Digital Transformation and New Quality Productive Forces Development: The Moderating Effects of Corporate ESG Performance and Intellectual Property Protection

Digital Transformation and New Quality Productive Forces Development: The Moderating Effects of Corporate ESG Performance and Intellectual Property Protection | 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 Article Digital Transformation and New Quality Productive Forces Development: The Moderating Effects of Corporate ESG Performance and Intellectual Property Protection Zhen Ren, Shengyang Zhong, Zhi Li, Ruihuan Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8115547/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 As global economies seek solutions to slowing productivity growth and sustainability challenges, China has advanced the concept of “New Quality Productive Forces” (NQPF) to guide innovation-led, green development. This study explores how corporate digital transformation (DT) contributes to regional NQPF, and how this relationship is moderated by two institutional forces: internal corporate ESG performance and external regional intellectual property protection (IPP). Using a matched panel dataset of 31,598 firm-year observations from Chinese A-share listed companies (2013–2022) and their corresponding regional data, we apply fixed-effect models and threshold regressions to empirically test the micro-to-macro productivity link. Results reveal that DT significantly enhances NQPF at the regional level, but this effect is conditional. Higher ESG performance amplifies the positive impact of DT, exhibiting an increasing marginal return. Meanwhile, IPP shows a non-linear moderating effect: only when protection surpasses a critical threshold does it unlock the productivity gains of corporate digitalization. These findings offer theoretical advances by clarifying the institutional boundaries of technology spillovers and contribute to the emerging NQPF literature by establishing a cross-level framework that links firm strategy to macroeconomic outcomes. Policy implications suggest that sustainable digitalization requires both strong internal ESG governance and robust external IP institutions to fully translate private innovation into public economic value. Business and commerce/Business and management Social science/Business and management Business and commerce/Economics Social science/Economics Business and commerce/Information systems and information technology Digital Transformation New Quality Productive Forces ESG Performance Intellectual Property Protection Figures Figure 1 1.Introduction The global economy is currently navigating a period of profound transformation, defined by the dual challenges of diminishing productivity growth and an urgent sustainability imperative. Traditional production factors are giving smaller returns. Although digital technologies are widespread (Siebel, 2019 ), a productivity jump has not happened. This is called the modern "productivity paradox" (Brynjolfsson et al., 2021). The effect of this change on firm and macroeconomic productivity is still debated (Aghion et al., 2019 ). Also, growing climate concerns and societal demands for sustainability place strong limits on the traditional, resource-intensive growth model (Friede et al., 2015 ; Gillan et al., 2021 ). This situation has created a key problem for policymakers and firms worldwide: how to start a new engine for innovation-driven productivity that also follows the limits of a green transition? To answer these common global challenges, new development models are being studied. For example, China has introduced the concept of "New Quality Productive Forces" (NQPF). It is a strategic framework to guide its next stage of high-quality development and get a lasting competitive advantage (Xi, 2024 ). Unlike traditional productivity, NQPF is an advanced productivity form. It is defined by new technological breakthroughs, innovative factor mixes, and deep industrial transformation (Chin et al., 2025 ; Liu & Lin, 2025 ). Its core characteristics are innovation-led and quality-driven. Importantly, it is seen as an advanced form of productivity that is naturally "green" (Liu & Li, 2025 ; Zhang et al., 2025 ). This new concept handles the two challenges of innovation and sustainability. It has already started to get significant attention in international academic discussions (Guo et al., 2025 ; Xue & Chen, 2025 ). Corporate Digital Transformation (DT) is seen as the main way to allow NQPF. This is because it provides the needed technological foundation for the factor jumps and industrial changes that NQPF requires. But this "micro-to-macro" link—from firm technological adoption to regional productivity transformation—is not direct or automatic. Its success depends on the institutional environment. We propose that this environment works at two key levels: (1) the internal firm commitment to sustainability, shown by Environmental, Social, and Governance (ESG) performance, which guides the quality and direction of digital investments (Gillan et al., 2021 ; Guo et al., 2025 ); and (2) the external institutional setting of the region, especially the strength of its Intellectual Property Protection (IPP), which shapes the innovation incentives for all firms there (Kim et al., 2012 ; Liu & Lin, 2025 ). To support this framework, a review of the growing literature shows a big gap at this cross-level point. Although some studies show a direct link between the digital economy and macroeconomic productivity (Liu & Li, 2025 ; Pan et al., 2022 ), and others separately study the macro-level impact of ESG (Guo et al., 2025 ) or IPP (Liu & Lin, 2025 ) on NQPF, the total mechanism of how firm actions produce these macro-outcomes remains a "black box." Importantly, an understanding of the factors that control this micro-to-macro change is missing. It remains unclear how a firm's internal ESG performance affects the change of its own digital efforts into public, regional productivity gains. And it is unclear whether this effect shows an "increasing marginal trend". Also, the role of the regional external IPP environment is not well understood. This includes whether its moderating effect is linear or shows the clear non-linear threshold effect we propose. China provides an ideal place for studying this cross-level mechanism. Its economy is both driven by firm energy and strong top-down regional governance. To build our test, we develop a new dataset by matching: (1) data from Chinese A-share listed companies (2013–2022) to measure Digital Transformation (DT) and ESG performance; and (2) data for the matching regions to measure New Quality Productive Forces (NQPF) and Intellectual Property Protection (IPP). Using this matched panel dataset, our study empirically tests: first, the total impact of corporate DT on regional NQPF; and second, the non-linear, threshold-based moderating effects of both corporate ESG and regional IPP. This study makes several new contributions to the literature at the intersection of digitalization, institutional economics, and regional economic development. First, we contribute to the NQPF theory by proposing and testing a cross-level analytical framework. Although most studies discuss NQPF at a macroeconomic level (Liu & Lin, 2025 ; Lv et al., 2025 ), our research connects the micro-macro divide by studying how corporate strategic actions (Digital Transformation) combine to shape regional productivity outcomes. Second, we provide new empirical evidence on the conditional nature of this micro-to-macro link. To the best of our knowledge, this is the first study to show that the impact of corporate digitalization on regional NQPF is not the same. Instead, it strongly depends upon the firm's internal institutional commitment (ESG performance) and the external institutional environment (IPP) (Guo et al., 2025 ). Third, and most importantly, we move beyond simple linear assumptions to find complex non-linear mechanisms. We show that the moderating effects of both ESG and Intellectual Property Protection are not linear but follow clear threshold patterns. We find that the stronger effect of ESG shows an "increasing marginal trend", while the helping effect of IPP is only "unlocked" after it crosses a critical threshold. This two-part finding provides detailed evidence on the complex interaction between technology and institutions (Kim et al., 2012 ). Finally, our findings offer more exact policy implications. To grow regional NQPF, policymakers cannot simply order corporate digitalization. They must also create a strong institutional environment where IPP is strong enough (passing the threshold) and at the same time encourage internal firm governance (ESG practices) to fully change corporate digital investments into macro-level productivity gains. 2. Literature Review and Hypothesis Development Regional economic outcomes, like an area's productivity, are not abstract phenomena. They are the emergent result of the aggregated actions and interactions of their key actors, primarily firms (Aghion et al., 2019 ). The strategic choices made by individual enterprises—about technology adoption, investment, and innovation—generate knowledge spillovers, shape regional supply chains, and cultivate specialized labor markets. These aggregated firm behaviors form the region's collective industrial structure and innovative capacity (Pan et al., 2022 ). Therefore, to understand the drivers of a macroeconomic concept like regional New Quality Productive Forces (NQPF), it is essential to adopt this cross-level perspective. This study builds its theoretical framework on this micro-to-macro logic. We argue that the transformation of regional productivity is driven by the strategic transformations happening within its constituent firms. 2.1 Digital Transformation and New Quality Productive Forces The core technological driver for NQPF is widely identified as Digital Transformation (DT) (Liu & Li, 2025 ). Following Marxist productivity theory, NQPF represents a qualitative leap in the key elements of production: the laborer, the means of labor, and the object of labor (Chin et al., 2025 ; Liu & Lin, 2025 ). Corporate DT is the catalyst for this three-fold leap, improving internal capabilities through new mechanisms. By using data analytics, artificial intelligence, and digital platforms, firms can optimize resource orchestration, improve supply-demand matching, and improve overall resource allocation efficiency. This, in turn, boosts their innovation output and productivity (Wu et al., 2020 ; Pan et al., 2022 ; Siebel, 2019 ). Specifically, this transformation of the three elements happens as follows: (1) Cultivating "high-quality laborers", as AI and automation create a complementary effect with high-skilled labor while substituting for low-skilled tasks, which upgrades the human capital structure (Akerman et al., 2015 ); (2) Forging "new-media means of labor", as digital platforms reconfigure supply chains, optimize production processes, and improve inter-firm collaboration (Tao et al., 2023 ); and (3) Expanding the "new-material objects of labor", moving beyond traditional physical inputs to include data as a core production factor and enabling the use of green, sustainable materials (Song et al., 2022 ; Yoo et al., 2012 ). This corporate-level efficiency, however, does not stay contained within the enterprise. As a large number of firms within a region adopts digital technologies, their aggregated actions generate important positive externalities that lift the entire area's productive capacity. First, widespread corporate digitalization builds a regional innovation ecosystem through greater knowledge spillovers. Second, it optimizes inter-firm collaboration, creating more resilient and efficient regional supply chains. Third, it collectively cultivates a high-skilled labor pool skilled in digital technologies, raising the quality of the regional labor factor. These aggregated effects directly contribute to the "innovative configuration of production factors" and "deep industrial transformation" that define NQPF at the regional level. By allowing this shift towards more innovative, efficient, and greener production models across the region, the aggregation of corporate DT acts as the primary engine for developing regional NQPF. Therefore, based on this micro-to-macro logic, we propose our first hypothesis: Hypothesis 1 (H1). Corporate digital transformation has a significant positive effect on regional new quality productive forces. 2.2 The Threshold-Moderating Role of ESG Performance Although digital transformation (DT) provides the technological engine, its ability to change into regional NQPF is not the same across all firms. This micro-to-macro process depends on the firm's internal institutional environment and strategic orientation (Zhou et al., 2022 ). Based on stakeholder theory, we argue that a firm's Environmental, Social, and Governance (ESG) performance is a critical internal moderator in this relationship (Friede et al., 2015 ; Gillan et al., 2021 ). High-ESG performance signals a firm's commitment to sustainable long-term value over short-term gains, meeting the expectations of diverse stakeholders (Friede et al., 2015 ; Gillan et al., 2021 ). This commitment builds an internal environment that strengthens the productive returns of DT through three primary channels, directly aligning with the core components of NQPF: (1) Environmental (E): High-ESG firms are strategically more likely to adopt green manufacturing and circular economy models. They guide their digital investments toward long-term, sustainable innovations (e.g., clean production processes), which directly supports the naturally "green" dimension of NQPF (Guo et al., 2025 ; Xue & Chen, 2025 ). (2) Social (S): Better "Social" performance improves a firm's ability to attract and retain the high-quality human capital that forms the "high-quality laborer" element of NQPF (Albuquerque et al., 2019 ; Chin et al., 2025 ). This upgraded talent pool is better at using digital tools, improving data integration, and doing complex innovations, thus maximizing the efficiency of DT initiatives. (3) Governance (G): Better "Governance" optimizes internal processes and ensures that managerial decision-making aligns with long-term value creation (Rezaee & Tuo, 2019 ). This disciplined governance ensures that digital investments are guided into real, high-return, NQPF-aligned projects rather than being diverted by short-term opportunism. Thus, a firm's internal commitment to sustainability is a key factor of whether its technological investments change into public productivity gains. Based on this three-pillar mechanism, we propose: Hypothesis a (H2a). Corporate ESG performance positively moderates the relationship between corporate digital transformation and regional new quality productive forces. Furthermore, this positive moderating effect may not be linear. We argue that the strengthening effect of ESG performance on the DT-NQPF link shows an "increasing marginal return." At low levels of ESG performance, a firm's sustainability efforts are often superficial, fragmented, or just for ceremonial compliance. As a result, its digital transformation initiatives may remain scattered, with only a weak alignment with NQPF goals. However, as corporate ESG performance improves, it signals a deep, structural, and cultural integration of sustainability into the core strategy (Gillan et al., 2021 ; Guo et al., 2025 ). At this substantive stage, the firm's entire operation—from talent acquisition to R&D direction (Albuquerque et al., 2019 )—is systemically aligned with high-quality, sustainable goals. Consequently, each additional unit of digital investment is guided more effectively toward generating real, high-quality innovations (e.g., green technologies, resilient supply chains) that produce strong positive externalities for regional NQPF. Thus, the strengthening effect of ESG is not constant; it becomes stronger as ESG performance improves. Hypothesis b (H2b). As corporate ESG performance optimizes, the positive moderating effect on the relationship between corporate digital transformation and regional new quality productive forces demonstrates an "increasing marginal trend." 2.3 The Threshold-Moderating Role of Intellectual Property Protection Beyond the firm's internal governance (ESG), the external institutional environment of the region plays a key role in shaping the outcomes of corporate digitalization. We argue that Intellectual Property Protection (IPP) is a critical external moderator. This is because digital transformation, while helping innovation, also introduces major intellectual property risks. Unlike traditional innovations, digital assets (such as algorithms, proprietary data, and software code) are often sent via online channels, making copying more hidden, rapid, and widespread. This high-risk environment can stop firms from doing deep digital innovation for fear of "free-riding" (Ang, 2010 ). A strong regional IPP framework directly fights this risk by increasing the costs and penalties for copying. It provides a credible legal safeguard, ensuring that firms can capture the economic returns from their digital investments (Saito, 2017 ). Also, strong IPP improves information transparency and secures inter-firm collaborations, which are needed for building the digital supply chains and innovation ecosystems that support regional NQPF (Liu & Lin, 2025 ). When firms see the regional IPP environment as strong, they are more motivated to invest in high-risk, high-return digital R&D, and it is these high-quality corporate activities that combine into regional productivity gains. Hypothesis a (H3a). Regional intellectual property protection positively moderates the relationship between corporate digital transformation and regional new quality productive forces. However, drawing from institutional economics, we argue that the impact of IPP is not always positive; it is likely dependent upon the level of IPP strength and shows a non-linear, threshold-based pattern. This non-linearity comes from the dual nature of IPP, especially in its early stages. First, getting and enforcing patents has direct costs, which can be a major burden (especially for smaller firms), possibly hurting their digital innovation incentives. Second, an early IPP system (i.e., below a critical threshold) can actively slow innovation in an economy that is still reliant on imitation. By limiting the knowledge spillovers and technology diffusion that imitation-based firms depend on—without yet being strong enough to encourage real, breakthrough innovation—a weak IPP regime can stall digital adoption and hurt overall innovation efficiency (Kim et al., 2012 ). Therefore, the positive moderating effect proposed in H3a likely only activates after regional IPP strength crosses a critical threshold. Only when the legal framework is strong enough can it provide a real deterrent and guarantee that the returns from real innovation are greater than the costs of both R&D and IP registration. This institutional security de-risks digital R&D, releasing corporate investment in transformative technologies rather than imitative applications. It is these high-quality, protected innovations that generate the major positive spillovers needed to drive a regional NQPF transformation. Hypothesis b (H3b). The moderating effect of regional IPP on the relationship between corporate DT and regional NQPF is non-linear and subject to a threshold. The positive moderation effect is significantly stronger, or only becomes significant, after IPP strength surpasses a critical value. 3. Methodology 3.1. Data Our study uses a comprehensive, matched panel dataset. The initial sample consists of all A-share listed companies on the Shanghai and Shenzhen stock exchanges for the period from 2013 to 2022. To ensure data quality, we apply a standard screening process: (1) we exclude firms designated as ST (Special Treatment) or *ST (delisting risk) due to their abnormal financial status; (2) we exclude firms in the financial industry, as their accounting standards and business models differ significantly from non-financial firms; and (3) we remove observations with severe missing data for our core variables. This process yields a final unbalanced panel dataset of 31598 firm-year observations. A key feature of our research design is the matching of corporate data with regional data. Corporate data, including information for Digital Transformation (DT), ESG performance, and financial controls, were primarily sourced from the China Stock Market and Accounting Research (CSMAR) database, the China Research Data Services Platform (CNRDS), and the Wind Information database. Regional data, used to construct our dependent variable (New Quality Productive Forces, NQPF) and our external moderating variable (Intellectual Property Protection, IPP), as well as regional control variables, were manually collected and compiled from various official publications, including the China Statistical Yearbook , the China Torch Statistical Yearbook , the China High-tech Industry Statistical Yearbook , the China Environmental Statistical Yearbook , and the National Intellectual Property Development Status Report . We matched the corporate data to the corresponding regional data based on the province where each firm is registered. To mitigate the impact of outliers, all continuous variables were winsorized at the 1% and 99% levels. All monetary variables were deflated to constant 2013 prices. 3.2. Measurements 3.2.1. Dependent Variable Our dependent variable, New Quality Productive Forces (NQPF) is a regional construct representing an advanced productivity state defined by high technology, high efficiency, and high quality (Lv et al., 2025 ). Given that NQPF is a novel and comprehensive concept, we constructed a composite evaluation index system based on its theoretical underpinnings. Specifically, and following the theoretical logic of this study rooted in Marxist productivity theory, we define NQPF through the qualitative leap and optimal configuration of its three fundamental elements: the laborer, the means of labor, and the objects of labor (Chin et al., 2025 ; Zhang et al., 2025 ; Liu & Lin, 2025 ). Drawing upon this framework (see Table 1 ) and indicator systems in related NQPF literature (Liu & Lin, 2025 ; Li & Ren, 2025 ), our measurement is structured around these three sub-systems. The "'High-Quality' Laborer" dimension captures the enhancement of human capital, operationalized through indicators of population health (e.g., medical insurance coverage), cultural quality (e.g., advanced human capital metrics), and skill level (e.g., R&D personnel and patent grants). The "'New-Media' Means of Labor" dimension reflects the advancement of production tools, focusing on information infrastructure (e.g., internet domain counts) and innovation infrastructure (e.g., national tech incubators). Finally, the "'New-Material' Objects of Labor" dimension measures the greening and advancement of production inputs, proxied by the supply of new materials (e.g., high-tech product development) and the exploration of new energy (e.g., green patents). We collected regional data for each indicator and utilized the Entropy Weight TOPSIS method to calculate a comprehensive NQPF index for each province in each year. This method objectively assigns weights based on the information content of each indicator, avoiding subjective bias. Table 1 Evaluation index system of NQPF. Overall Indicator Tier-1 Indicator Tier-2 Indicator Tier-3 Indicator New Quality Productive Forces "High-Quality" Laborer Physical Fitness Urban Basic Medical Insurance Fund Revenue Number of Healthcare Institutions Per Capita Consumption of Meat, Eggs, and Dairy Products Human Caliber Human Capital Upgrading Proportion of Employees with College Degrees or Above in Torch Specialized Industrial Bases Skill Competency Number of Employees in National-Level Technology Business Incubators Number of Domestic Patent Applications Granted "New Medium" Means of Labor Information Infrastructure Internet Domain Names per 10,000 People Computers per 100 People Fixed Asset Investment in Information Transmission Information Transmission, Software, and IT Services Innovation Infrastructure Number of National-Level Technology Business Incubators Number of National University Science Parks Number of Torch Program Specialized Industrial Bases "New Material" Objects of Labor New Material Supply Number of New Product Development Projects in High-Tech Industries Number of Strategic Emerging Industry Projects Facilitated by National Technology Transfer Demonstration Institutions Number of Major Technology Transfer Projects Facilitated by National Technology Transfer Demonstration Institutions New Energy Exploration Number of Green Patents Granted Investment in Industrial Pollution Control 3.2.2. Independent Variable Our independent variable is Corporate Digital Transformation (DT). Following prior literature (Yuan et al., 2021 ; Wu et al., 2021 ), we use a machine-learning-based text analysis approach. We first build a comprehensive dictionary of digital transformation keywords. Then, using Python, we process the "Management Discussion and Analysis" (MD&A) section of each firm's annual report to calculate the frequency of these keywords. The DT variable is thus measured as the ratio of the total frequency of these digital keywords to the total length of the MD&A section, reflecting the firm's strategic focus and investment in digitalization. 3.2.3. Moderating Variables We test two institutional moderators at their respective levels: Corporate ESG Performance (ESG) As our internal moderator, we measure ESG performance using the annual ESG ratings from the China Research Data Services Platform (CNRDS) database, a widely used source in related studies (Guo et al., 2025 ; Xue & Chen, 2025 ). This rating is standardized (divided by 100) for empirical analysis, following the methodology of Lei et al. ( 2023 ). Regional Intellectual Property Protection (IPP) As our external moderator, we measure IPP using a city-level index matched to firms' registered locations. Following the methodology of Shen and Huang ( 2019 ), this index is constructed based on the Revealed Comparative Advantage Comparative Advantage (RCA) of concluded intellectual property court cases within each city. The RCA-based metric reflects local governments' legal and enforcement capacity for innovation protection. 3.2.4. Control Variables To mitigate omitted variable bias, we include a comprehensive set of control variables at both the corporate and regional levels. Corporate-level Controls We control for firm-specific characteristics that may influence digitalization and performance, including Return on Equity (ROE), Intangible Asset Ratio (INT), Inventory Ratio (INV), and Average Age of Management (MTA). Regional-level Controls We control for regional macroeconomic conditions that may simultaneously affect both corporate behavior and regional NQPF, including Fiscal Autonomy (FIS), Government Science & Technology Support (GST), Regional Industrial Structure (RIS), Tax Collection and Management (TCM), and Pollution Control Efforts (PCE). Specifically, FIS is the ratio of local fiscal general budget revenue to budget expenditure; GST is the ratio of local fiscal science and technology expenditure to GDP; RIS is the ratio of the added value of the tertiary industry to the secondary industry; TCM is the ratio of local fiscal tax revenue to GDP; and PCE is the ratio of local fiscal environmental protection expenditure to general budget expenditure 3.3. Model Specification To empirically test the hypotheses developed in the previous section, we build a series of cross-level fixed effects models. Our research design matches corporate-level independent variables to their corresponding provincial-level dependent variable. First, to examine the baseline impact of corporate digital transformation on regional new quality productive forces (H1), we build the following panel data model: Where the subscripts i, j and t denote the firm, province, and year, respectively. NQPF is the dependent variable, and DT is the independent variable. X is a vector of all corporate-level and regional-level control variables specified in section 3.2.4 . To control for unobserved heterogeneity, we include firm fixed effects µ , province fixed effects λ , and year fixed effects φ . ε is the stochastic error term. We cluster standard errors at the firm level to account for potential correlations. Second, to test the linear moderating effects of corporate ESG performance (H2a) and regional IPP (H3a), we extend Model (1) by adding an interaction term: In this model, M is the moderating variable(either c orporate ESG or regional IPP). We are mainly interested in the coefficient β 3 , which captures the moderating effect. A significantly positive β 3 would provide support for H2a and H3a. Finally, to investigate the non-linear moderating effects of ESG performance (H2b) and the threshold-based moderation of regional IPP (H3b), we employ the panel threshold regression model developed by Hansen ( 1999 ). This model allows the relationship between DT and NQPF to change regime based on the value of a specific threshold variable. The general single-threshold model is specified as: Where M is the threshold variable (representing either corporate ESG or regional IPP), I (·) is an indicator function, and η is the threshold value to be estimated. This model will be used to test H2b (by setting M = ESG) and H3b (by setting M = IPP). For H3b, we will also test for a double-threshold effect to fully capture the nonlinear dynamic. 4. Analysis and Results This section presents the empirical evidence for our hypotheses. We begin by showing the statistical characteristics of our variables, followed by the baseline regression results. We then conduct a series of robustness checks to validate these findings. Finally, we investigate the core of our study: the moderating role of corporate ESG performance and the non-linear, threshold-based moderating effect of regional IPP. 4.1. Descriptive Statistics Table 2 presents the descriptive statistics for all variables used in our analysis, based on the final sample of 31598 firm-year observations. Our dependent variable, the regional New Quality Productive Forces (NQPF) index, has a mean of 0.331 and a standard deviation of 0.139. The wide range, from a minimum of 0.053 to a maximum of 0.676, shows significant regional differences in NQPF development across Chinese regions. This provides substantial variance for our econometric analysis. The core independent variable, Corporate Digital Transformation (DT), has a mean of 0.011. This suggests that, on average, the adoption of deep digital transformation is still in its early stages for many listed firms. However, its standard deviation (0.011) and maximum value (0.056) reveal significant differences among firms, which is consistent with the uneven landscape of digital adoption seen in related studies (Wu et al., 2021 ; Yuan et al., 2021 ). The moderating variables (corporate ESG and regional IPP) and all control variables also exhibit considerable variation, confirming the suitability of our dataset for this investigation. Table 2 Descriptive Statistics of Variables. Variable Type Variable Name Symbol Observations Mean Std. Dev. Min Max Dependent Variable New Quality Productivity Forces NQPF 31598 0.331 0.139 0.053 0.676 Independent Variable Firm Digitalization DT 31598 0.011 0.011 0 0.056 Moderator Variables (Threshold Variables) Corporate ESG Performance ESG 31598 0.274 0.110 0.016 0.793 Intellectual Property Protection IPP 31598 0.685 0.631 0 3.751 Control Variables Return on Equity ROE 31598 0.052 0.174 -6.85 2.379 Intangible Assets Ratio INT 31598 0.046 0.060 0 0.938 Inventory Ratio INV 31598 0.136 0.128 0 0.940 Average Management Age MTA 31598 3.898 0.066 3.572 4.141 Fiscal Autonomy FIS 31598 0.641 0.182 0.069 0.931 Government S&T Support GST 31598 0.007 0.003 0.002 0.013 Regional Industrial Structure RIS 31598 1.638 1.087 0.665 5.283 Tax Collection Intensity TCM 31598 0.091 0.034 0.035 0.188 Pollution Control Efforts PCE 31598 0.028 0.009 0.011 0.068 Figure 1 provides a dynamic visualization of the NQPF index over our sample period, illustrating its distribution using kernel density estimation. The plot reveals two key trends. First, the center of the kernel density curve shifts consistently to the right over the years. This indicates that the overall average level of NQPF across Chinese regions has been gradually increasing. Second, the distribution's shape has evolved: the main peak has decreased in height, while the curve's width has expanded, and it has developed a heavier right tail. This dynamic suggests a "Matthew effect" of polarization; while the average NQPF is rising, the absolute differences between high-performing regions and low-performing regions are simultaneously widening. This statistical and visual evidence of significant and growing variation in NQPF motivates our inquiry into its corporate-level drivers. 4.2. Baseline Results Table 3 presents the baseline regression results for the impact of corporate digital transformation (DT) on regional new quality productive forces (NQPF), based on Model (1). We employ a stepwise estimation approach to ensure the robustness of our findings. Column (1) reports the initial OLS regression, including only the core independent variable and fixed effects. The coefficient for DT is positive and significant (0.174, p < 0.05). Column (2) introduces regional-level control variables, and the DT coefficient remains strongly positive and significant (0.207, p < 0.01). Column (3) further incorporates our full set of corporate and regional-level control variables. In our preferred specification (Column 3), which controls for all covariates and fixed effects, the coefficient for corporate DT is 0.212 and is statistically significant at the 1% level. This result is not only statistically robust but also economically meaningful. It suggests that, holding all else constant, a 1 percentage point increase in corporate digital transformation is associated with a 0.212 unit increase in the NQPF index. This is an approximate 64.05% increase relative to the sample mean (0.212 / 0.331). This finding provides strong empirical support for our first hypothesis, confirming that corporate digital strategies aggregate to produce significant, positive macro-level productivity outcomes. Therefore, Hypothesis 1 (H1) —that corporate digital transformation has a significant positive effect on regional new quality productive force s —is supported. Table 3 Baseline Regression Results. NQPF(1) NQPF(2) NQPF(3) DT 0.174 ** 0.207 *** 0.212 *** (0.082) (0.077) (0.077) Coefficient 0.328 *** 0.427 *** 0.120 ** (0.001) (0.013) (0.058) Firm-level Controls No No Yes Region-level Controls No Yes Yes Firm Fixed Yes Yes Yes Region Fixed Yes Yes Yes Year Fixed Yes Yes Yes R 2 0.935 0.940 0.941 N 31598 31598 31598 *** , ** and * are significant at the significance levels of 1%, 5% and 10%, respectively. 4.3. Endogeneity and Robustness Checks Although our baseline model controls for a range of variables and fixed effects, the positive relationship found for Hypothesis 1 (DT → NQPF) could be subject to potential endogeneity concerns. Primarily, reverse causality could be an issue: regions with a higher level of NQPF might possess superior infrastructure and innovation ecosystems, which in turn attract more digitally advanced firms. To address this and ensure the robustness of our baseline finding, we conduct a series of rigorous tests, including an instrumental variable (IV) approach and multiple sensitivity analyses. Following related empirical literature, we use an instrumental variable approach to reduce endogeneity. We instrument for the Corporate Digital Transformation (DT) variable using historical telecommunication data from 1984, matched to the city where each firm is registered. This strategy, which uses historical infrastructure as a quasi-random source of variation, is a widely accepted approach in the literature for instrumenting digital adoption (Huang et al., 2019 ; Nunn & Qian, 2014 ). Specifically, we use the number of post offices per million people and the number of telephones per 100 people in 1984, interacted with the firm's previous-year (t-1) digitalization level. The logic is twofold: (1) Relevance: historical telecommunication infrastructure in a city is a strong predictor of the long-term, path-dependent development of modern digital infrastructure and later corporate digital adoption (Huang et al., 2019 ); and (2) Exclusion Restriction: this historical data from 1984 is highly unlikely to be correlated with the current error term affecting regional NQPF, thus meeting the exogeneity assumption. The 2SLS (Two-Stage Least Squares) results are presented in Table 4 . The first-stage regressions confirm (Columns 1 and 3, Table 4 ), the relevance and strength of our instruments. The coefficients on both historical post office and telephone IVs are positive and highly significant predictors of current corporate DT. In the crucial second-stage regressions (Columns 2 and 4, Table 4 ), the coefficient for the instrumented DT variable (e.g.,1.724 and 1.423) remains positive and is statistically significant at the 1% level. We also report the critical diagnostic statistics. In both specifications, the Kleibergen-Paap rk LM statistic (e.g.,347.112 and 342.842) is significant at the 1% level, which strongly rejects the null hypothesis of under identification. Furthermore, the Cragg-Donald Wald F-statistic (e.g.,4044.234 and 3564.032) far exceeds the Stock-Yogo critical values for weak identification (e.g., 16.38 at the 10% level), confirming that our instruments are strong. This IV-2SLS result, which accounts for endogeneity, confirms that our baseline finding is robust and that the positive impact of corporate digital transformation on regional new quality productive forces is causal, supporting H1. Table 4 Endogeneity Tests: Instrumental Variable Estimates. IV = Post Offices per Million Population in 1984×DT t−1 IV = Telephones per Hundred People in 1984×DT t−1 NQPF(1) NQPF(2) NQPF(3) NQPF(4) DT 0.004 *** 1.724 *** 0.124 *** 1.423 *** (0.000) (0.242) (0.005) (0.179) F statistic 594.23 *** 537.37 *** Kleibergen-Paap rk LM statistic 347.112 *** 342.842 *** Cragg-Donald Wald F statistic 4044.234 3564.032 [16.38] [16.38] Controls Yes Yes Yes Yes Firm Fixed Yes Yes Yes Yes Region Fixed Yes Yes Yes Yes Year Fixed Yes Yes Yes Yes N 31598 31598 31598 31598 *** , ** and * are significant at the significance levels of 1%, 5% and 10%, respectively. Figures in square brackets are the critical values for the Stock-Yogo test at the 10% significance level. We further test the stability of our baseline results by re-estimating Model (1) under several alternative specifications. The results are summarized in Table 5 . Lagged Independent Variable. To reduce potential reverse causality, we use the one-year lag of corporate digital transformation (L.DT). As shown in Column (1), the coefficient for L.DT (0.194, p < 0.01) remains positive and highly significant. Manufacturing Sector Only. The manufacturing industry is a primary focus for NQPF. We re-run the regression using only the subsample of manufacturing firms. Column (2) shows that the positive effect of DT (0.252, p < 0.05) holds. Excluding Municipalities. To ensure our results are not driven by China's four centrally-administered municipalities (Beijing, Shanghai, Tianjin, Chongqing), we exclude them from the sample. Column (3) shows that the DT coefficient (0.238, p < 0.05) remains robust. In sum, the findings from both the IV regression and the series of robustness checks consistently support our baseline conclusion. The positive and significant relationship between corporate digital transformation and regional new quality productive forces is robust. Table 5 Robustness Tests. NQPF(1) NQPF(2) NQPF(3) L.DT 0.194 *** (0.057) DT 0.252 ** 0.238 ** (0.100) (0.099) Coefficient 0.124 ** 0.147 ** 0.196 *** (0.058) (0.072) (0.069) Controls Yes Yes Yes Firm Fixed Yes Yes Yes Region Fixed Yes Yes Yes Year Fixed Yes Yes Yes R 2 0.941 0.945 0.941 N 31598 20811 25405 *** , ** and * are significant at the significance levels of 1%, 5% and 10%, respectively. 4.4. Further Analysis: Moderating Mechanisms Now that we have established the robust, positive baseline effect of corporate DT on regional NQPF, we now test our core hypotheses about the conditional nature of this relationship. We investigate how this micro-to-macro link is moderated by both the internal institutional environment (corporate ESG performance) and the external institutional environment (regional Intellectual Property Protection). 4.4.1. The Moderating Role of Corporate ESG Performance We first test Hypotheses H2a and H2b, which state that Corporate ESG performance positively moderates the DT-NQPF relationship, possibly in a non-linear, marginally increasing way. Column (1) of Table 6 presents the results of the linear interaction model (Model 2). The coefficient for the interaction term DT * ESG is positive and statistically significant at the 1% level (1.027, p < 0.01). This result provides strong support for Hypothesis H2a, indicating that high corporate ESG performance significantly strengthens the positive contribution of its digital transformation to regional NQPF. This aligns with our theory that firms with a strong internal commitment to sustainability (as signaled by ESG) are better able to channel their digital investments toward high-quality, green, and socially beneficial innovations that generate stronger positive externalities (Guo et al., 2025 ; Xue & Chen, 2025 ). Next, we test for the "increasing marginal trend" (H2b) using the panel threshold model (Model 3), with corporate ESG as the threshold variable. The results of the threshold test (reported in Table 7 ) confirm the existence of a single threshold for ESG (threshold = 0.208), which is statistically significant (F = 16.85, p < 0.01). The threshold regression results (Table 8 ) reveal a distinct non-linear pattern. When corporate ESG performance is below this critical threshold (ESG < 0.208), the coefficient for DT is 0.126 and only weakly significant (p 0.208), the coefficient for DT jumps to 0.362 and becomes highly significant (p < 0.01). This finding strongly supports Hypothesis H2b. It demonstrates that the strengthening effect of ESG is not linear; it exhibits an increasing marginal return. Only after corporate sustainable governance practices reach a sufficient level of maturity do its digital transformation efforts become a powerful, high-impact driver of regional NQPF. Table 6 The Moderating Role of Corporate ESG Performance. NQPF(1) NQPF(2) DT -0.061 -0.340 *** (0.118) (0.087) ESG -0.017 *** (0.006) DT×ESG 1.027 *** (0.391) IPP 0.001 (0.001) DT×IPP 0.718 *** (0.054) Coefficient 0.126 ** 0.136 ** (0.058) (0.057) Controls Yes Yes Firm Fixed Yes Yes Region Fixed Yes Yes Year Fixed Yes Yes R 2 0.941 0.942 N 31598 31598 *** , ** and * are significant at the significance levels of 1%, 5% and 10%, respectively. 4.4.2. The Threshold-Moderating Role of regional-level IPP We now turn to our final set of hypotheses, H3a and H3b, which examine the critical role of the regional IPP environment. This represents one of the core innovations of our study. Column (2) of Table 6 presents the linear interaction model for IPP. The coefficient for the interaction term DT * IPP is positive and highly significant (0.718, p < 0.01). This provides initial support for Hypothesis H3a, suggesting that a stronger external institutional environment for innovation protection does strengthen the productivity gains from corporate digitalization. However, H3b states that this relationship is non-linear and subject to a threshold. This reflects the idea that IPP must reach a critical strength to overcome the high risks of digital innovation (Ang, 2010 ). We test this using the panel threshold model (Model 3) with regional IPP as the threshold variable. The regression results across these three regimes (Table 8 ) reveal a striking non-linear pattern that strongly supports Hypothesis H3b. Regime 1 (Low IPP: IPP 0.10). Regime 2 (Medium IPP: 0.441 < IPP 0.10). Regime 3 (High IPP: IPP > 1.426): The coefficient for DT becomes significantly positive (1.067, p < 0.01). This shows that the positive moderating effect only becomes statistically detectable after the intensity of intellectual property protection surpasses a higher second threshold. Consequently, a weak IPP environment is not enough to protect digital investments and may even be harmful. Only after IPP strength crosses these critical thresholds can it effectively de-risk corporate innovation and unlock the full, aggregated potential of digital transformation to drive regional new quality productive forces. Table 7 Threshold Estimation and Threshold Test. Threshold Variables Threshold Type F-value P-value Threshold Estimate Critical Values 10% 5% 1% ESG Single 16.85 *** 0.007 0.208 10.118 12.061 15.165 Double 8.03 0.130 9.128 11.629 15.134 IPP Single 141.31 *** 0.000 0.441 11.920 13.610 18.381 Double 69.30 *** 0.000 1.426 11.824 13.417 21.489 Triple 24.57 0.667 53.093 67.851 83.639 *** , ** and * are significant at the significance levels of 1%, 5% and 10%, respectively. Based on 300 bootstrap replications. Table 8 Threshold Regression Results. The Role of ESG The Role of IPP 1 NQPF(1) NQPF(2) DT_1 0.126 * -0.027 (0.075) (0.079) DT_2 0.362 *** 0.065 (0.067) (0.073) DT_3 1.067 *** (0.083) Coefficient -0.177 *** -0.150 *** (0.042) (0.043) Controls Yes Yes Firm Fixed Yes Yes Region Fixed Yes Yes Coefficient Yes Yes R 2 0.684 0.689 N 16919 16157 4.5. Heterogeneity Analysis To further explore the boundary conditions of our baseline finding (H1), we conduct a series of heterogeneity analyses. The impact of corporate digital transformation (DT) on regional new quality productive forces (NQPF) may vary depending on firm characteristics, industry context, and regional endowments. We present these subsample regression results in Tables 9 , 10 , and 11 . 4.5.1. Corporate-Level Heterogeneity We first examine heterogeneity at the corporate level based on financing constraints and life cycle. Financing Constraints. Following Hadlock and Pierce ( 2010 ), we use the SA index to proxy for financing constraints and split the sample by its median. As shown in Columns (1) and (2) of Table 9 , the positive effect of DT on regional NQPF is significant only for the low-constraint subsample (Coef. = 0.193, p < 0.10). This suggests that firms with sufficient capital and resources are better able to change their digital investments into real, macro-level productivity gains. Firm Life Cycle. We divide the sample into growth, maturity, and decline stages based on the cash flow pattern methodology (Dickinson, 2011 ). Columns (3)-(5) of Table 9 show that the DT coefficient is positive and significant only for firms in the maturity stage (Coef. = 0.248, p < 0.05). This aligns with our theory, as mature firms have the stable resources, established market channels, and organizational capacity needed to execute complex digital strategies that can generate external spillovers. Table 9 Heterogeneity Analysis: Firm Characteristics. Low Financing Constraints High Financing Constraints Recession Stage Maturity Stage Growth Stage NQPF(1) NQPF(2) NQPF(3) NQPF(4) NQPF(5) DT 0.193 * 0.087 0.181 0.248 ** 0.054 (0.111) (0.098) (0.153) (0.103) (0.128) Coefficient 0.253 *** 0.084 0.098 0.079 0.138 (0.086) (0.076) (0.104) (0.074) (0.106) Controls Yes Yes Yes Yes Yes Firm Fixed Yes Yes Yes Yes Yes Region Fixed Yes Yes Yes Yes Yes Coefficient Yes Yes Yes Yes Yes R 2 0.954 0.956 0.947 0.948 0.962 N 15859 15739 7628 15909 8061 4.5.2. Industry-Level Heterogeneity Next, we explore variations across industry types (Table 10 ). High-tech vs. Low-tech Industries. Based on the official classification by the National Bureau of Statistics, we divide the sample into high-technology and low-technology industries. As shown in Columns (1) and (2) of Table 10 , the positive impact of DT is significant only within the high-technology industry subsample (Coef. = 0.269, p < 0.01). This is logical, as high-tech firms are the primary agents of innovation, and their digital advancements are more directly aligned with the core components of NQPF, such as developing high-quality labor and advanced means of labor. Competitive vs. Regulated Industries. Following Yuan et al. ( 2021 ), we classify industries as either competitive or regulated. The results in Columns (3) and (4) of Table 10 show that the DT coefficient is positive and significant only for firms in competitive industries (Coef. = 0.243, p < 0.01). This suggests that market competition acts as a key disciplining and motivating mechanism. Firms facing strong competition are forced to use their digital investments for real innovation rather than rent-seeking, which leads to a stronger, positive impact on the region's overall productivity. Table 10 Heterogeneity Analysis: Industry Characteristics. High-tech Industries Low-tech Industries Competitive Industries Regulated Industries NQPF(1) NQPF(2) NQPF(3) NQPF(4) DT 0.269 *** -0.031 0.243 *** -0.035 (0.088) (0.167) (0.084) (0.187) Coefficient 0.016 0.247 *** 0.141 ** 0.037 (0.075) (0.091) (0.067) (0.119) Controls Yes Yes Yes Yes Firm Fixed Yes Yes Yes Yes Region Fixed Yes Yes Yes Yes Coefficient Yes Yes Yes Yes R 2 0.945 0.939 0.942 0.945 N 18992 12606 23992 7606 4.5.3. Regional-Level Heterogeneity Finally, we investigate regional-level differences based on location and resource endowments (Table 11 ). Coastal vs. Non-coastal Regions. As shown in Columns (1) and (2) of Table 11 , the positive effect of DT is stronger and more significant for firms located in non-coastal regions (Coef. = 0.208, p 0.10). This finding suggests a "path-breaking" or "catch-up" effect. Although coastal regions may already operate at a high level of development, non-coastal regions seem to have greater marginal returns from digitalization, allowing them to use DT to overcome geographical and resource limitations. Resource-based vs. Non-resource-based Cities. The results in Columns (3) and (4) of Table 11 show that the DT coefficient is positive and significant only for firms in non-resource-based cities (Coef. = 0.141, p < 0.10). This highlights the "resource curse" or "path dependency" challenge. Firms in non-resource-based cities are forced to innovate through technology to make up for their lack of natural endowments, thus making their digital transformations a more powerful driver of regional NQPF. Table 11 Heterogeneity Analysis: Factor Endowments. Coastal Regions Non-coastal Regions Resource-based Cities Non-resource-based Cities NQPF(1) NQPF(2) NQPF(3) NQPF(4) DT 0.063 0.208 *** 0.104 0.141 * (0.093) (0.051) (0.173) (0.079) Coefficient 0.227 *** 0.054 0.262 ** 0.113 * (0.068) (0.035) (0.117) (0.060) Controls Yes Yes Yes Yes Firm Fixed Yes Yes Yes Yes Region Fixed Yes Yes Yes Yes Coefficient Yes Yes Yes Yes R 2 0.939 0.981 0.960 0.940 N 20267 11331 2627 28971 5. Discussion and Implications 5.1. Discussion of the Empirical Results Our empirical findings provide a clear understanding of how corporate strategic actions combine to influence regional productivity in the context of China's high-quality development. The results not only confirm the direct impact of digital transformation but also, more importantly, reveal that this link is deeply dependent on both internal and external institutional conditions. First, our baseline finding (Hypothesis 1 ) confirms that corporate digital transformation (DT) is a significant positive driver of regional new quality productive forces (NQPF) (see Table 3 ). This supports the micro-to-macro logic that the collective digital investments of firms generate significant positive externalities (Pan et al., 2022 ). This aggregation effect likely works through the channels we theorized in Section 2.1 : accelerating regional knowledge spillovers, improving the efficiency of inter-firm supply chains, and cultivating a high-skilled, digitally-native regional labor force, all of which are core components of NQPF. Second, we find that this micro-to-macro link is not uniform; it is powerfully moderated by the firm's internal strategic commitment to sustainability (Hypothesis 2). The positive linear moderation of ESG performance (H2a) (see Table 6 ) suggests that firms aligning their strategies with sustainable goals are more effective "converters" of digital investment into public productivity gains (Guo et al., 2025 ; Xue & Chen, 2025 ). Furthermore, the "increasing marginal return" (H2b) revealed in our threshold analysis (see Table 8 ) is a key insight. It implies that ESG is not merely a passive screening factor but an active amplifier. As corporate ESG performance matures from superficial "ceremonial" compliance to deep strategic integration, it becomes exponentially more effective at directing its digital innovations toward high-quality, green outcomes that directly fuel regional NQPF. Third, and most central to our study, we reveal the complex, non-linear moderating role of the external institutional environment (Hypothesis 3). Our findings for H3b (see Table 8 ) show that at low levels of regional Intellectual Property Protection (IPP), corporate DT has an insignificant, or even slightly negative, impact on regional NQPF. This supports the theoretical argument that in a weak institutional context, the high risks of digital innovation stop firms from doing the key R&D needed for macro-level spillovers (Ang, 2010 ). The positive effect is "unlocked" only after IPP strength crosses a critical threshold. This shows that a strong enough legal framework (IPP) is a prerequisite for de-risking corporate digital innovation and allowing its aggregation into real, regional productivity gains (Liu & Lin, 2025 ). Finally, our heterogeneity analysis (see Tables 9 – 11 ) supports this narrative. The positive effect of DT on NQPF is strongest among firms that are best positioned to execute complex innovations (i.e., mature firms with low financing constraints) (Dickinson, 2011 ; Hadlock & Pierce, 2010 ) and those with the strongest motivation to do so (i.e., high-tech firms in competitive industries). Also, the effect is most clear in non-coastal and non-resource-based regions, suggesting that DT serves as a critical "path-breaking" tool for regions less reliant on traditional endowments, forcing them to compete via innovation. 5.2. Theoretical Contributions This study makes several new contributions to the literature at the intersection of digitalization, institutional economics, and regional economic development. First, we contribute to the new NQPF literature by providing empirical validation for its micro-foundations. While most studies discuss NQPF at a macroeconomic level (Liu & Lin, 2025 ; Lv et al., 2025 ), our research connects the micro-macro divide by showing precisely how corporate strategic actions (Digital Transformation) combine to shape regional productivity outcomes. Second, we advance the literature on technology adoption by proposing and testing an institution-technology synergy model. We provide new empirical evidence that the success of this micro-to-macro link is not uniform. Instead, we show it is significantly dependent upon the alignment of both the firm's internal institutional commitment (ESG performance) and the external institutional environment (IPP) (Guo et al., 2025 ). Third, and most importantly, we contribute to institutional theory by moving beyond simple linear assumptions to show complex, non-linear moderating mechanisms. We reveal that the institutional effects of both ESG and Intellectual Property Protection are not linear, but follow distinct threshold patterns. Specifically, we find that the strengthening effect of ESG shows an "increasing marginal trend" (consistent with H2b), while the enabling effect of IPP is only "unlocked" after it crosses a critical activation threshold (consistent with H3b). This two-part finding provides detailed evidence on the complex, and often non-monotonic, interaction between technology and institutions (Kim et al., 2012 ). 5.3. Managerial and Policy Implications Our findings, which reveal a complex, institution-dependent link between corporate digitalization and regional NQPF, offer several critical, actionable implications for both managers and public policymakers. 5.3.1. Managerial Implications Our study provides two primary strategic insights for corporate decision-makers. First, our findings on H2b reframe ESG performance not as a mere compliance cost, but as a strategic multiplier. The evidence of an "increasing marginal return" (see Table 8 ) suggests that firms moving from superficial (or "ceremonial") to deep ESG integration can unlock significantly greater productivity returns from their existing digital transformation investments. This provides a clear, quantitative justification for managers to champion deep ESG integration as a core strategy to maximize the value generated from digitalization (Guo et al., 2025 ). Second, our discovery of a strong IPP threshold (H3b) carries direct implications for strategic location and investment decisions. Managers planning significant R&D or digital innovation investments must conduct due diligence on the regional IPP environment. Our results (see Table 8 ) strongly suggest that committing such investments in a region with an IPP framework below the critical activation threshold is a high-risk, low-return effort. To protect investments and maximize their contribution to NQPF, firms should prioritize locating high-stakes digital R&D in regions with a proven, robust IPP framework (Kim et al., 2012 ). 5.3.2. Policy Implications Our micro-to-macro findings offer several exact recommendations for regional governments that want to cultivate NQPF. Move Beyond Technological Determinism. Our results show that merely promoting corporate digitalization (H1) is not enough. The macro-level gains are "unlocked" by institutional quality. Policymakers must therefore adopt a synergistic approach, coupling digital infrastructure investment with robust institutional reforms. Build a "Threshold-Surpassing" IPP Regime. The most critical policy insight comes from H3b: a weak or "half-way" IPP system is ineffective and may even slow the digital economy's productivity spillovers. The policy goal must not be simply to "improve" IPP, but to aggressively strengthen it past the critical activation threshold identified in our analysis (see Table 8 ). Only a strong, credible IPP framework can de-risk corporate innovation and change digital investment into public productivity gains (Liu & Lin, 2025 ). Incentivize ESG as an "Efficiency Converter." The "increasing marginal return" of ESG (H2b) implies that firms with high ESG performance are the most efficient converters of digital inputs into NQPF outputs (see Table 8 ). Public policies, such as green credits or technology subsidies, should be given first to these high-ESG firms, as they will generate the greatest public return on investment for the region. Target High-Potential Regions and Industries. Our heterogeneity analysis (see Tables 9 – 11 ) provides a map for precision policymaking. The findings suggest that digital transformation investments give the highest marginal returns in non-coastal and non-resource-based regions. Furthermore, support should be focused on high-tech and competitive industries, as these are the primary sectors where the synergies of DT, ESG, and IPP are most effectively realized. 5.4. Limitations and Future Research Directions Although this study provides new insights into the micro-to-macro links driving NQPF, we acknowledge several limitations that offer clear pathways for future research. First, the measurement of NQPF remains a primary challenge. As NQPF is a new and complex macroeconomic concept, our three-element, theory-based index is a robust but not complete approximation. Future research could refine this regional index by adding other dimensions, such as the level of AI development or technological dynamism (Chin et al., 2025 ; Liu & Li, 2025 ). Or, scholars could try to develop and validate a corporate-level measure of NQPF, which would allow for a direct micro-to-micro analysis of how DT, ESG, and IPP interact within the enterprise (Guo et al., 2025 ; Xue & Chen, 2025 ). Second, our measurement of the key variables relies on effective but imperfect proxies. Our text-analysis-based DT variable captures strategic intent but not the specific type or quality of digital technology adopted. Similarly, our composite ESG rating, while comprehensive, masks the different effects of its individual "E", "S", and "G" components. Future research using more detailed data—such as corporate capital expenditures on AI, blockchain, or IoT, or separate analyses of the "E", "S", and "G" pillars—could unpack these mechanisms in greater detail. Third, while we have used a 2SLS-IV approach and multiple fixed effects to reduce endogeneity, establishing clear causality in a non-experimental, cross-level setting is very difficult. Our findings, while robust, are still based on correlational (panel) data. A good path for future research would be to use a quasi-natural experiment. For instance, an exogenous shock—such as the sudden implementation of a regional IPP pilot program or a new mandatory ESG disclosure rule—could provide cleaner identification of the causal effects we propose (cf. Zhang et al., 2025 ). Finally, our findings are based on Chinese A-share listed companies, which are typically large, mature, and publicly visible firms. The ability to generalize our "technology-institution" synergy model to small and medium-sized enterprises (SMEs), which face different resource constraints and innovation incentives, remains an open question. Furthermore, testing this model in other national contexts, such as different emerging markets or developed economies, would provide valuable insights into how the specific institutional environment (e.g., legal origins, market structures) shapes the relationship between digitalization and advanced productivity. 6. Conclusion This study investigates how corporate digital transformation (DT) contributes to regional new quality productive forces (NQPF) in the context of China's high-quality development agenda. Using a matched panel dataset matching Chinese A-share listed firms (2013–2022) to their corresponding provinces, our analysis reveals a strong positive relationship: the combination of corporate digitalization efforts significantly improves the NQPF of the regions in which they operate. This finding supports the "micro-to-macro" logic, suggesting that corporate technological investments generate large positive spillovers that lift regional productivity (Pan et al., 2022 ). However, this micro-to-macro link is not uniform; it strongly depends on the institutional environment. We find that the firm's internal commitment to sustainability—its ESG performance—acts as a powerful amplifier. Critically, this effect shows an "increasing marginal return": as corporate ESG performance improves, its digital investments become much more effective at driving regional NQPF (Guo et al., 2025 ; Xue & Chen, 2025 ). Furthermore, the external institutional environment—regional Intellectual Property Protection (IPP)—functions as a prerequisite. Our threshold analysis reveals that the positive impact of DT is suppressed or non-existent in regions with weak IPP. Only after IPP strength crosses a critical threshold is the "de-risking" effect activated, "unlocking" corporate digital innovation to generate significant macroeconomic productivity gains (Kim et al., 2012 ). The heterogeneity analysis further shapes these dynamics. The positive effect of corporate DT is most clear among firms with greater financial capacity (low financing constraints) and stable operational foundations (mature life-cycle stage) (Dickinson, 2011 ; Hadlock & Pierce, 2010 ). The effect is also significantly stronger in high-tech and competitive industries, where innovation is a core driver. Notably, we find the effect is strongest in non-coastal and non-resource-based regions, suggesting digital transformation serves as a critical "path-breaking" strategy for regions moving beyond traditional endowments. Overall, this study offers a comprehensive understanding of how the interaction between corporate technology adoption and multi-level institutional alignment shapes regional productivity. By using a "technology-institution" model within a cross-level framework (Lv et al., 2025 ), it contributes to the NQPF literature by showing that synergy, not singularity, is the key. Ultimately, our findings suggest that building new quality productive forces is a dual challenge. It requires not only promoting digital transformation (the engine) but also at the same time building the institutional "rules of the road"—both internal (corporate ESG governance) and external (robust IPP)—that are needed to guide technological potential into sustainable, high-quality development. Declarations ETHICAL APPROVAL: This article does not contain any studies with human participants performed by any of the authors INFORMED CONSENT: This article does not contain any studies with human participants performed by any of the authors Author Contribution Zhen Ren : Conceptualization, Methodology, Formal analysis, Funding acquisition, Investigation, Writing– original draft, Writing– review & editing. Shengyang Zhong: Methodology, Formal analysis, Validation,Writing– original draft. Zhi Li: Data curation, Formal analysis,Validation, Software. Ruihuan Fu: Conceptualization, Methodology, Formal analysis, Funding acquisition, Investigation. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References Aghion P, Jones BF, Jones CI (2019) Artificial intelligence and economic growth. The economics of artificial intelligence: An agenda. University of Chicago Press, pp 237–282 Akerman A, Gaarder I, Mogstad M (2015) The skill complementarity of broadband internet. Q J Econ 130(4):1781–1824 Albuquerque R, Koskinen Y, Zhang C (2019) Corporate social responsibility and firm risk: Theory and empirical evidence. Manage Sci 65(10):4451–4469 Ang JB (2010) Financial reforms, patent protection, and knowledge accumulation in India. World Dev 38(8):1070–1081 Branstetter LG, Fisman R, Foley CF (2006) Do stronger intellectual property rights increase international technology transfer? Empirical evidence from U.S. firm-level data. Q J Econ 121(1):321–349 Chin T, Li Z, Huang L, Li X (2025) How artificial intelligence promotes new quality productive forces of firms: A dynamic capability view. Technological Forecast Social Change 216:124128 Dickinson V (2011) Cash flow patterns as a proxy for firm life cycle. Acc Rev 86(6):1969–1994 Friede G, Busch T, Bassen A (2015) ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. J Sustainable Finance Invest 5(4):210–233 Gillan SL, Koch A, Starks LT (2021) Firms and social responsibility: A review of ESG and CSR research in corporate finance. J Corp Finance 66:101889 Guo Y, Hu J, Dou Y, Lin S (2025) Can ESG rating help shape enterprises' new-quality productivity? Evidence from China. Int Rev Econ Finance 104:104654 Hadlock CJ, Pierce JR (2010) New evidence on measuring financial constraints: Moving beyond the KZ index. Rev Financial Stud 23(5):1909–1940 Hansen BE (1999) Threshold effects in non-dynamic panels: Estimation, testing, and inference. J Econ 93(2):345–368 Huang Q, Yu Y, Zhang S (2019) Internet development and manufacturing productivity improvement: Internal mechanism and China's experience. China Industrial Econ (8): 5–23 Kim YK, Lee K, Park WG, Choo K (2012) Appropriate intellectual property protection and economic growth in countries at different levels of development. Res Policy 41(2):358–375 Lei L, Zhang D, Ji Q (2023) Common institutional ownership and corporate ESG performance. Econ Res J 58(4):133–151 Li Z, Ren Z (2025) How does the construction of a unified national market drive the development of new quality productive forces? J Beijing Technol Bus Univ (Social Sciences) 40(5):22–33 Liu H, Li X (2025) How digital technology can improve new quality productive forces? Perspective of total factor agricultural carbon productivity. J Asian Econ 98:101921 Liu Y, Lin Y (2025) Converting knowledge into Productivity: The role of intellectual property empowerment and digital economy in enhancing regional new quality productivity forces-Evidence from China. Int Rev Econ Finance 102:104316 Lv H, Lian Y, Hua X (2025) The relationship between FDI quality, new quality productivity forces, and economic resilience in China's Yangtze River Delta region: insights from an empirical spatial Durbin model. Humanit Social Sci Commun 12:1083 Nunn N, Qian N (2014) US food aid and civil conflict. Am Econ Rev 104(6):1630–1666 Pan W, Xie T, Wang Z, Ma L (2022) Digital economy: An innovation driver for total factor productivity. J Bus Res 139:303–311 Rezaee Z, Tuo L (2019) Are the quantity and quality of sustainability disclosures associated with the innate and discretionary earnings quality? J Bus Ethics 155(3):763–786 Saito Y (2017) Effects of patent protection on economic growth and welfare in a two-R&D-sector economy. Econ Model 62:124–129 Shen G, Huang S (2019) The impact of city-level intellectual property protection on FDI entry in China. J Financial Economic Res 40(12):143–157 Siebel TM (2019) Digital transformation: Survive and thrive in an era of mass extinction. Rosetta Books Song D, Zhu W, Ding H (2022) Can firm digitalization promote green technological innovation? An examination based on listed companies in heavy pollution industries. J Finance Econ 48(4):34–48 Tao F, Wang X, Xu Y, Zhu P (2023) Digital transformation, resilience of industrial chain and supply chain, and enterprise productivity. China Industrial Econ (5): 118–136 Wu F, Hu H, Lin H, Ren X (2021) Enterprise digital transformation and capital market performance: Empirical evidence from stock liquidity. J Manage World 37(7):130–144 Wu L, Hitt LM, Lou B (2020) Data analytics, innovation, and firm productivity. Manage Sci 66(5):2017–2039 Xi JP (2024) Advancing New Quality Productive Forccs Is Esscntial and a Key Priority for Fostering High-Quality Development. CPC Cent Comm Bimon (QIUSHI) (5): 2–8 Xue R, Chen J (2025) ESG performance and stability of New Quality Productivity Forces: From perspective of China's modernization construction. Int Rev Econ Finance 98:103911 Yoo Y, Boland RJ, Lyytinen K, Majchrzak A (2012) Organizing for innovation in the digitized world. Organ Sci 23(5):1398–1408 Yuan C, Xiao T, Geng C, Sheng Y (2021) Digital transformation and enterprise division of labor: Specialization or vertical integration. China Industrial Econ (9): 137–155 Zhang J, Wang X, Zhang W, Wang L (2025) Can the carbon emissions trading policy promote the development of new quality productive forces in manufacturing enterprises? Finance Res Lett 83:107617 Zhou Z, Wang S, Zhang Y (2022) Intellectual property protection and the information dilemma of corporate innovation. China Industrial Econ (6): 136–154 Additional Declarations No competing interests reported. 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-8115547","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":565495920,"identity":"9600df0e-6871-4cf7-a824-48ee0ea4f088","order_by":0,"name":"Zhen Ren","email":"","orcid":"","institution":"Capital University of Economics and Business","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Ren","suffix":""},{"id":565495921,"identity":"63b35e41-46c6-4f49-aaee-788f78ec155d","order_by":1,"name":"Shengyang Zhong","email":"","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shengyang","middleName":"","lastName":"Zhong","suffix":""},{"id":565495922,"identity":"a4f99038-6db7-48b5-b3cb-dd2624827eb7","order_by":2,"name":"Zhi Li","email":"","orcid":"","institution":"Capital University of Economics and Business","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Li","suffix":""},{"id":565495923,"identity":"1653646d-a003-459f-8afd-aefcb1afe455","order_by":3,"name":"Ruihuan Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBAC9gYgkdgAYRkAKcYGQlp4DsC0AFkkaAErk0gACxChRSL3mcTDHYflzSWfPyjmYbCR3XCA+dkD/FrSzSQSzxw23Dk7IcGYhyHNeMMBNnMDfFrsJdLYJBLbDjNuuJ1wAKjlcOKGAzxsEvhtgWix33DzYANQy3/itSRuuMHMANRygAgtPM+YLRLb0pM3nEljMJxjkGw88zCbGX4t7GmMN3+2WdtuOH78mcGbCjvZvuPNz/BqQQZsBuDIZCZWPUjtAxIUj4JRMApGwQgCAJu1Ro4CZyWlAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Ruihuan","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2025-11-14 13:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8115547/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8115547/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99313707,"identity":"399a5116-6eb7-4fb4-94f9-fafecbe57717","added_by":"auto","created_at":"2025-12-31 16:20:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":216765,"visible":true,"origin":"","legend":"","description":"","filename":"DigitalTransformationandNewQualityProductiveForcesDevelopmentTheModeratingEffectsofCorporateESGPerformanceandIntellectualPropertyProtection.docx","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/8b69c0d472bace0d8d2415d8.docx"},{"id":99022685,"identity":"8e4c2db5-2d38-4828-a5e0-940b3117c0a7","added_by":"auto","created_at":"2025-12-26 06:02:38","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6060,"visible":true,"origin":"","legend":"","description":"","filename":"48b092158f9047faa90d217b76e7e9a7.json","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/229206cde082dada9285477b.json"},{"id":99022696,"identity":"ae6a9a8b-a771-4c5a-8b7d-7d21024909b1","added_by":"auto","created_at":"2025-12-26 06:02:38","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172869,"visible":true,"origin":"","legend":"","description":"","filename":"48b092158f9047faa90d217b76e7e9a71enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/41aed9431893d18178bb2d27.xml"},{"id":99022695,"identity":"005bdaf3-4e77-4467-8ca7-9994dd95984c","added_by":"auto","created_at":"2025-12-26 06:02:38","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137756,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/0403a495e0079366745f51a1.png"},{"id":99022687,"identity":"e253c219-15b2-4c18-a02d-a26bb711636d","added_by":"auto","created_at":"2025-12-26 06:02:38","extension":"wmf","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1460,"visible":true,"origin":"","legend":"","description":"","filename":"image1.wmf","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/a6f4994fa336a0aa9d7b99e0.wmf"},{"id":99022688,"identity":"0109feb4-5bfe-4d92-a1e3-58dfa990f9c7","added_by":"auto","created_at":"2025-12-26 06:02:38","extension":"wmf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1694,"visible":true,"origin":"","legend":"","description":"","filename":"image2.wmf","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/49dfdaae1422c8a36f10e86f.wmf"},{"id":99313429,"identity":"20e8fd37-e895-481e-95ad-fec964d4abff","added_by":"auto","created_at":"2025-12-31 16:20:08","extension":"wmf","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1840,"visible":true,"origin":"","legend":"","description":"","filename":"image3.wmf","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/21118a4950a2a0e1a7e4a6f2.wmf"},{"id":99313339,"identity":"4234b762-41b5-4ec4-996c-335a4093d152","added_by":"auto","created_at":"2025-12-31 16:20:01","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32665,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/c5d8b854640cb37d260d8714.png"},{"id":99022693,"identity":"30c11518-18e1-42d6-9f48-1145cd87c6b4","added_by":"auto","created_at":"2025-12-26 06:02:38","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1630,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/a3a7147ad990df98bc64b843.png"},{"id":99314222,"identity":"0c253acc-363a-4f3c-b4bd-fe4f6b34231e","added_by":"auto","created_at":"2025-12-31 16:21:00","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2108,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/b1a33885e447c249899ac40e.png"},{"id":99022694,"identity":"5a605775-2306-4b48-a721-9f79ee50bdcb","added_by":"auto","created_at":"2025-12-26 06:02:38","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2428,"visible":true,"origin":"","legend":"","description":"","filename":"Onlineimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/a1a29792b7021034a4ba1dc1.png"},{"id":99022697,"identity":"c038bf1c-c053-4750-b05c-b385cc5de4d2","added_by":"auto","created_at":"2025-12-26 06:02:38","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170944,"visible":true,"origin":"","legend":"","description":"","filename":"48b092158f9047faa90d217b76e7e9a71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/fa8fb97d28008d125ff60f95.xml"},{"id":99022698,"identity":"19563676-1a71-462a-87f0-b62da14243db","added_by":"auto","created_at":"2025-12-26 06:02:38","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179650,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/d14af12e376b7fe4bff535a2.html"},{"id":99312933,"identity":"3f9e5a2d-e136-4622-929c-94f4072fe632","added_by":"auto","created_at":"2025-12-31 16:19:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKernel Density Estimation of New Quality Productivity Forces.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/47f9dd5203ef9d29dbb678f9.png"},{"id":101692321,"identity":"1250e877-00c3-48f1-b2b9-a3a902a51a93","added_by":"auto","created_at":"2026-02-02 16:17:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2010308,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8115547/v1/dbd2c024-996e-479f-9e65-a3ec90171bd9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Transformation and New Quality Productive Forces Development: The Moderating Effects of Corporate ESG Performance and Intellectual Property Protection","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eThe global economy is currently navigating a period of profound transformation, defined by the dual challenges of diminishing productivity growth and an urgent sustainability imperative. Traditional production factors are giving smaller returns. Although digital technologies are widespread (Siebel, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a productivity jump has not happened. This is called the modern \"productivity paradox\" (Brynjolfsson et al., 2021). The effect of this change on firm and macroeconomic productivity is still debated (Aghion et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Also, growing climate concerns and societal demands for sustainability place strong limits on the traditional, resource-intensive growth model (Friede et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gillan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This situation has created a key problem for policymakers and firms worldwide: how to start a new engine for innovation-driven productivity that also follows the limits of a green transition?\u003c/p\u003e \u003cp\u003eTo answer these common global challenges, new development models are being studied. For example, China has introduced the concept of \"New Quality Productive Forces\" (NQPF). It is a strategic framework to guide its next stage of high-quality development and get a lasting competitive advantage (Xi, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Unlike traditional productivity, NQPF is an advanced productivity form. It is defined by new technological breakthroughs, innovative factor mixes, and deep industrial transformation (Chin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu \u0026amp; Lin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Its core characteristics are innovation-led and quality-driven. Importantly, it is seen as an advanced form of productivity that is naturally \"green\" (Liu \u0026amp; Li, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This new concept handles the two challenges of innovation and sustainability. It has already started to get significant attention in international academic discussions (Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xue \u0026amp; Chen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorporate Digital Transformation (DT) is seen as the main way to allow NQPF. This is because it provides the needed technological foundation for the factor jumps and industrial changes that NQPF requires. But this \"micro-to-macro\" link\u0026mdash;from firm technological adoption to regional productivity transformation\u0026mdash;is not direct or automatic. Its success depends on the institutional environment. We propose that this environment works at two key levels: (1) the internal firm commitment to sustainability, shown by Environmental, Social, and Governance (ESG) performance, which guides the quality and direction of digital investments (Gillan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); and (2) the external institutional setting of the region, especially the strength of its Intellectual Property Protection (IPP), which shapes the innovation incentives for all firms there (Kim et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liu \u0026amp; Lin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo support this framework, a review of the growing literature shows a big gap at this cross-level point. Although some studies show a direct link between the digital economy and macroeconomic productivity (Liu \u0026amp; Li, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and others separately study the macro-level impact of ESG (Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) or IPP (Liu \u0026amp; Lin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) on NQPF, the total mechanism of how firm actions produce these macro-outcomes remains a \"black box.\" Importantly, an understanding of the factors that control this micro-to-macro change is missing. It remains unclear how a firm's internal ESG performance affects the change of its own digital efforts into public, regional productivity gains. And it is unclear whether this effect shows an \"increasing marginal trend\". Also, the role of the regional external IPP environment is not well understood. This includes whether its moderating effect is linear or shows the clear non-linear threshold effect we propose.\u003c/p\u003e \u003cp\u003eChina provides an ideal place for studying this cross-level mechanism. Its economy is both driven by firm energy and strong top-down regional governance. To build our test, we develop a new dataset by matching: (1) data from Chinese A-share listed companies (2013\u0026ndash;2022) to measure Digital Transformation (DT) and ESG performance; and (2) data for the matching regions to measure New Quality Productive Forces (NQPF) and Intellectual Property Protection (IPP). Using this matched panel dataset, our study empirically tests: first, the total impact of corporate DT on regional NQPF; and second, the non-linear, threshold-based moderating effects of both corporate ESG and regional IPP.\u003c/p\u003e \u003cp\u003eThis study makes several new contributions to the literature at the intersection of digitalization, institutional economics, and regional economic development. First, we contribute to the NQPF theory by proposing and testing a cross-level analytical framework. Although most studies discuss NQPF at a macroeconomic level (Liu \u0026amp; Lin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), our research connects the micro-macro divide by studying how corporate strategic actions (Digital Transformation) combine to shape regional productivity outcomes. Second, we provide new empirical evidence on the conditional nature of this micro-to-macro link. To the best of our knowledge, this is the first study to show that the impact of corporate digitalization on regional NQPF is not the same. Instead, it strongly depends upon the firm's internal institutional commitment (ESG performance) and the external institutional environment (IPP) (Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Third, and most importantly, we move beyond simple linear assumptions to find complex non-linear mechanisms. We show that the moderating effects of both ESG and Intellectual Property Protection are not linear but follow clear threshold patterns. We find that the stronger effect of ESG shows an \"increasing marginal trend\", while the helping effect of IPP is only \"unlocked\" after it crosses a critical threshold. This two-part finding provides detailed evidence on the complex interaction between technology and institutions (Kim et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Finally, our findings offer more exact policy implications. To grow regional NQPF, policymakers cannot simply order corporate digitalization. They must also create a strong institutional environment where IPP is strong enough (passing the threshold) and at the same time encourage internal firm governance (ESG practices) to fully change corporate digital investments into macro-level productivity gains.\u003c/p\u003e"},{"header":"2. Literature Review and Hypothesis Development","content":"\u003cp\u003eRegional economic outcomes, like an area's productivity, are not abstract phenomena. They are the emergent result of the aggregated actions and interactions of their key actors, primarily firms (Aghion et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The strategic choices made by individual enterprises\u0026mdash;about technology adoption, investment, and innovation\u0026mdash;generate knowledge spillovers, shape regional supply chains, and cultivate specialized labor markets. These aggregated firm behaviors form the region's collective industrial structure and innovative capacity (Pan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, to understand the drivers of a macroeconomic concept like regional New Quality Productive Forces (NQPF), it is essential to adopt this cross-level perspective. This study builds its theoretical framework on this micro-to-macro logic. We argue that the transformation of regional productivity is driven by the strategic transformations happening within its constituent firms.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Digital Transformation and New Quality Productive Forces\u003c/h2\u003e \u003cp\u003eThe core technological driver for NQPF is widely identified as Digital Transformation (DT) (Liu \u0026amp; Li, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Following Marxist productivity theory, NQPF represents a qualitative leap in the key elements of production: the laborer, the means of labor, and the object of labor (Chin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu \u0026amp; Lin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Corporate DT is the catalyst for this three-fold leap, improving internal capabilities through new mechanisms. By using data analytics, artificial intelligence, and digital platforms, firms can optimize resource orchestration, improve supply-demand matching, and improve overall resource allocation efficiency. This, in turn, boosts their innovation output and productivity (Wu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Siebel, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecifically, this transformation of the three elements happens as follows: (1) Cultivating \"high-quality laborers\", as AI and automation create a complementary effect with high-skilled labor while substituting for low-skilled tasks, which upgrades the human capital structure (Akerman et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); (2) Forging \"new-media means of labor\", as digital platforms reconfigure supply chains, optimize production processes, and improve inter-firm collaboration (Tao et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); and (3) Expanding the \"new-material objects of labor\", moving beyond traditional physical inputs to include data as a core production factor and enabling the use of green, sustainable materials (Song et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yoo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis corporate-level efficiency, however, does not stay contained within the enterprise. As a large number of firms within a region adopts digital technologies, their aggregated actions generate important positive externalities that lift the entire area's productive capacity. First, widespread corporate digitalization builds a regional innovation ecosystem through greater knowledge spillovers. Second, it optimizes inter-firm collaboration, creating more resilient and efficient regional supply chains. Third, it collectively cultivates a high-skilled labor pool skilled in digital technologies, raising the quality of the regional labor factor. These aggregated effects directly contribute to the \"innovative configuration of production factors\" and \"deep industrial transformation\" that define NQPF at the regional level.\u003c/p\u003e \u003cp\u003eBy allowing this shift towards more innovative, efficient, and greener production models across the region, the aggregation of corporate DT acts as the primary engine for developing regional NQPF. Therefore, based on this micro-to-macro logic, we propose our first hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(H1).\u003c/b\u003e \u003cem\u003eCorporate digital transformation has a significant positive effect on regional new quality productive forces.\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The Threshold-Moderating Role of ESG Performance\u003c/h2\u003e \u003cp\u003eAlthough digital transformation (DT) provides the technological engine, its ability to change into regional NQPF is not the same across all firms. This micro-to-macro process depends on the firm's internal institutional environment and strategic orientation (Zhou et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Based on stakeholder theory, we argue that a firm's Environmental, Social, and Governance (ESG) performance is a critical internal moderator in this relationship (Friede et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gillan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh-ESG performance signals a firm's commitment to sustainable long-term value over short-term gains, meeting the expectations of diverse stakeholders (Friede et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gillan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This commitment builds an internal environment that strengthens the productive returns of DT through three primary channels, directly aligning with the core components of NQPF: (1) Environmental (E): High-ESG firms are strategically more likely to adopt green manufacturing and circular economy models. They guide their digital investments toward long-term, sustainable innovations (e.g., clean production processes), which directly supports the naturally \"green\" dimension of NQPF (Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xue \u0026amp; Chen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). (2) Social (S): Better \"Social\" performance improves a firm's ability to attract and retain the high-quality human capital that forms the \"high-quality laborer\" element of NQPF (Albuquerque et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This upgraded talent pool is better at using digital tools, improving data integration, and doing complex innovations, thus maximizing the efficiency of DT initiatives. (3) Governance (G): Better \"Governance\" optimizes internal processes and ensures that managerial decision-making aligns with long-term value creation (Rezaee \u0026amp; Tuo, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This disciplined governance ensures that digital investments are guided into real, high-return, NQPF-aligned projects rather than being diverted by short-term opportunism.\u003c/p\u003e \u003cp\u003eThus, a firm's internal commitment to sustainability is a key factor of whether its technological investments change into public productivity gains. Based on this three-pillar mechanism, we propose:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003e \u003cb\u003ea (H2a).\u003c/b\u003e \u003cem\u003eCorporate ESG performance positively moderates the relationship between corporate digital transformation and regional new quality productive forces.\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFurthermore, this positive moderating effect may not be linear. We argue that the strengthening effect of ESG performance on the DT-NQPF link shows an \"increasing marginal return.\" At low levels of ESG performance, a firm's sustainability efforts are often superficial, fragmented, or just for ceremonial compliance. As a result, its digital transformation initiatives may remain scattered, with only a weak alignment with NQPF goals.\u003c/p\u003e \u003cp\u003eHowever, as corporate ESG performance improves, it signals a deep, structural, and cultural integration of sustainability into the core strategy (Gillan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). At this substantive stage, the firm's entire operation\u0026mdash;from talent acquisition to R\u0026amp;D direction (Albuquerque et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u0026mdash;is systemically aligned with high-quality, sustainable goals. Consequently, each additional unit of digital investment is guided more effectively toward generating real, high-quality innovations (e.g., green technologies, resilient supply chains) that produce strong positive externalities for regional NQPF. Thus, the strengthening effect of ESG is not constant; it becomes stronger as ESG performance improves.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003e \u003cb\u003eb (H2b).\u003c/b\u003e \u003cem\u003eAs corporate ESG performance optimizes, the positive moderating effect on the relationship between corporate digital transformation and regional new quality productive forces demonstrates an \"increasing marginal trend.\"\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The Threshold-Moderating Role of Intellectual Property Protection\u003c/h2\u003e \u003cp\u003eBeyond the firm's internal governance (ESG), the external institutional environment of the region plays a key role in shaping the outcomes of corporate digitalization. We argue that Intellectual Property Protection (IPP) is a critical external moderator. This is because digital transformation, while helping innovation, also introduces major intellectual property risks. Unlike traditional innovations, digital assets (such as algorithms, proprietary data, and software code) are often sent via online channels, making copying more hidden, rapid, and widespread. This high-risk environment can stop firms from doing deep digital innovation for fear of \"free-riding\" (Ang, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA strong regional IPP framework directly fights this risk by increasing the costs and penalties for copying. It provides a credible legal safeguard, ensuring that firms can capture the economic returns from their digital investments (Saito, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Also, strong IPP improves information transparency and secures inter-firm collaborations, which are needed for building the digital supply chains and innovation ecosystems that support regional NQPF (Liu \u0026amp; Lin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). When firms see the regional IPP environment as strong, they are more motivated to invest in high-risk, high-return digital R\u0026amp;D, and it is these high-quality corporate activities that combine into regional productivity gains.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003e \u003cb\u003ea (H3a).\u003c/b\u003e \u003cem\u003eRegional intellectual property protection positively moderates the relationship between corporate digital transformation and regional new quality productive forces.\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003eHowever, drawing from institutional economics, we argue that the impact of IPP is not always positive; it is likely dependent upon the level of IPP strength and shows a non-linear, threshold-based pattern. This non-linearity comes from the dual nature of IPP, especially in its early stages. First, getting and enforcing patents has direct costs, which can be a major burden (especially for smaller firms), possibly hurting their digital innovation incentives. Second, an early IPP system (i.e., below a critical threshold) can actively slow innovation in an economy that is still reliant on imitation.\u003c/p\u003e \u003cp\u003eBy limiting the knowledge spillovers and technology diffusion that imitation-based firms depend on\u0026mdash;without yet being strong enough to encourage real, breakthrough innovation\u0026mdash;a weak IPP regime can stall digital adoption and hurt overall innovation efficiency (Kim et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, the positive moderating effect proposed in H3a likely only activates after regional IPP strength crosses a critical threshold. Only when the legal framework is strong enough can it provide a real deterrent and guarantee that the returns from real innovation are greater than the costs of both R\u0026amp;D and IP registration. This institutional security de-risks digital R\u0026amp;D, releasing corporate investment in transformative technologies rather than imitative applications. It is these high-quality, protected innovations that generate the major positive spillovers needed to drive a regional NQPF transformation.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003e \u003cb\u003eb (H3b).\u003c/b\u003e \u003cem\u003eThe moderating effect of regional IPP on the relationship between corporate DT and regional NQPF is non-linear and subject to a threshold. The positive moderation effect is significantly stronger, or only becomes significant, after IPP strength surpasses a critical value.\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Data\u003c/h2\u003e\n \u003cp\u003eOur study uses a comprehensive, matched panel dataset. The initial sample consists of all A-share listed companies on the Shanghai and Shenzhen stock exchanges for the period from 2013 to 2022. To ensure data quality, we apply a standard screening process: (1) we exclude firms designated as ST (Special Treatment) or *ST (delisting risk) due to their abnormal financial status; (2) we exclude firms in the financial industry, as their accounting standards and business models differ significantly from non-financial firms; and (3) we remove observations with severe missing data for our core variables. This process yields a final unbalanced panel dataset of 31598 firm-year observations.\u003c/p\u003e\n \u003cp\u003eA key feature of our research design is the matching of corporate data with regional data. Corporate data, including information for Digital Transformation (DT), ESG performance, and financial controls, were primarily sourced from the China Stock Market and Accounting Research (CSMAR) database, the China Research Data Services Platform (CNRDS), and the Wind Information database. Regional data, used to construct our dependent variable (New Quality Productive Forces, NQPF) and our external moderating variable (Intellectual Property Protection, IPP), as well as regional control variables, were manually collected and compiled from various official publications, including the \u003cem\u003eChina Statistical Yearbook\u003c/em\u003e, the \u003cem\u003eChina Torch Statistical Yearbook\u003c/em\u003e, the \u003cem\u003eChina High-tech Industry Statistical Yearbook\u003c/em\u003e, the \u003cem\u003eChina Environmental Statistical Yearbook\u003c/em\u003e, and the \u003cem\u003eNational Intellectual Property Development Status Report\u003c/em\u003e. We matched the corporate data to the corresponding regional data based on the province where each firm is registered. To mitigate the impact of outliers, all continuous variables were winsorized at the 1% and 99% levels. All monetary variables were deflated to constant 2013 prices.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Measurements\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1. Dependent Variable\u003c/h2\u003e\n \u003cp\u003eOur dependent variable, New Quality Productive Forces (NQPF) is a regional construct representing an advanced productivity state defined by high technology, high efficiency, and high quality (Lv et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Given that NQPF is a novel and comprehensive concept, we constructed a composite evaluation index system based on its theoretical underpinnings. Specifically, and following the theoretical logic of this study rooted in Marxist productivity theory, we define NQPF through the qualitative leap and optimal configuration of its three fundamental elements: the laborer, the means of labor, and the objects of labor (Chin et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu \u0026amp; Lin, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eDrawing upon this framework (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and indicator systems in related NQPF literature (Liu \u0026amp; Lin, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Li \u0026amp; Ren, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), our measurement is structured around these three sub-systems. The \u0026quot;\u0026apos;High-Quality\u0026apos; Laborer\u0026quot; dimension captures the enhancement of human capital, operationalized through indicators of population health (e.g., medical insurance coverage), cultural quality (e.g., advanced human capital metrics), and skill level (e.g., R\u0026amp;D personnel and patent grants). The \u0026quot;\u0026apos;New-Media\u0026apos; Means of Labor\u0026quot; dimension reflects the advancement of production tools, focusing on information infrastructure (e.g., internet domain counts) and innovation infrastructure (e.g., national tech incubators). Finally, the \u0026quot;\u0026apos;New-Material\u0026apos; Objects of Labor\u0026quot; dimension measures the greening and advancement of production inputs, proxied by the supply of new materials (e.g., high-tech product development) and the exploration of new energy (e.g., green patents).\u003c/p\u003e\n \u003cp\u003eWe collected regional data for each indicator and utilized the Entropy Weight TOPSIS method to calculate a comprehensive NQPF index for each province in each year. This method objectively assigns weights based on the information content of each indicator, avoiding subjective bias.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEvaluation index system of NQPF.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall Indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTier-1 Indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTier-2 Indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTier-3 Indicator\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\" rowspan=\"18\"\u003e\n \u003cp\u003eNew Quality Productive Forces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e\u0026quot;High-Quality\u0026quot; Laborer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePhysical Fitness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Basic Medical Insurance Fund Revenue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Healthcare Institutions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Consumption of Meat, Eggs, and Dairy Products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHuman Caliber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman Capital Upgrading\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of Employees with College Degrees or Above in Torch Specialized Industrial Bases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSkill Competency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Employees in National-Level Technology Business Incubators\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Domestic Patent Applications Granted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u0026quot;New Medium\u0026quot; Means of Labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eInformation Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternet Domain Names per 10,000 People\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComputers per 100 People\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed Asset Investment in Information Transmission Information Transmission, Software, and IT Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eInnovation Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of National-Level Technology Business Incubators\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of National University Science Parks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Torch Program Specialized Industrial Bases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026quot;New Material\u0026quot; Objects of Labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eNew Material Supply\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of New Product Development Projects in High-Tech Industries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Strategic Emerging Industry Projects Facilitated by National Technology Transfer Demonstration Institutions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Major Technology Transfer Projects Facilitated by National Technology Transfer Demonstration Institutions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNew Energy Exploration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Green Patents Granted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvestment in Industrial Pollution Control\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\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2. Independent Variable\u003c/h2\u003e\n \u003cp\u003eOur independent variable is Corporate Digital Transformation (DT). Following prior literature (Yuan et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), we use a machine-learning-based text analysis approach. We first build a comprehensive dictionary of digital transformation keywords. Then, using Python, we process the \u0026quot;Management Discussion and Analysis\u0026quot; (MD\u0026amp;A) section of each firm\u0026apos;s annual report to calculate the frequency of these keywords. The DT variable is thus measured as the ratio of the total frequency of these digital keywords to the total length of the MD\u0026amp;A section, reflecting the firm\u0026apos;s strategic focus and investment in digitalization.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3. Moderating Variables\u003c/h2\u003e\n \u003cp\u003eWe test two institutional moderators at their respective levels:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCorporate ESG Performance (ESG)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAs our internal moderator, we measure ESG performance using the annual ESG ratings from the China Research Data Services Platform (CNRDS) database, a widely used source in related studies (Guo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xue \u0026amp; Chen, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This rating is standardized (divided by 100) for empirical analysis, following the methodology of Lei et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRegional Intellectual Property Protection (IPP)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAs our external moderator, we measure IPP using a city-level index matched to firms\u0026apos; registered locations. Following the methodology of Shen and Huang (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), this index is constructed based on the Revealed Comparative Advantage Comparative Advantage (RCA) of concluded intellectual property court cases within each city. The RCA-based metric reflects local governments\u0026apos; legal and enforcement capacity for innovation protection.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.4. Control Variables\u003c/h2\u003e\n \u003cp\u003eTo mitigate omitted variable bias, we include a comprehensive set of control variables at both the corporate and regional levels.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCorporate-level Controls\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe control for firm-specific characteristics that may influence digitalization and performance, including Return on Equity (ROE), Intangible Asset Ratio (INT), Inventory Ratio (INV), and Average Age of Management (MTA).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRegional-level Controls\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe control for regional macroeconomic conditions that may simultaneously affect both corporate behavior and regional NQPF, including Fiscal Autonomy (FIS), Government Science \u0026amp; Technology Support (GST), Regional Industrial Structure (RIS), Tax Collection and Management (TCM), and Pollution Control Efforts (PCE). Specifically, FIS is the ratio of local fiscal general budget revenue to budget expenditure; GST is the ratio of local fiscal science and technology expenditure to GDP; RIS is the ratio of the added value of the tertiary industry to the secondary industry; TCM is the ratio of local fiscal tax revenue to GDP; and PCE is the ratio of local fiscal environmental protection expenditure to general budget expenditure\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Model Specification\u003c/h2\u003e\n \u003cp\u003eTo empirically test the hypotheses developed in the previous section, we build a series of cross-level fixed effects models. Our research design matches corporate-level independent variables to their corresponding provincial-level dependent variable.\u003c/p\u003e\n \u003cp\u003eFirst, to examine the baseline impact of corporate digital transformation on regional new quality productive forces (H1), we build the following panel data model:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere the subscripts \u003cem\u003ei, j\u003c/em\u003e and \u003cem\u003et\u003c/em\u003e denote the firm, province, and year, respectively. NQPF is the dependent variable, and DT is the independent variable. X is a vector of all corporate-level and regional-level control variables specified in section \u003cspan class=\"InternalRef\"\u003e3.2.4\u003c/span\u003e. To control for unobserved heterogeneity, we include firm fixed effects \u003cem\u003e\u0026micro;\u003c/em\u003e, province fixed effects \u003cem\u003e\u0026lambda;\u003c/em\u003e, and year fixed effects \u003cem\u003e\u0026phi;\u003c/em\u003e. \u003cem\u003e\u0026epsilon;\u003c/em\u003e is the stochastic error term. We cluster standard errors at the firm level to account for potential correlations.\u003c/p\u003e\n \u003cp\u003eSecond, to test the linear moderating effects of corporate ESG performance (H2a) and regional IPP (H3a), we extend Model (1) by adding an interaction term:\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"EquationNumber\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn this model, M is the moderating variable(either \u003cstrong\u003ec\u003c/strong\u003eorporate ESG or regional IPP). We are mainly interested in the coefficient \u003cem\u003e\u0026beta;\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e, which captures the moderating effect. A significantly positive \u003cem\u003e\u0026beta;\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e would provide support for H2a and H3a.\u003c/p\u003e\n \u003cp\u003eFinally, to investigate the non-linear moderating effects of ESG performance (H2b) and the threshold-based moderation of regional IPP (H3b), we employ the panel threshold regression model developed by Hansen (\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). This model allows the relationship between DT and NQPF to change regime based on the value of a specific threshold variable. The general single-threshold model is specified as:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAA6CAYAAABvXuvJAAAQAElEQVR4AezdBbwkxREG8G7i7m4Qd3c9ohd3N+Lu7pAQt4t7AnF3d+LuSuzi7i5A7j9HHXPD7L6V2bdPih/N7M70tHxd8lV172OPw/KfRCARSAQSgUQgEUgEEoFEYBUR2KPkP4lAIpAIJAJLQCC7TAQSgURg8yKQBHTzrn3OPBFIBBKBRCARSAQSgaUgsFQCupQZZ6eJQCKQCCQCiUAikAgkAktFIAnoUuHPzhOBRCARWAoC2WkikAgkAktFIAnoUuHPzhOBRCARSAQSgUQgEdh8CGxeArr51jpnnAgkAolAIpAIJAKJwJpAIAnomliGHEQikAgkApsHgZxpIpAIJAJJQFMGEoFEIBFIBBKBRCARSARWFYEkoKsKd3SW10QgEUgEEoFEIBFIBDYvAklAN+/a58wTgUQgEdh8COSME4FEYE0gkAR0TSxDDiIRSAQSgUQgEUgEEoHNg0AS0M2z1jHTvCYCiUAikAgkAolAIrBUBJKALhX+7DwRSAQSgURg8yCQM00EEoFAIAloIJHXRCARSAQSgUQgEUgEEoFVQSAJ6KrAnJ0EAnlNBBKBRCARSAQSgUQgCWjKQCKQCCQCiUAisPERyBkmAmsKgSSga2o5cjCJQCKQCCQCiUAikAhsfASSgG78Nc4ZBgJ5TQQSgUQgEUgEEoE1gUAS0DWxDDmIRCARSAQSgURg4yKQM0sEuggkAe0ikt8TgUQgEUgEEoFEIBFIBBaKQBLQhcKbjScCgUBeE4FEIBFIBBKBRCAQSAIaSOQ1EUgEEoFEIBFIBDYeAjmjNYlAEtA1uSw5qNVA4N///nd517veVb71rW+tRndL7cMczdWclzqQTd75H//4x/Ke97yn/PKXv9zwSHzyk58sH/rQh8p///vfDT/XnGAikAhMj8BUBPR3v/tdueAFL1hOcYpTNOXVr371bj3+4x//KNe85jWbZ1Hnla985W513vrWt5YtW7bsqnPnO9+5/OxnP9tV55BDDinaPdOZzrSrTrTlepe73KX89re/3VX/61//ejnnOc/ZW1f9KI94xCPKP//5z13v5YfRCHCS1uVUpzrVkXC90Y1u1DjQf/3rX7s1YJ3PeMYzHql+4B9X7S6aBFnnhzzkIeU0pznNrvGQEbISg/7LX/5Sbnvb25Yvf/nL5fSnP33cbmSErEw6Xn216z/qUY/a1dYa+tDM0Vzvec97lkXjP8u82YDLXvayu9Yr8Lc2dP4LX/hCYRvabcM66o26nuEMZyjbtm0r//vf/9qvDv6ZPF3jGtfYbfxsJZsZnfl8y1vesrFfJz7xieN2c/3mN79ZznWuczXvn/e85y3ve9/7ymGHHdY86/tPu/7FLnax8tnPfrav2lLv0bl3vvOd5TnPec7C8R9qoh/+8IdL246d7WxnK+5p/+Mf/3izPiFr3fVVZ7OVz3/+8+UiF7lIudOd7lT++te/brbp53znRGAqAnrSk560fO5znyvPetazyjGOcYzyile8YjdnduxjH7u86U1vKve///3Lla985fLVr3613OIWtyjIynvf+95y9atfvbz73e9uCOavf/3r8qUvfals3769XOta12oMKIN7lKMcpdzsZjcrr3nNaxoDfOpTn7p8+tOfLuq//e1vLwcddFC5wx3usIu0nuc852naPMc5zlFqrYWRUDcKQ36hC12onP3sZy9HP/rR54Rrc7x+ohOdqDz/+c8v++yzT/nb3/5WHvSgB5Wf//znzRrc6la3Ko985CPLDW94w11rIMMho4OcWtOf/OQnBdEUkJAHa+H+nnvu2cgD2Vkkksc61rHK4x//+PKMZzyjcPoc9Pvf//5CVvSLYD/60Y8uF7/4xcvDH/7wctzjHtftpngXsXnyk5/cyC0Z/vvf/9486/7n0EMPbeQRVhe+8IUbMrvffvt1q62J7+Zoruc+97nLwx72sAKDNTGwwwdx2tOetnzsYx8rV7jCFRrbcsABB5Rf/epX5eCDDy7nO9/5yo1vfONy97vfvZApryD+P/3pT8v97ne/goz98Ic/LJe+9KUbHUcYyBxbcLKTnaxc6UpXKkc96lG9trBy/OMfvyBbD3jAA8qf/vSnQndkANlMnRrr7W53u3LrW9+60KGuDiCfSDYb+aMf/aj8+Mc/PhLh1o5CpsmnOd/jHvdobB4Z92yaAkttkeNp3pu0LjvymMc8pvzmN78pL33pS8t//vOfSV9dWr299967vOAFLyjHO97xyslPfvIG28tf/vLNeC51qUs1vg+xRqqR/ljfpsIm+g9fzS8jnmwJ//6d73xnEyEwzVSz7igEpiKgGkEQRYgiw0996lOFkXU/SmQpbnCDGzQKLNvyspe9rDz0oQ8t17nOdcoLX/jCglSqL0P17Gc/u2jzwAMPLG1Hrx1Cjjye7nSnU70wssgPEqzvMJyuymUuc5my5w6S01Q+/D+MhayPaFU/h99et5ff//73xdzhushJwP+Yxzxm4UQY3nDgV7va1cr+++/fbFsLRGSWODI4IzjWVIb6i1/8YkP6kYey45+TnOQkhUzE9x23Fv4v8owYcPwIjg6NVxDkigi411fMARmV2RoV2X/jG98o++67b9ljjz2abOopT3nKvqbW1D1zNvfAYNzgkDzbxXAcV2+oZ8YF87Oe9axFwFhrLWRQpvo+97lPE/AesIOYkk0kD7F84AMfWJCA73//+01AKxvDPhkTWURc47t7iy6wogs3velNi7noj11j99gygZt7o4pA72hHO1oTILBp3Xr0/g1veEOTIUVa73WvezWku1tvku8CRTsXdqUkAvr6m6SdcXUEPhIKggH6Mq6uZ2y+tXRkZBHj0ce4UmttAtOtW7c2meo//OEPTXUyZwfB+iDTbJl1ah7O8Z9lz9fQ6R2fam6+T1L4ITtKH/jAB8onPvGJYvcisJrk/W4dwQkSi8x2n63G92X3vxpzpFf82iR9/fnPfy6T6OskbY2rMzUBpYiM1eUud7lywhOesIn6ZcCiE0Ioc4GgcsyyGkgmw2v7KerF1TYvwyyaCuHTh8yTzCnBbis6hXUfKfBZkS3Qr+3/yHLKDiiIk+zUXnvtFV2u66utyte97nW7kfVFTIiBYVxkDa1l9GFNBQJI6ctf/vKGiMrmuCcDbj2+973vFdGwzLZMgnetIRmI7+4tspBJTk/wYv2jLwrI0JGrUWMhSwgOuUQoEOx4P66ctzOVSMYpdxBPJAc28XzIK334yle+Ul772tfO3SxCYF52E2AxrkE69sxnPrOQhXH1Vno26XMOkDMTLNrmjPfo9HWve91maxSRg/1xjnOccpWrXKWpAh/2QzZPkGtNPCCXsvLx3b1FFuTQOOhMBML0AXFBqJDhcf1/+9vfbgi3dzmAPgImS8rxC6YvcYlLNJm6cW2Oe4bkX/va124yrQfuSADAdiWZGNfeqGd0g51/3vOet9uOWV99c5ZZ/OhHP7q0bXuydclLXrI5jiNQM047OOTzNre5TRnSl6yF+bKVb37zmwv5M9dJCp1EwiUWTnCCEzSYSFZM8m5fHTYWT/jFL37R93jh95bd/yInaH3Ze7tJbR9lp0hgL8Bn59u6X2st7KzdPXxrUeObmoDKiiBBtspkGz7ykY80W7MxQOQD6ZNx4rhskclQML6yGVEvrpwHR4e0qu++rBNDTqD1414UTh/xZdQYYeAy8AgOEqQewIyr1tpkp/Tf13dZ4D+ckUW15fmOd7xjV0/IDcW1BWycux4M/GHe/jk5Aot8MjDt4XHsMoscPsMMWxgTbgZVQODqGEa8Z6041vi+0hXhetKTntRb7Wtf+1rhMBmN3go7btqCNTZEk/Pbcav5l9KR0bOc5SyNbDQ3O/8hh2c+85nL3ju24xBQ8t6uYt2Qc1u+nBKMyHu7zlCfbV86Y2q+iFm3XfIvELjABS5QrnrVqzZGQ+QqA0dHbPkaY7xnHZwxFETCIu7Pc5VBectb3lLIOifSbstRjJvf/OYT/ejmoIMOauo5/90ljUiBgMFWtkie/MU5StkLAQ9n2LYX7JAzoO3xrPTZkZG2vrbrm9sHP/jB9q3dPsOUbDnaETpDVtgs9kmgstsLnS/kGmE2fzpkXu0q5BIhkiGmX+25tutN83mvHYH59a53veZ4AxnmjJ7+9Kc3R2/GtUOmyJaxPPaxj22CFAES2SKLZJJsasNa0hW4OjPo3hCFL3rwgx/cHLGAXbSpDwmS17/+9XFrqis7hpyza45VfOYzn2nk0lY83zNVYwuqLKARWMO83QXizg7AoH1/6M9t/ZM4IvtkfOh+xrXHLt/73vdujuTxV3ZZ+Rg7I+Yv+Bv3/rzPcBS8Rp+Kz3zmvO0O+f6hhx7a/GaDXaKDbKT2X/SiFxX2jG468uPYmaOTninW94pXvGJzbMZRtq4tUmeIMjUB5dg5fgoqKyHLiF3HYstg2BqSDXNGlLOQmfRjgr4BI6CMNAITyo15EyhRqG00bTM2lIvjp2CMnPZsbzkGINPF4P3gBz8oIm31gKjOLEV/nB1HMK4gKN32OQdbhRYYSXMOVXvmyjAjoTe5yU2abEf33SG+z9s/YbOO1vn6179+c7a2PS6OXYbKFebtZ9bSdg6FROLaz6b5jDw523e3u92tcW5kQJEZt9Vfax259age8kmOLnrRi+4imu6/8Y1vLMhBkJfumGBHIc2N/HgHHlHPGiJuyJC1ZQT1IViKOvNeBQ9kj5GQcWEInPnj7Ltty1qc//znL0gE0k7u6KRjL46ryGw4/9p+L3QNFubXfjbtZ++TFWdu4eHstuwxHPWNMN3xjncs7SCgrw+yxo7AXHa91rpbNWQgbIE1aD9kk8ydgVWn/Wzaz86S22JFqEO26fhTn/rUYuub3etrEw5wVldAUuvO8Quu7QIhd7JGfe+6Z/70xhqrR+a06ZkiI/q4xz2uOXvNyTmfaN09m7cISjgmP6IyR/2TO3ZLANTF2/rSLZg4Y+uYxkte8pJm65osOILgu0SCsdVamwwZYjeEzGlT0OMs86te9apmFwbhME59kn048hPqzlLYLn4M0UNkJVvsHszS1tDvkAu+kB8JP6gP9xFmn+fVA21MUuDNz/HX7Ook78xbh26wM+wEP8H2yNThAMZB1yQvyOm8ffW9j3Mgu452+T0KjsPW0hd2mv3ue28Z9/gnOi0gY1uNAZ9hPxy/YVMcwZEswtc8U0dhByQPYOqvWZAv94cse0zbGALgDKfJIJYUXYTIQCIfIiHE0Y9SGHKOnqBw6H19mbDzggQJAWVERLNS8QTadrPsjygbWM6w7bfffs32v/a8z0gw9E984hObH8j4FT0DImuizixFxKA/2adxxZZYt30O0WIxxgwxclJrbX5kwQAznHDrvjfU93n7p7jWmSPtO7NpnREkTgARa48bCREZygLNgz/5csZUG095ylMKUkgJnD+VJULgR8kUsg97RIvixfgYS+Mmv6OcCeNBaQVMMlGMnb4RK6LCTgAAEABJREFUKu2QC05ZZpXiriTf3pm0wJUuMQR+CKF/JE7mZdRcZZyM1bjoEKPo6APCxuHLVrvfHoO5wwAWMGk/m/YzvOj6c5/73OZPWjFsCA2jZQfAerXXYFT7MmpIpKxOd7zeod9sizmJ2t1T6Bn998wPeNzbrUz5BcG0a8ORy4bK7ggE4Ct45Nz6mmT/kG16bQs+6iAJMPbeqDVUlxMTtMvMw5BdQ0o9c0XyBINsCf1iY5E/z4cs5EbQJwsq84GI0ul2H+y07X9zM84tW7aUu971rmXPPfdsglXrbw05sHiPLgpa6RL9jPuzXpFwGWVBqh9Jhh2SZbWVaA3j3ix9sDvInbGSSWOfpZ1FvEPW2Anjgnn0Yd52/pBleMf9RV3ZK3IiCCATte4MuhbVn3bxA3aXXdl3332LH+HRHcEiu0eHBKL0lR/0zpCFvYE9u4yTSA7Qa1eBNj1gc4fsc5626LD3BdauCtkwXlj5jmfZXaNP3Sw2P4wDvPjFLy7kTv0hy1QElDN2PodiMjIGx+hzNhyAbIwJcIgykUikzAdHOmrQzunZWvIDIoAQan0AyRao7/rlGJ7whCc0v272LNrzPkOHLHIQnLftbWMkGFFv2ivHLpOqzXGF8e22bR7IuXFyJpwS0kJZ/Uq2751uG/GdsvmlYRS/KpWBc3Yj7skIUsJ4Z97+rRtHYQ7WONqNqwgQ6SC43SwMEmGu1ossxDuzXPVtbhyJH5uILp0jRkCt+ag2GR4ZUIaonQlAlhAKzmXU+8g3x2pr0vwYEwSC4YMLUoJcwwARkCUx11FjmeS+tsmx7UQOVd+cKuKp/5XaoHscsmg85stxkp0+/TN3GMACJtG+MYRMudqSMR5/0sp3xQ/61It32AFkBOnihOiNNumhYwB+gBJ1x12RSHbAGU7y262LBFnX7nzYBgEsXZ93HfSJ4CJ3jg4IfpExzoy+sWvq9JXvfve7hR20/SebGHWQR4UMazvut6/W3zEicoVck3sYWkNBnh8dcnycROiXushiu50hPutPdk0mXUaUgzeevraRYjrubLegRh12wVzoRVt2zcu6Wkdrpq5ijgg+2VLgLmiXRODM3VNktGSXvaMg4vvss0/zQy/HLBB8OkDurBWbq96sRUYVvtZMMI7gzNpW+71Z59tuI/TdD0LbGCNG8OdL6Xj7nfZndoy/hKvir0vI3vFPvkeRVWy/1/4MD+vm3DDihfTCq11n3Gd2xTpFX7bS6Y/kUtxzlb1rtyPg2bZtW/OnFxFOusN20E3JAIkCckKO2+91P8/av3bIsb4U3xX9k1G8w/e1ULZv315kOQUq7H2MyWclvtNJ3I2+SUzEfVf67WgVG8vGuTdNWanuVATUhCyu7GI07IcAHIdtJsJi24Pw2wZkaERojG/Ub185egIsWt6yI4r2HsLpDBslQoCcMyLg/pwTA8zRRBuUWcTL4AOu1tpE4IyRbaB23XhnNa6ICyLAeDEIFts8ndli1DntScche0xRo8CBkBOWuOfclv6izXn6p9AMAdJlbaPN9pURQHiduZWxaT8TGSLdCMk082y30f7MsVEAThjpgyE5addpf+aonQeUxRQcTSsD5JYhlV2CKSwRaiTCn16RzZMZRj6dW+YEEK/2GKb9zGgaM6cvKyniHKUz3bZjvbSBlMRz8+BE6RHdiPvjrtYsZMoVGaC/t7/97YvvCmelXrTDQKnDEcLc2jsnCXdnolyj7qgrnUdqYW19++oxgAhzdz5kwtlRYzKOvnenvUdvtYc02YpnT9ioWmtvU5ydY0DWgsxNK/eyuzLA1t275I68uU/GzF3GnVwOrV/tCQn87TDZgZJtlf3cunVrQ/La9Xw2NraXTW77AzLHLrs/Tk+1oZAbmJGtKHyI4Msv/OMeWbIe3lEEImQN9mTAPYGSMdMDa+jeLIX9Q2oQXPbNdz5ulra678w632iHjBlPV9/5WkdYyGo3KRDvxpW/FBgGtoIMBNJvE+KeK/8b73Sv9AKJZQ9ibdiCbr1R3+lq24cJ9ATe5E/fUdjbdhvsLvJIPtgLssZ2yvDBljywQwKQcVnrWfuvtRZBNvz8fWL9CsD4w/Y418JnOzLkgu0aNR62hZ6RKzyrrx7/IeCz5n3P57k3FQGV1eyyaZEuIXnb295WGC7ZAwMyaALJENR6ZMNNUGxty7QhmaE0+uCEGREGV1ujCkYu2ma4GSP1aq3N33skGL7PWiycyAD5GFcowLg+EAHtIBSIIgczrn73GSIksxOFkhIGhjHuEbC2cW63MW3/Mi8ckfM0lLjdls/m61fkCOp973vf5k9oua+IrGVPOG8K7t48hZElUyJx2WhBhSwfZ4Bo9rVt3WQ/4c0JtuvUWpvzoN5VSucf9xAJhJcxQ56sF4LkHBjCLasq+ocrA4ik13pk+e40PfYrPTEvsg9XZEN2gdwxDONetl7ODiLFdE5d8xAQaotzoIPuR/Fccb/WI8aOSIdMuSIWgic7GL4rZEK9aKt95QjIqysiMKpe+x2fZTYZQraD3LnXLkgY+0LuGUvjjucMPzsgUDPWuD/PVZBtt4WskyWZJcTPuve1ixjTGfKpHFGnNPJWa23+pnH7fvuz85T0l8OstRbyQPaRULIvaEVKkT7zHUq/jIFsy34gErIl5i3LzX4akzp9BeYSBUhK4G7M9ITckMf2e+RNoU+11l2PrCW5JVuK93y3Heize4osp3d3vXj4B/oOF3qLlPTJ++FVJ7ogL3aYEG82Fjmmh2z4RA2sUGne+fbpuy6NGwa2Vumge6MK28Y+w1WBs2we3fM9CpnrtmENHTuzG+XcINJvTLLkEXDQk5XsFpmBb/Tls2QD/xb3XMOvxzgEBuZHB9hntsH60DvY4hOOXzkTb0zxXvc6a/90xS4NIkxOfKc38MNpuv0s8zscrDXd6RuH3RTBnsDFjoMggM3r1pXxVfiU7rN5v09MQAmVc1HYf7tTRsoWnMiXUNt29pwRYkgsEGLiXhQRq+0tGVMGw7YqYSK0omqCSMD6DE60wdhh+MaFFBhHPBviStBtQVqUcYXSj+rPfJAK27qEFimKusaPOIgqOGyKHc+Guo7rn3NDFpGUdn8MLYcrI20d2s+sG0fsV72OEsC9/dz5I+RQ8DBO+dvvjPpsXRFd52URd07YmVDk0IFvuPZhZj6Mgz81FLIYfRgTGWW0kIy4H1dzRyzJnntR3zo5/8LBuU+eZWRlDRhM94YoMhN+2GGbH9GQIXD+2RYgR9vXh8wZh0AHQweM1RrSwQjs2u+aOwxgYY7tZ7N+Js8CR9ugZIdTa7dFB9gPxwI4iXhmDemad2Vg4n5crYktWVl3P3botuvHhhyfXQHELd6b9WpXxrkp+CB/nKBjL7abnXXjbLttkxmEjIwy1O3n1hRxtk59DoqO2lni7K0fJyq75yoIYW8F+drkAMj9EPqlPY7bOX3OR+Zp27Ztzf8xy7NxhSz6wZUAw1pHXWMTSEhSIAlx35VdcEaRTHYx8nzWYj3InLUX9HTb8Zydc/Ze5gze9Ml39333ThBxts2fpuK73BcMCjAEwlHX/WUV+sDXsm/kxTisBzJknIiXe4sq9NSZfEkPW8760b+1hbXx2c5HiD0buvBJ1kGf2iZv5JD+2G30OxC2ALEaxx+8O20xP1yA3jhjLEgyDucj6QE/Pm2bi6zPD9a6828p9/XDN1gn+u/oENvsbG23Lh8h2aK97rN5v09EQC14GD9nHdqdIo4mwlCLhuMZ1m2RGF4LxAB5ZvE4EtuNCJ5IKoyVrQW/YLSNEPe801ecy2JEGPggDH31CChjaQ4MFSOL5XMGsht977hnTsaIJI8rIk71+woyxHHK0MoitesgrrIFnLE/pcF4t58P8XlU/8iAH4jYWmVQoi/KRBitkQwIo45YcMaMivXkFGWhkELRVbyLlDl/JWuNJHSVnxwgEkiswAFB0rbsRd/c4UIhnIv01xa0pz/bRRwysssgRP+uxs+BcDAcHWLifhTvI2oybkha3HclD9ZZm5TNPfUZN4GDjIioGTGGBdLH0KmjbpSQN1kthhrZC9nTdtQbda21Ftjb3nIeylr5gQ/DgOR03yPbggnyShc9J3N0SfYm5uJ+FHOHASy64486016NwTrKomi3/b5n1gtR4yg5Cc5CHYRPEGZ9zds9GG/fvr0IdmTj7HKYvzXwPAobIJCVJeGAaj0is6YOfUfgrS35sx7kTd8cpjrdQi6tk8BHu7Xu3FEh+3QWEWm/Y31kENg3WZj2M5/hL7tDHszLvXYxf0SIbMGg1tr8n7nIrgCB7KtPvwRg5tLWL236cZ5sJDIvWGIXYT7OvmnTPBFcWSx/yUH/7q9UjMV6Wme4R30BI9uKtBl/3HeFOawEreyKe0MU+GnX7wWQk26bdM+6sVvsWnyXUWfX2CBOmD3kp2ynwj3aETx4h4ySybgf19B3dcgb/8LG+QzfqDfUlW0TnApQtEmOBax+pyBw4Xfphf7ZWzZRvSEKvMgK2fI7C/qhXRjSG0kJZ/WtiWy+McCDTKg3RLHTQU7JGhtG9vkD6+DcvDG54gXmznYM0a82BCn0XP90zfqyUXAm86EL5NGaqBv6SE+HHo8xzVvoKd7GJhu/BOC8bU7z/ooEFJh+WSu6lNIVYcn+RCe11uZAsPMyHHLct0h+qcx5Ek4LZEvUOQOLgXg6eCx1TZCcN3PuiKNgMPz8vy+iIAQcCdA4WcZQm86BEYroP64iNQSQwlJQZ/aALutCmaPeIq6yv5wm4xjkQD8IAKIk0kaoZNHMhRHzfKgyqn/OztYxshB9MbCOUhBEhsafmZDJEGlbFwplHpRN1lpU5N1YD2f3rIG5ORvDUCFf6ijWWQYR4SEnnHKttXC+1lyddqEMfuXozB9ZimfIIFmTUbaOcZ9MCgYYQDLB8JEL44s6rrJsHCAyRF4U4xYx+1toHBN5R6bUN1ZkRIAl8yDrqR4jK0OmPc5VXSXkTfaVjCLkniOs5F6dSYo5M6zkg0EVlCHI7XfNjRwh2wKcWmvzR8WREetgjdv147MsBRyNPe7NexXIIDFkwxq32yMHxhhy4RkiJ9tL5gQ9CJrxsBEwdiSBbDjzLCNJvqybd8Ne0GV6THfIqDO6nkeh79qHJfmzHp6RFXbN526RXXT+jG7UupPQsh2CXJlQRxDiHTpDPvVLZ2RH6A1HE3XomswoGSczcV8ddemULCSi7ZiJ53RLcMgmsgnsnfmTU+sqABQckydzsysQ44WzORx88MFlJftGLvWLFMK9r2zZsqX5P68Zl0JfBEJImwAn1gSx57zIv2BI3Xahb3Qo1qD9bJ7PAlV2lK7UunO92u3JtjrihbiwOeYsc0X2yCo7xW5YWyQO1tZGG9bXD+m8I2gmb9Ysnqtjvl3/Qr+m1XdtrVTou6AcuWKnBScImPUnlwJPx9q0Q/bZ27bfcX/WYn3phXnpA5a17sSbbTImYxOEsc3snptXxAgAAA07SURBVDHArG2nZ+0/3mNjcAe+TdaOTWSH7VCw2360ioSrT8+RYJ+HKGwmn1RrLbiNI1mSR3CBiWSFfui8ZwKi0Ed6OvR49DVUYd9gS7+HanOSdlYkoEBl7DgIjgQJdVar3bg6tgtFH+37hJBjkD1AXDkUQkpIGKkQTNESIeL0AcA5yLgyWO32fNamzCnBYoBFV9rlaGrdqRDqKQw2A8FJ+2EEQiVqMQa/oOPc1VF36GIuyBAHE/OMPmQmRZEUl2CKskWtxhZ1Rl0ZOw6ZQx1Vx/1x/XMaSC9FUVdhzKTYbeMwsN6XQRFBCgj8iIrBRnTaRi3Wg0GwFtbE2vjbbBRQ25yv9Wfs3YcLDBBJ8sGgqKNuFGOLbbC4F1dORYYcMYh7ZJLMRJaDgyS3xhd1XBlOzsjczLPWWsiOrVzZBCTBVqt5qs/hOOtkzhTUuRlkQ9RvzLJNDK26ZIm8cWKMsIwJZ83pIhQcmnrTFGulTWSa3rTfNTeOUXBAhzwjQ84jIvAyZe61i+f0RWABi/az7mf6zLCHQe8+j+9w49AZMWOK+3HVjmfmIiNoPuTEGWJkmV0hO7ZIrQuMrYesFAdmLvQk2jNX9oLOyAh637sIYNRBSmVUkTqBLJIigHAV3Aqk9Bn140qv6GN8b18RF+OPe3RGP+yV+chMyVy39d24yRddaBNCddQ1f+/Ttxi/rCSnBjd4sndt/fIO3YGn82dIonvkXaDPKZO/lewbeYb3uEJW6FbMudba/B+ojEfAEPethfkhPcYd913Jh10Dv2qmt+6NKvCyPvwJjEbVc59tYk8EifTTvb6CHCFvEiF8AeJsjLXWQlfJB4JlnNYk+rW+5klnyApdbj8Pfddm+BcYwH5SfZ9mvsZpjLJqbI8jLYgQ3ZK9ZbvoBfvoL4Wwt20b2YeNe7bynf1FxH3vK8iXv70JAwFiu136yAaRY7IqgKK//C39NMe+Ntv3cAjzEUi073c/0wfrYtdBgEamZD8dB6Bn9NNaWSc+nv2mo912ut8n6d+c9a0/8s5O4RKOEMKg1tr837P0KXC2Y8H/y4jDd5rxdMc3y3d2QQBLLlZ6v9ba/F3ttk7HO/ykor24N9R1j6Ea6muHUnPmoiYZDYLZrRcAuXafzfV9x8u2iAAqwheRIDuOELjvzBOFJrQ7qg76L0VEEjkszrbbOPJFmCl199kQ31fqf4g+pmkDDow+RWUYOUnkkkwwILYxkIxp2py1LmPLaMvYcdqzttP3Hrkib4yPK4Jn/ZFHBtP9vveGvGfbR6TNWCA93bbNWdZIMAGL7vNpvyNenI+sc1/A2G0PWZQhlbWpdfeAsVt3nu/GgghYB1uqrogUUoYs+xt+Q8x/kjGSbdkZxJDcTfLOpHVkRq2lTItMKyfnukj71h0b242oIhtwbj93z3a9IE0g1n42z2fyJpAVkLMv49pCWqyB4xjWgfyNqz/ps9D3tn+REV+EvvMZSIzgGckaN0Z4hL0dV2+Rz8Lfwn0R/UgCCL668qYvei3QZOftkCDo7q9GIWt2ngRR+rcOEk3LGA/yizgKVtpzpzuObgkmkHUBlkw6oupoXbuuz3yKQrZ9H7IsjIDatmDkRfKiexGadLUsgWjUJEQGolJbFjKf7g1ZEExETwYH2Bwy5bUAsgd9hHje/hlj0bAIkHL0GTtCar6Bw7x9tt+fpP92/dX4LKtqjUW3okfZB7jIWMr2WIvVGEf0YZvI1pqMEcXjJOPZPNeQN+tLsWXTrDF544BrXRzhinHLwAhAZB9gHPfN0VxlSARisoPxbJ6rOTu3LaPq2MFKbSHHtogFgivVnee5DLhdBjJnyz7Wg72R3Z4kKzNP/913ZdE5IeRXxlEGvVtnlu/wZ9Nk+WTGZGJs09OpRdi3vjHSb1l3egVvdRTOT3DpGUI81HisK92V9abH5qq/vsLO2r1TR5ZUwNRXb5Z7sO/6Fzo3tL6TFTsRMq5IxUpjtR5hb1equ6jnbX+7iD74Efa1LW/tfgTa9Nx6tO8v+jOZJ2+OxLDF9NFu3jLGg/wi49aiPW+kXCCObAriHAEyPsdQjLtd12e7iriawNb3IcvCCChjIwKQ+ZFlFLHICjLCUv0mLzUuS0RQGIchJ6YtfTP8nI3tGtEyZbY1SngXIZwEUGpepKpv4+gW2LiHKCCMFtjWFCPp/jxlkv7naX+Wd2XcOEkBAGdka1gmzJrYzpmlzXne4TSsj/NvlNN5xHnai3fJm3NJHJC5IqLknsNEuqPeIq8wJV+CrlqPILzmyCDa5qV/Q+mbdmXyZeFW0if40EXk2BosEgdEU4aKASZ/1gMJtd7Oyy2y7762zdv2HSLGPiDIffWmvSfQtZXOrsk00qdF2re+8SE7nK5x2P6NOrKiEhHO/spC1XqEPEadWa7spl012/TmO64NZEzSgy1m8+nmuPrTPCPP2rW29I5/WYS+s+nspt8T2DlYaYzknb2dJCBcqa1Zngt2A49Z3l/pHRk7fzdYprEtb/Ge545OWI+4t1pXRI2Msb/kXyZ4WeMRlLL3bLQMemDALtq1EgzblXNsxpEeZ8CjTlwFbN7nL4dKWkTbrgsjoFi/c562J2x7OEPojKGo1TlSrFsmhGOmwAYzZGGk9MXZOApAKRhBGSkH4jlpn4fsU1sc3v77719kfBk897oF2ZT5c2jf2GQsCOoQBHSS/o2Hw5IdQIBd3VtE0T6DiABI4xuf86ecgmfWgYIuou9xbSJAgh9OrE/xxr3b9yzkzVaoz7beETLZrjCSi8Q5xuQwvD67xtccnbmkk0Pqm60mGX+OsdZ+gmHexkTv/NjLNrTzXghhjHvIK8LvHLI1oF/68VmUb00U9mDIPidpi8xZB8H3EBlggStMbbmyIbXWwt65R68WYd/65um4A9vOobWfI2d+MEUnzL39bJ7Pju/IrDoTPcpmsm8cp+w854kQIMhIumfz9O9dOt7nX8j50PoukH3a055WzCXaNoa+wqaGvUUe+uos+h7CTL/420X0RX/9CEsWr699emE72blOtqCvzqLuCSwlV9g8hZ9f5ngccau1FnIZc8bN2Gv8C++QEIJVrUe23+yKTL8foUs0RBtDXRdGQIca4KztIDXILYUUoRNIABIKCs0gus7a/rzvOQ/JELoy4Lav5m1z0vdFQ36cwVD4Uz8c1qTvTlvPHBEeB/ttEXKMDAjj6Gyesfg+bbtrrX7IG+MjA0XWaq3F/ONHLbUeWcHX2jwWMR6/zhWQCbhkLfzRczKxqHXn/OBP1+m5rKfv5B0RsVZDZYAXgdekbXKubFyttXAS5iTAY9fM23XStjZSvUMOOaT4wYy/HuHIhzNwMqa2sWV+/NUCMjDPnL0P+65/Wba+659usbcrkdV55j/uXXiHvx1Xb1HPrInsr2zkamPAxsg+08dYg2WORxbUD+LsCDmKNA3m8PPjQbsMsqbTvDtp3Q1LQJ298Oc3at1pnG3zY/6KrVeZm0U5wEnAJ5z+vI9fUDqrMck7Q9VBAvVt+45Bhs1QbXfbYRAdwnaAGQF1/lAdQi2ql5Hzfb0XmTbbq7LenFNgKsCQeZT1Xu9znHX8Ag1/js0ZVEX2UyYUSZq1zXHvkW8/xkH8bQ3HetAzv5aVDR33/np5Bld2jNNzJs781op9WyaGCBhcgnDSRfbGL5b9+ELmbF7bP8q/LFvfZYTZWL5v3jlOu4aybI75IF8wJ4vTtjFEfXrhf1biB3pDtDdNGxIrjtqwO/TRu8scDxvrCKQfpforII4HGNNKxW6l4E1AK/tJrlZ6Z5bnG5aAAsP5I4rozJUFQPrc3+BlTU1PNsrf1pT12rp1a7Edt6YGONBgRPzm6H+y4NfXtjQGajqbmQGBzbIeztH7axKOuPhzRDNAla/MiED6l92BSzxKWYv6aGfEdrsgjJ3YfdX6vyGetun9+cRF8qYNTUD9DUFbL7J9foXcD3XeXSQComD/Fxvbr9ZDdmKR/S2rbcrtYLy//eZIRa2bc7t9Wfh3+90s62F7zblG5+xXe7uxi/lm+86eHdm/bDYUjphv4lHKWtZHWXu7Q0es2OhPbMlqcKYNTUBrrQX7l4YeDXU+WTQC8Ec8a924pKzWnbJmnovGM9tfGYFaN8d60C02rtaNq1srr/ZyatS6U8aswXJGsLZ6rTXxIAupj5PL5YYmoJPDkDWHQiDbSQQSgUQgEUgEEoFEYCUEkoCuhFA+TwQSgUQgEUgE1j4COcJEYF0hkAR0XS1XDjYRSAQSgUQgEUgEEoH1j0AS0PW/hjmDQCCviUAikAgkAolAIrAuEEgCui6WKQeZCCQCiUAikAisXQRyZInAtAgkAZ0WsayfCCQCiUAikAgkAolAIjAXAklA54IvX04EAoG8JgKJQCKQCCQCicCkCCQBnRSprJcIJAKJQCKQCCQCaw+BHNG6RCAJ6Lpcthx0IpAIJAKJQCKQCCQC6xeBJKDrd+1y5IlAIJDXRCARSAQSgURgXSGQBHRdLVcONhFIBBKBRCARSATWDgI5klkR+D8AAAD//7RnaKcAAAAGSURBVAMARaqxIAj3pOYAAAAASUVORK5CYII=\"\u003e\u003c/p\u003e\n \u003cp\u003eWhere M is the threshold variable (representing either corporate ESG or regional IPP), \u003cem\u003eI\u003c/em\u003e(\u0026middot;) is an indicator function, and \u003cem\u003e\u0026eta;\u003c/em\u003e is the threshold value to be estimated. This model will be used to test H2b (by setting M\u0026thinsp;=\u0026thinsp;ESG) and H3b (by setting M\u0026thinsp;=\u0026thinsp;IPP). For H3b, we will also test for a double-threshold effect to fully capture the nonlinear dynamic.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Analysis and Results","content":"\u003cp\u003eThis section presents the empirical evidence for our hypotheses. We begin by showing the statistical characteristics of our variables, followed by the baseline regression results. We then conduct a series of robustness checks to validate these findings. Finally, we investigate the core of our study: the moderating role of corporate ESG performance and the non-linear, threshold-based moderating effect of regional IPP.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Descriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics for all variables used in our analysis, based on the final sample of 31598 firm-year observations. Our dependent variable, the regional New Quality Productive Forces (NQPF) index, has a mean of 0.331 and a standard deviation of 0.139. The wide range, from a minimum of 0.053 to a maximum of 0.676, shows significant regional differences in NQPF development across Chinese regions. This provides substantial variance for our econometric analysis. The core independent variable, Corporate Digital Transformation (DT), has a mean of 0.011. This suggests that, on average, the adoption of deep digital transformation is still in its early stages for many listed firms. However, its standard deviation (0.011) and maximum value (0.056) reveal significant differences among firms, which is consistent with the uneven landscape of digital adoption seen in related studies (Wu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The moderating variables (corporate ESG and regional IPP) and all control variables also exhibit considerable variation, confirming the suitability of our dataset for this investigation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\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\u003eNew Quality Productivity Forces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNQPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.676\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\u003eFirm Digitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModerator Variables (Threshold Variables)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorporate ESG Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntellectual Property Protection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eControl Variables\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntangible Assets Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.938\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\u003eINV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage Management Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFiscal Autonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment S\u0026amp;T Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegional Industrial Structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTax Collection Intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollution Control Efforts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.068\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\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a dynamic visualization of the NQPF index over our sample period, illustrating its distribution using kernel density estimation. The plot reveals two key trends. First, the center of the kernel density curve shifts consistently to the right over the years. This indicates that the overall average level of NQPF across Chinese regions has been gradually increasing. Second, the distribution's shape has evolved: the main peak has decreased in height, while the curve's width has expanded, and it has developed a heavier right tail. This dynamic suggests a \"Matthew effect\" of polarization; while the average NQPF is rising, the absolute differences between high-performing regions and low-performing regions are simultaneously widening. This statistical and visual evidence of significant and growing variation in NQPF motivates our inquiry into its corporate-level drivers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Baseline Results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the baseline regression results for the impact of corporate digital transformation (DT) on regional new quality productive forces (NQPF), based on Model (1). We employ a stepwise estimation approach to ensure the robustness of our findings. Column (1) reports the initial OLS regression, including only the core independent variable and fixed effects. The coefficient for DT is positive and significant (0.174, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Column (2) introduces regional-level control variables, and the DT coefficient remains strongly positive and significant (0.207, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Column (3) further incorporates our full set of corporate and regional-level control variables.\u003c/p\u003e \u003cp\u003eIn our preferred specification (Column 3), which controls for all covariates and fixed effects, the coefficient for corporate DT is 0.212 and is statistically significant at the 1% level. This result is not only statistically robust but also economically meaningful. It suggests that, holding all else constant, a 1 percentage point increase in corporate digital transformation is associated with a 0.212 unit increase in the NQPF index. This is an approximate 64.05% increase relative to the sample mean (0.212 / 0.331).\u003c/p\u003e \u003cp\u003eThis finding provides strong empirical support for our first hypothesis, confirming that corporate digital strategies aggregate to produce significant, positive macro-level productivity outcomes.\u003c/p\u003e \u003cp\u003eTherefore, \u003cb\u003eHypothesis\u003c/b\u003e \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e\u003cb\u003e1\u003c/b\u003e\u003c/span\u003e \u003cb\u003e(H1)\u003c/b\u003e\u0026mdash;that corporate digital transformation has a significant positive effect on regional new quality productive force\u003cem\u003es\u003c/em\u003e\u0026mdash;is supported.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Regression Results.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNQPF(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNQPF(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNQPF(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.174\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003csup\u003e***\u003c/sup\u003e\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(0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.077)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.328\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.427\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003csup\u003e**\u003c/sup\u003e\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(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm-level Controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\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\u003eRegion-level Controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\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\u003eFirm Fixed\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\u003eRegion Fixed\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\u003eYear Fixed\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\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31598\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 \u003csup\u003e***\u003c/sup\u003e, \u003csup\u003e**\u003c/sup\u003e and \u003csup\u003e*\u003c/sup\u003e are significant at the significance levels of 1%, 5% and 10%, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Endogeneity and Robustness Checks\u003c/h2\u003e \u003cp\u003eAlthough our baseline model controls for a range of variables and fixed effects, the positive relationship found for Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (DT \u0026rarr; NQPF) could be subject to potential endogeneity concerns. Primarily, reverse causality could be an issue: regions with a higher level of NQPF might possess superior infrastructure and innovation ecosystems, which in turn attract more digitally advanced firms. To address this and ensure the robustness of our baseline finding, we conduct a series of rigorous tests, including an instrumental variable (IV) approach and multiple sensitivity analyses.\u003c/p\u003e \u003cp\u003eFollowing related empirical literature, we use an instrumental variable approach to reduce endogeneity. We instrument for the Corporate Digital Transformation (DT) variable using historical telecommunication data from 1984, matched to the city where each firm is registered. This strategy, which uses historical infrastructure as a quasi-random source of variation, is a widely accepted approach in the literature for instrumenting digital adoption (Huang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nunn \u0026amp; Qian, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Specifically, we use the number of post offices per million people and the number of telephones per 100 people in 1984, interacted with the firm's previous-year (t-1) digitalization level. The logic is twofold: (1) Relevance: historical telecommunication infrastructure in a city is a strong predictor of the long-term, path-dependent development of modern digital infrastructure and later corporate digital adoption (Huang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); and (2) Exclusion Restriction: this historical data from 1984 is highly unlikely to be correlated with the current error term affecting regional NQPF, thus meeting the exogeneity assumption.\u003c/p\u003e \u003cp\u003eThe 2SLS (Two-Stage Least Squares) results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The first-stage regressions confirm (Columns 1 and 3, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the relevance and strength of our instruments. The coefficients on both historical post office and telephone IVs are positive and highly significant predictors of current corporate DT. In the crucial second-stage regressions (Columns 2 and 4, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the coefficient for the instrumented DT variable (e.g.,1.724 and 1.423) remains positive and is statistically significant at the 1% level. We also report the critical diagnostic statistics. In both specifications, the Kleibergen-Paap rk LM statistic (e.g.,347.112 and 342.842) is significant at the 1% level, which strongly rejects the null hypothesis of under identification. Furthermore, the Cragg-Donald Wald F-statistic (e.g.,4044.234 and 3564.032) far exceeds the Stock-Yogo critical values for weak identification (e.g., 16.38 at the 10% level), confirming that our instruments are strong.\u003c/p\u003e \u003cp\u003eThis IV-2SLS result, which accounts for endogeneity, confirms that our baseline finding is robust and that the positive impact of corporate digital transformation on regional new quality productive forces is causal, supporting H1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEndogeneity Tests: Instrumental Variable Estimates.\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eIV\u0026thinsp;=\u0026thinsp;Post Offices per Million Population in 1984\u0026times;DT\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIV\u0026thinsp;=\u0026thinsp;Telephones per Hundred People in 1984\u0026times;DT\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNQPF(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNQPF(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNQPF(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNQPF(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.724\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.423\u003csup\u003e***\u003c/sup\u003e\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(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.242)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.179)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e594.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e537.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap rk LM statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e347.112\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e342.842\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCragg-Donald Wald F statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4044.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3564.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[16.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[16.38]\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\u003eFirm Fixed\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\u003eRegion Fixed\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\u003eYear Fixed\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\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31598\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 \u003csup\u003e***\u003c/sup\u003e, \u003csup\u003e**\u003c/sup\u003e and \u003csup\u003e*\u003c/sup\u003e are significant at the significance levels of 1%, 5% and 10%, respectively. Figures in square brackets are the critical values for the Stock-Yogo test at the 10% significance level.\u003c/p\u003e \u003cp\u003eWe further test the stability of our baseline results by re-estimating Model (1) under several alternative specifications. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eLagged Independent Variable. To reduce potential reverse causality, we use the one-year lag of corporate digital transformation (L.DT). As shown in Column (1), the coefficient for L.DT (0.194, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) remains positive and highly significant.\u003c/p\u003e \u003cp\u003eManufacturing Sector Only. The manufacturing industry is a primary focus for NQPF. We re-run the regression using only the subsample of manufacturing firms. Column (2) shows that the positive effect of DT (0.252, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) holds.\u003c/p\u003e \u003cp\u003eExcluding Municipalities. To ensure our results are not driven by China's four centrally-administered municipalities (Beijing, Shanghai, Tianjin, Chongqing), we exclude them from the sample. Column (3) shows that the DT coefficient (0.238, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) remains robust.\u003c/p\u003e \u003cp\u003eIn sum, the findings from both the IV regression and the series of robustness checks consistently support our baseline conclusion. The positive and significant relationship between corporate digital transformation and regional new quality productive forces is robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness Tests.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNQPF(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNQPF(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNQPF(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL.DT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.194\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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(0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.238\u003csup\u003e**\u003c/sup\u003e\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 \u003cp\u003e(0.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.099)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.124\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003csup\u003e***\u003c/sup\u003e\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(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.069)\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\u003eFirm Fixed\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\u003eRegion Fixed\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\u003eYear Fixed\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\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25405\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 \u003csup\u003e***\u003c/sup\u003e, \u003csup\u003e**\u003c/sup\u003e and \u003csup\u003e*\u003c/sup\u003e are significant at the significance levels of 1%, 5% and 10%, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Further Analysis: Moderating Mechanisms\u003c/h2\u003e \u003cp\u003eNow that we have established the robust, positive baseline effect of corporate DT on regional NQPF, we now test our core hypotheses about the conditional nature of this relationship. We investigate how this micro-to-macro link is moderated by both the internal institutional environment (corporate ESG performance) and the external institutional environment (regional Intellectual Property Protection).\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1. The Moderating Role of Corporate ESG Performance\u003c/h2\u003e \u003cp\u003eWe first test Hypotheses H2a and H2b, which state that Corporate ESG performance positively moderates the DT-NQPF relationship, possibly in a non-linear, marginally increasing way.\u003c/p\u003e \u003cp\u003eColumn (1) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results of the linear interaction model (Model 2). The coefficient for the interaction term DT * ESG is positive and statistically significant at the 1% level (1.027, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This result provides strong support for Hypothesis H2a, indicating that high corporate ESG performance significantly strengthens the positive contribution of its digital transformation to regional NQPF. This aligns with our theory that firms with a strong internal commitment to sustainability (as signaled by ESG) are better able to channel their digital investments toward high-quality, green, and socially beneficial innovations that generate stronger positive externalities (Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xue \u0026amp; Chen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNext, we test for the \"increasing marginal trend\" (H2b) using the panel threshold model (Model 3), with corporate ESG as the threshold variable. The results of the threshold test (reported in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) confirm the existence of a single threshold for ESG (threshold\u0026thinsp;=\u0026thinsp;0.208), which is statistically significant (F\u0026thinsp;=\u0026thinsp;16.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eThe threshold regression results (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) reveal a distinct non-linear pattern. When corporate ESG performance is \u003cem\u003ebelow\u003c/em\u003e this critical threshold (ESG\u0026thinsp;\u0026lt;\u0026thinsp;0.208), the coefficient for DT is 0.126 and only weakly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). However, once corporate ESG performance \u003cem\u003esurpasses\u003c/em\u003e this threshold (ESG\u0026thinsp;\u0026gt;\u0026thinsp;0.208), the coefficient for DT jumps to 0.362 and becomes highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding strongly supports Hypothesis H2b. It demonstrates that the strengthening effect of ESG is not linear; it exhibits an increasing marginal return. Only after corporate sustainable governance practices reach a sufficient level of maturity do its digital transformation efforts become a powerful, high-impact driver of regional NQPF.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Moderating Role of Corporate ESG Performance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNQPF(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNQPF(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.340\u003csup\u003e***\u003c/sup\u003e\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(0.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.087)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.017\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u0026times;ESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.027\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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(0.391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\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 \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u0026times;IPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.718\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.126\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003csup\u003e**\u003c/sup\u003e\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(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.057)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm Fixed\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion Fixed\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear Fixed\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31598\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 \u003csup\u003e***\u003c/sup\u003e, \u003csup\u003e**\u003c/sup\u003e and \u003csup\u003e*\u003c/sup\u003e are significant at the significance levels of 1%, 5% and 10%, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2. The Threshold-Moderating Role of regional-level IPP\u003c/h2\u003e \u003cp\u003eWe now turn to our final set of hypotheses, H3a and H3b, which examine the critical role of the regional IPP environment. This represents one of the core innovations of our study.\u003c/p\u003e \u003cp\u003eColumn (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the linear interaction model for IPP. The coefficient for the interaction term DT * IPP is positive and highly significant (0.718, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This provides initial support for Hypothesis H3a, suggesting that a stronger external institutional environment for innovation protection does strengthen the productivity gains from corporate digitalization.\u003c/p\u003e \u003cp\u003eHowever, H3b states that this relationship is non-linear and subject to a threshold. This reflects the idea that IPP must reach a critical strength to overcome the high risks of digital innovation (Ang, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). We test this using the panel threshold model (Model 3) with regional IPP as the threshold variable.\u003c/p\u003e \u003cp\u003eThe regression results across these three regimes (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) reveal a striking non-linear pattern that strongly supports Hypothesis H3b.\u003c/p\u003e \u003cp\u003eRegime 1 (Low IPP: IPP\u0026thinsp;\u0026lt;\u0026thinsp;0.441): The coefficient for DT is negative but statistically insignificant (-0.027, p\u0026thinsp;\u0026gt;\u0026thinsp;0.10).\u003c/p\u003e \u003cp\u003eRegime 2 (Medium IPP: 0.441\u0026thinsp;\u0026lt;\u0026thinsp;IPP\u0026thinsp;\u0026lt;\u0026thinsp;1.426): The coefficient turns positive, though remains statistically insignificant (0.065, p\u0026thinsp;\u0026gt;\u0026thinsp;0.10).\u003c/p\u003e \u003cp\u003eRegime 3 (High IPP: IPP\u0026thinsp;\u0026gt;\u0026thinsp;1.426): The coefficient for DT becomes significantly positive (1.067, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eThis shows that the positive moderating effect only becomes statistically detectable after the intensity of intellectual property protection surpasses a higher second threshold. Consequently, a weak IPP environment is not enough to protect digital investments and may even be harmful. Only after IPP strength crosses these critical thresholds can it effectively de-risk corporate innovation and unlock the full, aggregated potential of digital transformation to drive regional new quality productive forces.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold Estimation and Threshold Test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThreshold Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThreshold Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThreshold Estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eCritical Values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.165\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\u003eDouble\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.381\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\u003eDouble\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.30\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.489\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\u003eTriple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e83.639\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 \u003csup\u003e***\u003c/sup\u003e, \u003csup\u003e**\u003c/sup\u003e and \u003csup\u003e*\u003c/sup\u003e are significant at the significance levels of 1%, 5% and 10%, respectively. Based on 300 bootstrap replications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold Regression Results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eThe Role of ESG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe Role of IPP\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNQPF(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNQPF(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.126\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.027\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(0.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.362\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\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(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.073)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.067\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(0.083)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.177\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.150\u003csup\u003e***\u003c/sup\u003e\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(0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.043)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm Fixed\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion Fixed\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Heterogeneity Analysis\u003c/h2\u003e \u003cp\u003eTo further explore the boundary conditions of our baseline finding (H1), we conduct a series of heterogeneity analyses. The impact of corporate digital transformation (DT) on regional new quality productive forces (NQPF) may vary depending on firm characteristics, industry context, and regional endowments. We present these subsample regression results in Tables\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, and \u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1. Corporate-Level Heterogeneity\u003c/h2\u003e \u003cp\u003eWe first examine heterogeneity at the corporate level based on financing constraints and life cycle.\u003c/p\u003e \u003cp\u003eFinancing Constraints. Following Hadlock and Pierce (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), we use the SA index to proxy for financing constraints and split the sample by its median. As shown in Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the positive effect of DT on regional NQPF is significant only for the low-constraint subsample (Coef. = 0.193, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). This suggests that firms with sufficient capital and resources are better able to change their digital investments into real, macro-level productivity gains.\u003c/p\u003e \u003cp\u003eFirm Life Cycle. We divide the sample into growth, maturity, and decline stages based on the cash flow pattern methodology (Dickinson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Columns (3)-(5) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e show that the DT coefficient is positive and significant only for firms in the maturity stage (Coef. = 0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This aligns with our theory, as mature firms have the stable resources, established market channels, and organizational capacity needed to execute complex digital strategies that can generate external spillovers.\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\u003eHeterogeneity Analysis: Firm Characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eLow Financing Constraints\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Financing Constraints\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecession Stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaturity Stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGrowth Stage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNQPF(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNQPF(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNQPF(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNQPF(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNQPF(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.193\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.248\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.054\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(0.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.128)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.138\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(0.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.106)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm Fixed\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion Fixed\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\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 \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2. Industry-Level Heterogeneity\u003c/h2\u003e \u003cp\u003eNext, we explore variations across industry types (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh-tech vs. Low-tech Industries. Based on the official classification by the National Bureau of Statistics, we divide the sample into high-technology and low-technology industries. As shown in Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the positive impact of DT is significant only within the high-technology industry subsample (Coef. = 0.269, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This is logical, as high-tech firms are the primary agents of innovation, and their digital advancements are more directly aligned with the core components of NQPF, such as developing high-quality labor and advanced means of labor.\u003c/p\u003e \u003cp\u003eCompetitive vs. Regulated Industries. Following Yuan et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we classify industries as either competitive or regulated. The results in Columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e show that the DT coefficient is positive and significant only for firms in competitive industries (Coef. = 0.243, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This suggests that market competition acts as a key disciplining and motivating mechanism. Firms facing strong competition are forced to use their digital investments for real innovation rather than rent-seeking, which leads to a stronger, positive impact on the region's overall productivity.\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\u003eHeterogeneity Analysis: Industry Characteristics.\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\u003eHigh-tech Industries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-tech Industries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCompetitive Industries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegulated Industries\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNQPF(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNQPF(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNQPF(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNQPF(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.269\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.035\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(0.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.187)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.247\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\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(0.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.119)\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\u003eFirm Fixed\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\u003eRegion Fixed\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\u003eCoefficient\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\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.5.3. Regional-Level Heterogeneity\u003c/h2\u003e \u003cp\u003eFinally, we investigate regional-level differences based on location and resource endowments (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoastal vs. Non-coastal Regions. As shown in Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, the positive effect of DT is stronger and more significant for firms located in non-coastal regions (Coef. = 0.208, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while it is insignificant for coastal regions (Coef. = 0.063, p\u0026thinsp;\u0026gt;\u0026thinsp;0.10). This finding suggests a \"path-breaking\" or \"catch-up\" effect. Although coastal regions may already operate at a high level of development, non-coastal regions seem to have greater marginal returns from digitalization, allowing them to use DT to overcome geographical and resource limitations.\u003c/p\u003e \u003cp\u003eResource-based vs. Non-resource-based Cities. The results in Columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e show that the DT coefficient is positive and significant only for firms in non-resource-based cities (Coef. = 0.141, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). This highlights the \"resource curse\" or \"path dependency\" challenge. Firms in non-resource-based cities are forced to innovate through technology to make up for their lack of natural endowments, thus making their digital transformations a more powerful driver of regional NQPF.\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\u003eHeterogeneity Analysis: Factor Endowments.\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\u003eCoastal Regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-coastal Regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResource-based Cities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-resource-based Cities\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNQPF(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNQPF(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNQPF(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNQPF(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003csup\u003e*\u003c/sup\u003e\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(0.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.227\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003csup\u003e*\u003c/sup\u003e\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(0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.060)\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\u003eFirm Fixed\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\u003eRegion Fixed\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\u003eCoefficient\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\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion and Implications","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\n\u003ch2\u003e5.1. Discussion of the Empirical Results\u003c/h2\u003e\n\u003cp\u003eOur empirical findings provide a clear understanding of how corporate strategic actions combine to influence regional productivity in the context of China's high-quality development. The results not only confirm the direct impact of digital transformation but also, more importantly, reveal that this link is deeply dependent on both internal and external institutional conditions.\u003c/p\u003e\n\u003cp\u003eFirst, our baseline finding (Hypothesis \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) confirms that corporate digital transformation (DT) is a significant positive driver of regional new quality productive forces (NQPF) (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This supports the micro-to-macro logic that the collective digital investments of firms generate significant positive externalities (Pan et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This aggregation effect likely works through the channels we theorized in Section \u003cspan class=\"InternalRef\"\u003e2.1\u003c/span\u003e: accelerating regional knowledge spillovers, improving the efficiency of inter-firm supply chains, and cultivating a high-skilled, digitally-native regional labor force, all of which are core components of NQPF.\u003c/p\u003e\n\u003cp\u003eSecond, we find that this micro-to-macro link is not uniform; it is powerfully moderated by the firm's internal strategic commitment to sustainability (Hypothesis 2). The positive linear moderation of ESG performance (H2a) (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) suggests that firms aligning their strategies with sustainable goals are more effective \"converters\" of digital investment into public productivity gains (Guo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xue \u0026amp; Chen, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the \"increasing marginal return\" (H2b) revealed in our threshold analysis (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) is a key insight. It implies that ESG is not merely a passive screening factor but an active amplifier. As corporate ESG performance matures from superficial \"ceremonial\" compliance to deep strategic integration, it becomes exponentially more effective at directing its digital innovations toward high-quality, green outcomes that directly fuel regional NQPF.\u003c/p\u003e\n\u003cp\u003eThird, and most central to our study, we reveal the complex, non-linear moderating role of the external institutional environment (Hypothesis 3). Our findings for H3b (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) show that at low levels of regional Intellectual Property Protection (IPP), corporate DT has an insignificant, or even slightly negative, impact on regional NQPF. This supports the theoretical argument that in a weak institutional context, the high risks of digital innovation stop firms from doing the key R\u0026amp;D needed for macro-level spillovers (Ang, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). The positive effect is \"unlocked\" only after IPP strength crosses a critical threshold. This shows that a strong enough legal framework (IPP) is a prerequisite for de-risking corporate digital innovation and allowing its aggregation into real, regional productivity gains (Liu \u0026amp; Lin, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFinally, our heterogeneity analysis (see Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e) supports this narrative. The positive effect of DT on NQPF is strongest among firms that are best positioned to execute complex innovations (i.e., mature firms with low financing constraints) (Dickinson, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hadlock \u0026amp; Pierce, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) and those with the strongest motivation to do so (i.e., high-tech firms in competitive industries). Also, the effect is most clear in non-coastal and non-resource-based regions, suggesting that DT serves as a critical \"path-breaking\" tool for regions less reliant on traditional endowments, forcing them to compete via innovation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\n\u003ch2\u003e5.2. Theoretical Contributions\u003c/h2\u003e\n\u003cp\u003eThis study makes several new contributions to the literature at the intersection of digitalization, institutional economics, and regional economic development.\u003c/p\u003e\n\u003cp\u003eFirst, we contribute to the new NQPF literature by providing empirical validation for its micro-foundations. While most studies discuss NQPF at a macroeconomic level (Liu \u0026amp; Lin, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lv et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), our research connects the micro-macro divide by showing precisely how corporate strategic actions (Digital Transformation) combine to shape regional productivity outcomes.\u003c/p\u003e\n\u003cp\u003eSecond, we advance the literature on technology adoption by proposing and testing an institution-technology synergy model. We provide new empirical evidence that the success of this micro-to-macro link is not uniform. Instead, we show it is significantly dependent upon the alignment of both the firm's internal institutional commitment (ESG performance) and the external institutional environment (IPP) (Guo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThird, and most importantly, we contribute to institutional theory by moving beyond simple linear assumptions to show complex, non-linear moderating mechanisms. We reveal that the institutional effects of both ESG and Intellectual Property Protection are not linear, but follow distinct threshold patterns. Specifically, we find that the strengthening effect of ESG shows an \"increasing marginal trend\" (consistent with H2b), while the enabling effect of IPP is only \"unlocked\" after it crosses a critical activation threshold (consistent with H3b). This two-part finding provides detailed evidence on the complex, and often non-monotonic, interaction between technology and institutions (Kim et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n\u003ch2\u003e5.3. Managerial and Policy Implications\u003c/h2\u003e\n\u003cp\u003eOur findings, which reveal a complex, institution-dependent link between corporate digitalization and regional NQPF, offer several critical, actionable implications for both managers and public policymakers.\u003c/p\u003e\n\u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\n\u003ch2\u003e5.3.1. Managerial Implications\u003c/h2\u003e\n\u003cp\u003eOur study provides two primary strategic insights for corporate decision-makers. First, our findings on H2b reframe ESG performance not as a mere compliance cost, but as a strategic multiplier. The evidence of an \"increasing marginal return\" (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) suggests that firms moving from superficial (or \"ceremonial\") to deep ESG integration can unlock significantly greater productivity returns from their existing digital transformation investments. This provides a clear, quantitative justification for managers to champion deep ESG integration as a core strategy to maximize the value generated from digitalization (Guo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSecond, our discovery of a strong IPP threshold (H3b) carries direct implications for strategic location and investment decisions. Managers planning significant R\u0026amp;D or digital innovation investments must conduct due diligence on the regional IPP environment. Our results (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) strongly suggest that committing such investments in a region with an IPP framework below the critical activation threshold is a high-risk, low-return effort. To protect investments and maximize their contribution to NQPF, firms should prioritize locating high-stakes digital R\u0026amp;D in regions with a proven, robust IPP framework (Kim et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\n\u003ch2\u003e5.3.2. Policy Implications\u003c/h2\u003e\n\u003cp\u003eOur micro-to-macro findings offer several exact recommendations for regional governments that want to cultivate NQPF.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMove Beyond Technological Determinism.\u003c/strong\u003e Our results show that merely promoting corporate digitalization (H1) is not enough. The macro-level gains are \"unlocked\" by institutional quality. Policymakers must therefore adopt a synergistic approach, coupling digital infrastructure investment with robust institutional reforms.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eBuild a \"Threshold-Surpassing\" IPP Regime.\u003c/strong\u003e The most critical policy insight comes from H3b: a weak or \"half-way\" IPP system is ineffective and may even slow the digital economy's productivity spillovers. The policy goal must not be simply to \"improve\" IPP, but to aggressively strengthen it past the critical activation threshold identified in our analysis (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Only a strong, credible IPP framework can de-risk corporate innovation and change digital investment into public productivity gains (Liu \u0026amp; Lin, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIncentivize ESG as an \"Efficiency Converter.\"\u003c/strong\u003e The \"increasing marginal return\" of ESG (H2b) implies that firms with high ESG performance are the most efficient converters of digital inputs into NQPF outputs (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Public policies, such as green credits or technology subsidies, should be given first to these high-ESG firms, as they will generate the greatest public return on investment for the region.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTarget High-Potential Regions and Industries.\u003c/strong\u003e Our heterogeneity analysis (see Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e) provides a map for precision policymaking. The findings suggest that digital transformation investments give the highest marginal returns in non-coastal and non-resource-based regions. Furthermore, support should be focused on high-tech and competitive industries, as these are the primary sectors where the synergies of DT, ESG, and IPP are most effectively realized.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\n\u003ch2\u003e5.4. Limitations and Future Research Directions\u003c/h2\u003e\n\u003cp\u003eAlthough this study provides new insights into the micro-to-macro links driving NQPF, we acknowledge several limitations that offer clear pathways for future research.\u003c/p\u003e\n\u003cp\u003eFirst, the measurement of NQPF remains a primary challenge. As NQPF is a new and complex macroeconomic concept, our three-element, theory-based index is a robust but not complete approximation. Future research could refine this regional index by adding other dimensions, such as the level of AI development or technological dynamism (Chin et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu \u0026amp; Li, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Or, scholars could try to develop and validate a corporate-level measure of NQPF, which would allow for a direct micro-to-micro analysis of how DT, ESG, and IPP interact within the enterprise (Guo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xue \u0026amp; Chen, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSecond, our measurement of the key variables relies on effective but imperfect proxies. Our text-analysis-based DT variable captures strategic intent but not the specific type or quality of digital technology adopted. Similarly, our composite ESG rating, while comprehensive, masks the different effects of its individual \"E\", \"S\", and \"G\" components. Future research using more detailed data\u0026mdash;such as corporate capital expenditures on AI, blockchain, or IoT, or separate analyses of the \"E\", \"S\", and \"G\" pillars\u0026mdash;could unpack these mechanisms in greater detail.\u003c/p\u003e\n\u003cp\u003eThird, while we have used a 2SLS-IV approach and multiple fixed effects to reduce endogeneity, establishing clear causality in a non-experimental, cross-level setting is very difficult. Our findings, while robust, are still based on correlational (panel) data. A good path for future research would be to use a quasi-natural experiment. For instance, an exogenous shock\u0026mdash;such as the sudden implementation of a regional IPP pilot program or a new mandatory ESG disclosure rule\u0026mdash;could provide cleaner identification of the causal effects we propose (cf. Zhang et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFinally, our findings are based on Chinese A-share listed companies, which are typically large, mature, and publicly visible firms. The ability to generalize our \"technology-institution\" synergy model to small and medium-sized enterprises (SMEs), which face different resource constraints and innovation incentives, remains an open question. Furthermore, testing this model in other national contexts, such as different emerging markets or developed economies, would provide valuable insights into how the specific institutional environment (e.g., legal origins, market structures) shapes the relationship between digitalization and advanced productivity.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study investigates how corporate digital transformation (DT) contributes to regional new quality productive forces (NQPF) in the context of China's high-quality development agenda. Using a matched panel dataset matching Chinese A-share listed firms (2013\u0026ndash;2022) to their corresponding provinces, our analysis reveals a strong positive relationship: the combination of corporate digitalization efforts significantly improves the NQPF of the regions in which they operate. This finding supports the \"micro-to-macro\" logic, suggesting that corporate technological investments generate large positive spillovers that lift regional productivity (Pan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, this micro-to-macro link is not uniform; it strongly depends on the institutional environment. We find that the firm's internal commitment to sustainability\u0026mdash;its ESG performance\u0026mdash;acts as a powerful amplifier. Critically, this effect shows an \"increasing marginal return\": as corporate ESG performance improves, its digital investments become much more effective at driving regional NQPF (Guo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xue \u0026amp; Chen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the external institutional environment\u0026mdash;regional Intellectual Property Protection (IPP)\u0026mdash;functions as a prerequisite. Our threshold analysis reveals that the positive impact of DT is suppressed or non-existent in regions with weak IPP. Only after IPP strength crosses a critical threshold is the \"de-risking\" effect activated, \"unlocking\" corporate digital innovation to generate significant macroeconomic productivity gains (Kim et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe heterogeneity analysis further shapes these dynamics. The positive effect of corporate DT is most clear among firms with greater financial capacity (low financing constraints) and stable operational foundations (mature life-cycle stage) (Dickinson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hadlock \u0026amp; Pierce, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The effect is also significantly stronger in high-tech and competitive industries, where innovation is a core driver. Notably, we find the effect is strongest in non-coastal and non-resource-based regions, suggesting digital transformation serves as a critical \"path-breaking\" strategy for regions moving beyond traditional endowments.\u003c/p\u003e \u003cp\u003eOverall, this study offers a comprehensive understanding of how the interaction between corporate technology adoption and multi-level institutional alignment shapes regional productivity. By using a \"technology-institution\" model within a cross-level framework (Lv et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), it contributes to the NQPF literature by showing that synergy, not singularity, is the key. Ultimately, our findings suggest that building new quality productive forces is a dual challenge. It requires not only promoting digital transformation (the engine) but also at the same time building the institutional \"rules of the road\"\u0026mdash;both internal (corporate ESG governance) and external (robust IPP)\u0026mdash;that are needed to guide technological potential into sustainable, high-quality development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eETHICAL APPROVAL:\u003c/h2\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eINFORMED CONSENT:\u003c/strong\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhen Ren : Conceptualization, Methodology, Formal analysis, Funding acquisition, Investigation, Writing\u0026ndash; original draft, Writing\u0026ndash; review \u0026amp; editing. Shengyang Zhong: Methodology, Formal analysis, Validation,Writing\u0026ndash; original draft. Zhi Li: Data curation, Formal analysis,Validation, Software. Ruihuan Fu: Conceptualization, Methodology, Formal analysis, Funding acquisition, Investigation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAghion P, Jones BF, Jones CI (2019) Artificial intelligence and economic growth. The economics of artificial intelligence: An agenda. University of Chicago Press, pp 237\u0026ndash;282\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkerman A, Gaarder I, Mogstad M (2015) The skill complementarity of broadband internet. Q J Econ 130(4):1781\u0026ndash;1824\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbuquerque R, Koskinen Y, Zhang C (2019) Corporate social responsibility and firm risk: Theory and empirical evidence. Manage Sci 65(10):4451\u0026ndash;4469\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAng JB (2010) Financial reforms, patent protection, and knowledge accumulation in India. World Dev 38(8):1070\u0026ndash;1081\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBranstetter LG, Fisman R, Foley CF (2006) Do stronger intellectual property rights increase international technology transfer? Empirical evidence from U.S. firm-level data. Q J Econ 121(1):321\u0026ndash;349\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChin T, Li Z, Huang L, Li X (2025) How artificial intelligence promotes new quality productive forces of firms: A dynamic capability view. Technological Forecast Social Change 216:124128\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDickinson V (2011) Cash flow patterns as a proxy for firm life cycle. Acc Rev 86(6):1969\u0026ndash;1994\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriede G, Busch T, Bassen A (2015) ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. J Sustainable Finance Invest 5(4):210\u0026ndash;233\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGillan SL, Koch A, Starks LT (2021) Firms and social responsibility: A review of ESG and CSR research in corporate finance. J Corp Finance 66:101889\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Hu J, Dou Y, Lin S (2025) Can ESG rating help shape enterprises' new-quality productivity? Evidence from China. Int Rev Econ Finance 104:104654\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadlock CJ, Pierce JR (2010) New evidence on measuring financial constraints: Moving beyond the KZ index. Rev Financial Stud 23(5):1909\u0026ndash;1940\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen BE (1999) Threshold effects in non-dynamic panels: Estimation, testing, and inference. J Econ 93(2):345\u0026ndash;368\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Q, Yu Y, Zhang S (2019) Internet development and manufacturing productivity improvement: Internal mechanism and China's experience. China Industrial Econ (8): 5\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim YK, Lee K, Park WG, Choo K (2012) Appropriate intellectual property protection and economic growth in countries at different levels of development. Res Policy 41(2):358\u0026ndash;375\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei L, Zhang D, Ji Q (2023) Common institutional ownership and corporate ESG performance. Econ Res J 58(4):133\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Ren Z (2025) How does the construction of a unified national market drive the development of new quality productive forces? J Beijing Technol Bus Univ (Social Sciences) 40(5):22\u0026ndash;33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Li X (2025) How digital technology can improve new quality productive forces? Perspective of total factor agricultural carbon productivity. J Asian Econ 98:101921\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Lin Y (2025) Converting knowledge into Productivity: The role of intellectual property empowerment and digital economy in enhancing regional new quality productivity forces-Evidence from China. Int Rev Econ Finance 102:104316\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv H, Lian Y, Hua X (2025) The relationship between FDI quality, new quality productivity forces, and economic resilience in China's Yangtze River Delta region: insights from an empirical spatial Durbin model. Humanit Social Sci Commun 12:1083\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNunn N, Qian N (2014) US food aid and civil conflict. Am Econ Rev 104(6):1630\u0026ndash;1666\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan W, Xie T, Wang Z, Ma L (2022) Digital economy: An innovation driver for total factor productivity. J Bus Res 139:303\u0026ndash;311\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezaee Z, Tuo L (2019) Are the quantity and quality of sustainability disclosures associated with the innate and discretionary earnings quality? J Bus Ethics 155(3):763\u0026ndash;786\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaito Y (2017) Effects of patent protection on economic growth and welfare in a two-R\u0026amp;D-sector economy. Econ Model 62:124\u0026ndash;129\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen G, Huang S (2019) The impact of city-level intellectual property protection on FDI entry in China. J Financial Economic Res 40(12):143\u0026ndash;157\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiebel TM (2019) Digital transformation: Survive and thrive in an era of mass extinction. Rosetta Books\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong D, Zhu W, Ding H (2022) Can firm digitalization promote green technological innovation? An examination based on listed companies in heavy pollution industries. J Finance Econ 48(4):34\u0026ndash;48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao F, Wang X, Xu Y, Zhu P (2023) Digital transformation, resilience of industrial chain and supply chain, and enterprise productivity. China Industrial Econ (5): 118\u0026ndash;136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu F, Hu H, Lin H, Ren X (2021) Enterprise digital transformation and capital market performance: Empirical evidence from stock liquidity. J Manage World 37(7):130\u0026ndash;144\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu L, Hitt LM, Lou B (2020) Data analytics, innovation, and firm productivity. Manage Sci 66(5):2017\u0026ndash;2039\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXi JP (2024) Advancing New Quality Productive Forccs Is Esscntial and a Key Priority for Fostering High-Quality Development. CPC Cent Comm Bimon (QIUSHI) (5): 2\u0026ndash;8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue R, Chen J (2025) ESG performance and stability of New Quality Productivity Forces: From perspective of China's modernization construction. Int Rev Econ Finance 98:103911\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo Y, Boland RJ, Lyytinen K, Majchrzak A (2012) Organizing for innovation in the digitized world. Organ Sci 23(5):1398\u0026ndash;1408\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan C, Xiao T, Geng C, Sheng Y (2021) Digital transformation and enterprise division of labor: Specialization or vertical integration. China Industrial Econ (9): 137\u0026ndash;155\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Wang X, Zhang W, Wang L (2025) Can the carbon emissions trading policy promote the development of new quality productive forces in manufacturing enterprises? Finance Res Lett 83:107617\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Z, Wang S, Zhang Y (2022) Intellectual property protection and the information dilemma of corporate innovation. China Industrial Econ (6): 136\u0026ndash;154\u003c/span\u003e\u003c/li\u003e\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":"Digital Transformation, New Quality Productive Forces, ESG Performance, Intellectual Property Protection","lastPublishedDoi":"10.21203/rs.3.rs-8115547/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8115547/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs global economies seek solutions to slowing productivity growth and sustainability challenges, China has advanced the concept of \u0026ldquo;New Quality Productive Forces\u0026rdquo; (NQPF) to guide innovation-led, green development. This study explores how corporate digital transformation (DT) contributes to regional NQPF, and how this relationship is moderated by two institutional forces: internal corporate ESG performance and external regional intellectual property protection (IPP). Using a matched panel dataset of 31,598 firm-year observations from Chinese A-share listed companies (2013\u0026ndash;2022) and their corresponding regional data, we apply fixed-effect models and threshold regressions to empirically test the micro-to-macro productivity link. Results reveal that DT significantly enhances NQPF at the regional level, but this effect is conditional. Higher ESG performance amplifies the positive impact of DT, exhibiting an increasing marginal return. Meanwhile, IPP shows a non-linear moderating effect: only when protection surpasses a critical threshold does it unlock the productivity gains of corporate digitalization. These findings offer theoretical advances by clarifying the institutional boundaries of technology spillovers and contribute to the emerging NQPF literature by establishing a cross-level framework that links firm strategy to macroeconomic outcomes. Policy implications suggest that sustainable digitalization requires both strong internal ESG governance and robust external IP institutions to fully translate private innovation into public economic value.\u003c/p\u003e","manuscriptTitle":"Digital Transformation and New Quality Productive Forces Development: The Moderating Effects of Corporate ESG Performance and Intellectual Property Protection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-26 06:02:33","doi":"10.21203/rs.3.rs-8115547/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":"6713eb47-509b-420f-9654-2100bc49ee52","owner":[],"postedDate":"December 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60207569,"name":"Business and commerce/Business and management"},{"id":60207570,"name":"Social science/Business and management"},{"id":60207571,"name":"Business and commerce/Economics"},{"id":60207572,"name":"Social science/Economics"},{"id":60207573,"name":"Business and commerce/Information systems and information technology"}],"tags":[],"updatedAt":"2026-02-02T16:15:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-26 06:02:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8115547","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8115547","identity":"rs-8115547","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}