From Innovation to Application: The Role of Government Venture Capital in Technology Commercialization

From Innovation to Application: The Role of Government Venture Capital in Technology Commercialization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Innovation to Application: The Role of Government Venture Capital in Technology Commercialization Jianmin Liu, Susu Wang, Fengbao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8277780/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Government venture capital (GVC), embodying both policy and market logics, represents a distinctive hybrid form of financing. This study investigates how GVC influences the process of technology commercialization by examining the interplay between its dual institutional logics. Analyzing a sample of A-share listed firms from 2012 to 2023, we find robust evidence that GVC significantly enhances the level of technology commercialization. Mechanism analyses reveal that this effect operates through three parallel mediation channels: providing resource support, leveraging network advantages, and facilitating benefit and risk sharing. Specifically, GVC alleviates financing constraints, attracts high-caliber talent, strengthens policy coordination and industrial chain integration, optimizes corporate governance, and enables collective risk bearing. Furthermore, the positive effect of GVC on commercialization is more pronounced for private enterprises and in asset-intensive or labor-intensive industries. Our findings offer important theoretical insights and empirical evidence for designing effective government-market coordination mechanisms to bridge the “valley of death” in technology commercialization. JEL code: G24, O38, O31 Government venture capital technology commercialization valley of death Figures Figure 1 Figure 2 Figure 3 1. Introduction As the critical final stage of the innovation value chain, technology commercialization bridges the formidable “Valley of Death” by translating abstract scientific discoveries into market-ready products, processes, and services. This translation is central to generating tangible economic growth and social progress, as it drives productivity gains, enhances competitive advantage, and helps address pressing global challenges. While knowledge creation is a fundamental starting point, its ultimate impact is contingent upon successful market entry and widespread diffusion (Dean et al., 2022 ). Consequently, understanding the mechanisms that effectively catalyze and accelerate this complex commercialization process constitutes a core scholarly and policy imperative for advancing sustainable development and strengthening national innovation capabilities (Takata et al., 2022 ). China’s statutory framework defines technology commercialization as activities aimed at enhancing productivity, encompassing the testing, development, application, and promotion of research outcomes to generate new technologies, processes, materials, products, and industries. Despite China’s leading position in patent filings and high-impact scientific publications—including being the first country to hold over 4 million valid domestic invention patents in 2023—the commercialization rate of these patents remains a critical challenge. According to the 2023 China Patent Survey, only 39.6% of invention patents in China have reached industrialization. Even among enterprises, the primary drivers of innovation, the industrialization rate for invention patents stands at 51.3%, a figure that still trails behind that of developed economies. This gap between research output and practical application underscores an urgent need to strengthen the mechanisms that translate scientific and technological achievements into market-ready solutions. Previous research on the commercialization of corporate technological achievements has primarily been conducted from the perspectives of innovation management, policy science, and organizational behavior, focusing on the barriers and drivers within this process. One stream of literature focuses on the characteristics and motivations of the key actors involved. Extensive studies have analyzed the evolving roles of universities and research institutions in technology transfer, examining how factors such as individual motivations for academic entrepreneurship, team composition, and institutional policies—particularly regarding intellectual property ownership and incentive systems—influence commercialization outcomes (Diánez-González and Camelo-Ordaz, 2016 ; Goethner et al., 2012 ; Perkmann et al., 2013 ; Visintin and Pittino 2014 ).Another stream shifts focus to the interaction and collaboration among multiple actors. Here, the “triple helix” model (university-industry-government) is widely applied to analyze inter-organizational dynamics within national innovation systems, emphasizing that effective institutional arrangements and interface management are crucial for overcoming technology transfer bottlenecks (Etzkowitz and Leydesdorff, 2000 ; Leydesdorff, 2012 ). Furthermore, the role of government support systems—including science and technology programs, tax incentives, and science parks—in facilitating technology transfer has been a significant area of inquiry (Howell, 2017a ; Park and Leydesdorff, 2010 ). In summary, prior work on the drivers of technology commercialization has coalesced around three primary dimensions: the attributes of the involved actors, the nature of organizational interactions, and the surrounding policy environment. The underlying mechanisms are frequently explained through the “resource endowment effect,” the “collaborative network effect,” and the “institutional incentive effect.” However, a key limitation in this body of work is its practical logic. Past research has often been confined to examining single subjects or linear relationships. More critically, it has frequently conflated the distinct roles and operational pathways of direct government intervention versus market-based mechanisms in the commercialization process. This conflation has led to theoretical and practical paradoxes when scrutinizing factors within either domain, thereby obscuring the inherent limitations and potential complementary effects of government and market tools. The commercialization of corporate technological achievements is an innovative activity characterized by significant positive externalities, high uncertainty, and systemic complexity. Overcoming its inherent challenges requires the synergistic combination of government support and market mechanisms; relying solely on either proves insufficient. Regarding government support, while intervention can theoretically fulfill functions such as innovation incentivization, resource pooling, and signaling (Howell, 2017b ), and can address incentive gaps caused by positive externalities (Guo et al., 2016 ), it faces intrinsic functional limitations. Issues like information asymmetry in direct interventions, lagged responses to market signals, and the potential subordination of efficiency to policy objectives prevent government action from independently and effectively resolving the challenges firms face in technology commercialization. In practice, it may even induce forms of “government failure,” such as distorting genuine market demand, dampening corporate initiative, and reinforcing a path-dependent cycle of “high investment, low efficiency, and weak incentive.” Particularly in today’s complex environment, the “limited variety” and “redundant inefficiency” of policy tools have weakened their effectiveness in supporting corporate innovation. Standalone policy instruments often fail to integrate multifaceted resources and struggle to meet the intricate, sustained demands of corporate innovation (Goulder and Parry, 2008 ; Wang and Zou, 2018 ). This over-reliance can stifle companies’ intrinsic motivation, ultimately hindering the commercialization of their technological achievements. Conversely, traditional market mechanisms also exhibit significant limitations in allocating resources for such endeavors. Factors like externalities, public good undersupply, monopoly, and information asymmetry frequently lead to market inefficiencies (Marchand and Russell, 1973 ). While the market’s “invisible hand” plays a fundamental role in guiding resource allocation, incentivizing innovation, and fostering collaboration, its inherent constraints—including underinvestment in activities with positive externalities, avoidance of high-risk and long-cycle projects, and the potential for individually rational actions to yield collectively irrational outcomes—prevent it from single-handedly solving commercialization challenges. In practice, it may even exacerbate vicious cycles among resource allocation, coordination, and governance dilemmas. Information economics suggests that a firm's response to its environment is highly contingent on the timeliness, accuracy, and completeness of information, whereas markets are typically plagued by delays and asymmetries in information transmission (Stiglitz and Rothschild, 1976 ). Furthermore, in technology commercialization, the private sector often curtails investment due to a focus on short-term financial objectives (Ren et al., 2025 ). Simultaneously, the positive externalities of technological innovation mean private returns are lower than social returns, further depressing long-term R&D incentives (Jones and Summers, 2022 ). This, in turn, weakens firms' capacity to facilitate the smooth transition of technological achievements through resource integration and capability reconfiguration (Li and Jia, 2018 ). The efficient commercialization of corporate technological achievements necessitates a coordinated mechanism to address both “government failure” and “market failure.” The synergistic interaction between government support and market mechanisms has thus become a critical pathway to overcome this dual-failure dilemma and enhance commercialization efficiency. This synergy operates on multiple levels: at the institutional level, where governments, markets, and firms break down barriers through information sharing and resource integration, thereby enhancing policy transparency and allocation efficiency (Hailu, 2024 ; Tang et al., 2025 ); and at the instrumental level, where policy tools guide factor aggregation and incentivize behaviors, strengthening firms’ resource mobilization capacity and risk resilience (Bustinza et al., 2019 ). Coordination at the subject level can mitigate issues of “incentive misalignment” and “duplicative incentives” stemming from information asymmetry, while coordination at the tool level helps establish a multi-channel financing system, enabling firms to more broadly integrate and leverage market resources. Government Venture Capital (GVC) represents an institutional innovation that synergizes government and market forces, serving as a key policy instrument for driving efficient technology commercialization and enhancing corporate innovation capabilities. At its core, GVC is a hybrid tool embodying both policy and market attributes. Its policy orientation is characterized by alignment with national strategic goals and public interests, emphasizing support for key sectors and critical technologies (Colombo et al., 2016 ). Its market orientation finds expression in the delegation of investment decisions to professional institutions, utilizing market-based mechanisms such as equity investment and risk-sharing to pursue optimal resource allocation (Callagher et al., 2015a ; Guerini and Quas, 2016 ), with a focus on investment efficiency and sustainable returns. These two logics are not mechanically superimposed but are organically integrated and functionally complementary within a unified governance framework. Unlike traditional government support mechanisms like direct grants or special subsidies, GVC participates in corporate innovation through equity investment. It adheres to principles such as “investing early, investing small, and investing in hard tech,” and relies on market mechanisms for project selection, post-investment management, and exit. This model better aligns incentive structures with the actual needs of portfolio firms. As an organic synthesis of public intent and market principles within the innovation domain, GVC mitigates the resource misallocation and weak incentives associated with traditional administrative methods, while also correcting the market’s chronic underinvestment in early-stage technology commercialization due to high risks and positive externalities. Functionally, GVC fulfills a dual mission of “strategic guidance” and “value creation.” Beyond seeking financial returns, it prioritizes using capital linkages to help technologies bridge the “valley of death,” accelerate intellectual property commercialization, and catalyze synergistic investments from social capital. This facilitates the advancement of industrial infrastructure and supply chain modernization, fostering a virtuous cycle within the innovation ecosystem and maximizing societal welfare (Bertoni et al., 2019a ; Callagher et al., 2015b ; Croce et al., 2019 ). Compared to private venture capital (PVC), GVC’s public and long-term orientation leads it to place greater emphasis on building sustainable innovation capacity and mitigating commercialization risks when supporting firm growth (Alperovych et al., 2020 ; Bertoni and Tykvová, 2015a ). In practice, GVC often embeds itself into the governance of portfolio companies through board representation and specific covenants. This allows it to guide the standardization of R&D management, optimize technology transfer processes, and provide systematic support in areas like IP strategy and industry-academia collaboration (Bottazzi et al., 2008 ; Cumming, 2007 ). This dual model of “governance embedding plus resource empowerment” aligns with national innovation-driven development strategies and effectively enhances the success rate and overall benefits of technology commercialization. Existing research on GVC has predominantly examined its functional positioning, operational contexts, economic impacts, and underlying mechanisms. First, regarding functional positioning, a consensus exists among scholars that the core function of GVC is to address market failures inherent in PVC. Seminal work by Lerner ( 2002 ) demonstrates that governments can provide “patient capital” to early-stage, high-risk technology firms through state-backed venture funds, thereby mitigating financing gaps arising from information asymmetry and externalities. Empirical studies, such as those by Brander et al. ( 2015 ), further indicate that GVCs not only offer direct funding but also exert a critical “certification” or “signaling” effect. Specifically, GVC investment signals positive project quality to the market, thereby crowding in additional private capital and generating a significant capital leverage effect. Second, in terms of operational contexts, research has concentrated on GVC efficacy within specific sectors and developmental stages. Substantial evidence suggests that GVCs play a particularly vital role in supporting hard-technology innovation (e.g., biotechnology, clean energy) and cultivating regional innovation ecosystems (Colombo et al., 2016 ). In these domains where market failures are more acute, the long-term orientation and strategic patience of GVCs prove crucial. Third, scholarly conclusions regarding GVC performance exhibit divergence. Some studies affirm its positive impacts, such as enhancing the innovation output (e.g., patents), follow-on financing capacity, and survival rates of portfolio firms (Bertoni and Tykvová, 2015b ). Conversely, other studies raise concerns about a potential “crowding-out effect,” positing that GVC may distort markets through non-commercial investment decisions, thereby displacing more efficient private capital (Cumming and MacIntosh, 2006 ). Recent research increasingly suggests that the relationship between GVC and PVC is complementary rather than purely substitutive, with outcomes contingent upon the specific design, governance structure, and contextual fit of the GVC program (Grilli and Murtinu, 2014 ). Finally, concerning underlying mechanisms, prior investigations have largely focused on isolated pathways—such as the aforementioned signaling and capital supplementation mechanisms. However, they lack an integrated theoretical framework capable of systematically elucidating the multiple, parallel channels through which GVC influences firm development and technology commercialization. The global landscape of technological competition has been undergoing rapid restructuring. Concurrently, the challenge of tackling critical core technologies has intensified, and the complexity of deeply integrating innovation chains with industrial chains has grown significantly. In this context, the environment for corporate technology commercialization has shifted from one characterized by “predictable technological risks” to one facing “systemic transformation uncertainties.” Against this backdrop, GVC—a hybrid instrument combining policy guidance with market-based mechanisms—holds exceptional strategic value for bridging the “valley of death” in technology commercialization and enhancing the overall efficiency of innovation systems. Nonetheless, research on its operational mechanisms and supporting empirical evidence remains limited. To address this gap, this study investigates the impact of GVC on the efficiency of corporate technology commercialization and subsequent innovation levels from a dual-logic perspective that integrates government and market incentives. The potential contributions of this research are as follows: First, by treating the dual nature of GVC as the logical starting point of this study, we move beyond the conventional “government-guiding-the-market” paradigm. This paper reconceptualizes GVC as an institutionally integrated entity characterized by “a single entity with dual attributes.” This perspective transcends the binary and oppositional analytical framework—where “government” and “market” are treated as separate actors—that dominates prior research. Previous studies have largely regarded GVC as a simple superposition or an external interaction between two independent entities. Their analytical logic has thus either focused on how governments can “guide” the market or on how markets “respond” to government policies. This “dual-subject” approach fails to capture the essence of GVC as an institutional innovation: it is not an external collaboration between two distinct entities, but rather the organic integration of governmental and market logics within a unified governance framework and capital vehicle. In contrast, this paper treats GVC as a singular entity that simultaneously embodies and synergistically operationalizes both “government advantages” and “market advantages.” Building on this new logical foundation, we systematically demonstrate how GVC, as an integrated entity, leverages its inherent dual advantages to synergistically influence the entire process of corporate technology commercialization. This approach not only clarifies the intrinsic transmission mechanism of “mutual reinforcement and functional complementarity” between GVC’s dual attributes but also offers a novel theoretical lens for understanding the micro-level integration of a “proactive government” and an “efficient market,” thereby moving beyond traditional approaches that merely juxtapose or oppose the two. Second, this paper develops a systematic mediation model to bridge the typically fragmented research streams on GVC and technology commercialization. Moving beyond examining GVC’s financial functions or isolated commercialization barriers in isolation, we propose an integrated framework in which GVC influences technology commercialization through three sequentially linked mediators: resource support, network advantages, and the joint sharing of benefits and risks. This framework theoretically synthesizes insights from the resource-based view, social network theory, and incentive theory, thereby providing a comprehensive and coherent lens for understanding how GVC promotes technology commercialization through multiple, interconnected pathways. The main innovations of this paper are as follows: First, regarding the logical relationships among the mechanisms, this paper develops a “trinity” synergistic chain that highlights systemic innovation. It moves beyond prior literature that treats GVC as a singular, isolated mechanism. Instead, we construct an interlinked, systematic intermediary model consisting of three components: “resource support–network advantage–restructuring of benefits and risks.” These three mechanisms are not merely parallel; they exhibit a clear logical progression and synergistic relationship, collectively forming a systemic solution that supports the commercialization of technology from innovation to application. First, “resource support” serves as the foundational layer and prerequisite. We argue that GVC leverages policy endorsement to mobilize market capital and, through platform-based initiatives, attracts high-end talent. This provides the essential financial and human capital for technology commercialization, establishing the material foundation for crossing the “Valley of Death” and addressing the question of “whether commercialization can be initiated.” Second, “network advantage” acts as the enabling layer and an efficiency amplifier. Building on resource infusion, GVC further activates and integrates internal and external networks. At a macro level, it unlocks policy toolkits to provide institutional safeguards; at a meso level, it functions as a “chain leader” to foster industry-university-research collaboration and reduce transaction costs; at a micro level, it embeds governance structures to optimize corporate decision-making and curb short-termism. This mechanism efficiently translates initial resource inputs into systemic synergies, addressing the question of “how to achieve efficient commercialization.” Finally, “benefit sharing and risk pooling” constitutes the incentive layer, ensuring sustainability. While the first two mechanisms address capabilities and efficiency, unfair distribution of innovation benefits or excessive risk concentration can render commercialization efforts unsustainable. GVC reshapes incentive structures through instruments such as “public subordinated capital” and market-aligned exit arrangements. This enables firms to “dare to commercialize” and incentivizes private capital to “willingly follow,” thereby resolving the question of “willingness to sustain commercialization.” Second, this study substantially enriches the research content, achieving dual expansion in both depth and breadth through the mutual validation of theory and empirical evidence. By integrating theoretical frameworks with deepened empirical analysis, it moves beyond addressing the basic question of “whether GVC is effective” to critically examine subsequent and more nuanced issues: “why it is effective,” “through which pathways it achieves its effects,” and “under what conditions and for whom it is most effective.” This approach yields robust and insightful conclusions regarding GVC’s role in technology commercialization. Third, the study focuses intensely on the journey from innovation to application, specifically on overcoming the “Valley of Death” that spans the technology commercialization process. Its central argument is tightly organized around this critical transition—analyzing how to cross this chasm. Furthermore, it provides a detailed analysis of how GVC leverages its dual attributes to design targeted solutions for the inherent high risks, long cycles, and positive externalities of the “Valley of Death.” It employs “patient capital” to hedge against uncertainty, utilizes market mechanisms to screen viable technologies, and harnesses public credibility to attract follow-on private investment. In essence, GVC constructs an acceleration mechanism that bridges the gap between “technology readiness” and “market readiness.” 2. Theoretical Analysis and Research Hypotheses Enterprises face three major challenges in the process of technology commercialization. First, regarding sustained resource investment, the process inherently involves high risk, substantial capital commitment, and long cycles. Particularly during the pilot testing and engineering validation phases, there is a significant demand for capital to procure equipment, validate processes, and facilitate market entry. However, most enterprises, especially small and medium-sized enterprises, lack the internal capacity to independently sustain the substantial costs of this commercialization journey (Doh and Kim, 2014). Although governments provide supportive policies such as fiscal subsidies and tax incentives, in practice, barriers to accessing these funds are high, approval procedures are complex, and the time required for disbursement is often prolonged. Consequently, it is difficult to meet enterprises’ need for “timely” capital infusion (Meuleman and De Maeseneire, 2012). Furthermore, market-based funding sources like venture capital and angel investments tend to favor mature projects, demonstrating limited appetite for supporting early-stage technology commercialization. This situation creates a pronounced “first-round financing gap.” Second, regarding collaborative challenges, the inherent heterogeneity in objectives and institutional logics among participants, combined with the absence of effective governance mechanisms, can contribute to systemic failures within the innovation chain from laboratory to market. As noted by Lucena et al. (2025), when firms engage in technology commercialization, they face complementary challenges between internal R&D activities and external knowledge sourcing. If firms cannot effectively access university R&D through contractual agreements, their capacity to decompose R&D processes to capture returns from innovation is constrained. Bailey et al. (2025) identify divergent pathways for universities and enterprises in commercializing biomedical technologies. While concept validation centers help bridge the “valley of death” by providing non-dilutive funding and incubation guidance, factors such as the principal investigator’s commercialization experience, technology type, and gender can still lead to significant variances in collaboration strategy. Furthermore, Cunningham et al. (2025), using text mining, reveal that university technology transfer research is increasingly focused on spin-offs and quantitative metrics, while topics essential for sustaining institutionalized collaboration—such as institutional context and the role of technology transfer offices—are receiving declining attention. A case study in Colombia further illustrates that institutional fragmentation, insufficient R&D investment, and a lack of inter-organizational trust often confine collaborative innovation to ad hoc projects. This hinders the development of an inclusive ecosystem capable of integrating traditional knowledge with modern innovation processes (Valencia-Arias et al., 2025). Third, at the internal governance level, enterprises face structural conflicts between intellectual property (IP) allocation and long-term incentives. Fabiano et al. (2021) found that, under the “professor’s privilege” system in some European contexts, founding scientists often retain patent rights. This makes it difficult for firms to consolidate key IP for subsequent development, creating a dilemma of “fragmented property rights.” Kim and Koo (2023) further revealed that excessive pay dispersion within top management teams can stifle collaborative innovation, hindering the cross-functional cooperation essential for technology commercialization. Regarding incentive design, Cockburn, Henderson, and Stern (1999) demonstrated through a multi-task principal-agent model that when firms offer strong performance incentives for short-term, applied research alone, scientists may reduce investment in the long-term, curiosity-driven basic research that generates valuable knowledge spillovers, leading to a “temporal mismatch” in the innovation ecosystem. Governance structure flaws further exacerbate these commercialization barriers. Severe information asymmetry plagues the process. Evidence from the UK Parliament indicates that the absence of credible valuation mechanisms between university technology transfer offices and firms hinders price discovery, leaving many patents in a “valuation vacuum” (“MIP0039 - Evidence on Managing intellectual property and technology transfer”, 2025). This information problem forms a vicious cycle with weak corporate IP management. Panel data from German firms confirms that manager-owned enterprises show significantly lower efficiency in translating R&D investments into patents, suggesting that insufficient separation of ownership and control leads to less professional IP strategy (Grünebaum, 2021). More fundamentally, as assessed by the World Intellectual Property Organization, most companies lack specialized training systems for technology transfer. Consequently, R&D personnel are often neither equipped to protect laboratory outcomes nor capable of identifying market opportunities, resulting in a critical “capability gap” in commercialization (Lorenz and World Intellectual Property Organization, 2022). The compound effect of these three challenges concentrates the associated risks disproportionately on the originating firms or individuals, while external parties may capture a significant share of the benefits. This misalignment means that market pricing often fails to reflect the true costs, resulting in a low-level equilibrium trap characterized by high sunk costs, high coordination friction, and high accountability risks. Consequently, this situation severely impedes the sustained and efficient diffusion of new technologies into the market. GVC embodies a hybrid policy instrument that integrates both governmental and market attributes. Its operational mechanism adheres to a dual logic: the administrative logic of public policy, which prioritizes social benefits and strategic planning, and the market logic of capital allocation, which emphasizes risk-return efficiency. These two logics do not merely coexist in tension; rather, within a specific institutional framework, they can synergize, complement each other, and undergo mutual transformation. This hybrid governance principle was formally articulated in China’s 2008 Guiding Opinions on the Standardized Establishment and Operation of Venture Capital Guidance Funds, which established the operational model of “government guidance with market-driven operation.” The guidelines emphasize that both government and private capital must jointly adhere to contractual agreements and market rules. The core intent is to leverage the market’s decisive role in resource allocation, utilizing market mechanisms to enhance the efficiency of public goods provision and the quality of public services in domains such as technology commercialization. Compared to conventional venture capital, GVC is characterized by three defining attributes: policy-driven objectives, market-oriented operations, and outcome-enabling mechanisms that empower technology commercialization (Grilli and Murtinu, 2014; Guerini and Quas, 2016). By leveraging public fiscal funds to amplify impact and steer investment direction, GVC effectively bridges the chasm—often termed the “valley of death”—between “technology readiness” and “market readiness,” transforming it into what can be conceptualized as a “commercialization acceleration zone.” It does this by utilizing the patient, long-term nature of state capital to hedge against the prolonged cycles and high uncertainty inherent in frontier technologies. Concurrently, it applies the market’s rigorous selection criteria and active post-investment governance to enhance the commercial viability of technology pathways, business models, and entrepreneurial teams (Bertoni et al., 2019b; Munari and Toschi, 2015). By addressing this critical disconnect, GVC not only elevates the efficiency of transforming technology into capital but also improves the capital market’s allocation efficiency toward “hard tech” sectors. Furthermore, the state-affiliated nature of GVC inherently provides portfolio firms with “political linkages.” These linkages facilitate preferential access to scarce policy resources, such as government procurement programs, key R&D initiatives, demonstration project orders, and interest subsidies for technology financing. By leveraging what can be termed “public credit,” GVC acts as a credible third-party endorser of a technology’s potential. This certification significantly reduces the due diligence costs and trust barriers for potential collaborators across industry, academia, and research institutes, as well as for downstream clients and follow-on private investors. This “endorsement effect” is instrumental in attracting subsequent rounds of private capital, thereby establishing a relay-style investment pathway that guides ventures from angel investment through venture capital and private equity, and ultimately to industrial capital (Guerini and Quas, 2016; Y. Wang et al., 2017). Compared to direct government subsidies, government-guided venture capital funds delegate micro-level investment decisions to professional market entities. By sharing profit rights and clarifying responsibility boundaries between public and private capital, these funds mitigate excessive administrative intervention while leveraging the market's informational advantages to improve the accuracy of technology valuation. This establishes a sustainable commercialization mechanism centered on “technology value discovery—risk sharing—profit distribution.” At its core, this mechanism operates as a synergistic dual-engine system: “public capital credit enhancement” coupled with “technology merit screening.” The public sector provides patient capital and facilitates resource integration, while market-oriented partners contribute commercialization expertise and governance discipline. The synergy between these elements enables government venture capital to significantly outperform traditional subsidies in terms of the scope, duration, and impact intensity of technology commercialization (Alperovych et al., 2020; Bertoni et al., 2019b), offering full-cycle support for laboratory technologies to successfully navigate the competitive “Darwinian sea” of the market. Policy-based finance, functioning as a specialized financial instrument that embodies state priorities, serves as a crucial platform for advancing national innovation strategies. GVC, established through partnerships between fiscal funds and private capital, represents a deep integration of “strategic public intent with market-driven execution.” Compared to purely private venture capital or direct state investment, it more fully embodies the synergistic principle of “an enabling state and an efficient market.” This hybrid governance structure enables GVC to significantly accelerate, amplify, and solidify the process of technology commercialization within portfolio firms. Based on the preceding theoretical analysis, the following hypothesis is advanced: H1: Government venture capital investment positively influences the intensity and success of technology commercialization in investee enterprises. According to market failure theory, exclusive reliance on market mechanisms often results in inefficient allocation of resources for innovation, impeding the optimal path for technology commercialization (Arrow, 1972; Nelson, 1959). Frontier technologies, characterized by high investment, long cycles, and positive externalities, often yield private returns that fall below their social returns. This divergence leads to underinvestment by private markets and creates a commercialization vacuum. Consequently, advancing technology commercialization necessitates the coordinated interplay of the market’s “invisible hand” and the government’s “visible hand.” GVC addresses this need by deploying public funds through market-conforming instruments like equity investment. It retains the strategic advantages of policy-driven credibility and resource orchestration while incorporating professional fund management and market-aligned incentive structures. Possessing this hybrid “government-market” attribute, GVC plays a crucial role in bridging the “valley of death” in technology transfer. The following section elucidates the underlying mechanisms by examining three distinct pathways through which GVC influences this process and proposes corresponding research hypotheses. 2.1. Government venture capital and sustained corporate resource support The policy backing of GVC provides firms with two key resources to advance the commercialization of new technologies: capital and talent. As investment capital underpinned by state assets, GVC possesses greater credibility in terms of endorsement and subsequent resource acquisition (Guerini and Quas, 2016). According to signaling theory, its investment acts as an implicit certification, effectively communicating governmental priorities to external stakeholders and creating a positive endorsement effect for corporate financing (Colonnelli et al., 2024). In capital markets, when a GVC institution invests in a technology-based firm, its public credit backing signals to the market that the firm’s technology holds industrialization potential. This reduces information asymmetry and attracts follow-on private capital. Consequently, it helps alleviate the early-stage funding shortages of the “valley of death,” lowers financing costs, and establishes a solid financial foundation for moving technologies from the laboratory to pilot and mass production stages (Brander et al., 2015). Beyond capital, GVC leverages its policy advantages to facilitate talent assembly for portfolio firms through two distinct pathways. First, it utilizes linkages such as “government subsidies—research platforms—talent programs” to help firms recruit high-end R&D personnel at competitive rates (Cadorin et al., 2021). Second, leveraging its public-sector connections, GVC builds bridges between “government-industry-academia-research-application” entities, supplying firms with scarce talent like postdoctoral researchers and engineers. This creates a synergistic aggregation effect through the organic integration of projects, platforms, and talent. The combined impact of this capital leverage and talent aggregation significantly shortens the cycle from patent to prototype to scaled production. The market-oriented operation of GVC delivers dual reinforcement through coordinated capital and talent support, distinct from traditional fiscal subsidies. GVC allocates funds through market-based mechanisms that emphasize risk-sharing, profit-sharing, and professional governance. By adopting a public-private partnership model that pools fiscal capital with private limited partners (LPs), GVC directly amplifies capital supply via equity investments. For instance, Shandong Province's New and Old Growth Drivers Transformation Fund leveraged an initial fiscal allocation of 4.77 billion yuan to attract a total investment of 355.8 billion yuan, with approximately 70% directed toward major industrial technology breakthroughs and commercialization projects. Regarding talent, market-oriented general partners (GPs) establish dedicated post-investment talent networks for their portfolio companies. By leveraging their industry connections, they bridge enterprises with critical human resources such as technical advisors, industrial engineers, and sales experts. This creates a value-added service model often described as “investing with a team” (Cadorin et al., 2021). Furthermore, tolerance mechanisms and structured profit-sharing arrangements enhance the appeal to private capital and high-end talent. By sharing a portion of investment returns with market participants and maintaining high failure-tolerance thresholds, these mechanisms encourage research teams to pursue high-risk technology commercialization while incentivizing private capital to co-invest (Cumming et al., 2017). The simultaneous injection of capital and talent enables enterprises to accomplish parallel critical tasks—such as pilot line construction and core team formation—in the early stages, thereby significantly increasing the success rate of technology commercialization. In summary, through two core mechanisms—policy-backed credit endorsement and market-driven leverage amplification and talent networking—GVC provides enterprises with a sustainable, scalable, and actionable resource package for technology commercialization, combining critical financial and human capital. H2: The hybrid, policy-and-market nature of government venture capital positively enhances the resource endowment of portfolio firms in terms of financial and human capital, which in turn facilitates technology commercialization. 2.2. Triple network advantages of government venture capital and policy support, on-chain enterprise collaboration, and internal corporate governance The policy backing of GVC can unlock network advantages at macro, meso, and micro levels, systematically enhancing the efficiency of technology commercialization. At the macro level, GVC’s entry itself serves as a strong signal of governmental confidence in a firm’s technological trajectory and growth potential (Guerini and Quas, 2016), triggering coordination within the public policy ecosystem. Research suggests GVC can orchestrate multi-dimensional policy resources—including fiscal funds, tax incentives, subsidized loans, land allocations, application scenarios, talent programs, and failure-tolerance mechanisms—forming an institutional support system covering the entire chain from proof-of-concept to pilot-scale and mass production. This system helps mitigate the “valley of death” risks in technology commercialization (Ellwood et al., 2022; Son et al., 2022). At the meso (industry) level, acting as an anchor investor, GVC injects public credibility into industrial chains. Through equity investments, it integrates universities, research institutes, upstream and downstream firms, and financial institutions into a collaborative framework. This reduces information asymmetry and redundant negotiation costs, thereby enhancing industrial chain synergy. At the micro (firm) level, government investors, often through board seats, special voting rights, or governance clauses, embed themselves in corporate oversight. This strengthens external monitoring, curbs managerial short-termism, and can introduce independent directors or specialized committees to optimize governance. Consequently, it improves R&D transparency and accelerates the execution of technology transfer. The interplay of policy orchestration, inter-firm coordination, and governance optimization collectively forms the systemic network advantage through which GVC empowers technology commercialization. The market-oriented operation of GVC significantly amplifies its network advantages by effectively bridging public policy objectives with market discipline. First, through competitive selection processes and capital raising from a diverse pool of investors, professional GPs can transform the GVC’s credit into a powerful composite signal that blends public credibility with market validation. This attracts a broader network of private capital, industrial partners, and technical talent, creating a robust ecosystem of “capital, technology, and application scenarios,” thereby enhancing the leverage of public policy resources. Second, leveraging market-based pricing and evaluation mechanisms, GPs identify and focus on high-potential technology sectors. They utilize industrial mapping and active post-investment management to strategically align stakeholders along the value chain, matching technology maturity with commercial readiness. This precise orchestration fosters efficient linkages between R&D entities, manufacturers, and end-users, which shortens the overall cycle of technology transfer. Third, market-oriented GPs continuously provide value-added support to their portfolio companies. Through mechanisms such as equity incentives, co-investment arrangements, and board representation, they deploy industry experts, technical advisors, and professional managers. This deep engagement strengthens corporate governance and enhances the firm’s execution capabilities in research, development, and commercialization. In sum, while the public sector provides the foundational institutional network, the market mechanism effectively scales and enhances this network with diverse resources. This synergy enables GVC to cultivate sustainable and scalable network advantages that are critical for successful technology commercialization. Based on the above analysis, the following hypothesis is proposed: H3: Government venture capital derives its unique strength from dual policy and market advantages, which enable invested firms to develop triple network advantages—in policy support, inter-firm collaboration, and internal corporate governance. This configuration ultimately promotes technology commercialization. 2.3. The Dual Nature of Government Venture Capital: Benefit Distribution and Risk Sharing The policy advantages of government venture capital can promote shared benefits and risk-sharing. By embedding itself as “public subordinated capital” within the technology transfer chain, the government transforms the public nature of fiscal funds into risk mitigation tools through institutional arrangements. Specifically, the “government-first-loss” clause established in Article 18 of the Interim Measures for the Management of Venture Capital Sub-funds Established by the National Science and Technology Achievement Transformation Guidance Fund clearly defines the subordinate position of government capital during sub-fund liquidation. This provides a credible “safety cushion” for researchers and early-stage social capital, significantly reducing the wait-and-see attitude caused by concerns over “state-owned asset loss.” On the revenue side, the government adopts a contractual structure of “principal + low fixed returns, with excess returns transferred,” shifting its own revenue function from “profit maximization” to “maximizing positive externalities.” This approach retains the high-energy incentives of the private sector while avoiding the dampening effect on innovation investment caused by excessive revenue fragmentation (Brander et al., 2015). Furthermore, cities like Wuhan have leveraged social capital exceeding five times the initial investment into angel-stage ventures through concessionary designs—such as charging only bank deposit interest or recovering only principal. This approach achieves leveraged benefit sharing and risk pooling, where “small fiscal investments” mobilize “large social capital.” The policy advantages of GVC enable shared benefits and risk sharing. Acting as “public subordinated capital” within the technology commercialization chain, the government transforms fiscal funds’ public nature into risk-mitigation tools via institutional arrangements. A key embodiment is the “government-first-loss” clause (stipulated in Article 18 of the Interim Measures for Venture Capital Sub-funds of the National Science and Technology Achievement Transformation Guidance Fund), which explicitly defines the subordinate position of government capital upon sub-fund liquidation. This clause creates a credible “safety cushion” for researchers and early-stage private investors, thereby significantly reducing wait-and-see attitudes driven by fears of “state-owned asset loss.” Regarding returns, the government employs a contractual structure of “principal plus low fixed returns, with excess returns transferred,” reframing its revenue function from “profit maximization” to “maximizing positive externalities.” This structure retains private-sector-style high-powered incentives while circumventing the discouragement of innovation investment that results from excessive revenue fragmentation (Brander et al., 2015). In practice, cities such as Wuhan have leveraged concessionary designs—like charging only bank deposit interest or recovering solely the principal—to attract private capital exceeding five times the initial public investment into angel-stage projects. This demonstrates a model of leveraged benefit sharing and risk pooling, wherein “small fiscal investments” galvanize “large private capital.” The market-oriented operations of GVC facilitate benefit and risk sharing among portfolio firms. Within a “fund of funds plus sub-funds” governance structure, government capital operates as a limited partner, delegating project screening, valuation, and post-investment management to professional GPs. This arrangement channels capital toward technological ventures with the highest marginal commercialization efficiency through competitive market mechanisms (Alperovych et al., 2020). Pilot programs in regions like Chengdu and Tianjin exemplify a “funding-first, equity-later” model. Fiscal science and technology funds are initially disbursed as grants and subsequently convert to equity at market valuation upon the achievement of predefined milestones, with exits governed by the principle of “appropriate return.” For liquidity, a multi-tiered exit matrix—encompassing public markets (e.g., STAR Market, Beijing Stock Exchange), regional equity market innovation boards, and government repurchase protocols—mitigates the exit risk premium demanded by private capital. Concurrently, entrepreneurial teams capture liquidity premiums to share in higher valuation gains. Thus, market mechanisms orchestrate a dual redistribution of benefits and risks. Based on the above analysis, the following hypothesis is proposed: H4: Government venture capital leverages dual policy and market advantages to provide portfolio firms with access to profit sharing and risk sharing, thereby facilitating technology commercialization. 3. Research Design 3.1. Samples and Data The study sample consists of A-share listed companies from 2012 to 2023. The sample selection followed these criteria: (1) excluding ST firms; (2) removing financial institutions; (3) omitting observations with missing data; and (4) winsorizing all continuous variables at the 1st and 99th percentiles to mitigate the influence of extreme values. This procedure resulted in a final unbalanced panel of 37,588 firm-year observations. All data were obtained from the CSMAR and CV Source databases. 3.2. Variable Measurement 3.2.1. Technology Commercialization Building on the work of Jaffe et al. ( 1993 ) and Maggioni and Uberti ( 2009 ), we use patent citation frequency as our core measure. Specifically, the frequency with which a technology’s patent is cited by subsequent patents reflects its foundational influence on later research or applications. This influence captures the absorption and, ultimately, the technology commercialization that occurs as knowledge diffuses through subsequent innovations (Hung and Wang, 2009 ). Thus, patent citations signal not only a technology’s scientific value but also the market’s recognition of its practical utility and commercial potential. To address the typical right-skewed distribution of citation counts and mitigate the undue influence of outliers in our regression analyses, we apply a natural logarithmic transformation to the yearly count of citations received by a firm’s patents. 3.2.2. Government Venture Capital Following the definition of GVC outlined by Chen et al. ( 2021 ), we first extracted approximately 230,000 corporate investment events spanning 2012–2023 from the CV Source database. Specific details on government-guided funds were then sourced from the Wind database. We manually reviewed these corporate investors and verified their status against the fund records: a firm was classified as having received GVC if an investor in a given year was a government-guided fund or if the firm had a state-owned capital background. The GVC investment variable is coded as 1 for the year of initial investment and all subsequent years, and 0 otherwise. 3.2.3. Control Variables To address potential confounding effects from firm heterogeneity, we include a comprehensive set of firm-level control variables in our regression models. The definitions of all variables are summarized in Table 1 . Furthermore, to reduce the influence of extreme values, all continuous variables were trimmed at the 1st and 99th percentiles. Table 1 Variable Definitions Variable Type Variable Name Variable Symbol Variable Definition Dependent variable Technology Commercialization TC The number of invention patents granted to the company in a specific year that were transferred, expressed as a logarithm. Independent variable Government Venture Capital GVC Dummy variable: 1 if the company received government venture capital in a given year, 0 otherwise. Control variable Return on Equity ROE Net Profit / Average Net Assets × 100% Inventory Ratio Ir Net Inventory / Total Assets Fixed Assets Ratio Far Net Fixed Assets / Total Assets Is there a loss? Loss Net profit for the year < 0 takes 1, otherwise takes 0 Percentage of Independent Directors Indep Number of Independent Directors/Directors Top Ten Shareholders' Shareholding Ratios Top10 Top 10 Shareholders' Equity / Total Equity Management Expense Ratio Mer Administrative Expenses / Operating Revenue Major Shareholder Fund Misappropriation Mfund Other Receivables/Total Assets Capital Intensity Cap Total Assets / Operating Revenue Years in Operation Age ln(Current Year - Year of Company Establishment + 1) ------------------------------------------------ Insert Table 1 about here ------------------------------------------------ 3.3. Model Construction This paper constructs the following benchmark regression model: $$\:{TC}_{i,t}={\beta\:}_{0}+{\beta\:}_{1}{GVC}_{i,t}+{\beta\:}_{3}Controls+\sum\:firm+\sum\:year+\sum\:industry+{\epsilon\:}_{i,t}\:\:\:\:\:\left(1\right)$$ In the model, TC i,t measures the level of technology commercialization for firm i in year t; GVC i,t indicates whether firm i received government venture capital investment in year t; Controls denotes a vector of firm-level control variables.; We include firm (∑Firm), year (∑Year), and industry (∑Industry) fixed effects to account for time-invariant unobserved heterogeneity, common macroeconomic shocks, and industry-specific factors, respectively. ε i,t is the idiosyncratic error term. The coefficient β 1 captures the net effect of government venture capital on our key dependent variable, technology commercialization. 4. Empirical Findings and Analysis 4.1. Descriptive Statistics Table 2 reports the descriptive statistics for the key variables. GVC is a dummy variable with a mean of 0.094, implying that GVC investment was present in approximately 9.4% of the 37,588 firm-year observations. Our measure for technology commercialization is the natural logarithm of one plus the number of patent transfers. This variable has a mean of 0.525 and a standard deviation of 1.049. Its values range from 0 to 8.391, indicating considerable heterogeneity in patent-based commercialization activities across the firms in our sample. Table 2 Descriptive Statistics VarName Obs Mean SD Min Max TC 37588 0.525 1.049 0.000 8.391 GVC 37588 0.094 0.292 0.000 1.000 ROE 37588 0.063 0.131 -0.962 0.414 Ir 37588 0.133 0.120 0.000 0.778 Far 37588 0.204 0.154 0.002 0.725 Loss 37588 0.143 0.351 0.000 1.000 Indep 37588 37.645 5.447 0.000 60.000 Top10 37588 0.591 0.154 0.000 0.910 Mer 37588 0.457 2.612 0.000 39.165 Mfund 37588 0.013 0.021 0.000 0.202 Cap 37588 2.520 2.064 0.000 19.481 Age 37588 2.952 0.328 0.000 3.638 ------------------------------------------------ Insert Table 2 about here ------------------------------------------------ 4.2. Benchmark Regression Column (1) of Table 3 displays the baseline results from a univariate regression. Column (2) introduces the set of firm-level control variables. Column (3) further augments the specification by including firm, year, and industry fixed effects. Across all specifications, the coefficient on the key explanatory variable, GVC, is positive and statistically significant. These regression results suggest that government venture capital investment has a positive effect on technology commercialization, thus providing support for Hypothesis 1. Table 3 Benchmark Regression Results (1) (2) (3) TC TC TC GVC 0.238*** 0.231*** 0.054*** (12.858) (12.500) (3.442) ROE 0.185*** 0.047 (3.522) (0.961) Ir -0.210*** 0.112 (-4.526) (1.310) Far -0.269*** -0.054 (-7.351) (-0.787) Loss 0.014 -0.003 (0.708) (-0.202) Indep 0.006*** -0.000 (6.439) (-0.118) Top10 -0.205*** 0.165*** (-5.574) (2.593) Mer -0.001 -0.000 (-0.466) (-0.166) Mfund 0.585** 1.177*** (2.265) (4.172) Cap -0.047*** -0.001 (-17.392) (-0.196) Age 0.130*** 0.910*** (7.712) (9.946) _cons 0.503*** 0.181*** -2.276*** (88.591) (2.628) (-7.966) Stkcd No No Yes Year No No Yes Industry No No Yes N 37588 37563 37264 adj. R2 0.004 0.026 0.408 t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01 ----------------------------------------------- Insert Table 3 about here ------------------------------------------------ 4.3 Heterogeneity Test 4.3.1. Heterogeneity Analysis: The Role of Financing Constraints Firms face varying degrees of financing constraints due to differences in internal capital reserves and external financing capacity. These differences may affect their sensitivity to GVC and their efficiency in utilizing such resources, thereby influencing the effectiveness of technology commercialization. To examine this heterogeneity, we follow prior literature and employ the SA index to measure firm-level financing constraints. Firms are classified into high- and low-constraint groups based on the annual industry median of the SA index. Specifically, for each industry-year cohort, we calculate the median SA index. A firm is assigned to the high financing constraints group if its SA index is above the industry-year median; otherwise, it is classified into the low financing constraints group. The subgroup regression results are presented in Table 4 . In Column (1) for the low-constraint group, the coefficient on GVC is 0.059, which is positive and statistically significant at the 5% level. In contrast, the coefficient for the high-constraint group in Column (2) is 0.030 and statistically insignificant. This pattern suggests that the positive effect of GVC on technology commercialization is primarily driven by firms facing lower financing constraints, with no discernible average effect observed among firms with higher constraints. A plausible explanation for this finding lies in firm-level absorptive capacity and operational maturity. Firms with lower financing constraints typically possess more robust management systems, stronger technological assimilation capabilities, and more established market channels. These attributes likely enable them to utilize GVC more effectively—leveraging not only the financial capital but also the policy resources and certification benefits associated with government funding—to translate technological assets into market value. Conversely, firms with higher financing constraints may be hampered by more fundamental operational inefficiencies, weaker technological foundations, or lower market adaptability. For these firms, the marginal benefit of government capital may be attenuated, resulting in an insignificant average treatment effect. 4.3.2. Heterogeneity Analysis: The Role of Ownership Type Differences in ownership structure give rise to variations in governance models, incentive mechanisms, and institutional linkages. These factors likely influence how firms respond to GVC and allocate resources, which may subsequently affect the efficiency of technology commercialization. To investigate this, we segment our sample into three ownership categories: private enterprises, state-owned enterprises (SOEs), and other enterprises (e.g., foreign-invested and collective firms). The subgroup regression results are presented in Table 4. For private enterprises in Column (3), the coefficient on GVC is 0.105, which is positive and statistically significant at the 1% level. In contrast, the coefficient for SOEs in Column (4) is 0.037 and statistically insignificant. The coefficient for other enterprises in Column (5) is 0.033, significant at the 10% level. These results suggest that the positive effect of GVC on technology commercialization is most pronounced for private enterprises, present but weaker for other enterprises, and indistinguishable from zero for SOEs. This pattern can be interpreted through the lens of institutional theory and managerial incentives. Private enterprises typically operate with greater flexibility, face harder budget constraints, and are driven by strong market-oriented incentives for innovation. This combination may enable them to utilize GVC funding and associated policy resources more effectively to generate tangible commercial outputs. Other enterprises may also realize some efficiency gains, potentially due to policy alignment. For SOEs, however, agency problems and softer budget constraints—stemming from their inherent institutional design—may dampen innovation incentives. Consequently, government investment in SOEs might act more as a substitute for, rather than a complement to, their own innovation efforts, leading to a marginal and statistically insignificant effect. 4.3.3. Heterogeneity Analysis: The Role of Industry Characteristics The effect of GVC is likely moderated by industry context. Differences in factor intensity, innovation patterns, and capital requirements across industries may systematically influence how firms absorb external funding and translate it into commercial outcomes. To test for this heterogeneity, we categorize industries into three types based on the CSRC 2012 classification: technology-intensive, asset-intensive, and labor-intensive. The estimation results by industry group are presented in Table 4. The coefficient on GVC is 0.049 (significant at the 5% level) for labor-intensive industries [Column (8)], and 0.064 (significant at the 10% level) for asset-intensive industries [Column (6)]. In contrast, the coefficient for technology-intensive industries [Column (7)] is 0.035 and statistically indistinguishable from zero. The results indicate that GVC has a discernible positive effect on technology commercialization in asset-intensive and labor-intensive industries, but not in technology-intensive ones. This pattern can be understood through the lens of resource complementarity and need. Asset-intensive industries require substantial capital for equipment and technological upgrades; GVC directly alleviates these financing constraints, enabling critical investments. In labor-intensive industries, GVC may foster commercialization not primarily through R&D funding, but by providing capital and managerial expertise that improve production efficiency and process standardization, thereby facilitating market adoption of incremental innovations. Conversely, the null effect in technology-intensive sectors may stem from two factors. First, firms in these industries often possess strong internal R&D capabilities and alternative funding sources, reducing the marginal value of GVC. Second, the inherently long cycles and high risks of breakthrough innovation in these sectors may dilute the measurable short- to medium-term impact of equity investment, making it difficult for GVC to yield statistically significant commercial outcomes within our observation window. Table 4 Heterogeneity Test (1) (2) (3) (4) (5) (6) (7) (8) Financing constraints Type of enterprise Distinguish industries Low High Private State-Owned Other Asset Technology Labor TC TC TC TC TC TC TC TC GVC 0.059** 0.030 0.105*** 0.037 0.033* 0.064* 0.035 0.049** (2.445) (1.274) (3.469) (0.650) (1.649) (1.780) (1.380) (2.188) _cons -2.278*** -2.423*** -2.577*** 0.285 -1.936*** -2.550*** -2.448*** -1.597*** (-5.180) (-5.743) (-3.919) (0.187) (-5.593) (-3.801) (-5.226) (-3.802) Controls Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Stkcd FE Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes N 18028 18067 8587 3094 25342 6621 18110 12167 Adj R 2 0.4027 0.4178 0.4820 0.4519 0.3793 0.3760 0.4067 0.4140 t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01 ------------------------------------------------ Insert Table 4 about here ------------------------------------------------ 4.4. Endogeneity Test 4.4.1. Addressing Endogeneity: An Exogenous Policy Shock To strengthen causal inference and address potential endogeneity concerns such as sample selection bias and omitted variables, we leverage the exogenous shock of the “National Industry-Finance Cooperation Pilot Cities” policy. This policy, designed to deepen integration between industry and finance and to enhance financial support for the real economy, shares conceptual overlap with the objectives of GVC. To isolate the effect of GVC from this concurrent policy, we employ a Propensity Score Matching-Difference-in-Differences (PSM-DID) design. The logic of this test is as follows: if the observed improvement in firms’ technology commercialization is primarily driven by the broader industry-finance policy rather than by GVC itself, then controlling for this policy shock should materially attenuate the coefficient on GVC in our baseline model. Our multi-period DID specification allows us to test this directly. a. Parallel Trend Test The validity of the difference-in-differences (DID) estimator relies on the parallel trends assumption. We test this assumption by examining the dynamic treatment effects around the policy implementation year (period 0). Figure 1 plots the estimated coefficients for the periods preceding (t = -12 to -1) and following (t = 0 to 6) the policy shock. As shown, the estimated coefficients for all pre-treatment periods fluctuate around zero and are statistically insignificant, with their confidence intervals consistently encompassing zero. This pattern indicates that the treatment group (firms in pilot cities) and the control group (firms in non-pilot cities) followed parallel trends in the outcome variable prior to the policy intervention, thus satisfying a core prerequisite for our DID analysis. ------------------------------------------------ Insert Fig. 1 about here ------------------------------------------------ b. Multitemporal DID regression results This study constructed the following multi-period DID model: $$\:TC={\beta\:}_{0}+{\beta\:}_{1}DID+\sum\:\gamma\:Controls+\mu\:+\lambda\:+\epsilon\:\:\:\left(2\right)$$ Among these, DID serves as the core explanatory variable, representing the interaction term between the treatment group (enterprises in pilot cities) and the post-policy implementation period. The regression results in Table 5 show that the coefficient for the core variable DID is -0.016, with a t-value of 1.917, which is less than 1.96. This result suggests that the “National Industry-Finance Cooperation Pilot Cities” policy itself did not exert a statistically significant direct effect on firms’ technology commercialization. Consequently, the positive relationship between GVC and technology commercialization is unlikely to be driven by this concurrent policy shock. The findings from our baseline regression thus remain robust, and potential endogeneity bias from omitting this policy variable is substantially mitigated. Table 5 DID Regression Results (1) TC DID -0.016 (1.917) ROE 0.072 (1.381) Ir 0.098 (1.083) Far -0.005 (-0.069) Loss 0.005 (0.304) Indep 0.001 (0.362) Top10 0.089 (1.312) Mer -0.000 (-0.029) Mfund 1.029*** (3.394) Cap -0.002 (-0.505) Age 0.903*** (9.267) _cons -2.245*** (-7.381) Stkcd Yes Year Yes Industry Yes N 12190 adj. R2 0.440 t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01 ------------------------------------------------ Insert Table 5 about here ------------------------------------------------ 4.4.2. Placebo Test To assess whether our baseline results might be driven by random chance rather than a true causal relationship, we conduct a placebo test by randomly reassigning the treatment status (GVC investment) across firms within our sample period. We repeat this random reassignment and re-estimate our baseline model 500 times, storing the estimated coefficient, t-statistic, and p-value from each iteration. The distribution of the placebo coefficients is plotted in Fig. 2 . The “true” estimated coefficient from our main analysis lies far in the right tail of this distribution, which is tightly clustered around zero. Correspondingly, Fig. 3 shows that the vast majority of the placebo tests yield statistically insignificant results (p > 0.10). These findings confirm that the significant positive effect of GVC on technology commercialization identified in our benchmark regression is unlikely to be spurious and is robust to this falsification test. ------------------------------------------------ Insert Figs. 2 and 3 about here ------------------------------------------------ 4.4.3. Double Machine Learning for Causal Inference To empirically assess the causal effect of GVC on technology commercialization, this study employs the Double Machine Learning (DML) framework proposed by Chernozhukov et al. ( 2018 ). We adopt this advanced method to address two critical challenges faced by traditional causal inference approaches in this context. First, the relationship is likely confounded by a high-dimensional set of control variables, including multifaceted firm characteristics and industry attributes. Standard parametric models often struggle to capture the complex, potentially nonlinear relationships within such data. Second, threats to identification persist from potential endogeneity biases, such as omitted variables or measurement error. The DML framework is particularly suited to this setting, as it is designed to provide consistent estimates of causal effects even when using flexible, non-parametric machine learning models to control for high-dimensional confounders. DML achieves “double robustness,” meaning it yields consistent causal estimates even if one of the underlying predictive models is slightly misspecified. The core idea is to separate the estimation of the treatment effect from the modeling of complex confounding relationships. We implement a partially linear model within the DML framework. To effectively handle high-dimensional data and automatically model nonlinearities and interaction effects, we utilize a stacking ensemble learner as the base predictor, constructed via Python’s pystacked library. Random Forest is specified as one of the base learners within the ensemble due to its proven efficacy in such tasks. The model employs 5-fold cross-validation (k = 5) to prevent overfitting and ensure the robustness of the predictions. The DML estimation results provide strong evidence for a positive causal effect. The estimated coefficient for GVC on technology commercialization is 0.188, with a standard error of 0.017. This effect is highly statistically significant at the 1% level (t = 10.939, z = 10.940, p < 0.001). The 95% confidence interval, [0.154, 0.221], excludes zero, confirming the statistical robustness of the finding. The model, estimated using 37,588 observations, demonstrates good fit, with an estimated constant term of -0.060 (p < 0.001).In summary, the application of the double machine learning framework offers a rigorous approach to causal identification in a high-dimensional setting. The results clearly demonstrate that government venture capital exerts a significant and positive causal effect on firms’ technology commercialization activities. This finding robustly supports the core theoretical proposition that GVC promotes enterprise-level technology commercialization. Table 6 Results of Dual Machine Learning Tests TC Coefficient Std. err. z P>|z| [95% conf. interval] GVC 0.188 0.017 10.940 0.000 [0.154, 0.221] _cons -0.060 0.005 -11.790 0.000 [-0.070, -0.050] ------------------------------------------------ Insert Table 6 about here ------------------------------------------------ 4.4.4. Addressing Potential Omitted Variable Bias To further address concerns regarding endogeneity from omitted variables, we augment our baseline model with a series of macro- and firm-level controls. Specifically, we incorporate the regional “intellectual property protection intensity (IPPI)” and the “number of regional universities (RU)” to account for institutional quality and knowledge spillovers. At the firm level, we control for the “proportion of R&D personnel (R&D)” and the “digital transformation level (DT).” The results of this expanded specification are reported in Column (2) of Table 7 .All four additional controls show a statistically significant positive association with technology commercialization. After their inclusion, the coefficient on our core explanatory variable, GVC, decreases modestly from 0.054 to 0.044 but remains statistically significant at the 5% level. This result demonstrates that the positive effect of government venture capital on technology commercialization is robust to controlling for these dimensions of institutional context, human capital, and firm capabilities, suggesting that omitted variable bias does not substantially threaten our main finding. Table 7 Regression Results for Omitted Variables (1) (2) TC TC GVC 0.054*** 0.044** (3.442) (2.521) ROE 0.047 0.049 (0.961) (0.872) Ir 0.112 0.054 (1.310) (0.484) Far -0.054 0.031 (-0.787) (0.378) Loss -0.003 -0.003 (-0.202) (-0.168) Indep -0.000 0.001 (-0.118) (0.393) Top10 0.165*** 0.381*** (2.593) (4.918) Mer -0.000 -0.000 (-0.166) (-0.107) Mfund 1.177*** 1.592*** (4.172) (4.579) Cap -0.001 0.004 (-0.196) (0.655) Age 0.910*** 0.780*** (9.946) (7.498) RU 0.001 (0.894) DT 0.002*** (5.376) IPPI 0.899* (1.720) R&D 0.002*** (2.671) _cons -2.276*** -2.201*** (-7.966) (-6.306) Stkcd Yes Yes Year Yes Yes Industry Yes Yes N 37288 33126 adj. R 2 0.409 0.401 t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01 ------------------------------------------------ Insert Table 7 about here ------------------------------------------------ 4.5. Robustness Test 4.5.1. Robustness to Regional Fixed Effects Systematic variation across cities—such as differences in resource endowments, industrial bases, and local policy support (e.g., municipal industrial funds or talent programs)—could directly influence firms’ innovative outcomes. If unaccounted for, this cross-sectional heterogeneity might introduce omitted variable bias. To address this concern, we augment our baseline model by incorporating city-level fixed effects. The regression results including these regional controls are presented in Column (1) of Table 8 . The coefficient on GVC is 0.055 and remains highly significant (p < 0.01). This finding indicates that the positive effect of GVC on firm-level technology commercialization is robust to controlling for all time-invariant heterogeneity at the city level, strengthening confidence that our core results are not driven by latent regional factors. 4.5.2. Robustness to Additional Firm-Level Controls A firm’s capacity for technology commercialization may be influenced not only by GVC but also by its internal financial health and operational capabilities. Omitting these factors could lead to bias in estimating the GVC effect. To more comprehensively account for such potential confounders, we expand our baseline model by incorporating a set of supplementary firm-level variables: leverage ratio (lev), controlling for financial constraints and risk posture; asset turnover ratio (ato), capturing operational efficiency; cash flow from operations (cashflow), accounting for internal liquidity available for innovation; and sales growth rate (growth), reflecting market expansion and growth momentum. The results from this augmented specification are presented in Column (2) of Table 8. The coefficient on GVC is 0.055 and remains statistically significant at the 1% level, virtually unchanged from the baseline estimate. This finding indicates that the positive effect of GVC on technology commercialization is robust to controlling for these additional dimensions of firm financial structure, operational efficiency, liquidity, and growth potential, further strengthening the credibility of our core results. 4.5.3. Robustness to Alternative Clustering Levels In panel data regressions, the disturbance terms may exhibit within-group correlation. An inappropriate choice of clustering for standard errors can lead to biased inference. To ensure our core findings are not sensitive to this choice, we re-estimate our model using different clustering levels to verify the robustness of the GVC coefficient. While maintaining firm, year, and industry fixed effects, we first cluster standard errors at the industry level. We then employ two-way clustering at the province-by-industry level to account for potential multi-dimensional heteroskedasticity and autocorrelation. The results under these alternative specifications are presented in Columns (3) and (4) of Table 8. In both cases, the coefficient on GVC is 0.054 and remains highly significant (p < 0.01). The magnitude and statistical significance of this coefficient are virtually identical to the baseline estimates. This consistency demonstrates that the positive effect of GVC on technology commercialization is robust and our inference is not materially affected by the choice of clustering level, further supporting the statistical reliability of our main conclusion. 4.5.4. Robustness to Excluding Central State-Owned Enterprises The innovation behavior of central state-owned enterprises (SOEs) may systematically differ from that of other firms due to their distinct access to policy support, financing, and administrative resources. To ensure our estimates of the GVC effect are not driven by these unique entities and to enhance the generalizability of our findings, we exclude all central SOEs from the sample and re-estimate our model. The results from this restricted sample are presented in Column (5) of Table 8. The coefficient on GVC is 0.047 and remains positive and statistically significant at the 1% level. While the point estimate is slightly attenuated compared to the baseline, its sign and high significance are unchanged. This indicates that GVC exerts a robust positive effect on technology commercialization even within a more general population of firms that excludes centrally administered SOEs. The finding further supports the reliability of our core conclusion, confirming that the role of GVC is not attributable solely to a subset of enterprises with privileged institutional standing. Table 8 Robustness Test Results (1) (2) (3) (4) (5) TC TC TC TC TC GVC 0.055*** 0.055*** 0.054*** 0.054*** 0.047*** (3.509) (3.520) (3.814) (3.575) (2.992) Controls Yes Yes Yes Yes Yes _cons -2.287*** -2.126*** -2.276*** -2.274*** -2.174*** (-7.901) (-7.427) (-6.046) (-9.797) (-7.441) year FE Yes Yes Yes Yes Yes Stkcd FE Yes Yes Yes Yes Yes industry FE Yes Yes Yes Yes Yes city FE Yes No No No No N 37267 37288 37288 37273 35468 Adj R2 0.412 0.411 0.409 0.409 0.386 t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01. ------------------------------------------------ Insert Table 8 about here ------------------------------------------------ 5. Mechanism Analysis Our theoretical framework posits that GVC facilitates firm-level technology commercialization through three core mechanisms: (1) resource provision, (2) network advantages, and (3) structured profit-sharing and risk-bearing. The resource provision mechanism operates through capital leverage and talent aggregation. The network advantage mechanism functions via policy signaling, supply-chain synergies, and governance optimization. Finally, the structured profit-sharing and risk-bearing mechanism is enacted through explicit contractual arrangements that share downside risk and allocate upside returns. Conceptually, GVC acts as a form of “public subordinated capital.” Its structural terms—often prioritizing loss absorption before claiming profits—can transform high-risk, long-cycle technological projects, which might otherwise remain shelved by private markets, into viable, long-term collaborations that align incentives among multiple stakeholders. 5.1. The Resource Provision Mechanism Our theoretical analysis posits that GVC supports technology commercialization by providing two key resources: financial capital and human talent. This dual provision alleviates critical financing constraints and human capital bottlenecks in the commercialization process. Financially, GVC not only directly addresses firms’ liquidity shortfalls but also, through a positive signaling effect, helps attract follow-on private investment, thereby improving the overall financing environment. In parallel, the official endorsement, policy support, and resource empowerment associated with GVC enhance a firm’s ability to attract and retain high-skilled talent, laying the necessary human capital foundation for commercialization. To empirically test these mechanisms, we estimate mediation models for both the financial and human capital pathways. For the financial support mechanism, we use the SA index (Hadlock and Pierce, 2010 ) as a proxy for financing constraints, where a lower value indicates fewer constraints, capturing the capital leverage effect of GVC. For the human capital aggregation mechanism, we employ the natural logarithm of one plus the number of employees holding a master’s degree or higher, measuring the stock of high-level human capital to capture GVC’s role in talent attraction and retention. The results, presented in Table 9 , support the dual-channel resource provision mechanism. Column (1) shows a significant negative coefficient on GVC (p < 0.01), indicating that GVC effectively alleviates financing constraints. Column (2) shows a significant positive coefficient on GVC (p < 0.01), demonstrating that GVC promotes the aggregation of high-skilled talent. Column (3) presents the full model. The coefficient on GVC remains positive and significant. Both the SA index and the talent variable are also positive and significant. These findings confirm that both the alleviation of financing constraints and the aggregation of high-level human capital serve as significant mediating channels through which GVC promotes technology commercialization. Thus, the resource provision mechanism is empirically validated, providing support for Hypothesis 2. Table 9 Resource Support Mechanism (1) (2) (3) Financing Constraints Human Capital TC GVC -0.003*** 0.069*** 0.056*** (-2.937) (2.931) (3.587) Financing Constraints 0.990*** (11.534) Human Capital 0.012*** (3.349) Constant -3.848*** 0.126 1.533*** (-208.935) (0.296) (3.514) Controls Yes Yes Yes Observations 37280 37288 37280 Adj. R² 0.966 0.721 0.411 t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01 ------------------------------------------------ Insert Table 9 about here ------------------------------------------------ 5.2. Network Advantages Theoretical analysis posits that GVC fosters technology commercialization by building multi-level network advantages, which operate through three distinct channels: (1) macro-level policy resource allocation, (2) meso-level industrial chain synergy enhancement, and (3) micro-level corporate governance optimization. To empirically test this theoretical mechanism (H3), we construct a multiple mediation model using variables for policy support, intra-chain collaboration, and internal governance as parallel mediators. The policy support mechanism is measured by a dummy variable indicating whether a firm receives explicit policy support from the central government. This variable captures the institutional certification and resource allocation effects conferred by GVC. The intra-chain enterprise collaboration mechanism is proxied by the natural logarithm of the number of R&D alliances a firm participates in plus one [ln (number of R&D alliances + 1)]. This metric reflects the firm’s embeddedness in collaborative innovation networks and its capacity for resource integration. For the internal governance mechanism, we employ the proportion of institutional investor shareholding as a proxy variable. Existing research (Chung and Zhang 2011 ) indicates that this metric effectively represents corporate governance quality and the intensity of external oversight. The regression results are presented in Table 10 . Column (1) shows that the coefficient on GVC is significantly positive at the 1% level, indicating that GVC significantly increases the probability of a firm obtaining central policy support. Column (2) shows that the coefficient on GVC is significantly positive at the 1% level, suggesting that GVC effectively promotes corporate participation in R&D alliances and enhances coordination capabilities within industrial chains. Column (3) shows that the coefficient on GVC is significantly positive at the 1% level, demonstrating that GVC helps increase the proportion of institutional investor holdings, thereby improving the level of corporate internal governance. Column (4) presents the results of the full model. All three mediating variables—policy support, intra-chain collaboration, and institutional ownership—exhibit a significant positive effect on technology commercialization at the 1% level, while the direct effect of GVC also remains significant. These results indicate that GVC promotes technology commercialization through three concurrent pathways: securing policy support, enhancing intra-chain collaboration, and optimizing corporate governance. The network advantage mechanism is therefore effective, confirming Hypothesis 3. Table 10 Network Advantage Mechanism (1) (2) (3) (4) Policy Support Intra-chain Enterprise Collaboration Internal Governance TC GVC 0.013*** 0.069*** 0.016*** 0.050*** (3.237) (2.931) (11.742) (2.881) Policy Support 0.097*** (3.747) Intra-chain Enterprise Collaboration 0.114*** (18.051) Internal Governance 0.244*** (3.477) Constant -0.127* 0.126 -0.423*** -1.956*** (-1.794) (0.296) (-17.613) (-6.327) Controls Yes Yes Yes Yes Observations 32252 37288 37288 32252 Adj. R² 0.790 0.721 0.926 0.421 t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01 ------------------------------------------------ Insert Table 10 about here ------------------------------------------------ 5.3. The Benefit-Sharing and Risk-Sharing Mechanism Our theoretical framework posits that GVC promotes technology commercialization through two interrelated channels: structuring benefit-sharing to align incentives and providing risk-sharing to mitigate uncertainties. The benefit-sharing mechanism addresses the incentive problem of “unwillingness to commercialize” by designing revenue arrangements that properly reward innovation actors, thereby enhancing conversion efficiency. Conversely, the risk-sharing mechanism tackles the constraint of “daring not to commercialize.” By using public capital to absorb early-stage risks, GVC provides a risk buffer for firms, enabling them to pursue higher-risk, higher-reward commercialization projects. To test these mechanisms, we construct the following proxy variables: Benefit-sharing: We employ a firm’s information disclosure rating as an ordered discrete variable, ranging from 1 (Excellent) to 4 (Unqualified). A lower score indicates higher information transparency, which we argue reflects a more rational and transparent benefit-distribution structure fostered by GVC’s involvement in corporate governance. Risk-sharing: We measure a firm’s risk-bearing capacity using the industry-adjusted standard deviation of its return on assets (ROA) from years t-2 to t + 2. A higher value indicates a greater ability and willingness to endure volatility, capturing the risk-buffering effect provided by GVC’s role as “patient” or “junior” capital. The regression results are presented in Table 11 . Column (1) shows that the coefficient on GVC is negative and significant at the 5% level, indicating that GVC significantly improves firms’ benefit-sharing mechanisms (as reflected in higher disclosure ratings). Column (2) shows that the coefficient on GVC is positive and significant at the 1% level, suggesting that GVC enhances firms’ risk-bearing capacity. The results provide empirical support for the dual channels of the benefit- and risk-sharing mechanism. GVC appears to facilitate technology commercialization both by optimizing incentive structures and by mitigating risk exposures. Hypothesis 4 is therefore supported. Table 11 Benefit-sharing and Risk-sharing Mechanism (1) (2) (3) Benefit-sharing Risk-sharing TC GVC -0.019** 0.003*** 0.032* (-1.966) (4.380) (1.863) Risk-sharing -0.008 (-0.711) Benefit-sharing 0.530*** (3.341) Constant 1.636*** -0.020* -1.940*** (8.706) (-1.856) (-5.713) Controls Yes Yes Yes Observations 32027 36512 31293 Adj. R² 0.438 0.382 0.430 t statistics in parentheses, * p < 0.1, ** p < 0.05, *** p < 0.01 ------------------------------------------------ Insert Table 11 about here ------------------------------------------------ 6. Conclusions and Implications 6.1. Research Findings Drawing on a sample of Chinese A-share listed companies from 2012 to 2023, our empirical analysis yields three main findings. First, GVC exerts a significant positive effect on firms’ technology commercialization. Second, this effect operates through three distinct pathways: the resource provision effect (by alleviating financing constraints and aggregating high-skilled talent), the network advantage effect (by securing policy support, enhancing industrial chain collaboration, and optimizing internal governance), and the benefit- and risk-sharing effect (by structuring incentives and buffering risk). This conclusion is robust, as it withstands a battery of endogeneity checks and robustness tests. Third, we find significant heterogeneity in this catalytic effect. The positive influence of GVC on technology commercialization is more pronounced in private and other non-state-owned enterprises, as well as in asset-intensive and labor-intensive industries. 6.2 Research Implications In light of this, this paper explores potential policy implications at the formulation level: First, the scope and precision of government venture capital support should be further expanded, with a particular focus on targeted assistance for private enterprises and other businesses, as well as asset-intensive and labor-intensive industries facing prominent bottlenecks in technology transfer. This can be achieved by establishing specialized guiding funds for specific sectors and optimizing the regional layout of science and technology innovation funds, thereby enhancing the inclusiveness and structural alignment of policy support. Second, improve the market-oriented operation and governance mechanisms of government venture capital. Clarify the principle of “government guidance, market-driven decision-making, and professional operation.” Enhance capital allocation efficiency and sustainability by establishing error tolerance and correction mechanisms, improving exit channels, and introducing socialized performance evaluations. This will prevent administrative intervention from distorting market signals. Third, strengthen synergies between government-backed venture capital and other policy tools. Integrate these with tax incentives, first-unit purchase policies, science and technology talent programs, and open application scenarios to form a unified “investment-lending-subsidy-service” support system. Amplify resource infusion and institutional empowerment through policy stacking. Finally, implement differentiated and refined government venture capital support strategies: prioritize early-stage R&D and concept validation for technology-intensive industries; strengthen pilot-scale scaling and industrialization support for asset-intensive sectors; and focus on process innovation and digital transformation for labor-intensive industries—achieving targeted precision support. Drawing on these findings, we propose several implications for the design and implementation of GVC policies: First, policymakers should enhance the scope and precision of GVC support. Targeted assistance should be directed toward private and other non-state-owned enterprises, as well as asset-intensive and labor-intensive industries where bottlenecks in technology commercialization are most acute. This can be achieved by establishing dedicated sectoral guiding funds and optimizing the regional distribution of sci-tech innovation funds to improve the inclusiveness and structural relevance of policy support. Second, it is crucial to improve the market-oriented operation and governance mechanisms of GVC. The principle of “government guidance, market-based decision-making, and professional operation” must be clarified. Operational efficiency and long-term sustainability can be enhanced by implementing formal error-tolerance and correction mechanisms, improving capital exit channels, and introducing third-party performance evaluations. These steps are essential to prevent administrative interventions from distorting market signals. Third, strengthening the synergy between GVC and other policy tools is key. GVC initiatives should be strategically integrated with complementary measures such as R&D tax incentives, first-purchase policies, science and technology talent programs, and the provision of open application scenarios. This integration can form a cohesive “investment-loan-subsidy-service” support system, amplifying resource infusion and institutional empowerment through policy stacking effects. Finally, differentiated and refined GVC support strategies should be implemented. Support for technology-intensive industries should prioritize early-stage R&D and proof-of-concept activities. For asset-intensive sectors, the focus should shift to pilot scaling and industrialization support. For labor-intensive industries, GVC should facilitate process innovation and digital transformation. Such an approach ensures targeted and precise support aligned with distinct industry needs. For enterprises, GVC should be strategically embraced not merely as a financing channel, but as a critical resource for navigating the “valley of death” in the innovation process. Firms should proactively engage with GVC institutions, leveraging their policy credibility to attract follow-on private investment, diversify funding sources, and alleviate critical financial constraints during technology commercialization. Furthermore, firms should fully utilize the network resources and governance improvements facilitated by GVC. This involves actively integrating into regional innovation ecosystems and industrial chains, strengthening collaborative R&D with universities, research institutes, and supply-chain partners to enhance the systemic efficiency of commercialization. Internally, firms should seize the opportunity to optimize governance structures, refine systems for disclosing, evaluating, and incentivizing innovation outputs, and bolster capabilities in intellectual property management—thereby transforming external support into endogenous, sustainable drivers of growth. As an institutional innovation that integrates state guidance with market mechanisms, GVC serves as a pivotal lever for bridging the “last mile” in technology commercialization. Through synergistic policy optimization and enhanced firm-level capabilities, it can help cultivate a thriving innovation ecosystem characterized by effective governance, efficient markets, and dynamic firms. Such an ecosystem provides essential support for achieving higher levels of technological self-reliance and high-quality industrial development. Declarations Conflict of interest The authors declare that they have no conflicts of interest. Author Contribution J. 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Firm performance and state innovation funding: Evidence from China’s Innofund program. Research Policy , 46 (6), 1142–1161. https://doi.org/10.1016/j.respol.2017.05.001 Additional Declarations No competing interests reported. Supplementary Files ReasearchData.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8277780","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556731599,"identity":"cd4fcffd-7c63-481a-b857-a95996fcc11a","order_by":0,"name":"Jianmin Liu","email":"","orcid":"","institution":"Nantong Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianmin","middleName":"","lastName":"Liu","suffix":""},{"id":556731600,"identity":"3eb95881-f5e4-4b31-918a-e06696dab361","order_by":1,"name":"Susu Wang","email":"","orcid":"","institution":"Nanchang 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16:01:52","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":276052,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8277780/v1/8491bca4681f99ba237de78f.html"},{"id":97906957,"identity":"7439d336-05c5-4a26-a486-b010dd158368","added_by":"auto","created_at":"2025-12-10 16:01:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59908,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Parallel Trend Test\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8277780/v1/63c29bed9ec5654362c66f73.png"},{"id":97906964,"identity":"726a3cbb-181f-4525-b1b6-887a9aed4329","added_by":"auto","created_at":"2025-12-10 16:01:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47323,"visible":true,"origin":"","legend":"\u003cp\u003eFalse Estimation Coefficients\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8277780/v1/4f16a39d3ecd328e8f74bf15.png"},{"id":97907007,"identity":"4eb66b25-3841-4b2f-8bf4-f7d77eaa364c","added_by":"auto","created_at":"2025-12-10 16:01:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72718,"visible":true,"origin":"","legend":"\u003cp\u003eFalse p-Values\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8277780/v1/b1a71413f742654b3922088a.png"},{"id":99796716,"identity":"99c6e8f8-2a89-4814-b1b8-d2e2c2badef2","added_by":"auto","created_at":"2026-01-08 13:43:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1993211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8277780/v1/87fb6b5b-e34e-4ea4-911a-c1e5ab7bea1e.pdf"},{"id":98421668,"identity":"9f0777a3-1118-4d05-af79-6a1cb538d9af","added_by":"auto","created_at":"2025-12-17 16:28:54","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6326331,"visible":true,"origin":"","legend":"","description":"","filename":"ReasearchData.zip","url":"https://assets-eu.researchsquare.com/files/rs-8277780/v1/8c18ecf5fce9377462acf63f.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Innovation to Application: The Role of Government Venture Capital in Technology Commercialization","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs the critical final stage of the innovation value chain, technology commercialization bridges the formidable \u0026ldquo;Valley of Death\u0026rdquo; by translating abstract scientific discoveries into market-ready products, processes, and services. This translation is central to generating tangible economic growth and social progress, as it drives productivity gains, enhances competitive advantage, and helps address pressing global challenges. While knowledge creation is a fundamental starting point, its ultimate impact is contingent upon successful market entry and widespread diffusion (Dean et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, understanding the mechanisms that effectively catalyze and accelerate this complex commercialization process constitutes a core scholarly and policy imperative for advancing sustainable development and strengthening national innovation capabilities (Takata et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eChina\u0026rsquo;s statutory framework defines technology commercialization as activities aimed at enhancing productivity, encompassing the testing, development, application, and promotion of research outcomes to generate new technologies, processes, materials, products, and industries. Despite China\u0026rsquo;s leading position in patent filings and high-impact scientific publications\u0026mdash;including being the first country to hold over 4\u0026nbsp;million valid domestic invention patents in 2023\u0026mdash;the commercialization rate of these patents remains a critical challenge. According to the 2023 China Patent Survey, only 39.6% of invention patents in China have reached industrialization. Even among enterprises, the primary drivers of innovation, the industrialization rate for invention patents stands at 51.3%, a figure that still trails behind that of developed economies. This gap between research output and practical application underscores an urgent need to strengthen the mechanisms that translate scientific and technological achievements into market-ready solutions.\u003c/p\u003e\u003cp\u003ePrevious research on the commercialization of corporate technological achievements has primarily been conducted from the perspectives of innovation management, policy science, and organizational behavior, focusing on the barriers and drivers within this process. One stream of literature focuses on the characteristics and motivations of the key actors involved. Extensive studies have analyzed the evolving roles of universities and research institutions in technology transfer, examining how factors such as individual motivations for academic entrepreneurship, team composition, and institutional policies\u0026mdash;particularly regarding intellectual property ownership and incentive systems\u0026mdash;influence commercialization outcomes (Di\u0026aacute;nez-Gonz\u0026aacute;lez and Camelo-Ordaz, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Goethner et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Perkmann et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Visintin and Pittino \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).Another stream shifts focus to the interaction and collaboration among multiple actors. Here, the \u0026ldquo;triple helix\u0026rdquo; model (university-industry-government) is widely applied to analyze inter-organizational dynamics within national innovation systems, emphasizing that effective institutional arrangements and interface management are crucial for overcoming technology transfer bottlenecks (Etzkowitz and Leydesdorff, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Leydesdorff, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, the role of government support systems\u0026mdash;including science and technology programs, tax incentives, and science parks\u0026mdash;in facilitating technology transfer has been a significant area of inquiry (Howell, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Park and Leydesdorff, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In summary, prior work on the drivers of technology commercialization has coalesced around three primary dimensions: the attributes of the involved actors, the nature of organizational interactions, and the surrounding policy environment. The underlying mechanisms are frequently explained through the \u0026ldquo;resource endowment effect,\u0026rdquo; the \u0026ldquo;collaborative network effect,\u0026rdquo; and the \u0026ldquo;institutional incentive effect.\u0026rdquo; However, a key limitation in this body of work is its practical logic. Past research has often been confined to examining single subjects or linear relationships. More critically, it has frequently conflated the distinct roles and operational pathways of direct government intervention versus market-based mechanisms in the commercialization process. This conflation has led to theoretical and practical paradoxes when scrutinizing factors within either domain, thereby obscuring the inherent limitations and potential complementary effects of government and market tools.\u003c/p\u003e\u003cp\u003eThe commercialization of corporate technological achievements is an innovative activity characterized by significant positive externalities, high uncertainty, and systemic complexity. Overcoming its inherent challenges requires the synergistic combination of government support and market mechanisms; relying solely on either proves insufficient. Regarding government support, while intervention can theoretically fulfill functions such as innovation incentivization, resource pooling, and signaling (Howell, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e), and can address incentive gaps caused by positive externalities (Guo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), it faces intrinsic functional limitations. Issues like information asymmetry in direct interventions, lagged responses to market signals, and the potential subordination of efficiency to policy objectives prevent government action from independently and effectively resolving the challenges firms face in technology commercialization. In practice, it may even induce forms of \u0026ldquo;government failure,\u0026rdquo; such as distorting genuine market demand, dampening corporate initiative, and reinforcing a path-dependent cycle of \u0026ldquo;high investment, low efficiency, and weak incentive.\u0026rdquo; Particularly in today\u0026rsquo;s complex environment, the \u0026ldquo;limited variety\u0026rdquo; and \u0026ldquo;redundant inefficiency\u0026rdquo; of policy tools have weakened their effectiveness in supporting corporate innovation. Standalone policy instruments often fail to integrate multifaceted resources and struggle to meet the intricate, sustained demands of corporate innovation (Goulder and Parry, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wang and Zou, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This over-reliance can stifle companies\u0026rsquo; intrinsic motivation, ultimately hindering the commercialization of their technological achievements. Conversely, traditional market mechanisms also exhibit significant limitations in allocating resources for such endeavors. Factors like externalities, public good undersupply, monopoly, and information asymmetry frequently lead to market inefficiencies (Marchand and Russell, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). While the market\u0026rsquo;s \u0026ldquo;invisible hand\u0026rdquo; plays a fundamental role in guiding resource allocation, incentivizing innovation, and fostering collaboration, its inherent constraints\u0026mdash;including underinvestment in activities with positive externalities, avoidance of high-risk and long-cycle projects, and the potential for individually rational actions to yield collectively irrational outcomes\u0026mdash;prevent it from single-handedly solving commercialization challenges. In practice, it may even exacerbate vicious cycles among resource allocation, coordination, and governance dilemmas. Information economics suggests that a firm's response to its environment is highly contingent on the timeliness, accuracy, and completeness of information, whereas markets are typically plagued by delays and asymmetries in information transmission (Stiglitz and Rothschild, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Furthermore, in technology commercialization, the private sector often curtails investment due to a focus on short-term financial objectives (Ren et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Simultaneously, the positive externalities of technological innovation mean private returns are lower than social returns, further depressing long-term R\u0026amp;D incentives (Jones and Summers, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This, in turn, weakens firms' capacity to facilitate the smooth transition of technological achievements through resource integration and capability reconfiguration (Li and Jia, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe efficient commercialization of corporate technological achievements necessitates a coordinated mechanism to address both \u0026ldquo;government failure\u0026rdquo; and \u0026ldquo;market failure.\u0026rdquo; The synergistic interaction between government support and market mechanisms has thus become a critical pathway to overcome this dual-failure dilemma and enhance commercialization efficiency. This synergy operates on multiple levels: at the institutional level, where governments, markets, and firms break down barriers through information sharing and resource integration, thereby enhancing policy transparency and allocation efficiency (Hailu, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); and at the instrumental level, where policy tools guide factor aggregation and incentivize behaviors, strengthening firms\u0026rsquo; resource mobilization capacity and risk resilience (Bustinza et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Coordination at the subject level can mitigate issues of \u0026ldquo;incentive misalignment\u0026rdquo; and \u0026ldquo;duplicative incentives\u0026rdquo; stemming from information asymmetry, while coordination at the tool level helps establish a multi-channel financing system, enabling firms to more broadly integrate and leverage market resources. Government Venture Capital (GVC) represents an institutional innovation that synergizes government and market forces, serving as a key policy instrument for driving efficient technology commercialization and enhancing corporate innovation capabilities. At its core, GVC is a hybrid tool embodying both policy and market attributes. Its policy orientation is characterized by alignment with national strategic goals and public interests, emphasizing support for key sectors and critical technologies (Colombo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Its market orientation finds expression in the delegation of investment decisions to professional institutions, utilizing market-based mechanisms such as equity investment and risk-sharing to pursue optimal resource allocation (Callagher et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e; Guerini and Quas, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with a focus on investment efficiency and sustainable returns. These two logics are not mechanically superimposed but are organically integrated and functionally complementary within a unified governance framework. Unlike traditional government support mechanisms like direct grants or special subsidies, GVC participates in corporate innovation through equity investment. It adheres to principles such as \u0026ldquo;investing early, investing small, and investing in hard tech,\u0026rdquo; and relies on market mechanisms for project selection, post-investment management, and exit. This model better aligns incentive structures with the actual needs of portfolio firms. As an organic synthesis of public intent and market principles within the innovation domain, GVC mitigates the resource misallocation and weak incentives associated with traditional administrative methods, while also correcting the market\u0026rsquo;s chronic underinvestment in early-stage technology commercialization due to high risks and positive externalities. Functionally, GVC fulfills a dual mission of \u0026ldquo;strategic guidance\u0026rdquo; and \u0026ldquo;value creation.\u0026rdquo; Beyond seeking financial returns, it prioritizes using capital linkages to help technologies bridge the \u0026ldquo;valley of death,\u0026rdquo; accelerate intellectual property commercialization, and catalyze synergistic investments from social capital. This facilitates the advancement of industrial infrastructure and supply chain modernization, fostering a virtuous cycle within the innovation ecosystem and maximizing societal welfare (Bertoni et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Callagher et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e; Croce et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Compared to private venture capital (PVC), GVC\u0026rsquo;s public and long-term orientation leads it to place greater emphasis on building sustainable innovation capacity and mitigating commercialization risks when supporting firm growth (Alperovych et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bertoni and Tykvov\u0026aacute;, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e). In practice, GVC often embeds itself into the governance of portfolio companies through board representation and specific covenants. This allows it to guide the standardization of R\u0026amp;D management, optimize technology transfer processes, and provide systematic support in areas like IP strategy and industry-academia collaboration (Bottazzi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Cumming, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This dual model of \u0026ldquo;governance embedding plus resource empowerment\u0026rdquo; aligns with national innovation-driven development strategies and effectively enhances the success rate and overall benefits of technology commercialization.\u003c/p\u003e\u003cp\u003eExisting research on GVC has predominantly examined its functional positioning, operational contexts, economic impacts, and underlying mechanisms. First, regarding functional positioning, a consensus exists among scholars that the core function of GVC is to address market failures inherent in PVC. Seminal work by Lerner (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) demonstrates that governments can provide \u0026ldquo;patient capital\u0026rdquo; to early-stage, high-risk technology firms through state-backed venture funds, thereby mitigating financing gaps arising from information asymmetry and externalities. Empirical studies, such as those by Brander et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), further indicate that GVCs not only offer direct funding but also exert a critical \u0026ldquo;certification\u0026rdquo; or \u0026ldquo;signaling\u0026rdquo; effect. Specifically, GVC investment signals positive project quality to the market, thereby crowding in additional private capital and generating a significant capital leverage effect. Second, in terms of operational contexts, research has concentrated on GVC efficacy within specific sectors and developmental stages. Substantial evidence suggests that GVCs play a particularly vital role in supporting hard-technology innovation (e.g., biotechnology, clean energy) and cultivating regional innovation ecosystems (Colombo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In these domains where market failures are more acute, the long-term orientation and strategic patience of GVCs prove crucial. Third, scholarly conclusions regarding GVC performance exhibit divergence. Some studies affirm its positive impacts, such as enhancing the innovation output (e.g., patents), follow-on financing capacity, and survival rates of portfolio firms (Bertoni and Tykvov\u0026aacute;, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e). Conversely, other studies raise concerns about a potential \u0026ldquo;crowding-out effect,\u0026rdquo; positing that GVC may distort markets through non-commercial investment decisions, thereby displacing more efficient private capital (Cumming and MacIntosh, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Recent research increasingly suggests that the relationship between GVC and PVC is complementary rather than purely substitutive, with outcomes contingent upon the specific design, governance structure, and contextual fit of the GVC program (Grilli and Murtinu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Finally, concerning underlying mechanisms, prior investigations have largely focused on isolated pathways\u0026mdash;such as the aforementioned signaling and capital supplementation mechanisms. However, they lack an integrated theoretical framework capable of systematically elucidating the multiple, parallel channels through which GVC influences firm development and technology commercialization.\u003c/p\u003e\u003cp\u003eThe global landscape of technological competition has been undergoing rapid restructuring. Concurrently, the challenge of tackling critical core technologies has intensified, and the complexity of deeply integrating innovation chains with industrial chains has grown significantly. In this context, the environment for corporate technology commercialization has shifted from one characterized by \u0026ldquo;predictable technological risks\u0026rdquo; to one facing \u0026ldquo;systemic transformation uncertainties.\u0026rdquo; Against this backdrop, GVC\u0026mdash;a hybrid instrument combining policy guidance with market-based mechanisms\u0026mdash;holds exceptional strategic value for bridging the \u0026ldquo;valley of death\u0026rdquo; in technology commercialization and enhancing the overall efficiency of innovation systems. Nonetheless, research on its operational mechanisms and supporting empirical evidence remains limited. To address this gap, this study investigates the impact of GVC on the efficiency of corporate technology commercialization and subsequent innovation levels from a dual-logic perspective that integrates government and market incentives. The potential contributions of this research are as follows:\u003c/p\u003e\u003cp\u003eFirst, by treating the dual nature of GVC as the logical starting point of this study, we move beyond the conventional \u0026ldquo;government-guiding-the-market\u0026rdquo; paradigm. This paper reconceptualizes GVC as an institutionally integrated entity characterized by \u0026ldquo;a single entity with dual attributes.\u0026rdquo; This perspective transcends the binary and oppositional analytical framework\u0026mdash;where \u0026ldquo;government\u0026rdquo; and \u0026ldquo;market\u0026rdquo; are treated as separate actors\u0026mdash;that dominates prior research. Previous studies have largely regarded GVC as a simple superposition or an external interaction between two independent entities. Their analytical logic has thus either focused on how governments can \u0026ldquo;guide\u0026rdquo; the market or on how markets \u0026ldquo;respond\u0026rdquo; to government policies. This \u0026ldquo;dual-subject\u0026rdquo; approach fails to capture the essence of GVC as an institutional innovation: it is not an external collaboration between two distinct entities, but rather the organic integration of governmental and market logics within a unified governance framework and capital vehicle. In contrast, this paper treats GVC as a singular entity that simultaneously embodies and synergistically operationalizes both \u0026ldquo;government advantages\u0026rdquo; and \u0026ldquo;market advantages.\u0026rdquo; Building on this new logical foundation, we systematically demonstrate how GVC, as an integrated entity, leverages its inherent dual advantages to synergistically influence the entire process of corporate technology commercialization. This approach not only clarifies the intrinsic transmission mechanism of \u0026ldquo;mutual reinforcement and functional complementarity\u0026rdquo; between GVC\u0026rsquo;s dual attributes but also offers a novel theoretical lens for understanding the micro-level integration of a \u0026ldquo;proactive government\u0026rdquo; and an \u0026ldquo;efficient market,\u0026rdquo; thereby moving beyond traditional approaches that merely juxtapose or oppose the two.\u003c/p\u003e\u003cp\u003eSecond, this paper develops a systematic mediation model to bridge the typically fragmented research streams on GVC and technology commercialization. Moving beyond examining GVC\u0026rsquo;s financial functions or isolated commercialization barriers in isolation, we propose an integrated framework in which GVC influences technology commercialization through three sequentially linked mediators: resource support, network advantages, and the joint sharing of benefits and risks. This framework theoretically synthesizes insights from the resource-based view, social network theory, and incentive theory, thereby providing a comprehensive and coherent lens for understanding how GVC promotes technology commercialization through multiple, interconnected pathways.\u003c/p\u003e\u003cp\u003eThe main innovations of this paper are as follows:\u003c/p\u003e\u003cp\u003eFirst, regarding the logical relationships among the mechanisms, this paper develops a \u0026ldquo;trinity\u0026rdquo; synergistic chain that highlights systemic innovation. It moves beyond prior literature that treats GVC as a singular, isolated mechanism. Instead, we construct an interlinked, systematic intermediary model consisting of three components: \u0026ldquo;resource support\u0026ndash;network advantage\u0026ndash;restructuring of benefits and risks.\u0026rdquo; These three mechanisms are not merely parallel; they exhibit a clear logical progression and synergistic relationship, collectively forming a systemic solution that supports the commercialization of technology from innovation to application. First, \u0026ldquo;resource support\u0026rdquo; serves as the foundational layer and prerequisite. We argue that GVC leverages policy endorsement to mobilize market capital and, through platform-based initiatives, attracts high-end talent. This provides the essential financial and human capital for technology commercialization, establishing the material foundation for crossing the \u0026ldquo;Valley of Death\u0026rdquo; and addressing the question of \u0026ldquo;whether commercialization can be initiated.\u0026rdquo; Second, \u0026ldquo;network advantage\u0026rdquo; acts as the enabling layer and an efficiency amplifier. Building on resource infusion, GVC further activates and integrates internal and external networks. At a macro level, it unlocks policy toolkits to provide institutional safeguards; at a meso level, it functions as a \u0026ldquo;chain leader\u0026rdquo; to foster industry-university-research collaboration and reduce transaction costs; at a micro level, it embeds governance structures to optimize corporate decision-making and curb short-termism. This mechanism efficiently translates initial resource inputs into systemic synergies, addressing the question of \u0026ldquo;how to achieve efficient commercialization.\u0026rdquo; Finally, \u0026ldquo;benefit sharing and risk pooling\u0026rdquo; constitutes the incentive layer, ensuring sustainability. While the first two mechanisms address capabilities and efficiency, unfair distribution of innovation benefits or excessive risk concentration can render commercialization efforts unsustainable. GVC reshapes incentive structures through instruments such as \u0026ldquo;public subordinated capital\u0026rdquo; and market-aligned exit arrangements. This enables firms to \u0026ldquo;dare to commercialize\u0026rdquo; and incentivizes private capital to \u0026ldquo;willingly follow,\u0026rdquo; thereby resolving the question of \u0026ldquo;willingness to sustain commercialization.\u0026rdquo;\u003c/p\u003e\u003cp\u003eSecond, this study substantially enriches the research content, achieving dual expansion in both depth and breadth through the mutual validation of theory and empirical evidence. By integrating theoretical frameworks with deepened empirical analysis, it moves beyond addressing the basic question of \u0026ldquo;whether GVC is effective\u0026rdquo; to critically examine subsequent and more nuanced issues: \u0026ldquo;why it is effective,\u0026rdquo; \u0026ldquo;through which pathways it achieves its effects,\u0026rdquo; and \u0026ldquo;under what conditions and for whom it is most effective.\u0026rdquo; This approach yields robust and insightful conclusions regarding GVC\u0026rsquo;s role in technology commercialization.\u003c/p\u003e\u003cp\u003eThird, the study focuses intensely on the journey from innovation to application, specifically on overcoming the \u0026ldquo;Valley of Death\u0026rdquo; that spans the technology commercialization process. Its central argument is tightly organized around this critical transition\u0026mdash;analyzing how to cross this chasm. Furthermore, it provides a detailed analysis of how GVC leverages its dual attributes to design targeted solutions for the inherent high risks, long cycles, and positive externalities of the \u0026ldquo;Valley of Death.\u0026rdquo; It employs \u0026ldquo;patient capital\u0026rdquo; to hedge against uncertainty, utilizes market mechanisms to screen viable technologies, and harnesses public credibility to attract follow-on private investment. In essence, GVC constructs an acceleration mechanism that bridges the gap between \u0026ldquo;technology readiness\u0026rdquo; and \u0026ldquo;market readiness.\u0026rdquo;\u003c/p\u003e"},{"header":"2. Theoretical Analysis and Research Hypotheses","content":"\u003cp\u003eEnterprises face three major challenges in the process of technology commercialization. First, regarding sustained resource investment, the process inherently involves high risk, substantial capital commitment, and long cycles. Particularly during the pilot testing and engineering validation phases, there is a significant demand for capital to procure equipment, validate processes, and facilitate market entry. However, most enterprises, especially small and medium-sized enterprises, lack the internal capacity to independently sustain the substantial costs of this commercialization journey (Doh and Kim, 2014). Although governments provide supportive policies such as fiscal subsidies and tax incentives, in practice, barriers to accessing these funds are high, approval procedures are complex, and the time required for disbursement is often prolonged. Consequently, it is difficult to meet enterprises\u0026rsquo; need for \u0026ldquo;timely\u0026rdquo; capital infusion (Meuleman and De Maeseneire, 2012). Furthermore, market-based funding sources like venture capital and angel investments tend to favor mature projects, demonstrating limited appetite for supporting early-stage technology commercialization. This situation creates a pronounced \u0026ldquo;first-round financing gap.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eSecond, regarding collaborative challenges, the inherent heterogeneity in objectives and institutional logics among participants, combined with the absence of effective governance mechanisms, can contribute to systemic failures within the innovation chain from laboratory to market. As noted by Lucena et al. (2025), when firms engage in technology commercialization, they face complementary challenges between internal R\u0026amp;D activities and external knowledge sourcing. If firms cannot effectively access university R\u0026amp;D through contractual agreements, their capacity to decompose R\u0026amp;D processes to capture returns from innovation is constrained. Bailey et al. (2025) identify divergent pathways for universities and enterprises in commercializing biomedical technologies. While concept validation centers help bridge the \u0026ldquo;valley of death\u0026rdquo; by providing non-dilutive funding and incubation guidance, factors such as the principal investigator\u0026rsquo;s commercialization experience, technology type, and gender can still lead to significant variances in collaboration strategy. Furthermore, Cunningham et al. (2025), using text mining, reveal that university technology transfer research is increasingly focused on spin-offs and quantitative metrics, while topics essential for sustaining institutionalized collaboration\u0026mdash;such as institutional context and the role of technology transfer offices\u0026mdash;are receiving declining attention. A case study in Colombia further illustrates that institutional fragmentation, insufficient R\u0026amp;D investment, and a lack of inter-organizational trust often confine collaborative innovation to ad hoc projects. This hinders the development of an inclusive ecosystem capable of integrating traditional knowledge with modern innovation processes (Valencia-Arias et al., 2025).\u003c/p\u003e\n\u003cp\u003eThird, at the internal governance level, enterprises face structural conflicts between intellectual property (IP) allocation and long-term incentives. Fabiano et al. (2021) found that, under the \u0026ldquo;professor\u0026rsquo;s privilege\u0026rdquo; system in some European contexts, founding scientists often retain patent rights. This makes it difficult for firms to consolidate key IP for subsequent development, creating a dilemma of \u0026ldquo;fragmented property rights.\u0026rdquo; Kim and Koo (2023) further revealed that excessive pay dispersion within top management teams can stifle collaborative innovation, hindering the cross-functional cooperation essential for technology commercialization. Regarding incentive design, Cockburn, Henderson, and Stern (1999) demonstrated through a multi-task principal-agent model that when firms offer strong performance incentives for short-term, applied research alone, scientists may reduce investment in the long-term, curiosity-driven basic research that generates valuable knowledge spillovers, leading to a \u0026ldquo;temporal mismatch\u0026rdquo; in the innovation ecosystem. Governance structure flaws further exacerbate these commercialization barriers. Severe information asymmetry plagues the process. Evidence from the UK Parliament indicates that the absence of credible valuation mechanisms between university technology transfer offices and firms hinders price discovery, leaving many patents in a \u0026ldquo;valuation vacuum\u0026rdquo;\u0026nbsp;(\u0026ldquo;MIP0039 - Evidence on Managing intellectual property and technology transfer\u0026rdquo;,\u0026nbsp;2025). This information problem forms a vicious cycle with weak corporate IP management. Panel data from German firms confirms that manager-owned enterprises show significantly lower efficiency in translating R\u0026amp;D investments into patents, suggesting that insufficient separation of ownership and control leads to less professional IP strategy\u0026nbsp;(Gr\u0026uuml;nebaum,\u0026nbsp;2021). More fundamentally, as assessed by the World Intellectual Property Organization, most companies lack specialized training systems for technology transfer. Consequently, R\u0026amp;D personnel are often neither equipped to protect laboratory outcomes nor capable of identifying market opportunities, resulting in a critical \u0026ldquo;capability gap\u0026rdquo; in commercialization (Lorenz and World Intellectual Property Organization, 2022).\u003c/p\u003e\n\u003cp\u003eThe compound effect of these three challenges concentrates the associated risks disproportionately on the originating firms or individuals, while external parties may capture a significant share of the benefits. This misalignment means that market pricing often fails to reflect the true costs, resulting in a low-level equilibrium trap characterized by high sunk costs, high coordination friction, and high accountability risks. Consequently, this situation severely impedes the sustained and efficient diffusion of new technologies into the market.\u003c/p\u003e\n\u003cp\u003eGVC embodies a hybrid policy instrument that integrates both governmental and market attributes. Its operational mechanism adheres to a dual logic: the administrative logic of public policy, which prioritizes social benefits and strategic planning, and the market logic of capital allocation, which emphasizes risk-return efficiency. These two logics do not merely coexist in tension; rather, within a specific institutional framework, they can synergize, complement each other, and undergo mutual transformation. This hybrid governance principle was formally articulated in China\u0026rsquo;s 2008 Guiding Opinions on the Standardized Establishment and Operation of Venture Capital Guidance Funds, which established the operational model of \u0026ldquo;government guidance with market-driven operation.\u0026rdquo; The guidelines emphasize that both government and private capital must jointly adhere to contractual agreements and market rules. The core intent is to leverage the market\u0026rsquo;s decisive role in resource allocation, utilizing market mechanisms to enhance the efficiency of public goods provision and the quality of public services in domains such as technology commercialization.\u003c/p\u003e\n\u003cp\u003eCompared to conventional venture capital, GVC is characterized by three defining attributes: policy-driven objectives, market-oriented operations, and outcome-enabling mechanisms that empower technology commercialization (Grilli and Murtinu, 2014; Guerini and Quas, 2016). By leveraging public fiscal funds to amplify impact and steer investment direction, GVC effectively bridges the chasm\u0026mdash;often termed the \u0026ldquo;valley of death\u0026rdquo;\u0026mdash;between \u0026ldquo;technology readiness\u0026rdquo; and \u0026ldquo;market readiness,\u0026rdquo; transforming it into what can be conceptualized as a \u0026ldquo;commercialization acceleration zone.\u0026rdquo; It does this by utilizing the patient, long-term nature of state capital to hedge against the prolonged cycles and high uncertainty inherent in frontier technologies. Concurrently, it applies the market\u0026rsquo;s rigorous selection criteria and active post-investment governance to enhance the commercial viability of technology pathways, business models, and entrepreneurial teams (Bertoni et al., 2019b; Munari and Toschi, 2015). By addressing this critical disconnect, GVC not only elevates the efficiency of transforming technology into capital but also improves the capital market\u0026rsquo;s allocation efficiency toward \u0026ldquo;hard tech\u0026rdquo; sectors. Furthermore, the state-affiliated nature of GVC inherently provides portfolio firms with \u0026ldquo;political linkages.\u0026rdquo; These linkages facilitate preferential access to scarce policy resources, such as government procurement programs, key R\u0026amp;D initiatives, demonstration project orders, and interest subsidies for technology financing. By leveraging what can be termed \u0026ldquo;public credit,\u0026rdquo; GVC acts as a credible third-party endorser of a technology\u0026rsquo;s potential. This certification significantly reduces the due diligence costs and trust barriers for potential collaborators across industry, academia, and research institutes, as well as for downstream clients and follow-on private investors. This \u0026ldquo;endorsement effect\u0026rdquo; is instrumental in attracting subsequent rounds of private capital, thereby establishing a relay-style investment pathway that guides ventures from angel investment through venture capital and private equity, and ultimately to industrial capital (Guerini and Quas, 2016; Y. Wang et al., 2017).\u003c/p\u003e\n\u003cp\u003eCompared to direct government subsidies, government-guided venture capital funds delegate micro-level investment decisions to professional market entities. By sharing profit rights and clarifying responsibility boundaries between public and private capital, these funds mitigate excessive administrative intervention while leveraging the market\u0026apos;s informational advantages to improve the accuracy of technology valuation. This establishes a sustainable commercialization mechanism centered on \u0026ldquo;technology value discovery\u0026mdash;risk sharing\u0026mdash;profit distribution.\u0026rdquo; At its core, this mechanism operates as a synergistic dual-engine system: \u0026ldquo;public capital credit enhancement\u0026rdquo; coupled with \u0026ldquo;technology merit screening.\u0026rdquo; The public sector provides patient capital and facilitates resource integration, while market-oriented partners contribute commercialization expertise and governance discipline. The synergy between these elements enables government venture capital to significantly outperform traditional subsidies in terms of the scope, duration, and impact intensity of technology commercialization (Alperovych et al., 2020; Bertoni et al., 2019b), offering full-cycle support for laboratory technologies to successfully navigate the competitive \u0026ldquo;Darwinian sea\u0026rdquo; of the market.\u003c/p\u003e\n\u003cp\u003ePolicy-based finance, functioning as a specialized financial instrument that embodies state priorities, serves as a crucial platform for advancing national innovation strategies. GVC, established through partnerships between fiscal funds and private capital, represents a deep integration of \u0026ldquo;strategic public intent with market-driven execution.\u0026rdquo; Compared to purely private venture capital or direct state investment, it more fully embodies the synergistic principle of \u0026ldquo;an enabling state and an efficient market.\u0026rdquo; This hybrid governance structure enables GVC to significantly accelerate, amplify, and solidify the process of technology commercialization within portfolio firms. Based on the preceding theoretical analysis, the following hypothesis is advanced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1:\u003c/strong\u003e Government venture capital investment positively influences the intensity and success of technology commercialization in investee enterprises.\u003c/p\u003e\n\u003cp\u003eAccording to market failure theory, exclusive reliance on market mechanisms often results in inefficient allocation of resources for innovation, impeding the optimal path for technology commercialization (Arrow, 1972; Nelson, 1959). Frontier technologies, characterized by high investment, long cycles, and positive externalities, often yield private returns that fall below their social returns. This divergence leads to underinvestment by private markets and creates a commercialization vacuum. Consequently, advancing technology commercialization necessitates the coordinated interplay of the market\u0026rsquo;s \u0026ldquo;invisible hand\u0026rdquo; and the government\u0026rsquo;s \u0026ldquo;visible hand.\u0026rdquo; GVC addresses this need by deploying public funds through market-conforming instruments like equity investment. It retains the strategic advantages of policy-driven credibility and resource orchestration while incorporating professional fund management and market-aligned incentive structures. Possessing this hybrid \u0026ldquo;government-market\u0026rdquo; attribute, GVC plays a crucial role in bridging the \u0026ldquo;valley of death\u0026rdquo; in technology transfer. The following section elucidates the underlying mechanisms by examining three distinct pathways through which GVC influences this process and proposes corresponding research hypotheses.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.1. Government venture capital and sustained corporate resource support\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe policy backing of GVC provides firms with two key resources to advance the commercialization of new technologies: capital and talent. As investment capital underpinned by state assets, GVC possesses greater credibility in terms of endorsement and subsequent resource acquisition (Guerini and Quas, 2016). According to signaling theory, its investment acts as an implicit certification, effectively communicating governmental priorities to external stakeholders and creating a positive endorsement effect for corporate financing (Colonnelli et al., 2024). In capital markets, when a GVC institution invests in a technology-based firm, its public credit backing signals to the market that the firm\u0026rsquo;s technology holds industrialization potential. This reduces information asymmetry and attracts follow-on private capital. Consequently, it helps alleviate the early-stage funding shortages of the \u0026ldquo;valley of death,\u0026rdquo; lowers financing costs, and establishes a solid financial foundation for moving technologies from the laboratory to pilot and mass production stages (Brander et al., 2015). Beyond capital, GVC leverages its policy advantages to facilitate talent assembly for portfolio firms through two distinct pathways. First, it utilizes linkages such as \u0026ldquo;government subsidies\u0026mdash;research platforms\u0026mdash;talent programs\u0026rdquo; to help firms recruit high-end R\u0026amp;D personnel at competitive rates (Cadorin et al., 2021). Second, leveraging its public-sector connections, GVC builds bridges between \u0026ldquo;government-industry-academia-research-application\u0026rdquo; entities, supplying firms with scarce talent like postdoctoral researchers and engineers. This creates a synergistic aggregation effect through the organic integration of projects, platforms, and talent. The combined impact of this capital leverage and talent aggregation significantly shortens the cycle from patent to prototype to scaled production.\u003c/p\u003e\n\u003cp\u003eThe market-oriented operation of GVC delivers dual reinforcement through coordinated capital and talent support, distinct from traditional fiscal subsidies. GVC allocates funds through market-based mechanisms that emphasize risk-sharing, profit-sharing, and professional governance. By adopting a public-private partnership model that pools fiscal capital with private limited partners (LPs), GVC directly amplifies capital supply via equity investments. For instance, Shandong Province\u0026apos;s New and Old Growth Drivers Transformation Fund leveraged an initial fiscal allocation of 4.77 billion yuan to attract a total investment of 355.8 billion yuan, with approximately 70% directed toward major industrial technology breakthroughs and commercialization projects. Regarding talent, market-oriented general partners (GPs) establish dedicated post-investment talent networks for their portfolio companies. By leveraging their industry connections, they bridge enterprises with critical human resources such as technical advisors, industrial engineers, and sales experts. This creates a value-added service model often described as \u0026ldquo;investing with a team\u0026rdquo; (Cadorin et al., 2021). Furthermore, tolerance mechanisms and structured profit-sharing arrangements enhance the appeal to private capital and high-end talent. By sharing a portion of investment returns with market participants and maintaining high failure-tolerance thresholds, these mechanisms encourage research teams to pursue high-risk technology commercialization while incentivizing private capital to co-invest (Cumming et al., 2017). The simultaneous injection of capital and talent enables enterprises to accomplish parallel critical tasks\u0026mdash;such as pilot line construction and core team formation\u0026mdash;in the early stages, thereby significantly increasing the success rate of technology commercialization.\u003c/p\u003e\n\u003cp\u003eIn summary, through two core mechanisms\u0026mdash;policy-backed credit endorsement and market-driven leverage amplification and talent networking\u0026mdash;GVC provides enterprises with a sustainable, scalable, and actionable resource package for technology commercialization, combining critical financial and human capital.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2:\u003c/strong\u003e The hybrid, policy-and-market nature of government venture capital positively enhances the resource endowment of portfolio firms in terms of financial and human capital, which in turn facilitates technology commercialization.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.2. Triple network advantages of government venture capital and policy support, on-chain enterprise collaboration, and internal corporate governance\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe policy backing of GVC can unlock network advantages at macro, meso, and micro levels, systematically enhancing the efficiency of technology commercialization. At the macro level, GVC\u0026rsquo;s entry itself serves as a strong signal of governmental confidence in a firm\u0026rsquo;s technological trajectory and growth potential (Guerini and Quas, 2016), triggering coordination within the public policy ecosystem. Research suggests GVC can orchestrate multi-dimensional policy resources\u0026mdash;including fiscal funds, tax incentives, subsidized loans, land allocations, application scenarios, talent programs, and failure-tolerance mechanisms\u0026mdash;forming an institutional support system covering the entire chain from proof-of-concept to pilot-scale and mass production. This system helps mitigate the \u0026ldquo;valley of death\u0026rdquo; risks in technology commercialization (Ellwood et al., 2022; Son et al., 2022). At the meso (industry) level, acting as an anchor investor, GVC injects public credibility into industrial chains. Through equity investments, it integrates universities, research institutes, upstream and downstream firms, and financial institutions into a collaborative framework. This reduces information asymmetry and redundant negotiation costs, thereby enhancing industrial chain synergy. At the micro (firm) level, government investors, often through board seats, special voting rights, or governance clauses, embed themselves in corporate oversight. This strengthens external monitoring, curbs managerial short-termism, and can introduce independent directors or specialized committees to optimize governance. Consequently, it improves R\u0026amp;D transparency and accelerates the execution of technology transfer. The interplay of policy orchestration, inter-firm coordination, and governance optimization collectively forms the systemic network advantage through which GVC empowers technology commercialization.\u003c/p\u003e\n\u003cp\u003eThe market-oriented operation of GVC significantly amplifies its network advantages by effectively bridging public policy objectives with market discipline. First, through competitive selection processes and capital raising from a diverse pool of investors, professional GPs can transform the GVC\u0026rsquo;s credit into a powerful composite signal that blends public credibility with market validation. This attracts a broader network of private capital, industrial partners, and technical talent, creating a robust ecosystem of \u0026ldquo;capital, technology, and application scenarios,\u0026rdquo; thereby enhancing the leverage of public policy resources. Second, leveraging market-based pricing and evaluation mechanisms, GPs identify and focus on high-potential technology sectors. They utilize industrial mapping and active post-investment management to strategically align stakeholders along the value chain, matching technology maturity with commercial readiness. This precise orchestration fosters efficient linkages between R\u0026amp;D entities, manufacturers, and end-users, which shortens the overall cycle of technology transfer. Third, market-oriented GPs continuously provide value-added support to their portfolio companies. Through mechanisms such as equity incentives, co-investment arrangements, and board representation, they deploy industry experts, technical advisors, and professional managers. This deep engagement strengthens corporate governance and enhances the firm\u0026rsquo;s execution capabilities in research, development, and commercialization. In sum, while the public sector provides the foundational institutional network, the market mechanism effectively scales and enhances this network with diverse resources. This synergy enables GVC to cultivate sustainable and scalable network advantages that are critical for successful technology commercialization.\u003c/p\u003e\n\u003cp\u003eBased on the above analysis, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3:\u003c/strong\u003e Government venture capital derives its unique strength from dual policy and market advantages, which enable invested firms to develop triple network advantages\u0026mdash;in policy support, inter-firm collaboration, and internal corporate governance. This configuration ultimately promotes technology commercialization.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.3. The Dual Nature of Government Venture Capital: Benefit Distribution and Risk Sharing\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe policy advantages of government venture capital can promote shared benefits and risk-sharing. By embedding itself as \u0026ldquo;public subordinated capital\u0026rdquo; within the technology transfer chain, the government transforms the public nature of fiscal funds into risk mitigation tools through institutional arrangements. Specifically, the \u0026ldquo;government-first-loss\u0026rdquo; clause established in Article 18 of the Interim Measures for the Management of Venture Capital Sub-funds Established by the National Science and Technology Achievement Transformation Guidance Fund clearly defines the subordinate position of government capital during sub-fund liquidation. This provides a credible \u0026ldquo;safety cushion\u0026rdquo; for researchers and early-stage social capital, significantly reducing the wait-and-see attitude caused by concerns over \u0026ldquo;state-owned asset loss.\u0026rdquo; On the revenue side, the government adopts a contractual structure of \u0026ldquo;principal + low fixed returns, with excess returns transferred,\u0026rdquo; shifting its own revenue function from \u0026ldquo;profit maximization\u0026rdquo; to \u0026ldquo;maximizing positive externalities.\u0026rdquo; This approach retains the high-energy incentives of the private sector while avoiding the dampening effect on innovation investment caused by excessive revenue fragmentation (Brander et al., 2015). Furthermore, cities like Wuhan have leveraged social capital exceeding five times the initial investment into angel-stage ventures through concessionary designs\u0026mdash;such as charging only bank deposit interest or recovering only principal. This approach achieves leveraged benefit sharing and risk pooling, where \u0026ldquo;small fiscal investments\u0026rdquo; mobilize \u0026ldquo;large social capital.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe policy advantages of GVC enable shared benefits and risk sharing. Acting as \u0026ldquo;public subordinated capital\u0026rdquo; within the technology commercialization chain, the government transforms fiscal funds\u0026rsquo; public nature into risk-mitigation tools via institutional arrangements. A key embodiment is the \u0026ldquo;government-first-loss\u0026rdquo; clause (stipulated in Article 18 of the Interim Measures for Venture Capital Sub-funds of the National Science and Technology Achievement Transformation Guidance Fund), which explicitly defines the subordinate position of government capital upon sub-fund liquidation. This clause creates a credible \u0026ldquo;safety cushion\u0026rdquo; for researchers and early-stage private investors, thereby significantly reducing wait-and-see attitudes driven by fears of \u0026ldquo;state-owned asset loss.\u0026rdquo; Regarding returns, the government employs a contractual structure of \u0026ldquo;principal plus low fixed returns, with excess returns transferred,\u0026rdquo; reframing its revenue function from \u0026ldquo;profit maximization\u0026rdquo; to \u0026ldquo;maximizing positive externalities.\u0026rdquo; This structure retains private-sector-style high-powered incentives while circumventing the discouragement of innovation investment that results from excessive revenue fragmentation (Brander et al., 2015). In practice, cities such as Wuhan have leveraged concessionary designs\u0026mdash;like charging only bank deposit interest or recovering solely the principal\u0026mdash;to attract private capital exceeding five times the initial public investment into angel-stage projects. This demonstrates a model of leveraged benefit sharing and risk pooling, wherein \u0026ldquo;small fiscal investments\u0026rdquo; galvanize \u0026ldquo;large private capital.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe market-oriented operations of GVC facilitate benefit and risk sharing among portfolio firms. Within a \u0026ldquo;fund of funds plus sub-funds\u0026rdquo; governance structure, government capital operates as a limited partner, delegating project screening, valuation, and post-investment management to professional GPs. This arrangement channels capital toward technological ventures with the highest marginal commercialization efficiency through competitive market mechanisms (Alperovych et al., 2020). Pilot programs in regions like Chengdu and Tianjin exemplify a \u0026ldquo;funding-first, equity-later\u0026rdquo; model. Fiscal science and technology funds are initially disbursed as grants and subsequently convert to equity at market valuation upon the achievement of predefined milestones, with exits governed by the principle of \u0026ldquo;appropriate return.\u0026rdquo; For liquidity, a multi-tiered exit matrix\u0026mdash;encompassing public markets (e.g., STAR Market, Beijing Stock Exchange), regional equity market innovation boards, and government repurchase protocols\u0026mdash;mitigates the exit risk premium demanded by private capital. Concurrently, entrepreneurial teams capture liquidity premiums to share in higher valuation gains. Thus, market mechanisms orchestrate a dual redistribution of benefits and risks.\u003c/p\u003e\n\u003cp\u003eBased on the above analysis, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4:\u003c/strong\u003e Government venture capital leverages dual policy and market advantages to provide portfolio firms with access to profit sharing and risk sharing, thereby facilitating technology commercialization.\u003c/p\u003e"},{"header":"3. Research Design","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Samples and Data\u003c/h2\u003e\u003cp\u003eThe study sample consists of A-share listed companies from 2012 to 2023. The sample selection followed these criteria: (1) excluding ST firms; (2) removing financial institutions; (3) omitting observations with missing data; and (4) winsorizing all continuous variables at the 1st and 99th percentiles to mitigate the influence of extreme values. This procedure resulted in a final unbalanced panel of 37,588 firm-year observations. All data were obtained from the CSMAR and CV Source databases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Variable Measurement\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Technology Commercialization\u003c/h2\u003e\u003cp\u003eBuilding on the work of Jaffe et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and Maggioni and Uberti (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), we use patent citation frequency as our core measure. Specifically, the frequency with which a technology\u0026rsquo;s patent is cited by subsequent patents reflects its foundational influence on later research or applications. This influence captures the absorption and, ultimately, the technology commercialization that occurs as knowledge diffuses through subsequent innovations (Hung and Wang, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, patent citations signal not only a technology\u0026rsquo;s scientific value but also the market\u0026rsquo;s recognition of its practical utility and commercial potential. To address the typical right-skewed distribution of citation counts and mitigate the undue influence of outliers in our regression analyses, we apply a natural logarithmic transformation to the yearly count of citations received by a firm\u0026rsquo;s patents.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Government Venture Capital\u003c/h2\u003e\u003cp\u003eFollowing the definition of GVC outlined by Chen et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we first extracted approximately 230,000 corporate investment events spanning 2012\u0026ndash;2023 from the CV Source database. Specific details on government-guided funds were then sourced from the Wind database. We manually reviewed these corporate investors and verified their status against the fund records: a firm was classified as having received GVC if an investor in a given year was a government-guided fund or if the firm had a state-owned capital background. The GVC investment variable is coded as 1 for the year of initial investment and all subsequent years, and 0 otherwise.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3. Control Variables\u003c/h2\u003e\u003cp\u003eTo address potential confounding effects from firm heterogeneity, we include a comprehensive set of firm-level control variables in our regression models. The definitions of all variables are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Furthermore, to reduce the influence of extreme values, all continuous variables were trimmed at the 1st and 99th percentiles.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Definitions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariable Symbol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariable Definition\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnology Commercialization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe number of invention patents granted to the company in a specific year that were transferred, expressed as a logarithm.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGovernment Venture Capital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDummy variable: 1 if the company received government venture capital in a given year, 0 otherwise.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eControl variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReturn on Equity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eROE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet Profit / Average Net Assets \u0026times; 100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInventory Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet Inventory / Total Assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed Assets Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet Fixed Assets / Total Assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIs there a loss?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLoss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNet profit for the year\u0026thinsp;\u0026lt;\u0026thinsp;0 takes 1, otherwise takes 0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of Independent Directors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of Independent Directors/Directors\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTop Ten Shareholders' Shareholding Ratios\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTop10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTop 10 Shareholders' Equity / Total Equity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManagement Expense Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdministrative Expenses / Operating Revenue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMajor Shareholder Fund Misappropriation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMfund\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOther Receivables/Total Assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCapital Intensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Assets / Operating Revenue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYears in Operation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eln(Current Year - Year of Company Establishment\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e------------------------------------------------\u003c/p\u003e\u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here\u003c/p\u003e\u003cp\u003e------------------------------------------------\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Model Construction\u003c/h2\u003e\u003cp\u003eThis paper constructs the following benchmark regression model:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{TC}_{i,t}={\\beta\\:}_{0}+{\\beta\\:}_{1}{GVC}_{i,t}+{\\beta\\:}_{3}Controls+\\sum\\:firm+\\sum\\:year+\\sum\\:industry+{\\epsilon\\:}_{i,t}\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the model, TC\u003csub\u003ei,t\u003c/sub\u003e measures the level of technology commercialization for firm i in year t; GVC\u003csub\u003ei,t\u003c/sub\u003e indicates whether firm i received government venture capital investment in year t; Controls denotes a vector of firm-level control variables.; We include firm (\u0026sum;Firm), year (\u0026sum;Year), and industry (\u0026sum;Industry) fixed effects to account for time-invariant unobserved heterogeneity, common macroeconomic shocks, and industry-specific factors, respectively. ε\u003csub\u003ei,t\u003c/sub\u003e is the idiosyncratic error term. The coefficient β\u003csub\u003e1\u003c/sub\u003e captures the net effect of government venture capital on our key dependent variable, technology commercialization.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Empirical Findings and Analysis","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Descriptive Statistics\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reports the descriptive statistics for the key variables. GVC is a dummy variable with a mean of 0.094, implying that GVC investment was present in approximately 9.4% of the 37,588 firm-year observations. Our measure for technology commercialization is the natural logarithm of one plus the number of patent transfers. This variable has a mean of 0.525 and a standard deviation of 1.049. Its values range from 0 to 8.391, indicating considerable heterogeneity in patent-based commercialization activities across the firms in our sample.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVarName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTop10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMfund\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. Benchmark Regression\u003c/h2\u003e\n \u003cp\u003eColumn (1) of Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e displays the baseline results from a univariate regression. Column (2) introduces the set of firm-level control variables. Column (3) further augments the specification by including firm, year, and industry fixed effects. Across all specifications, the coefficient on the key explanatory variable, GVC, is positive and statistically significant. These regression results suggest that government venture capital investment has a positive effect on technology commercialization, thus providing support for Hypothesis 1.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBenchmark Regression Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.238***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(12.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(12.500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.442)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.522)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.961)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.210***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-4.526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.310)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.269***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-7.351)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.787)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.439)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTop10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.205***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-5.574)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.593)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.466)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.166)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMfund\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.585**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.177***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.047***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-17.392)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.910***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.276***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(88.591)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-7.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStkcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eadj. R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003et statistics in parentheses, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e-----------------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Heterogeneity Test\u003c/h2\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.3.1. Heterogeneity Analysis: The Role of Financing Constraints\u003c/h2\u003e\n \u003cp\u003eFirms face varying degrees of financing constraints due to differences in internal capital reserves and external financing capacity. These differences may affect their sensitivity to GVC and their efficiency in utilizing such resources, thereby influencing the effectiveness of technology commercialization. To examine this heterogeneity, we follow prior literature and employ the SA index to measure firm-level financing constraints. Firms are classified into high- and low-constraint groups based on the annual industry median of the SA index. Specifically, for each industry-year cohort, we calculate the median SA index. A firm is assigned to the high financing constraints group if its SA index is above the industry-year median; otherwise, it is classified into the low financing constraints group. The subgroup regression results are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. In Column (1) for the low-constraint group, the coefficient on GVC is 0.059, which is positive and statistically significant at the 5% level. In contrast, the coefficient for the high-constraint group in Column (2) is 0.030 and statistically insignificant. This pattern suggests that the positive effect of GVC on technology commercialization is primarily driven by firms facing lower financing constraints, with no discernible average effect observed among firms with higher constraints. A plausible explanation for this finding lies in firm-level absorptive capacity and operational maturity. Firms with lower financing constraints typically possess more robust management systems, stronger technological assimilation capabilities, and more established market channels. These attributes likely enable them to utilize GVC more effectively\u0026mdash;leveraging not only the financial capital but also the policy resources and certification benefits associated with government funding\u0026mdash;to translate technological assets into market value. Conversely, firms with higher financing constraints may be hampered by more fundamental operational inefficiencies, weaker technological foundations, or lower market adaptability. For these firms, the marginal benefit of government capital may be attenuated, resulting in an insignificant average treatment effect.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ch2\u003e4.3.2. Heterogeneity Analysis: The Role of Ownership Type\u003c/h2\u003e\n \u003cp\u003eDifferences in ownership structure give rise to variations in governance models, incentive mechanisms, and institutional linkages. These factors likely influence how firms respond to GVC and allocate resources, which may subsequently affect the efficiency of technology commercialization. To investigate this, we segment our sample into three ownership categories: private enterprises, state-owned enterprises (SOEs), and other enterprises (e.g., foreign-invested and collective firms). The subgroup regression results are presented in Table\u0026nbsp;4. For private enterprises in Column (3), the coefficient on GVC is 0.105, which is positive and statistically significant at the 1% level. In contrast, the coefficient for SOEs in Column (4) is 0.037 and statistically insignificant. The coefficient for other enterprises in Column (5) is 0.033, significant at the 10% level. These results suggest that the positive effect of GVC on technology commercialization is most pronounced for private enterprises, present but weaker for other enterprises, and indistinguishable from zero for SOEs. This pattern can be interpreted through the lens of institutional theory and managerial incentives. Private enterprises typically operate with greater flexibility, face harder budget constraints, and are driven by strong market-oriented incentives for innovation. This combination may enable them to utilize GVC funding and associated policy resources more effectively to generate tangible commercial outputs. Other enterprises may also realize some efficiency gains, potentially due to policy alignment. For SOEs, however, agency problems and softer budget constraints\u0026mdash;stemming from their inherent institutional design\u0026mdash;may dampen innovation incentives. Consequently, government investment in SOEs might act more as a substitute for, rather than a complement to, their own innovation efforts, leading to a marginal and statistically insignificant effect.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e4.3.3. Heterogeneity Analysis: The Role of Industry Characteristics\u003c/h2\u003e\n \u003cp\u003eThe effect of GVC is likely moderated by industry context. Differences in factor intensity, innovation patterns, and capital requirements across industries may systematically influence how firms absorb external funding and translate it into commercial outcomes. To test for this heterogeneity, we categorize industries into three types based on the CSRC 2012 classification: technology-intensive, asset-intensive, and labor-intensive. The estimation results by industry group are presented in Table\u0026nbsp;4. The coefficient on GVC is 0.049 (significant at the 5% level) for labor-intensive industries [Column (8)], and 0.064 (significant at the 10% level) for asset-intensive industries [Column (6)]. In contrast, the coefficient for technology-intensive industries [Column (7)] is 0.035 and statistically indistinguishable from zero. The results indicate that GVC has a discernible positive effect on technology commercialization in asset-intensive and labor-intensive industries, but not in technology-intensive ones. This pattern can be understood through the lens of resource complementarity and need. Asset-intensive industries require substantial capital for equipment and technological upgrades; GVC directly alleviates these financing constraints, enabling critical investments. In labor-intensive industries, GVC may foster commercialization not primarily through R\u0026amp;D funding, but by providing capital and managerial expertise that improve production efficiency and process standardization, thereby facilitating market adoption of incremental innovations. Conversely, the null effect in technology-intensive sectors may stem from two factors. First, firms in these industries often possess strong internal R\u0026amp;D capabilities and alternative funding sources, reducing the marginal value of GVC. Second, the inherently long cycles and high risks of breakthrough innovation in these sectors may dilute the measurable short- to medium-term impact of equity investment, making it difficult for GVC to yield statistically significant commercial outcomes within our observation window.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHeterogeneity Test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFinancing constraints\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eType of enterprise\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDistinguish industries\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eState-Owned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLabor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.445)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.469)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.650)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.649)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.780)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e_cons\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.278***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.423***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.577***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.936***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.550***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.448***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.597***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-5.180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-5.743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.919)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-5.593)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-5.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.802)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eYear FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStkcd FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIndustry FE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAdj R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003et statistics in parentheses, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table\u0026nbsp;4 about here\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4. Endogeneity Test\u003c/h2\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.1. Addressing Endogeneity: An Exogenous Policy Shock\u003c/h2\u003e\n \u003cp\u003eTo strengthen causal inference and address potential endogeneity concerns such as sample selection bias and omitted variables, we leverage the exogenous shock of the \u0026ldquo;National Industry-Finance Cooperation Pilot Cities\u0026rdquo; policy. This policy, designed to deepen integration between industry and finance and to enhance financial support for the real economy, shares conceptual overlap with the objectives of GVC. To isolate the effect of GVC from this concurrent policy, we employ a Propensity Score Matching-Difference-in-Differences (PSM-DID) design. The logic of this test is as follows: if the observed improvement in firms\u0026rsquo; technology commercialization is primarily driven by the broader industry-finance policy rather than by GVC itself, then controlling for this policy shock should materially attenuate the coefficient on GVC in our baseline model. Our multi-period DID specification allows us to test this directly.\u003c/p\u003e\n \u003cp\u003ea. Parallel Trend Test\u003c/p\u003e\n \u003cp\u003eThe validity of the difference-in-differences (DID) estimator relies on the parallel trends assumption. We test this assumption by examining the dynamic treatment effects around the policy implementation year (period 0). Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e plots the estimated coefficients for the periods preceding (t = -12 to -1) and following (t\u0026thinsp;=\u0026thinsp;0 to 6) the policy shock. As shown, the estimated coefficients for all pre-treatment periods fluctuate around zero and are statistically insignificant, with their confidence intervals consistently encompassing zero. This pattern indicates that the treatment group (firms in pilot cities) and the control group (firms in non-pilot cities) followed parallel trends in the outcome variable prior to the policy intervention, thus satisfying a core prerequisite for our DID analysis.\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003eb. Multitemporal DID regression results\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThis study constructed the following multi-period DID model:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:TC={\\beta\\:}_{0}+{\\beta\\:}_{1}DID+\\sum\\:\\gamma\\:Controls+\\mu\\:+\\lambda\\:+\\epsilon\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eAmong these, DID serves as the core explanatory variable, representing the interaction term between the treatment group (enterprises in pilot cities) and the post-policy implementation period. The regression results in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e show that the coefficient for the core variable DID is -0.016, with a t-value of 1.917, which is less than 1.96. This result suggests that the \u0026ldquo;National Industry-Finance Cooperation Pilot Cities\u0026rdquo; policy itself did not exert a statistically significant direct effect on firms\u0026rsquo; technology commercialization. Consequently, the positive relationship between GVC and technology commercialization is unlikely to be driven by this concurrent policy shock. The findings from our baseline regression thus remain robust, and potential endogeneity bias from omitting this policy variable is substantially mitigated.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDID Regression Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.381)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.362)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTop10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.312)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMfund\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.029***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.505)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.903***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.267)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.245***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-7.381)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStkcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eadj. R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003et statistics in parentheses, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.2. Placebo Test\u003c/h2\u003e\n \u003cp\u003eTo assess whether our baseline results might be driven by random chance rather than a true causal relationship, we conduct a placebo test by randomly reassigning the treatment status (GVC investment) across firms within our sample period. We repeat this random reassignment and re-estimate our baseline model 500 times, storing the estimated coefficient, t-statistic, and p-value from each iteration. The distribution of the placebo coefficients is plotted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The \u0026ldquo;true\u0026rdquo; estimated coefficient from our main analysis lies far in the right tail of this distribution, which is tightly clustered around zero. Correspondingly, Fig. 3 shows that the vast majority of the placebo tests yield statistically insignificant results (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10). These findings confirm that the significant positive effect of GVC on technology commercialization identified in our benchmark regression is unlikely to be spurious and is robust to this falsification test.\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and 3 about here\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.3. Double Machine Learning for Causal Inference\u003c/h2\u003e\n \u003cp\u003eTo empirically assess the causal effect of GVC on technology commercialization, this study employs the Double Machine Learning (DML) framework proposed by Chernozhukov et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). We adopt this advanced method to address two critical challenges faced by traditional causal inference approaches in this context. First, the relationship is likely confounded by a high-dimensional set of control variables, including multifaceted firm characteristics and industry attributes. Standard parametric models often struggle to capture the complex, potentially nonlinear relationships within such data. Second, threats to identification persist from potential endogeneity biases, such as omitted variables or measurement error. The DML framework is particularly suited to this setting, as it is designed to provide consistent estimates of causal effects even when using flexible, non-parametric machine learning models to control for high-dimensional confounders. DML achieves \u0026ldquo;double robustness,\u0026rdquo; meaning it yields consistent causal estimates even if one of the underlying predictive models is slightly misspecified. The core idea is to separate the estimation of the treatment effect from the modeling of complex confounding relationships. We implement a partially linear model within the DML framework. To effectively handle high-dimensional data and automatically model nonlinearities and interaction effects, we utilize a stacking ensemble learner as the base predictor, constructed via Python\u0026rsquo;s pystacked library. Random Forest is specified as one of the base learners within the ensemble due to its proven efficacy in such tasks. The model employs 5-fold cross-validation (k\u0026thinsp;=\u0026thinsp;5) to prevent overfitting and ensure the robustness of the predictions. The DML estimation results provide strong evidence for a positive causal effect. The estimated coefficient for GVC on technology commercialization is 0.188, with a standard error of 0.017. This effect is highly statistically significant at the 1% level (t\u0026thinsp;=\u0026thinsp;10.939, z\u0026thinsp;=\u0026thinsp;10.940, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The 95% confidence interval, [0.154, 0.221], excludes zero, confirming the statistical robustness of the finding. The model, estimated using 37,588 observations, demonstrates good fit, with an estimated constant term of -0.060 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).In summary, the application of the double machine learning framework offers a rigorous approach to causal identification in a high-dimensional setting. The results clearly demonstrate that government venture capital exerts a significant and positive causal effect on firms\u0026rsquo; technology commercialization activities. This finding robustly supports the core theoretical proposition that GVC promotes enterprise-level technology commercialization.\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Dual Machine Learning Tests\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. err.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[95% conf. interval]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.154, 0.221]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.070, -0.050]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table 6 about here\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.4. Addressing Potential Omitted Variable Bias\u003c/h2\u003e\n \u003cp\u003eTo further address concerns regarding endogeneity from omitted variables, we augment our baseline model with a series of macro- and firm-level controls. Specifically, we incorporate the regional \u0026ldquo;intellectual property protection intensity (IPPI)\u0026rdquo; and the \u0026ldquo;number of regional universities (RU)\u0026rdquo; to account for institutional quality and knowledge spillovers. At the firm level, we control for the \u0026ldquo;proportion of R\u0026amp;D personnel (R\u0026amp;D)\u0026rdquo; and the \u0026ldquo;digital transformation level (DT).\u0026rdquo; The results of this expanded specification are reported in Column (2) of Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.All four additional controls show a statistically significant positive association with technology commercialization. After their inclusion, the coefficient on our core explanatory variable, GVC, decreases modestly from 0.054 to 0.044 but remains statistically significant at the 5% level. This result demonstrates that the positive effect of government venture capital on technology commercialization is robust to controlling for these dimensions of institutional context, human capital, and firm capabilities, suggesting that omitted variable bias does not substantially threaten our main finding.\u003c/p\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression Results for Omitted Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.442)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.521)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.961)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.310)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.484)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.787)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.378)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.168)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTop10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.593)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.918)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMfund\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.177***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.592***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.579)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.655)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.910***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.498)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.894)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.376)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.899*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.720)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u0026amp;D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.671)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.276***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.201***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-7.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-6.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStkcd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003et statistics in parentheses, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e about here\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5. Robustness Test\u003c/h2\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003e4.5.1. Robustness to Regional Fixed Effects\u003c/h2\u003e\n \u003cp\u003eSystematic variation across cities\u0026mdash;such as differences in resource endowments, industrial bases, and local policy support (e.g., municipal industrial funds or talent programs)\u0026mdash;could directly influence firms\u0026rsquo; innovative outcomes. If unaccounted for, this cross-sectional heterogeneity might introduce omitted variable bias. To address this concern, we augment our baseline model by incorporating city-level fixed effects. The regression results including these regional controls are presented in Column (1) of Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. The coefficient on GVC is 0.055 and remains highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding indicates that the positive effect of GVC on firm-level technology commercialization is robust to controlling for all time-invariant heterogeneity at the city level, strengthening confidence that our core results are not driven by latent regional factors.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ch2\u003e4.5.2. Robustness to Additional Firm-Level Controls\u003c/h2\u003e\n \u003cp\u003eA firm\u0026rsquo;s capacity for technology commercialization may be influenced not only by GVC but also by its internal financial health and operational capabilities. Omitting these factors could lead to bias in estimating the GVC effect. To more comprehensively account for such potential confounders, we expand our baseline model by incorporating a set of supplementary firm-level variables: leverage ratio (lev), controlling for financial constraints and risk posture; asset turnover ratio (ato), capturing operational efficiency; cash flow from operations (cashflow), accounting for internal liquidity available for innovation; and sales growth rate (growth), reflecting market expansion and growth momentum. The results from this augmented specification are presented in Column (2) of Table\u0026nbsp;8. The coefficient on GVC is 0.055 and remains statistically significant at the 1% level, virtually unchanged from the baseline estimate. This finding indicates that the positive effect of GVC on technology commercialization is robust to controlling for these additional dimensions of firm financial structure, operational efficiency, liquidity, and growth potential, further strengthening the credibility of our core results.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e4.5.3. Robustness to Alternative Clustering Levels\u003c/h2\u003e\n \u003cp\u003eIn panel data regressions, the disturbance terms may exhibit within-group correlation. An inappropriate choice of clustering for standard errors can lead to biased inference. To ensure our core findings are not sensitive to this choice, we re-estimate our model using different clustering levels to verify the robustness of the GVC coefficient. While maintaining firm, year, and industry fixed effects, we first cluster standard errors at the industry level. We then employ two-way clustering at the province-by-industry level to account for potential multi-dimensional heteroskedasticity and autocorrelation. The results under these alternative specifications are presented in Columns (3) and (4) of Table\u0026nbsp;8. In both cases, the coefficient on GVC is 0.054 and remains highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The magnitude and statistical significance of this coefficient are virtually identical to the baseline estimates. This consistency demonstrates that the positive effect of GVC on technology commercialization is robust and our inference is not materially affected by the choice of clustering level, further supporting the statistical reliability of our main conclusion.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ch2\u003e4.5.4. Robustness to Excluding Central State-Owned Enterprises\u003c/h2\u003e\n \u003cp\u003eThe innovation behavior of central state-owned enterprises (SOEs) may systematically differ from that of other firms due to their distinct access to policy support, financing, and administrative resources. To ensure our estimates of the GVC effect are not driven by these unique entities and to enhance the generalizability of our findings, we exclude all central SOEs from the sample and re-estimate our model. The results from this restricted sample are presented in Column (5) of Table\u0026nbsp;8. The coefficient on GVC is 0.047 and remains positive and statistically significant at the 1% level. While the point estimate is slightly attenuated compared to the baseline, its sign and high significance are unchanged. This indicates that GVC exerts a robust positive effect on technology commercialization even within a more general population of firms that excludes centrally administered SOEs. The finding further supports the reliability of our core conclusion, confirming that the role of GVC is not attributable solely to a subset of enterprises with privileged institutional standing.\u003c/p\u003e\n \u003c/div\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRobustness Test Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.520)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.814)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.287***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.126***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.276***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.274***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.174***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-7.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-7.427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-6.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-9.797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-7.441)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyear FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStkcd FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eindustry FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecity FE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdj R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003et statistics in parentheses, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003cp\u003eInsert Table\u0026nbsp;8 about here\u003c/p\u003e\n \u003cp\u003e------------------------------------------------\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Mechanism Analysis","content":"\u003cp\u003eOur theoretical framework posits that GVC facilitates firm-level technology commercialization through three core mechanisms: (1) resource provision, (2) network advantages, and (3) structured profit-sharing and risk-bearing. The resource provision mechanism operates through capital leverage and talent aggregation. The network advantage mechanism functions via policy signaling, supply-chain synergies, and governance optimization. Finally, the structured profit-sharing and risk-bearing mechanism is enacted through explicit contractual arrangements that share downside risk and allocate upside returns. Conceptually, GVC acts as a form of \u0026ldquo;public subordinated capital.\u0026rdquo; Its structural terms\u0026mdash;often prioritizing loss absorption before claiming profits\u0026mdash;can transform high-risk, long-cycle technological projects, which might otherwise remain shelved by private markets, into viable, long-term collaborations that align incentives among multiple stakeholders.\u003c/p\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e5.1. The Resource Provision Mechanism\u003c/h2\u003e\u003cp\u003eOur theoretical analysis posits that GVC supports technology commercialization by providing two key resources: financial capital and human talent. This dual provision alleviates critical financing constraints and human capital bottlenecks in the commercialization process. Financially, GVC not only directly addresses firms\u0026rsquo; liquidity shortfalls but also, through a positive signaling effect, helps attract follow-on private investment, thereby improving the overall financing environment. In parallel, the official endorsement, policy support, and resource empowerment associated with GVC enhance a firm\u0026rsquo;s ability to attract and retain high-skilled talent, laying the necessary human capital foundation for commercialization. To empirically test these mechanisms, we estimate mediation models for both the financial and human capital pathways. For the financial support mechanism, we use the SA index (Hadlock and Pierce, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) as a proxy for financing constraints, where a lower value indicates fewer constraints, capturing the capital leverage effect of GVC. For the human capital aggregation mechanism, we employ the natural logarithm of one plus the number of employees holding a master\u0026rsquo;s degree or higher, measuring the stock of high-level human capital to capture GVC\u0026rsquo;s role in talent attraction and retention. The results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, support the dual-channel resource provision mechanism. Column (1) shows a significant negative coefficient on GVC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that GVC effectively alleviates financing constraints. Column (2) shows a significant positive coefficient on GVC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), demonstrating that GVC promotes the aggregation of high-skilled talent. Column (3) presents the full model. The coefficient on GVC remains positive and significant. Both the SA index and the talent variable are also positive and significant. These findings confirm that both the alleviation of financing constraints and the aggregation of high-level human capital serve as significant mediating channels through which GVC promotes technology commercialization. Thus, the resource provision mechanism is empirically validated, providing support for Hypothesis 2.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResource Support Mechanism\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinancing Constraints\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHuman Capital\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.069***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.937)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.931)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.587)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancing Constraints\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.990***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(11.534)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman Capital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.012***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.349)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.848***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.533***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-208.935)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.296)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.514)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj. R\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003et statistics in parentheses, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e------------------------------------------------\u003c/p\u003e\u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e about here\u003c/p\u003e\u003cp\u003e------------------------------------------------\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Network Advantages\u003c/h2\u003e\u003cp\u003eTheoretical analysis posits that GVC fosters technology commercialization by building multi-level network advantages, which operate through three distinct channels: (1) macro-level policy resource allocation, (2) meso-level industrial chain synergy enhancement, and (3) micro-level corporate governance optimization. To empirically test this theoretical mechanism (H3), we construct a multiple mediation model using variables for policy support, intra-chain collaboration, and internal governance as parallel mediators. The policy support mechanism is measured by a dummy variable indicating whether a firm receives explicit policy support from the central government. This variable captures the institutional certification and resource allocation effects conferred by GVC. The intra-chain enterprise collaboration mechanism is proxied by the natural logarithm of the number of R\u0026amp;D alliances a firm participates in plus one [ln (number of R\u0026amp;D alliances\u0026thinsp;+\u0026thinsp;1)]. This metric reflects the firm\u0026rsquo;s embeddedness in collaborative innovation networks and its capacity for resource integration. For the internal governance mechanism, we employ the proportion of institutional investor shareholding as a proxy variable. Existing research (Chung and Zhang \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) indicates that this metric effectively represents corporate governance quality and the intensity of external oversight. The regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Column (1) shows that the coefficient on GVC is significantly positive at the 1% level, indicating that GVC significantly increases the probability of a firm obtaining central policy support. Column (2) shows that the coefficient on GVC is significantly positive at the 1% level, suggesting that GVC effectively promotes corporate participation in R\u0026amp;D alliances and enhances coordination capabilities within industrial chains. Column (3) shows that the coefficient on GVC is significantly positive at the 1% level, demonstrating that GVC helps increase the proportion of institutional investor holdings, thereby improving the level of corporate internal governance. Column (4) presents the results of the full model. All three mediating variables\u0026mdash;policy support, intra-chain collaboration, and institutional ownership\u0026mdash;exhibit a significant positive effect on technology commercialization at the 1% level, while the direct effect of GVC also remains significant. These results indicate that GVC promotes technology commercialization through three concurrent pathways: securing policy support, enhancing intra-chain collaboration, and optimizing corporate governance. The network advantage mechanism is therefore effective, confirming Hypothesis 3.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNetwork Advantage Mechanism\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolicy Support\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntra-chain Enterprise Collaboration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInternal Governance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.069***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.050***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.237)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.931)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(11.742)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.881)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolicy Support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.097***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.747)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntra-chain Enterprise Collaboration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.114***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(18.051)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal Governance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.244***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.477)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.127*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.423***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.956***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.794)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.296)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-17.613)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(-6.327)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj. R\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003et statistics in parentheses, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e------------------------------------------------\u003c/p\u003e\u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e about here\u003c/p\u003e\u003cp\u003e------------------------------------------------\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e5.3. The Benefit-Sharing and Risk-Sharing Mechanism\u003c/h2\u003e\u003cp\u003eOur theoretical framework posits that GVC promotes technology commercialization through two interrelated channels: structuring benefit-sharing to align incentives and providing risk-sharing to mitigate uncertainties. The benefit-sharing mechanism addresses the incentive problem of \u0026ldquo;unwillingness to commercialize\u0026rdquo; by designing revenue arrangements that properly reward innovation actors, thereby enhancing conversion efficiency. Conversely, the risk-sharing mechanism tackles the constraint of \u0026ldquo;daring not to commercialize.\u0026rdquo; By using public capital to absorb early-stage risks, GVC provides a risk buffer for firms, enabling them to pursue higher-risk, higher-reward commercialization projects. To test these mechanisms, we construct the following proxy variables: Benefit-sharing: We employ a firm\u0026rsquo;s information disclosure rating as an ordered discrete variable, ranging from 1 (Excellent) to 4 (Unqualified). A lower score indicates higher information transparency, which we argue reflects a more rational and transparent benefit-distribution structure fostered by GVC\u0026rsquo;s involvement in corporate governance. Risk-sharing: We measure a firm\u0026rsquo;s risk-bearing capacity using the industry-adjusted standard deviation of its return on assets (ROA) from years t-2 to t\u0026thinsp;+\u0026thinsp;2. A higher value indicates a greater ability and willingness to endure volatility, capturing the risk-buffering effect provided by GVC\u0026rsquo;s role as \u0026ldquo;patient\u0026rdquo; or \u0026ldquo;junior\u0026rdquo; capital. The regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. Column (1) shows that the coefficient on GVC is negative and significant at the 5% level, indicating that GVC significantly improves firms\u0026rsquo; benefit-sharing mechanisms (as reflected in higher disclosure ratings). Column (2) shows that the coefficient on GVC is positive and significant at the 1% level, suggesting that GVC enhances firms\u0026rsquo; risk-bearing capacity. The results provide empirical support for the dual channels of the benefit- and risk-sharing mechanism. GVC appears to facilitate technology commercialization both by optimizing incentive structures and by mitigating risk exposures. Hypothesis 4 is therefore supported.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBenefit-sharing and Risk-sharing Mechanism\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenefit-sharing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRisk-sharing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGVC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.019**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-1.966)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.380)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(1.863)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk-sharing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-0.711)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenefit-sharing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.530***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.341)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.636***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.020*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.940***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(8.706)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-1.856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(-5.713)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31293\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj. R\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003et statistics in parentheses, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e------------------------------------------------\u003c/p\u003e\u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e about here\u003c/p\u003e\u003cp\u003e------------------------------------------------\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusions and Implications","content":"\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e6.1. Research Findings\u003c/h2\u003e\u003cp\u003eDrawing on a sample of Chinese A-share listed companies from 2012 to 2023, our empirical analysis yields three main findings. First, GVC exerts a significant positive effect on firms\u0026rsquo; technology commercialization. Second, this effect operates through three distinct pathways: the resource provision effect (by alleviating financing constraints and aggregating high-skilled talent), the network advantage effect (by securing policy support, enhancing industrial chain collaboration, and optimizing internal governance), and the benefit- and risk-sharing effect (by structuring incentives and buffering risk). This conclusion is robust, as it withstands a battery of endogeneity checks and robustness tests. Third, we find significant heterogeneity in this catalytic effect. The positive influence of GVC on technology commercialization is more pronounced in private and other non-state-owned enterprises, as well as in asset-intensive and labor-intensive industries.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Research Implications\u003c/h2\u003e\u003cp\u003eIn light of this, this paper explores potential policy implications at the formulation level: First, the scope and precision of government venture capital support should be further expanded, with a particular focus on targeted assistance for private enterprises and other businesses, as well as asset-intensive and labor-intensive industries facing prominent bottlenecks in technology transfer. This can be achieved by establishing specialized guiding funds for specific sectors and optimizing the regional layout of science and technology innovation funds, thereby enhancing the inclusiveness and structural alignment of policy support. Second, improve the market-oriented operation and governance mechanisms of government venture capital. Clarify the principle of \u0026ldquo;government guidance, market-driven decision-making, and professional operation.\u0026rdquo; Enhance capital allocation efficiency and sustainability by establishing error tolerance and correction mechanisms, improving exit channels, and introducing socialized performance evaluations. This will prevent administrative intervention from distorting market signals. Third, strengthen synergies between government-backed venture capital and other policy tools. Integrate these with tax incentives, first-unit purchase policies, science and technology talent programs, and open application scenarios to form a unified \u0026ldquo;investment-lending-subsidy-service\u0026rdquo; support system. Amplify resource infusion and institutional empowerment through policy stacking. Finally, implement differentiated and refined government venture capital support strategies: prioritize early-stage R\u0026amp;D and concept validation for technology-intensive industries; strengthen pilot-scale scaling and industrialization support for asset-intensive sectors; and focus on process innovation and digital transformation for labor-intensive industries\u0026mdash;achieving targeted precision support.\u003c/p\u003e\u003cp\u003eDrawing on these findings, we propose several implications for the design and implementation of GVC policies: First, policymakers should enhance the scope and precision of GVC support. Targeted assistance should be directed toward private and other non-state-owned enterprises, as well as asset-intensive and labor-intensive industries where bottlenecks in technology commercialization are most acute. This can be achieved by establishing dedicated sectoral guiding funds and optimizing the regional distribution of sci-tech innovation funds to improve the inclusiveness and structural relevance of policy support. Second, it is crucial to improve the market-oriented operation and governance mechanisms of GVC. The principle of \u0026ldquo;government guidance, market-based decision-making, and professional operation\u0026rdquo; must be clarified. Operational efficiency and long-term sustainability can be enhanced by implementing formal error-tolerance and correction mechanisms, improving capital exit channels, and introducing third-party performance evaluations. These steps are essential to prevent administrative interventions from distorting market signals. Third, strengthening the synergy between GVC and other policy tools is key. GVC initiatives should be strategically integrated with complementary measures such as R\u0026amp;D tax incentives, first-purchase policies, science and technology talent programs, and the provision of open application scenarios. This integration can form a cohesive \u0026ldquo;investment-loan-subsidy-service\u0026rdquo; support system, amplifying resource infusion and institutional empowerment through policy stacking effects. Finally, differentiated and refined GVC support strategies should be implemented. Support for technology-intensive industries should prioritize early-stage R\u0026amp;D and proof-of-concept activities. For asset-intensive sectors, the focus should shift to pilot scaling and industrialization support. For labor-intensive industries, GVC should facilitate process innovation and digital transformation. Such an approach ensures targeted and precise support aligned with distinct industry needs.\u003c/p\u003e\u003cp\u003eFor enterprises, GVC should be strategically embraced not merely as a financing channel, but as a critical resource for navigating the \u0026ldquo;valley of death\u0026rdquo; in the innovation process. Firms should proactively engage with GVC institutions, leveraging their policy credibility to attract follow-on private investment, diversify funding sources, and alleviate critical financial constraints during technology commercialization. Furthermore, firms should fully utilize the network resources and governance improvements facilitated by GVC. This involves actively integrating into regional innovation ecosystems and industrial chains, strengthening collaborative R\u0026amp;D with universities, research institutes, and supply-chain partners to enhance the systemic efficiency of commercialization. Internally, firms should seize the opportunity to optimize governance structures, refine systems for disclosing, evaluating, and incentivizing innovation outputs, and bolster capabilities in intellectual property management\u0026mdash;thereby transforming external support into endogenous, sustainable drivers of growth. As an institutional innovation that integrates state guidance with market mechanisms, GVC serves as a pivotal lever for bridging the \u0026ldquo;last mile\u0026rdquo; in technology commercialization. Through synergistic policy optimization and enhanced firm-level capabilities, it can help cultivate a thriving innovation ecosystem characterized by effective governance, efficient markets, and dynamic firms. Such an ecosystem provides essential support for achieving higher levels of technological self-reliance and high-quality industrial development.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ. Liu: Conceptualization, Formal analysis, Funding acquisition and Writing-review and editing. S. Wang: Writing-original draft, Data curation, Investigation and Methodology. F. Zhang: Writing-draft, Resources, Supervision, Software, Visualization and Validation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlperovych, Y., Groh, A., \u0026amp; Quas, A. (2020). 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Firm performance and state innovation funding: Evidence from China\u0026rsquo;s Innofund program. \u003cem\u003eResearch Policy\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(6), 1142\u0026ndash;1161. https://doi.org/10.1016/j.respol.2017.05.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Government venture capital, technology commercialization, valley of death","lastPublishedDoi":"10.21203/rs.3.rs-8277780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8277780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGovernment venture capital (GVC), embodying both policy and market logics, represents a distinctive hybrid form of financing. This study investigates how GVC influences the process of technology commercialization by examining the interplay between its dual institutional logics. Analyzing a sample of A-share listed firms from 2012 to 2023, we find robust evidence that GVC significantly enhances the level of technology commercialization. Mechanism analyses reveal that this effect operates through three parallel mediation channels: providing resource support, leveraging network advantages, and facilitating benefit and risk sharing. Specifically, GVC alleviates financing constraints, attracts high-caliber talent, strengthens policy coordination and industrial chain integration, optimizes corporate governance, and enables collective risk bearing. Furthermore, the positive effect of GVC on commercialization is more pronounced for private enterprises and in asset-intensive or labor-intensive industries. Our findings offer important theoretical insights and empirical evidence for designing effective government-market coordination mechanisms to bridge the “valley of death” in technology commercialization.\u003c/p\u003e\n\u003cp\u003eJEL code: G24, O38, O31\u003c/p\u003e","manuscriptTitle":"From Innovation to Application: The Role of Government Venture Capital in Technology Commercialization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 15:47:15","doi":"10.21203/rs.3.rs-8277780/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e1a7fe62-46b2-49d2-9c4e-8996b43340d9","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T15:24:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-10 15:47:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8277780","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8277780","identity":"rs-8277780","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}