leadership, industrial disruption, and technological route uncertainty. The layout of such industries not only helps to grasp the initiative of the global technological revolution, but also serves as a key path to reconstruct the competitive advantage of the industry. With the deepening development of artificial intelligence, big data, and cloud computing technology, computing power resources are gradually replacing traditional production factors and becoming an important driving force for future industrial innovation. This article calculates the future industrial innovation network based on cooperative patents in 285 cities from 2011 to 2021, and measures urban computing infrastructure through the construction of supercomputing centers. The impact mechanism and effect of supercomputing center construction on urban future industrial joint innovation are tested through a difference in differences model. The following conclusions are reached: (1) The construction of supercomputing centers can positively promote the enhancement of the strength of future industrial innovation networks, and this conclusion has been tested through robustness methods such as adjusting regression samples, adjusting model settings, using dual machine learning models, and endogeneity tests, indicating that the empowerment of computing infrastructure can promote the development of future industrial innovation networks. At the same time, computing infrastructure mainly strengthens the external innovation network connection of cities, and its innovation network reconstruction function is more reflected in the external connection level that breaks through administrative boundaries, but it has limited innovation synergy effects among innovation subjects within cities. (2) The computing infrastructure mainly affects the future development of industrial innovation networks through mechanisms such as knowledge innovation linkage, innovation factor adsorption, and innovation boundary expansion. Computing infrastructure helps to strengthen knowledge cooperation links in the innovation chain, promote the flow and aggregation of urban innovation resources, expand technological boundaries, and thus promote the improvement of future industrial innovation network levels. It is worth noting that the policy combination formed by the construction of computing infrastructure, the optimization of innovation ecology policies, and the smooth flow of data elements can generate positive synergies, further promoting the development of future industrial innovation networks. (3) Heterogeneity testing reveals that in the central and western regions, regions with higher levels of market integration and information costs, the construction of computing infrastructure has a more significant impact on the strength of future industrial innovation network connections. The construction of computing infrastructure has positively promoted the centrality of innovation network nodes, while its negative impact is close to centrality, indicating that it is promoting the formation of an unbalanced development pattern in the future industrial innovation network of cities. The results of the spatial double difference model show that the construction of supercomputing centers promotes the enhancement of collaborative innovation capabilities in neighboring cities through spatial spillover effects, and the spillover effects become stronger with increasing distance. Based on the above conclusions, this article has the following policy implications. The first is that the government should optimize the layout of computing infrastructure and promote the deepening of urban innovation networks. The second is that the government should form a diversified policy combination, creating a synergistic effect between computing power and innovation ecology. Thirdly, the government needs to promote the construction of computing infrastructure according to local conditions and narrow the regional innovation gap.

Amid intensifying global technological competition, the implementation of China’s 14th Five-Year Plan, and the comprehensive push for high-quality regional development, provincial laboratories have emerged as key pillars in the nation’s regional strategic science and technology system. These laboratories are rapidly evolving from single-function research entities into integrated “task-force-style” platforms that combine basic research, technological innovation, and results commercialization. However, on the path toward high-level scientific and technological self-reliance, they continue to face structural challenges, including weak theoretical foundations, insufficient cross-sector collaboration, and inadequate policy support. Based on the strategic framework built around three pillars—dynamic mechanism, ecological networks, and institutional safeguards,this study analyzes the drivers, organizational logic, and strategic priorities underpinning the clustered development of provincial laboratories. Drawing on case studies of leading laboratories such as Zhejiang Zhijiang, Guangdong Songshan Lake Materials, Jiangsu Gusu, and Henan Zhongyuan Graphene, the study outlines a pathway for breakthrough through fostering collaborative innovation ecosystems, enabling cross-disciplinary cooperation, implementing flexible operational models, and enhancing targeted policy support. The findings provide a replicable strategic model and practical guidance for advancing the systematic deployment of China’s regional science and technology capacity as part of its broader national innovation agenda.