Government technology support, digital finance development and corporate innovation performance

Resource limitations and externalities of enterprise innovation impose significant burdens on enterprise research and development (R&D) investment, while firms are unable to fully capture the benefits of innovation (Dimos & Pugh, 2016). In competitive markets, the benefits from R&D tend to fall short of the associated costs, weakening firms’ incentives to innovate (Bakker, 2013) and resulting in market failure (Tassey, 2004). Simple market forces are difficult to motivate innovation performance. As a remedy, government technology support has become a widely adopted policy tool across countries. Government technology support behavior encompasses a range of financial and policy instruments aimed at promoting enterprise innovation (Szücs, 2020; Kim, Choi, & Lee, 2021).

Recent studies have examined the impact of government technology support on corporate innovation performance, yielding three main perspectives. First, the crowding-in effect suggests that direct government subsidies alleviate funding constraints, lower R&D costs and signal support for innovation, thereby encouraging broader participation in innovative activities. Second, the crowding-out view argues that subsidies may replace firms’ own R&D investment, reducing their initiative. Third, some scholars propose a non-linear relationship, where moderate support promotes innovation, but excessive subsidies may reduce firms’ motivation by fostering dependence. In this regard, how can government technology support curb “market failure”? Will the effects of policy implementation be in line with the government’s original objectives?

A review of the literature indicates that such support aims to address financing gaps and direct resources toward innovation. However, funding remains a core challenge. Innovation requires substantial, long-term investment (Stanko & Henard, 2017), which internal funds and traditional bank loans often cannot sustain. There is information asymmetry between the government and funding providers, which hinders the formation of the most effective policy support and investment choices, leading to the flow of funds towards non-research activities. Moreover, the difficulty in evaluating innovation output also makes it difficult for enterprises to obtain external financing or the financing cost is very high (Carpenter & Petersen, 2002).

The development of digital finance helps overcome the limitations of traditional financial discrimination and expands financing channels for technology-based enterprises. It enables firms previously excluded from bank credit to access funding, thereby enhancing their motivation and success in innovation (Xu, Yao, & Zhou, 2023; Ozili, 2018). By leveraging big data, the Internet of Things, artificial intelligence (AI) and cloud computing, financial technology reduces information asymmetry, lowers risk premiums and operating costs and improves resource allocation efficiency, creating new opportunities for corporate innovation (Spiezia, 2011; Hao, Wang, Yan, Irfan, & Chang, 2023). Government support for innovation primarily functions as financial compensation for market failures, making financial support essential to its effectiveness. Therefore, the role of government technology support in corporate innovation cannot be separated from financial support, but there is a lack of related research. Especially for digital finance development, government technology support can effectively solve the corporate innovation incentive problem. How is the mechanism? Relevant research has not been carried out so far.

As a representative emerging market economy, China has accumulated substantial experience in government-led technological support, which has played a crucial role in promoting corporate innovation. Since the early 21st century, the Chinese Government has launched strategic initiatives such as building an innovative nation and pursuing innovation-driven development. Central government investment in R&D has steadily increased, aiming to establish a national innovation system with enterprises at its core. From 1999 to 2020, government expenditure on science and technology rose from 54.39bn to 109.50bn yuan, with an average annual growth rate of 14.9%. In recent years, corporate innovation in China has achieved significant progress, making Chinese data particularly valuable for generating generalizable insights.

To this end, this paper explores the mechanism through which Chinese Government technology support affects corporate innovation, with a focus on its impact under digital finance development. First, it theoretically and systematically deduces the impact mechanism of government technology support on corporate innovation performance and further examines how digital finance development influences this relationship. Second, based on the construction of a regulatory effect model for digital finance development, a moderated mediation model is employed to analyze the path through which government technology support affects corporate innovation performance under digital finance and to explore the mechanism for alleviating financing constraints. Furthermore, the influence of corporate governance and external supervisory factors on the above relationships is also examined.

This study makes several important contributions. First, it enriches the existing literature by incorporating digital finance development into the research framework linking government technology support and corporate innovation performance. While prior studies have primarily focused on the direct effects of government support or firm-level characteristics, they have largely neglected the role of digital finance, leading to fragmented and incomplete analyses. By systematically examining how digital finance influences the effectiveness of government support in promoting innovation, this paper addresses a critical gap and extends current theoretical understanding. Second, from a mechanism perspective, this study constructs an integrated analytical framework that considers multiple influencing factors. In contrast to earlier research that examined single-factor effects, this paper incorporates corporate governance and external supervision into the theoretical model, offering a more comprehensive and realistic explanation of the pathways through which government technology support impacts innovation under digital finance development. This multifaceted approach enhances the explanatory power and practical relevance of the theoretical mechanism.

The remainder of this paper is structured as follows: “Literature review” is a literature review, “Theoretical analysis and research hypothesis” presents the theoretical hypotheses, “Methodology and data” presents the methodology and data, “Empirical results” analyzes the regression results, “Further research” presents a further analysis and “Conclusions and recommendations” presents the conclusions and policy implications.

Government technology support has a crowding-in effect on corporate innovation performance. The financial support from the government helps alleviate the high R&D cost faced by enterprises, thereby stimulating their enthusiasm for innovation activities (Guellec & Van Pottelsberghe De La Potterie, 2003). Szücs (2020), using matching and difference-in-differences (DID) estimations based on the European Commission’s three most recent framework programs, finds that technology subsidies increase R&D investment, particularly among R&D-intensive firms and micro, small and medium enterprises. Fiscal incentives implemented by the government can effectively correct market failures, promote fair competition and encourage firms to conduct research activities (Bronzini & Piselli, 2016; Xinle, Zhen, & Xinting, 2022). Moreover, government subsidies send a credible signal to external investors, enhancing firms’ access to R&D funding and improving financing opportunities (Wu, 2017).

Government technology support has a crowding-out effect on corporate innovation performance. Clausen (2009) found that government support has limitations in fostering technological innovation within firms. When selecting companies for support, the government tends to prioritize low-risk firms with high short-term returns, which results in firms with strong long-term growth prospects but lower short-term returns being overlooked (Jia & Ma, 2017). When the cost of applying for government subsidies is lower than the cost of financing through capital markets, firms are more likely to rely on government funds rather than market-based financing for innovation, weakening their research investment and having minimal substantive impact on innovation (Boeing, 2016).

Government technology support has a non-linear effect on corporate innovation performance. Some scholars have noted that when policy incentives are below a certain threshold, they can effectively stimulate technological innovation and industrial development. However, excessive policy incentives may cause “inert behavior” in enterprise research, weakening their research investment behavior (Shao & Wang, 2023). Liu, Chen, Liu, and Yu (2019) argued that as government technology support increases, corporate R&D investment also increases. However, when government technology support exceeds the optimal subsidy level, it will squeeze out corporate R&D investment.

Throughout previous research literature, studies have dealt with the impact of governmental technology support on corporate innovation. However, the theoretical deduction is not systematic. Studies from different countries suggest that under varying economic environments, the economic consequences of government technology support on corporate innovation may differ.

With the wide application of digital technology in the financial sector, the rapid development of digital finance based on information technology and digital finance – driven by information technology, big data and cloud computing – has rapidly developed, addressing the limitations of traditional finance and providing a solid foundation for innovation. Li, Shao, Chang, and Albu (2022) found that digital finance reduces information asymmetry, promotes resource allocation efficiency and improves corporate technological innovation. They further emphasized that its ability to ease financing constraints is the key to its innovation-enhancing effect. Zhang (2023) proposed that digital finance reduces costs and improves the breadth and quality of financial services, thereby diversifying firms’ financing channels. Similarly, Demertzis, Merler, and Wolff (2018) noted that digital inclusive finance broadens financing avenues for micro-enterprises and enables efficient information matching, thereby stimulating innovation. Based on USA bank data, Jagtiani and Lemieux (2018) demonstrated that digital finance reshapes traditional financial services by improving access to credit for previously excluded but high-potential firms. Across different cultural contexts, there is a broad consensus that digital finance fosters corporate innovation through expanded financing channels, reduced financing costs and mitigated information asymmetry.

Previous studies have largely treated digital financial development as a factor that enhances corporate innovation performance, without adequately examining its underlying mechanisms as both an institutional and technological phenomenon. However, the expansion of financing channels and reduction of financing costs through digital finance do not inherently guarantee the promotion of innovation. In contrast, government support for science and technology has a more direct and established causal relationship with corporate innovation. Therefore, a key question is whether and in what ways digital finance development can help address the substantial capital demands and high costs associated with government-supported innovation initiatives. Clarifying this relationship is essential to understanding how digital finance can effectively contribute to innovation. However, existing literature has largely overlooked the causal links and transmission pathways between digital financial development, government technological support and corporate innovation.

The nature of corporate innovation is characterized by high inputs, prolonged times and inherent uncertainty. Decision-making in innovation is largely influenced by the compensation for input factors, specifically the goal of maximizing the benefits and cost reimbursements associated with innovation (Montalvo, 2008). However, in contexts where factor markets are distorted and the pricing mechanisms for protecting technological knowledge are imperfect, the resources invested by enterprises in research fail to fully capture the expected benefits and adequate cost compensation that green innovation ideally promises. As a result, corporate management is often inclined to reduce innovation activities.

On the one hand, corporate innovation performance is trapped in resource limitations. The commercialization of innovation outputs inherently involves substantial uncertainty and extended gestation periods (Brav, Jiang, Ma, & Tian, 2018), necessitating continuous commitments of human capital and financial resources. Under the prevailing accounting regulatory framework, innovation-related research expenditures must undergo immediate expense recognition. This regulatory constraint systematically elevates operational cost structures through incremental depreciation and/or amortization charges, thereby exerting measurable downward pressure on short-term profitability metrics (Lee, Wu, & Tseng, 2018). Furthermore, enterprises face significant challenges in relying exclusively on endogenous financing to ensure the smooth execution of innovation projects. Given the high risk, uncertain returns and other inherent challenges associated with innovation, it becomes particularly difficult for companies to secure external funding.

On the other hand, the innovation performance of enterprises is trapped in innovation compensation. Innovation involves public knowledge externalities, as technological innovation is essentially a process of producing new knowledge (OECD, 1992). The knowledge possessed by innovative firms is initially private. However, once this private knowledge enters the market, other firms, in pursuit of higher profits, can gradually transform it into public knowledge through mechanisms such as learning, analysis and imitation. Due to the non-excludability and non-rivalry of public knowledge, other firms can adopt innovative technologies with low investment and risk, while innovators are unable to prevent the diffusion of their new knowledge. As private knowledge becomes public, firms that bear the costs of R&D often fail to capture adequate returns, resulting in positive externalities that weaken their motivation to innovate.

A properly designed institutional environment can induce innovation, bring a competitive advantage and provide innovation compensation (Porter & Linde, 1995; Requate, 2005). As an effective regulator to compensate for market failure, government technology support can effectively incentivize enterprises to improve their innovation performance. On the one hand, government technology support is directly given to enterprises supporting R&D funding (Feldman & Kelley, 2006), which is beneficial for enterprises to obtain external innovation resources. On the other hand, government technology support can compensate for the technological spillover effect of innovation and reduce the externality of innovation activities (Zhu, Luo, Zhao, Wang, & Wang, 2021).

Government technology support is direct financial support provided based on objective evaluations of enterprises, which is beneficial for enterprises to obtain external innovation resources. Government technology support is actually an objective evaluation of a company’s ability to reserve, absorb and develop new technological knowledge. The government assesses whether firms are capable of adapting to technological innovation and, in consideration of internal factors such as organizational governance mechanisms, subsequently decides whether to provide technological support to the firms. In particular, the government provides subsidies to enterprises in the form of cash, which directly alleviates the financial difficulties of enterprises in R&D and strengthens the motivation of enterprises to innovate. At the same time, government technology support also has implicit credit guarantee functions (Feldman & Kelley, 2006) and attracting external investment functions (Meuleman & De Maeseneire, 2012), which can increase external investors’ confidence in subsidized enterprises, help enterprises obtain external resources and stimulate their innovation activities.

Government technology support induces a halo effect, leading to the flow of innovative resources to innovative enterprises. Under the backdrop of high-quality growth and industrial transformation and upgrading in China, technological innovation and progress are showing an increasingly fierce trend. New technology represented by AI, 5G, augmented reality, virtual reality and new energy has led the world into the era of “technological explosion.” Enterprises with advanced technology have competitive advantages in the market and sustainable development characteristics, and the government technology support gives the enterprise a halo of “government.” Based on signaling theory, government technology support is a recognition of a company’s future development capabilities, which can convey positive signals of high-quality development to the outside world (Gu, Tang, & Wu, 2020; Feldman & Kelley, 2006) and help attract the inflow of external resources such as capital and human resources to advanced technology R&D activities. In order to maintain the halo image and obtain more resources, enterprises will also invest quality resources in innovation.

Government technology support generates a market compensation mechanism for innovation investment, leading to the optimization of the allocation of R&D resources within the enterprise. Corporate innovation activities are characterized by long investment cycles and investment uncertainty, which reduce market investment willingness and require government intervention to compensate for uncertainty (Song & Wen, 2023). Government technology support facilitates the transformation of enterprises toward knowledge-intensive industries, increases the frequency of technology transactions and promotes the development of regional technology trading platforms and pricing mechanisms. As these mechanisms take shape, transaction uncertainty declines and firms gain clearer expectations of returns on innovation investment, mitigating the failure of market-based compensation. The advancement of technology platforms further enhances access to cutting-edge technologies and supports the commercialization of innovations (Rodrik, 2006).

Digital finance, as defined by Savina (2018), represents a financial model emerging from the integration of finance and technology within the digital economy. In this context, financial technology serves as the enabler, while inclusive and precise financial services form the core. This core characteristic enhances the efficiency of resource allocation and helps address inequities in financial access. Leveraging technologies such as big data and cloud computing, digital finance reduces information asymmetry and lowers risk premiums in investment and financing activities (Gomber, Kauffman, Parker, & Weber, 2018). By broadening the coverage of financial services and improving accessibility, digital finance can support a wider range of financial actors, including micro and small enterprises and start-ups, through platforms such as digital banking, digital payments, supply chain finance, inclusive finance and insurance (Ozili, 2018). Furthermore, digital finance facilitates data integration among government agencies, research institutions and financial entities, thereby reducing information asymmetry between the government and firms. This, in turn, enhances the precision of policy implementation and streamlines the approval process for innovation projects (Fuster, Plosser, Schnabl, & Vickery, 2019). Through functions such as digital payments, transfers and lending, digital finance also accelerates the disbursement of government support funds, promoting the timely advancement of enterprise innovation.

Under digital finance development, government technology support is more likely to induce a halo effect and alleviate the mismatch of R&D resources. On the one hand, digital finance development makes enterprise procurement, production and sales information recordable and traceable, which significantly improves the service efficiency of financial institutions. This improved transparency allows governments to collect enterprise-related information more effectively, mitigating moral hazard and adverse selection issues. As a result, firms engaged in emerging technologies are more likely to receive government support. On the other hand, digital finance development reduces capital circulation costs and accelerates capital turnover, improving the timeliness of government fund allocation. Digital finance development makes it possible for enterprises with new technologies to create a “halo effect” with the government technology support, which not only accelerates the creation of the “halo effect” but also increases the speed of the “halo” capital circulation. Enterprises are more willing to invest in government technology support in their innovation activities to reduce R&D costs and optimize their investment structure so as to obtain more innovation performance and form a virtuous circle.

Under digital finance development, government technology support is more likely to generate a market compensation mechanism for innovation investment, resulting in the protection of technological knowledge. With the intervention of digital finance, firms’ innovation outcomes can be more effectively rewarded through efficient financial markets. On the one hand, digital finance development can, through the innovation of financial products, make technology more efficiently allocated to where it is needed and improve the speed of technology transactions in the market. Leveraging big data, blockchain and other digital tools, digital finance improves the precision of matching between technology suppliers and demanders, reduces search and matching costs, streamlines transaction processes and lowers technology transaction costs. Under digital finance development, the government technology support further accelerates the transition of firms toward knowledge-intensive upgrading, enhances the dynamism of the technology market and contributes to the formation of a robust technology pricing mechanism. On the other hand, digital finance development optimizes capital flows and resource allocation while promoting the development of online platforms for technology transactions. These platforms enhance transparency and fairness, break down barriers to technology exchange and create favorable conditions for government support to catalyze the institutionalization of technology pricing.

Government technology support can effectively alleviate the financing constraints faced by enterprises and promote enterprise innovation. Government technology support is the direct financial support for enterprise innovation projects, which can effectively solve the financial bottleneck of enterprises in the research stage and help enterprises overcome the financing constraints. The government technology support also has the function of invisible credit guarantee; the enterprise that obtains the government technology support can transmit the signal of good development prospects to the outside world and help the enterprise obtain financing from external investors.

Under digital finance development, the financing constraint alleviation mechanism of government technology support for enterprise innovation will be amplified. On the one hand, relying on digital finance development, digital finance enhances the efficiency of financial services and accelerates capital turnover, enabling faster disbursement of government support to firms’ key technological areas. As a result, enterprises engaged in high-quality R&D can more rapidly access private compensation for innovation, thereby easing their financing constraints. On the other hand, government support becomes more effective in guiding innovation investment under the moderating influence of digital finance development, making innovative firms more likely to receive credit support (Tian & Shao, 2023). In traditional financial markets, decentralized small-scale investors are often excluded due to high transaction costs and technological limitations. Digital finance overcomes these barriers by integrating fragmented capital through scenario-based services, data analytics and financial innovation. When aligned with government policy signals, these decentralized funds are more likely to be directed toward enterprises engaged in critical technological innovation, thus further relieving R&D financing constraints for firms with high innovation potential.

In traditional financial markets, information asymmetry often imposes external financing constraints on firms, thereby hindering innovation activities. As digital finance development promotes the increasing prosperity of the financial market, information asymmetry is reduced and enterprise financing constraints are eased. On the one hand, digital finance makes use of the large amount of soft information, such as behavioral data, precipitated by enterprises on the Internet, to construct a credit assessment model for innovative enterprises by means of big data analysis (Duarte, Siegel, & Young, 2012). This form of risk assessment not only enables governments to evaluate innovation risk more transparently but also allows investors to better understand firms’ production and R&D activities, thereby narrowing the information gap between firms and external stakeholders. On the other hand, technologies such as smart contracts, online payment systems and data-sharing platforms improve the speed and efficiency of information dissemination, foster stronger trust between financial institutions and enterprises and further alleviate financing constraints stemming from information asymmetry.

To investigate the effect of government technology support on corporate innovation performance, the empirical regression equation is shown in Equation (1).

The dependent variable Lnapp_it/Lngrant_it denotes the corporate i’s innovation performance in year t, measured by the number of patent applications and grants. The independent variable Rdsub_it denotes the level of government technology support for firm i in year t, control denotes the control variables in the model and Firm, Region and Year denote fixed effects controlling for individual-level, region-level and year-level firms, respectively.

On the basis of Equation (1), the interaction terms of digital finance development, digital finance development and government technology support are added to examine the enhancing effect of digital finance development on the promotion of corporate innovation performance by government technology support:

Rdsub $×$ Lindex_t is the interaction item of government technology support and digital finance development, and when its coefficient is positive and significant, it indicates that digital finance development has a positive moderating effect on government technology support on corporate innovation performance.

For Hypothesis 2a, this article constructs a moderated effect model with mediation to test it. In the model, Kz represents the financing constraints of the corporate, specifically:

Through the results of empirical analysis, we first examine whether the coefficient of λ1 in Equation (3) is significant or not and then determine whether the mediated moderating effect is established based on whether the significance level of the coefficient of φ1 in Equation (4) has changed. When the coefficient of φ3 in Equation (4) is significant and the significance level of φ1 decreases or is no longer significant, it means that the mediated regulation is established, and the regulation partially and/or completely works through the mediating variable. When the significance level of the coefficient φ1 does not change, it means that the mediated regulation is not valid. The Kz index is constructed based on Kaplan and Zingales (1997), and the larger the Kz index, the higher the financing constraint faced by listed companies ^[1]. The Kz index is constructed based on financial indicators such as net cash flow from operations, dividends, cash holdings, gearing ratio and Tobin’s Q.

Dependent variable: corporate innovation performance (Lnapp/Lngrant). The paper selects the number of innovative patent applications and authorizations of a company in the current year and logarithmically processes them as substitute variables for the corporate innovation performance. That is, the more innovative patent applications a company has, the higher its innovation performance; the more patents authorized for corporate innovation, the higher the innovation performance of the corporation. The data are sourced from the China Research Data Service Platform (CNRDS) database.

Independent variable: government technology support (Rdsub). The logarithmic value of subsidies for technology projects in the government grants in the notes to the financial statements of listed companies is selected to measure the government’s support for technology within the enterprise and whether it is a government subsidy for technology according to whether the relevant introduction involves the keywords of “research and development,” “innovation,” “scientific research,” “new products,” “R&D,” “patents,” “major projects,” “Torch Plan,” “Spark Plan,” “973 Plan” and so on.

Moderating variable: digital financial development (Lindex). In 2016, the G20 Advanced Principles of Digital Inclusive Finance issued by the central bank proposed the use of digital technology to promote the development of inclusive finance and expand the ecosystem of digital financial services infrastructure, and digital inclusive financial development has become the most representative industry of digital financial development. Given this, this paper refers to the Digital Finance Development Index compiled by the Digital Finance Research Center of Peking University to indicate digital finance development (Guo et al., 2020).

Control variables. Similarly, following previously published studies, we introduce the following control variables in this study: gearing (Lev), firm age (Lnage), firm size (Lnsize), profitability (Roe), cash flow level (Cash), development level (Growth), market power (Market), board size (Lnboardsize), nature of property rights (Soe) and regional-level factors that influence corporate innovation, including human capital (Hum), industry share (Indrate) and regional gross domestic product (Lngdp). The variables are reported in Table A1 ( Appendix).

This paper selects the “Peking University Digital Finance Index” (2011–2022) released in 2022. In order to maintain comparability of sample data, data from A-share listed companies from 2011–2022 are selected. The screening process is as follows: ① excludes special treatment (ST) corporates, ② excludes samples with outliers, ③ excludes samples with missing data and ④ excludes listed companies in the financial industry. According to the National Economic Classification of Industries (GB/T4754-2011), which was unified in 2012, the sample corporates are divided into 20 industries. After the screening of the above conditions, 26,787 observations were obtained, covering 18 industries. The data on government technology support and control variables mainly come from the China Stock Market & Accounting Research Database and the China Statistical Yearbook. The data on digital finance development come from the Digital Finance Development Index compiled by the Digital Finance Research Center of Peking University. The data on corporate innovation patents come from the Chinese Research Data Services Platform (CNRDS) database. In this paper, STATA software was selected for empirical analysis, all continuous variables were subjected to a top and bottom 1% tailing process and the standard errors were treated as firm-level clusters.

Table 1 reports the descriptive statistics of variables. The descriptive statistics reveal substantial variation in firms’ innovation performances. The number of patent applications (Lnapp) ranges from 0 to 7.188, with a mean of 2.888, while patent grants (Lngrant) range from 0 to 6.830, with an average of 2.612, suggesting considerable heterogeneity in innovation outcomes across firms. Government technology support (Rdsub) also exhibits notable dispersion, with values ranging from 10.24 to 20.43, indicating that some firms receive significantly more support than others, which may enhance the effectiveness of such support in promoting innovation. Furthermore, the large difference between the maximum and minimum values of the digital finance development index (Lindex) suggests substantial regional variation in digital finance development across provinces, potentially leading to heterogeneous effects on firm innovation.

Before regression analysis, the variance inflation factor (VIF) was tested, and the VIF of each variable in the least squares (ordinary least squares (OLS)) model was less than 5, and the average VIF was 2.15, and there was no serious multicollinearity problem among the variables.

We use OLS regression to examine the role of government technology support on corporate innovation performance. Columns (1) and (3) of Table 2 present the results based on Equation (1), where the dependent variables are the number of patent applications and patent grants, respectively. The regression coefficient of government technology support (Rdsub) is positive and significant at the 1% level, indicating that government technology support shows a significant positive relationship with corporate innovation performance, i.e. the higher the government technology support received by the corporate, the higher the amount of corporate innovation patent applications and authorizations, i.e. the higher the corporate innovation performance, and Hypothesis 1 is established. The economic significance of the regression coefficient in column (1) indicates that for every 1 unit increase in government technology support, the number of innovative patent applications of enterprises increases by 2.4%. The economic significance of the regression coefficient in column (3) indicates that for every 1 unit increase in government technology support, the number of innovation patents granted to enterprises increases by 1.9%. Meanwhile, focusing on the regression results of control variables in columns (1) and (3), it can be found that the regression coefficients of corporate age (Lnage), corporate size (Lnsize), corporate development level (Growth) and board size (Lnboardsize) are all positive, among which the coefficients of corporate size and board of directors’ size are all significant, which indicates that the higher the age of the corporate, the higher the size, the higher the development level and the higher the board size, the higher the corporate innovation performance, which is consistent with existing research.

Secondly, the moderating effect of digital financial development on government technology support on corporate innovation performance is tested, and columns (2) and (4) in Table 2 are based on the regression of Equation (2) on corporate innovation performance. The estimated coefficient of the interaction term between government technology support and digital financial development (Rdsub $×$ Lindex) is positive and significant at the 1% level, indicating that digital financial development has a significant positive moderating effect on government technology support for corporate innovation performance, and Hypothesis 2 holds.

Table 3 shows the results of the impact path test of government technology support to alleviate corporate financing constraints and promote corporate innovation performance under digital financial development. Columns (2) and (5) are the regression results of Equation (3) on corporate financing constraints, and the estimated coefficients of the interaction term (Rdsub×Lindex) between government science and technology support and digital financial development on corporate financing constraints are negative and pass the test at the 1% significance level, respectively, which indicates that government technology support can significantly reduce the financing constraints faced by corporates under the moderating effect of digital financial development. Column (3) and column (6) are the regression results of model (4) on corporate innovation performance, and the estimated coefficients of the interaction term (Rdsub×Lindex) between government technology support and digital financial development are positive and pass the test at the 1% level of significance, indicating that under the moderating effect of digital financial development, the effective alleviation of financing constraints of financing corporates is the main path for government technology support to affect corporate innovation performance, and Hypothesis 2a is established.

As the model setting is based on the researcher’s subjective experience and theoretical assumptions, selectivity bias may exist. In addition, the estimation results are subject to uncertainty due to potential parameter estimation errors. Moreover, the relationship between government technology support and corporate innovation performance may suffer from omitted variable bias and reverse causality. Therefore, this paper conducts a series of endogeneity tests to address these issues.

In this paper, two-stage least squares (2SLS) is performed to mitigate the potential endogeneity problem. Using the average value of government technology support received by all firms in the same industry in the same province, except itself, as an instrumental variable, the validity of the instrument is tested as follows: in the underidentification test, the Kleibergen–Paap rk LM statistic is significant at the 1% level; in the weak instrument test, the Kleibergen–Paap rk Wald F statistic substantially exceeds the critical value corresponding to the 10% significance level. The first-stage regression results of the 2SLS estimation are reported in column (1) of Table 4, where the coefficient on the instrumental variable (Rdsub_IV) is 0.323 and statistically significant at the 1% level. This indicates that government R&D support received by other firms in the same province and industry has a significant positive effect on the government support received by the focal firm. The second-stage regression results are presented in columns (2)–(5) of Table 4, where the coefficients and significance levels of the key explanatory variables are consistent with those of the baseline model, confirming the robustness of the results.

There may still be omitted variables in the process of studying government technology support and corporate innovation performance. Therefore, in order to verify the robustness of the research results, referring to Frank (2000), the knofound command was used to test and quantify the robustness of causal inference regarding omitted variables (Frank, 2000). Table 5 shows the percentage of deviation required to achieve ineffective inference, with higher deviation percentages indicating more robust results. The deviation percentage of government technology support (Rdsub) in the regression estimation of enterprise innovation performance exceeded 50%, indicating a high robustness of causal inference.

Figure 1 shows the sensitivity plot of government technology support in the regression results of enterprise patent application, where the estimate marker is the part where the estimated value exceeds the threshold. The larger the part, the stronger the robustness of causal inference. Therefore, the causal inference in this study is robust.

Exogenous policy shocks. The impact of government technology support on corporate innovation performance is often related to the level of regional technological development, and in order to more robustly assess whether government technology support can improve innovation performance, the regression results are examined using the policy exogenous shock methodology, drawing on the endogeneity treatment idea of Fuchs-Schündeln and Hassan (2016). In this paper, we adopt the demonstration zone for the transfer and transformation of scientific and technological achievements as an exogenous shock policy, which is evaluated by the DID method. Considering that the demonstration zones for the transfer and transformation of scientific and technological achievements were successively approved in batches in 2016, 2017, 2018 and 2021, a multi-temporal double-difference model is used to study the impact of government scientific and technological support on innovation performance, and Equation (6) is constructed:

Where Lnapp_it/Lngrant_it is the dependent variable innovation performance; DID_t is the policy variable, the product of the treatment group dummy variable and the policy implementation time dummy variable. The principle of setting up the treatment group dummy variable is that cities covered by the national technology transfer demonstration zones are assigned a value of 1 and those not covered are assigned a value of 0 during the sample period; the principle of setting up the dummy variable of the time of implementation of science and technology transfer demonstration zones policy is that the cities are assigned a value of 0 before they are approved and a value of 1 afterward.

It is necessary to test whether the trends before the policy intervention are parallel before DID, i.e. the individuals in the treatment group who are affected by the establishment of the demonstration zone for the transfer of scientific and technological achievements and the individuals in the control group who are not affected must keep the same trend of change before the pilot. The first five periods of the approved demonstration zone for the transfer and transformation of scientific and technological achievements in the prefecture-level city where the corporate is located are used as the base period for the parallel trend test, and the test results are shown in Figure 2. The coefficients of the first five periods of the approved demonstration zone for the transfer and transformation of scientific and technological achievements are not significant, which meets the requirements of the parallel trend assumption.

Table 6 reports the regression results of the double-difference model of the effect of the policy of science and technology transfer demonstration zones on the innovation performance, and the regression coefficients are all positive and significant; the national science and technology achievement transfer demonstration zone policy helps to improve innovation performance. The national science and technology achievement transfer demonstration zone policy brings about technological upgrading in the region and improves the innovation motivation of corporates.

In addition to considering exogenous policy shocks, several robustness checks are conducted, including excluding the effects of the competitive environment in external markets, substituting key variables and performing subsample regressions. The results consistently support the hypotheses proposed in this study. Detailed empirical results and discussions are provided in the  Supplementary material.

The paper divides the sample into state-owned corporates and non-state-owned corporates according to the nature of property rights to examine the impact of government technology support on corporate innovation performance and the moderating role of digital financial development. As shown in Table 7, the regression coefficients of the interaction term between government technology support and digital financial development (Rdsub $×$ Lindex) in SOEs are positive and significant at the 1% level, while the interaction term in non-SOEs is not significant. The p-value of the intergroup difference tests is 0.0161 for columns (3) and (7) and 0.0325 for columns (4) and (8), indicating that the positive moderating effect of digital financial development on government technology support on corporate innovation performance is more significant in SOEs than in non-SOEs, and the innovation-driven effect of digital finance development is more significant in SOEs. The main reason for this is that the objectives of SOEs and non-SOEs are different, as SOEs tend to consider economic, environmental and social benefits, while non-SOEs are more inclined to consider economic benefits, and as a result, SOEs are more likely to utilize the funds gained through digital finance development for innovative R&D activities, whereas non-SOEs are more likely to utilize the excess funds for chasing economic benefits.

Government technology subsidies are a common policy tool to promote firm-level R&D. However, the receipt of subsidies does not necessarily ensure their effective use for innovation. In this context, internal corporate governance and external monitoring play a critical role. Robust internal governance can mitigate managerial opportunism and enhance the efficiency of subsidy utilization, while external oversight similarly helps ensure that funds are used as intended.

The effectiveness of innovation largely depends on a firm’s specific governance environment. Sound corporate governance facilitates risk identification and strategic response by management, thereby creating a supportive environment for innovation. Prior research shows that the impact of innovation policies varies across firms with different governance levels (Xia, Gao, & Wei, 2022). In well-governed firms, stronger monitoring mechanisms increase the likelihood of detecting opportunistic behavior by controlling shareholders and executives, thereby mitigating agency problems (Manne, 1965). Furthermore, digital financial development more effectively eases financing constraints for firms with lower financing costs, thus exerting a stronger innovation-promoting effect on these firms (Amore & Bennedsen, 2016).

Building on this, the paper incorporates internal governance into the analytical framework linking government technology support, digital financial development and corporate innovation performance, aiming to examine how the impact of digital finance on the effectiveness of government support varies across different levels of internal governance. A corporate governance index is constructed using principal component analysis (PCA) based on seven indicators: the dual role of chairman and general manager (managerial decision-making power), executive compensation and executive shareholding (incentive mechanisms), the proportion of independent directors and board size (board monitoring) and institutional ownership and ownership balance (measured as the shareholding ratio of the second to fifth largest shareholders relative to that of the largest shareholder). The first principal component extracted from PCA serves as a composite index of internal governance, where higher values indicate stronger governance. Firms are then divided into high- and low-government groups based on the median value of this index.

As shown in Panel A in Table 8, the moderating effect of digital financial development varies significantly with the level of internal governance. For firms with low internal governance, the interaction term between government technology support and digital financial development (Rdsub × Lindex) is negative. In contrast, for firms with high internal governance, the interaction term is positive and statistically significant at the 1% level. The p-values of the intergroup difference tests are 0.0653 for columns (3) and (7) and 0.0220 for columns (4) and (8), suggesting that under strong internal governance, digital financial development more effectively enhances the positive impact of government support on corporate innovation performance.

As key information intermediaries in external markets, analysts serve an important monitoring function. By evaluating and disclosing firm-specific information, analysts can mitigate managerial incentives for regulatory arbitrage and encourage innovation aimed at restoring investor confidence (Bushman, Chen, Engel, & Smith, 2004). Moreover, through deep analysis and comprehensive interpretation of corporate data, analysts enhance the overall information supply to capital markets, thereby reducing information asymmetry between firms and external investors, as well as between firms and the government. This enables the government to more accurately assess subsidy applicants, increasing the likelihood that firms engaged in high-quality innovation receive technological support and ultimately improving innovation efficiency. To this end, this paper incorporates external monitoring into the analytical framework linking government technology support, digital financial development and corporate innovation performance, aiming to examine how the quality of external supervision influences the moderating effect of digital finance. Analyst attention is used as a proxy for external monitoring quality, with firms above the sample median classified as having high analyst attention and those below as low.

As reported in Panel B in Table 8, the moderating effect of digital financial development differs significantly across monitoring quality. For firms with low analyst attention, the interaction term between government support and digital finance (Rdsub × Lindex) is positive but statistically insignificant. In contrast, for firms with high analyst attention, the interaction term is positive and significant at the 1% level. The p-values for the intergroup difference tests are 0.0430 for columns (3) and (7) and 0.0367 for columns (4) and (8), indicating that under stronger external supervision, digital finance more effectively enhances the positive impact of government support on corporate innovation performance.

Prior research lacks a deep and systematic theoretical analysis, often remaining at the level of surface phenomena and offering limited exploration of the causal mechanisms linking government technology support and corporate innovation. Moreover, the role of digital financial development in this relationship has received insufficient attention. This paper addresses these gaps by constructing a theoretical framework that elucidates the causal link between government technology support and corporate innovation and investigates the moderating role and mechanisms of digital financial development. Using panel data of China’s A-share listed firms from 2011 to 2022, the study confirms the following theoretical propositions: (1) government technology support significantly promotes corporate innovation performance, with digital finance development exerting a positive moderating effect; (2) both government support and digital finance help alleviate financing constraints and enhance firms’ risk-taking capacity, thereby improving innovation performance; (3) the positive moderating role of digital finance is more pronounced in state-owned enterprises and (4) under strong internal governance and effective external supervision, digital financial development more effectively enhances the impact of government technology support on corporate innovation performance.

On the basis of the above empirical analysis, this paper puts forward the policy recommendations to make full use of the government technology support and digital finance development and to construct the internal governance and external supervision mechanism of the company so as to realize the efficiency change.

Firstly, in order to promote corporate innovation, the government should increase technology support and rationalize the allocation of resources. Specifically, the government should improve the evaluation mechanism for technology support programs and accelerate the disbursement of innovation subsidies. In addition to enhancing the scientific rigor and transparency of enterprise selection by shifting from ex ante approval to mid-term and ex-post supervision, it is essential to establish a comprehensive evaluation system for the use of funds during and after allocation. This includes a full-process assessment of enterprises’ use of technological subsidies and personnel inputs. Accelerating the disbursement of government subsidies and optimizing oversight mechanisms can help ensure more efficient resource allocation and promote innovation outcomes.

Secondly, accelerate digital finance development and promote the effective allocation of financial resources. Specifically, increase investment in basic research on digital technology, encourage financial institutions to carry out digital financial transformation, precisely increase investment in basic research on digital technology, encourage financial institutions to carry out digital financial transformation, accurately guide local legal person banks to carry out digital transformation practices and strengthen the foundation for improving the quality and efficiency of enterprises through digital financial services.

Thirdly, strengthening internal corporate governance can enhance the execution of innovation strategies, while external supervision helps foster a favorable innovation environment. Specifically, firms should integrate board member, supervisor, and executive recruitment; performance evaluation, incentives, compensation and equity arrangements with assessments of technological innovation and the efficiency of new technology investments. In parallel, the quality and technical expertise of directors, supervisors and executives should be improved through professional training and the recruitment of high-level, interdisciplinary talent in relevant fields. Moreover, the external monitoring system should be further developed, with particular emphasis on mechanisms such as analyst coverage. These efforts aim to achieve effective coordination between internal governance and external supervision, thereby improving innovation performance.

This study also has some limitations. Firstly, due to the reality that there are many ways in which government technology support can be measured, the measurement may be flawed, and it is also difficult to measure its relationship with factors such as regional endowments, which are not yet sufficiently detailed and specific. Secondly, governments in different regions may focus their policies on different levels of technology to meet the needs of the strategic layout of these regions. These policies create uncertainty and affect the need for relevant firms to adjust their corporate innovation strategies.

Future directions worthy of research include, firstly, the large differences in the way government technology support is provided in different regions of China, as well as different levels of support. Digital finance also suffers from unbalanced and insufficient development. Therefore, future research needs to consider how to promote government technology support and digital financial development according to local conditions, stratification and categorization, as well as how to continuously innovate the institutional mechanism within the corporate to build a set of adaptive incentive structures to adapt to the government’s technology support and digital finance development conditions and to continuously improve the corporate innovation performance. Second, in the process of successive transitioning from old to new kinetic energy for China’s economic growth, it is important to explore how to coordinate government technology support means and digital financial development so that they can better play the incentive effect of corporate innovation, as well as how to construct a comprehensive indicator to measure the level of government technology support, which are all worthy of research in the future.

The supplementary material for this article can be found online.