Research on the Impact of Government-Led Technical Standardization on Regional Innovation Performance

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Utilizing a comprehensive provincial-level panel dataset from 2013 to 2022 and employing a two-way fixed effects approach, we provide robust empirical evidence demonstrating that government-led standardization significantly enhances regional innovation performance. Specifically, this positive impact operates through dual mediating pathways: facilitating technology trajectory extension and enabling technology trajectory transformation. Furthermore, our analysis reveals that institutional development serves as a critical moderator, where regions with more robust institutional frameworks experience substantially stronger innovation effects from standardization policies. By elucidating the complete "policy-trajectory-performance" transmission mechanism, this research advances theoretical understanding of standardization-innovation dynamics and offers valuable insights for designing effective innovation policies in emerging economies. Government-led Technical Standardization Regional Innovation Performance Technological Trajectory Evolution Level of Institutional Development Figures Figure 1 Figure 2 1 Introduction InnovationInnovation is widely recognized as the core driver of regional economic growth and industrial upgrading (Pierre-Alexandre et al., 2022). Against this backdrop, as the gap in economic development between regions continues to widen, regional innovation performance has increasingly become a crucial indicator for evaluating regional development quality and policy effectiveness (Grillitsch & Sotarauta, 2020 ). Although existing studies have extensively discussed innovation mechanisms at the enterprise and national levels, systematic investigations into the institutional and policy factors affecting regional innovation performance—especially the role of government-led technical standardization policies—remain limited (Ortega & Serna, 2020 ). In recent years, government-led technical standardization has become a key policy tool for promoting innovation and industrial transformation in emerging economies such as China (Wang et al., 2023 ). Studies by Blind, Ramel, & Rochell ( 2022 ) on the impact of standards and patents on long-term economic growth, as well as Tassey’s ( 2017 ) systematic analysis of the economic role of standards, have highlighted the strategic significance of standardization activities at the macroeconomic level. Unlike market-driven or industry-led standardization paths, government-led standardization features stronger goal orientation and resource mobilization capabilities. By setting unified norms, reducing institutional frictions, and clarifying technical directions, it can effectively guide innovation resources to concentrate on policy-prioritized fields within regions (Huppenbauer, 2023 ; Mazzucato, 2013 ). Existing studies have confirmed that participation in standardization can improve enterprise performance (Wakke, Blind, & Ramel, 2016 ; Oh et al., 2016 ). However, most of these literatures focus on the micro or industry level, ignoring the macro-regulatory role of the regional institutional environment and failing to fully reveal the process mechanism through which government-led standardization affects innovation performance (Hamidi et al., 2019 ). It is particularly worth noting that existing theories have obvious limitations in explaining the role of government-led standardization in the Chinese context. While the technology trajectory theory profoundly reveals the evolutionary logic of technology itself, it overemphasizes the inherent law of technological development and underestimates the governments ability to proactively shape technology trajectories as a powerful institutional force in transition economies. Although the institutional theory emphasizes the constraints and guiding effects of the institutional environment on economic activities, it fails to deeply analyze how specific institutional tools such as "government-led standardization" interact with technology trajectories at different development stages, thereby generating differentiated innovation effects. This theoretical "fragmentation" makes it difficult for existing studies to systematically explain the internal mechanism through which government-led standardization affects regional innovation performance. To fill this research gap, this study integrates the technology trajectory theory (Dosi, 1982 ) and institutional theory (North, 1990 ) to construct a moderated mediation model. This model aims to explain how government-led standardization affects regional innovation performance through technology trajectory evolution (including trajectory extension and transformation) (Geels, 2002 ; Juliao-Rossi et al., 2020 ; Sun et al., 2021 ) and to examine the boundary role of institutional development level in the above path (Rodriguez-Pose & Di Cataldo, 2015). The contributions of this study are as follows: First, it introduces a regional policy perspective, enriching the theoretical discussion on the relationship between standardization and innovation. Second, it identifies "technology trajectory evolution" as a mediating mechanism, clarifying the process through which policy tools affect innovation performance. Third, it proposes and empirically tests the moderating role of institutional development level, providing practical implications for improving the effectiveness of standardization policies in regional innovation governance. 2. Theoretical Background and Hypotheses 2.1 Government-Led Technical Standardization and Regional Innovation Performance Government-led technical standardization has transcended mere technical specifications and evolved into a critical strategic policy tool. Its role in innovation governance has become increasingly prominent, particularly in transition economies like China that can effectively coordinate institutional resources (Hu & Liu, 2022 ). Unlike the gradual and spontaneous nature of market-led paths, the core advantage of the government-led path lies in its ability to leverage public authority to proactively shape and define emerging technical paradigms, thereby systematically guiding and accelerating the evolution of regional innovation systems. From the perspective of macroeconomic growth, standards and patents have been identified as key drivers of long-term economic performance. Research by Blind, Ramel, & Rochell ( 2022 ) reveals the profound impact of standards and patents on economic growth, emphasizing their foundational role in national innovation systems. This macro-level effect is supported by solid empirical evidence: early pioneering studies, such as the cross-country panel analysis by Blind & Jungmittag ( 2008 )有 covering twelve sectors across four countries, have already confirmed that standards make a significant positive contribution to sector-level productivity growth. This provides a logical starting point for cross-level verification in exploring their impact at the regional level in this study. From the perspective of micro-behavioral mechanisms, the realization of the aforementioned macro-level effects ultimately depends on the active response and capacity improvement of market entities. The findings of Wakke, Blind, & Ramel ( 2016 ) are crucial: they confirm that enterprises’participation in formal standardization activities can significantly improve their performance. Furthermore, the empirical analysis by Blind, Pohlisch, & Rainville ( 2019 ) shows that standardization and innovation together constitute the key driving forces for enterprises to achieve success in public procurement.This clarifies at the micro level why government-led standardization policies can aggregate into regional performance—by enhancing individual enterprises’ technical capabilities, market reputation, and collaboration efficiency, it thereby drives regional innovation at the aggregated level. The latest theoretical advancements further position standards as the core architecture of modern innovation ecosystems, noting that they provide critical coordination functions for innovation activities by reducing technical uncertainty, guiding R&D resource allocation, and promoting knowledge diffusion (Blind, Kenney, Leiponen, et al., 2023). However, a rigorous theoretical framework must include a dialectical examination of potential policy risks. Academic research clearly indicates that while standards offer numerous advantages, their inherent institutional rigidity may also trigger countervailing forces such as "path dependence" and "technology lock-in" (Tassey, 2000 , 2017 ; Swann, 2010 ). For instance, when government-led standards become disconnected from rapidly changing market demands or form a closed system that excludes external competition, they may instead inhibit disruptive innovation and increase compliance burdens for small and medium-sized enterprises (Zhao et al., 2021 ). Recent research by Blind & Münch ( 2024 ) provides new evidence for this, revealing the complex interactive relationship between standards, innovation, and regulation, and pointing out that under specific conditions, such interactions may produce inhibitory effects.Blind et al. ( 2017 ) further corroborates this by demonstrating how standards and regulation can impact innovation differently across uncertain market environments. Nevertheless, these potential negative impacts precisely highlight the critical role of policy design and institutional environment. Against the backdrop of an underdeveloped institutional environment and flawed market coordination mechanisms, a well-designed, forward-looking, and open government-led standardization policy is likely to generate positive effects that far outweigh its potential costs in avoiding market fragmentation, providing clear technical signals, and guiding scarce resources—resulting in a significant net positive effect Based on the above discussion, this paper proposes the following hypothesis: H1: Government-led technical standardization promotes regional innovation performance. 2.2 The Mediating Role of Technological Trajectory Evolution The technology trajectory theory provides a classic framework for understanding the laws of technological change. This theory argues that technological innovation is not a random process but evolves cumulatively in a predetermined direction under the guidance of specific "technical paradigms" (Dosi, 1982 ). Geels’(2002) multi-level perspective further deepens this understanding, clarifying that the transformation of socio-technical systems is typically accompanied by two processes: the continuous strengthening (i.e., extension) of existing technical trajectories and the fundamental breakthrough (i.e., transformation) of new technical trajectories. In this study, following this theoretical vein, regional technology trajectory evolution is specifically operationalized into two dimensions: technology trajectory extension (corresponding to incremental innovation) and technology trajectory transformation (corresponding to radical innovation) (Schilling, 2013 ). Government-led technical standardization can act on both dimensions simultaneously, thereby driving the evolution of technology trajectories. First, in terms of the technology trajectory extension path, the government creates a predictable environment for enterprises to accumulate knowledge and improve efficiency within a relatively stable paradigm by providing clear technical specifications and innovation guidelines (Zhou et al., 2018 ). This deepening and expansion of existing technical paths aligns closely with the mechanism emphasized by Blind et al. ( 2023 )—whereby standards promote knowledge diffusion and adoption by "reducing uncertainty"—and primarily supports the cumulative effects of incremental innovation.In particular, in complex technology fields such as biotechnology, studies have shown that standards play a core coordinating role at the interface of product and process development, which is precisely the micro-level manifestation of the systematic extension of technological trajectories (Blind, Lorenz, & Raven, 2017 ). Second, in the more critical technology trajectory transformation path, government-led standardization also plays a vital role. It does not always lead to lock-in; on the contrary, by issuing forward-looking cutting-edge technical standards, the government can clarify the strategic direction of technological breakthroughs for enterprises and research institutions. More importantly, through supporting policy resources such as R&D subsidies and market access, the government can effectively share the high risks faced by enterprises in exploring disruptive technologies, thereby encouraging them to break free from dependence on existing technical paths and achieve leaps in technical paradigms (Geels, 2002 ). This mechanism is closely linked to the "resource allocation guidance" attribute inherent in standards (Blind et al., 2023 ). Research by Aldieri, Barra, Vinci, & Zotti ( 2021 )有 on the combined impact of different types of innovation (e.g., product innovation and process innovation) on enterprise productivity indirectly supports the importance of technology trajectory extension and transformation as parallel and complementary paths. It indicates that the synergy of multiple innovation models is key to improving overall performance. To summarize, government-led technical standardization constitutes a critical mediating mechanism for enhancing regional innovation performance by driving both the extension (incremental path) and transformation (radical path) of technology trajectories. Based on the above analysis, this paper proposes the following research hypotheses: H2: Government-led technical standardization can improve regional innovation performance by promoting technological trajectory evolution, i.e., technological trajectory evolution plays a mediating role. H2a: Government-led technical standardization promotes technological trajectory extension. H2b: Government-led technical standardization promotes technological trajectory transition. 2.3 The Moderating Role of Institutional Development Level high level of institutional development serves as the cornerstone for any policy tool to exert its effectiveness. It provides a stable, transparent, and predictable macro-environment for various innovation activities by optimizing the allocation efficiency of innovation resources, safeguarding a fair competitive market environment, and improving legal protection (especially in terms of intellectual property rights) (Wang et al., 2017; Papageorgiadis & Sharma, 2016 ). The institutional environment plays a decisive role in either constraining or promoting the effectiveness of technical elements. In their research on the impact of standards and patents on long-term economic growth, Blind, Ramel, & Rochell ( 2022 ) explicitly emphasize that a sound institutional framework is a prerequisite for ensuring these technical elements generate positive economic benefits. This view resonates in the broader field of technical policy. Research by Blind & Schubert ( 2024 ) on open-source software reveals a highly relevant conclusion: the complementary effect between open-source software and R&D investment strongly depends on national-level institutional and environmental factors. This provides strong cross-field evidence for the hypothesis regarding the moderating role of institutions in this study, from another important dimension of technical governance. Specifically, in regions with a high level of institutional development: an efficient policy implementation system ensures that standardization strategies are accurately communicated and implemented; a sound market mechanism facilitates the rapid diffusion and efficient integration of standard-related knowledge and technologies; and strict intellectual property protection encourages enterprises to engage in bold re-innovation based on standards without excessive fear of innovation outcomes misappropriation. All these factors work together to ensure that the signals of standardization policies are clearly received, supporting resources are accurately allocated, and ultimately maximizing their innovation-promoting effects. Conversely, in regions with weak institutions, even well-designed policies may see their effectiveness greatly diminished—or even produce counterproductive results—due to distorted implementation, inefficient resource allocation, or inadequate property rights protection. Therefore, the level of institutional development essentially sets a critical "effectiveness boundary" for the effectiveness of government-led standardization policies. This study proposes the following hypothesis: H3: A higher level of institutional development positively moderates the relationship between government-led technical standardization and regional innovation performance, i.e., the higher the institutional development, the more significant the promoting effect of standardization policies on regional innovation. 2.4 Theoretical Model Based on the above analysis, this paper proposes the relevant theoretical model, as shown in Fig. 1 . 3.1 Data Sources and Sample Selection This paper uses samples of industrial enterprises from 30 Chinese provinces from 2013 to 2022 to test the hypotheses. The sample data are obtained from databases such as CSMAR, Wind, and the Statistical Yearbooks of various provinces. The data for this period are selected because China’s government-led technical standardization work was comprehensively promoted during this stage, and the relevant policy system was gradually improved, providing a typical scenario for studying its impact on regional innovation performance. The sample covers industrial enterprises in 30 provinces. Initially, we selected normally operating industrial enterprises (excluding ST companies and those with shareholders’equity less than zero). Next, we excluded samples with missing key variables. Finally, we winsorized the number of enterprise patent authorizations at the 1% and 99% percentiles (adding 1 and taking the natural logarithm) to eliminate the impact of extreme values. Eventually, an effective sample of 4996 observations was obtained. 3.2 Variable Definition and Measurement (1) Regional Innovation Performance Regional innovation performance measures the output of regional technological innovation activities. The number of patent authorizations, due to their legally confirmed validity, can well reflect the actual level of a region in knowledge output, R&D capabilities, and innovation efficiency. Compared with the number of applications, authorized patents can more accurately reflect the efficiency of intellectual property transformation and R&D quality ((Hegde et al., 2023 ;Wang et al., 2022 ). This paper uses the total number of annual authorized patents (including inventions, utility models, and designs) in each province as the measure of regional innovation performance. (2) Government-Led Technical Standardization Government-led technical standardization reflects the government’s leading role in standard formulation, promotion, and implementation, especially in the diffusion of emerging industry technologies and institutional supplementation. This process helps guide industrial technical directions, reduce institutional frictions, and improve the efficiency of collaborative innovation (Hu & Liu, 2022 ). Referring to their approach, this paper uses the number of new technical standards in the industrial field issued by each provincial government every year to measure the level of government-led technical standardization. (3) Technological Trajectory Evolution Technological trajectory evolution describes the path changes of regional innovation activities, divided into incremental "trajectory extension" and breakthrough "trajectory transition" (Aghion et al., 2019 ). This paper uses invention patents to represent breakthrough innovation and utility model and design patents to represent incremental innovation. Taking enterprises as the unit, the number of patents is winsorized at the 1% and 99% percentiles, then added 1 and taken the natural logarithm. The realization of technological transition or extension is judged based on industry averages. (4) Institutional Development Level Institutional development level is a core indicator for measuring the maturity of regional market mechanisms, government governance capabilities, and policy implementation efficiency. A higher degree of marketization and more institutions can provide a stable environment for standardization policies(Long & Liao, 2022 ). This paper uses the "marketization index" from the Report on China’s Provincial Marketization Index to measure the institutional development level of each province. Control Variables (5) Per Capita GDP Per capita GDP can reflect the regional economic development level and resource carrying capacity, and is an important variable for measuring regional innovation potential, which has been widely used in regional innovation research (Yang et al., 2021 ). This paper calculates it as the ratio of each province’s GDP to its population. (6) Proportion of the Tertiary Industry The service industry has a high knowledge intensity and can promote the effective flow of innovation resources. A higher proportion of the tertiary industry indicates a stronger regional capacity to carry high-value-added industries and innovation factors(Lemstra & de Mesquita, 2023 ). This paper uses the proportion of the added value of the tertiary industry in GDP to measure the optimization of the industrial structure. (7) Education Level Education provides talents and knowledge for innovation and is an important indicator for measuring regional human capital accumulation. Higher education, in particular, has a significant promoting effect on innovation activities(Zhang et al., 2023 ). This paper uses the number of students in higher education per 100,000 people to measure education level. (8) Total Population and Population Density Total population and population density reflect the regional social foundation and the agglomeration capacity of innovation resources. A high population density can enhance knowledge spillover and collaborative innovation effects(Gordon & McCann, 2005 ). This paper uses the total permanent population and population density (number of people per square kilometer) to control for urbanization and scale effects, respectively. In the econometric analysis, we referred to the panel data model processing principles elaborated by Zong ( 2022 ) to ensure the validity and robustness of the estimation results. 4. Results This paper uses StataMP 18 for regression analysis to test the theoretical framework based on a panel data model with provincial and year fixed effects to control for time trends and regional heterogeneity. The correlation between variables is shown in Table 1 . Table 2 presents the results of the main regression analysis. Table 1 Variable Correlation Analysis Variable Name (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Regional Innovation Performance 1.000 Government-Led Technical Standardization 0.693*** 1.000 Institutional Development Level 0.659*** 0.467*** 1.000 Technological Trajectory Transition 0.660*** 0.457*** 0.520*** 1.000 Technological Trajectory Extension 0.680*** 0.489*** 0.417*** 0.651*** 1.000 Per Capita GDP 0.512*** 0.300*** 0.691*** 0.443*** 0.353*** 1.000 Proportion of Tertiary Industry 0.240*** 0.091 0.388*** 0.302*** 0.241*** 0.692*** 1.000 Education Level 0.220*** 0.150*** 0.571*** 0.130* 0.097* 0.651*** 0.618*** 1.000 Total Population 0.617*** 0.613*** 0.484*** 0.382*** 0.377*** 0.007 -0.173*** -0.075 1.000 Population Density 0.233*** -0.022 0.547*** 0.214*** 0.167*** 0.669*** 0.609*** 0.414*** -0.003 1.000 ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. 4.1 Direct Effect of Government-Led Technical Standardization on Regional Innovation Performance To test the direct impact of government-led technical standardization on regional innovation performance, this paper conducts regression analysis based on a panel data model with provincial and year fixed effects to control for time trends and regional heterogeneity. As shown in Table 2 , the results of the benchmark model (Model 1) indicate that government-led technical standardization has a significantly positive impact on regional innovation performance (β = 0.384, p < 0.01), verifying Hypothesis 1. Models 2 to 5 gradually introduce control variables such as economic scale, infrastructure, education level, population size, and density. The coefficients of the core variable remain stable and significant, and the R²value gradually increases, indicating that the model fits well and the positive effect of government-led standardization on innovation performance is robust. Table 2 Direct Effect Test of Government-Led Technical Standardization on Regional Innovation Performance (1) (2) (3) (4) (5) Variable Name Regional Innovation Performance Regional Innovation Performance Regional Innovation Performance Regional Innovation Performance Regional Innovation Performance Government-Led Technical Standardization 0.384*** (4.10) 0.373*** (4.24) 0.378*** (4.36) 0.378***(4.38) 0.251*** (3.88) Per Capita GDP 0.379*** (6.16) 0.410*** (6.74) 0.412*** (5.94) 0.039 (0.41) Proportion of Tertiary Industry 0.123 (1.54) 0.123 (1.51) 0.067 (1.03) Education Level 0.010 (0.08) -0.026 (-0.29) Total Population 5.672*** (6.13) Population Density 2.250** (2.25) Constant Term 0.330*** (3.44) -0.393*** (-2.55) -0.764*** (-2.92) -0.793* (-1.68) 1.629 (0.86) Number of Observations 300 300 300 300 300 R² 0.848 0.858 0.858 0.858 0.925 F 39.95 44.27 43.09 41.99 42.96 Province Fixed Effects Yes Yes Yes Yes Yes Time Fixed Effects Yes Yes Yes Yes Yes ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics. 4.2 Test of the Mediating Effect of Technological Trajectory Evolution Further analysis of the mediating effect of technological trajectory evolution examines the mechanisms of technological trajectory transition and extension respectively. As shown in Table 3 , government-led technical standardization has a significantly positive impact on technological trajectory transition (β = 0.163, p < 0.01), and trajectory transition also has a significant impact on regional innovation performance (β = 0.244, p < 0.01). After introducing trajectory transition, the direct effect of standardization remains significant (β = 0.232, p < 0.01), indicating the existence of a partial mediating effect, which verifies Hypotheses H2 and H2a. Table 3 Mediating Effect Test of Technological Trajectory Transition (1) (2) (3) Variable Name Regional Innovation Performance Technological Trajectory Transition Regional Innovation Performance Government-Led Technical Standardization 0.251*** (3.88) 0.163** (2.52) 0.232*** (3.85) Technological Trajectory Transition 0.121*** (2.75) Per Capita GDP 0.039 (0.41) 0.176*** (1.12) 0.017 (0.20) Proportion of Tertiary Industry 0.067 (1.03) 0.146 (0.75) 0.049 (0.77) Education Level -0.026(-0.29) -0.144 (-1.15) -0.008 (-0.10) Total Population 5.672*** (6.13) 3.455*** (3.84) 5.254*** (6.07) Population Density 2.250** (2.25) 3.713 (1.45) 2.510** (2.09) Constant Term 1.629 (0.86) -1.564 (-0.46) 1.819 (1.06) Number of Observations 300 300 300 R² 0.925 0.798 0.928 F 42.96 39.33 42.18 Province Fixed Effects Yes Yes Yes Time Fixed Effects Yes Yes Yes ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics. Similarly, the results in Table 4 show that government-led technical standardization also has a significantly positive impact on technological trajectory extension (β = 0.148, p < 0.01), and extension has a significant impact on innovation performance (β = 0.193, p < 0.01). The direct effect of the standardization variable remains positive and significant in the model (β = 0.215, p < 0.01), verifying Hypotheses H2 and H2b. Table 4 Mediating Effect Test of Technological Trajectory Extension (1) (2) (3) Variable Name Regional Innovation Performance Technological Trajectory Extension Regional Innovation Performance Government-Led Technical Standardization 0.251***(3.88) 0.148**(2.07) 0.215***(4.08) Technological Trajectory Extension 0.214***(3.87) Per Capita GDP 0.039(0.41) 0.046(0.39) 0.027(0.32) Proportion of Tertiary Industry 0.067(1.03) -0.193(-1.12) 0.114(1.50) Education Level -0.026(-0.29) -0.330***(-2.61) 0.054(0.68) Total Population 5.672***(6.13) 2.458***(3.44) 5.078***(5.84) Population Density 2.250**(2.25) 1.538(0.84) 2.587**(2.03) Constant Term 1.629(0.86) 1.554(0.63) 1.254(0.71) Number of Observations 300 300 300 R² 0.925 0.845 0.934 F 42.96 38.08 46.80 Province Fixed Effects Yes Yes Yes Year Fixed Effects Yes Yes Yes ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics. In summary, government-led technical standardization can not only directly promote regional innovation performance but also have an indirect positive impact through the transition and extension of technological trajectories. 4.3 Test of the Moderating Effect of Institutional Development Level Table 5 shows the test results of the moderating effect of institutional development level. After adding the interaction term between institutional development level and government-led standardization into the model, the results show that the interaction term is significantly positive (β = 0.336, p < 0.01), and institutional development level itself has a significant impact on innovation performance (β = 0.169, p < 0.01), verifying Hypothesis H3. This indicates that in regions with more institutional foundations, the positive impact of government-led standardization on innovation performance is stronger, suggesting that the institutional environment plays a key role in improving the effectiveness of policy tools. Table 5 Moderating Effect Test of Institutional Development Level (1) (2) Variable Name Regional Innovation Performance Regional Innovation Performance Government-Led Technical Standardization 0.251*** (3.88) 0.068** (2.46) Institutional Development Level 0.169*** (2.98) Government-Led Technical Standardization × Institutional Development Level 0.336*** (9.22) Per Capita GDP 0.039 (0.41) 0.020 (0.38) Proportion of Tertiary Industry 0.067 (1.03) -0.021 (-0.77) Education Level -0.026 (-0.29) 0.134*** (3.14) Total Population 5.672*** (6.13) 3.858*** (5.00) Population Density 2.250**(2.25) 1.389(1.31) Constant Term 1.629 (0.86) 1.630 (1.10) Number of Observations 300 300 R² 0.925 0.959 F 42.96 57.54 Province Fixed Effects Yes Yes Time Fixed Effects Yes Yes ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics. The slope depicted in Fig. 2 demonstrates that under conditions of a high institutional development level, the positive correlation between government-led technical standardization and regional innovation performance exhibits a greater magnitude compared to scenarios characterized by a low institutional development level. This observation serves to validate the positive moderating effect of institutional development level on the association between government-led technical standardization and regional innovation performance. 4.4 Robustness Test To test the stability of the research results, this study uses an alternative measure of the dependent variable for verification. Borrowing from Pan et al. ( 2020 ), this study employs provincial patent application counts as a proxy for regional innovation performance, consistent with previous empirical frameworks in Chinese innovation research. The empirical results of the impact of government-led technical standardization on the alternative variable of regional innovation performance are shown in Table 6 , which are consistent with the previous results, passing the robustness test. Table 6 Direct Effect Test of Government-Led Technical Standardization on Alternative Variables of Regional Innovation Performance (1) (2) (3) (4) Variable Name Regional Innovation Performance Regional Innovation Performance Regional Innovation Performance Regional Innovation Performance Government-Led Technical Standardization 0.129***(5.13) 0.111***(4.54) 0.108***(4.48) 0.040**(1.72) Technological Trajectory Transition 0.108***(4.05) Technological Trajectory Extension 0.140***(3.74) Institutional Development Level 0.002(0.03) Government-Led Technical Standardization × Institutional Development Level 0.136***(4.27) Per Capita GDP 0.041(0.62) 0.022(0.35) 0.035(0.52) 0.007(0.12) Proportion of Tertiary Industry -0.006(-0.13) -0.022(-0.45) 0.021(0.37) -0.070(-1.49) Education Level 0.032(-0.29) 0.047(0.70) 0.079(1.18) 0.050(0.81) Total Population 4.703***(6.77) 4.328***(6.68) 4.360***(6.42) 3.858***(5.00) Population Density 2.729**(2.42) 2.327**(2.33) 2.514**(2.30) 4.079***(6.01) Constant Term 1.137(0.70) 1.307(0.90) 0.920(0.59) 1.911**(2.06) Number of Observations 300 300 300 300 R² 0.967 0.969 0.970 0.972 F 125.26 116.19 138.47 127.59 Province Fixed Effects Yes Yes Yes Yes Time Fixed Effects Yes Yes Yes Yes ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics. 5. Discussion Based on Chinese provincial panel data, this study systematically examines the impact mechanism of government-led technical standardization on regional innovation performance. First, this study confirms the direct promoting effect of government-led technical standardization on regional innovation performance (H1), which supplements the previous research perspective that mainly focuses on traditional policy tools such as fiscal subsidies. Second, the mediation effect analysis shows that technical standardization improves innovation performance through the dual paths of technology trajectory extension and transformation (H2). This finding, especially regarding the trajectory transformation path, forms an interesting dialogue with Sandrini’s ( 2023 ) research on "innovation, competition, and incomplete adoption of superior technology". This study shows that government-led standardization can serve as a coordination mechanism in specific contexts to alleviate market failures, promote more complete adoption and diffusion of new technologies, and thus drive a fundamental leap in technology trajectories. Finally, the moderation effect analysis reveals that the level of institutional development positively strengthens the effect of standardization policies (H3). (This is consistent with the conclusion of Blind, Ramel, & Rochell ( 2022 ) that emphasizes the institutional framework for exerting the long-term growth effect of standards, jointly highlighting the key role of the institutional foundation in the success of technical policies. 6. Conclusions 6.1 Theoretical Contributions At the theoretical level, this paper mainly expands and deepens the theoretical system of research on government-led technical standardization and regional innovation performance from the following aspects: First, this paper broadens the research perspective and theoretical boundaries of technical standardization. Previous studies have mainly focused on market-led or industry self-governed technical standardization models, with insufficient attention to government-led technical standardization, especially in terms of how it affects regional innovation performance(Hu & Liu, 2022 ).Based on a summary of previous studies, this paper explicitly takes government-led technical standardization as an independent type of policy tool, constructs an analytical framework of "government-led technical standardization—technological trajectory evolution—regional innovation performance", and responds to the practical needs of theoretical construction for policy-oriented technical standardization mechanisms in current research(Lehr, 1992 ).In particular, this study echoes the findings of Zi & Blind ( 2014 ), which, through a case analysis of public research institutions in Germany, reveals the key role of researchers as participants in the source of standardization knowledge. Building on this, this paper extends the analytical perspective to the starting point of the innovation value chain, emphasizing the leading role of the government in integrating various elements including public R&D resources to promote the standardization process, thereby enriching the theoretical connotation of the government-led model. Second, this paper opens the "black box" of the intermediate mechanism through which government-led technical standardization affects regional innovation performance. Existing literature mostly discusses the impact of standardization on innovation from the perspectives of knowledge diffusion or technical lock-in (Allen & Sriram, 2000 ;Uyarra et al., 2020 ), but lacks a systematic description of meso-level mechanisms. This paper introduces the concept of "technological trajectory evolution" and divides it into two paths: "trajectory extension" and "trajectory transition". On this basis, it explores its mediating role in the process of government-led technical standardization affecting innovation performance, helping to clarify key processes such as how policy-oriented technical standards guide enterprises’innovation paradigm selection, path evolution, and performance changes (Dosi, 1982 ; Juliao-Rossi et al., 2020 ;Sun et al., 2021 ).Third, this paper introduces "institutional development level" as a moderating variable, enriching the application scenarios of the institutional environment in regional innovation research. Although existing studies have paid attention to the indirect role of institutional level in regional innovation (Rodríguez-Pose & Di Cataldo, 2015 ).there is still a lack of empirical tests analyzing it in the chain of policy tool—innovation performance mechanisms. This paper finds that institutional development level can significantly enhance the effect of government-led technical standardization policies, providing a sound institutional foundation for regional innovation performance and expanding the theoretical application boundaries of the institutional perspective in innovation research. Finally, this paper sinks the scale of innovation performance research to provincial panel data analysis, strengthening the meso-level empirical basis for policy mechanism research. Currently, academic circles mostly analyze the impact of policies on innovation at the national or industry level(Wang et al., 2023 ), but ignore the differentiated role of the government as a technical standard setter at the regional level. This paper conducts multi-dimensional empirical tests using panel data of 30 provinces over ten years, further verifying the applicability and robustness of the policy—mechanism—performance chain at the meso level.This approach aligns with recent calls in the literature for more granular analyses of standardization dynamics, as exemplified by Yao et al. ( 2024 ) work on strategic maneuvers in SEP follow-on innovations. In summary, this paper not only responds to the academic concerns about deepening the mechanism of government-led technical standardization and expanding empirical paths but also provides theoretically relevant support for regional innovation governance practices. 6.2 Management Implications (1) Government Level: The government should take technical standardization as a policy tool, formulate forward-looking strategies, clarify key fields and development paths to promote regional innovation. Meanwhile, it should improve the legal and regulatory system, enhance the authority and coordination of policies, and encourage enterprises to participate in standard formulation through incentives such as fiscal subsidies and tax preferences. (2) Enterprise Level: Enterprises should actively participate in government-led technical standardization, combine their own technical advantages to enhance industry influence. In addition, they should build a multi-dimensional innovation system focusing on both basic and applied research, pay attention to emerging technologies such as artificial intelligence and big data promote technological upgrading of products and services, and explore new technological trajectories. 6.3 Research Limitations and Future Research This study still has the following limitations: First, although using the number of patent authorizations to measure regional innovation performance is representative, it is not comprehensive. Future studies can introduce supplementary indicators such as new product sales revenue. Second, the data mainly come from industrial technical standards, with limited industry coverage. Future studies can expand to other industries or refine industry types. Third, it does not discuss the possibility of failure of a single standard project. Future studies can further explore the different role orientations of the government in the standardization process and their adaptive mechanisms. Attention should be paid to emerging technologies such as artificial intelligence, big data, the Internet of Things, and blockchain, explore their applications in enterprise products and services, and open up new technological trajectories. Declarations Author Contribution WYX critically reviewed and revised the entire manuscript. QMY developed the research framework, led data collection and empirical analysis, and completed the initial draft of the manuscript. YXY contributed to data processing and result visualization. LLG assisted with the literature review and methodology section. STT participated in the discussion of results and provided policy implications. All authors contributed to the revision and finalization of the manuscript and approved the version submitted. References Aghion, P., Hepburn, C., Teytelboym, A., & Zenghelis, D. (2019). Path dependence, innovation and the economics of climate change. In Handbook on Green Growth (pp. 67–83). Edw- ard Elgar Publishing. https://doi.org/10.4337/9781788110686.00011 Aldieri, L., Barra, C., Vinci, C. P., & Zotti, R. (2021). The joint impact of different types of. innovation on firm's productivity: Evidence from Italy. Economics of Innovation and New . Technology 30 (2), 151–182. https://doi.org/10.1080/10438599.2019.1685211 Allen, R. H., & Sriram, R. D. (2000). The role of standards in innovation. 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A., Bunn, T., Dalla, D., Gozzi, E., Hausmann, R., O’Connell, N., & Rodrik, D. (2022). The new paradigm of economic complexity. Research Policy , 51 (3), 104568. https://doi.org/10.1016/j.respol.2022.104568 Rodríguez-Pose, A., & Di Cataldo, M. (2015). Quality of government and innovative performance in the regions of Europe. Journal of Economic Geography , 15 (4), 673–706. https://doi.org/10.1093/jeg/lbu023 Sandrini, L. (2023). Innovation, competition, and incomplete adoption of a superior technology. Economics of Innovation and New Technology , 32 (6), 783–803. https://doi.org/10.1080/10438599.2021.2024432 Schilling, M. A. (2013). Strategic management of technological innovation (4th ed.). McGraw-Hill Education. Sun, Y., Li, L., Chen, Y., & Kataev, M. Y. (2021). An empirical study on innovation ecosystem, technological trajectory transition, and innovation performance. Journal of Global Information Management , 29 (4), 148–171. https://doi.org/10.4018/JGIM.20210701.oa7 Swann, G. P. (2010). The economics of standardization: An update (Report No. BIS-10-458). UK Department for Business Innovation and Skills. Tassey, G. (2000). Standardization in technology-based markets. Research Policy , 29 (4–5), 587–602. https://doi.org/10.1016/S0048-7333(99)00091-8 Tassey, G. (2017). The roles and impacts of technical standards on economic growth and implications for innovation policy. Annals of Science and Technology Policy , 1 (3), 215–316. https://doi.org/10.1561/110.00000003 Uyarra, E., Zabala-Iturriagagoitia, J. M., Flanagan, K., & Magro, E. (2020). Public procurement, innovation and industrial policy: Rationales, roles, capabilities and implementation. Research Policy , 49 (1), 103844. https://doi.org/10.1016/j.respol.2019.103844 Wakke, P., Blind, K., & Ramel, F. (2016). The impact of participation within formal standardization on firm performance. Journal of Productivity Analysis , 45 (3), 317–330. https://doi.org/10.1007/s11123-016-0465-3 Wang, H., Zhao, T., Cooper, S. Y., Wang, S., Harrison, R. T., & Yang, Z. (2023). Effective policy mixes in entrepreneurial ecosystems: A configurational analysis in China. Small Business Economics , 60 (4), 1509–1542. https://doi.org/10.1007/s11187-022-00682-1 Wang, S., Zheng, Y., & Wang, Q. (2023). Technical standardization and total factor productivity in innovation-driven development: Evidence from China. PLOS ONE , 18 (10), e0287109. https://doi.org/10.1371/journal.pone.0287109 Wang, Y., Wu, D., & Li, H. (2022). Efficiency measurement and productivity progress of regional green technology innovation in China: A comprehensive analytical framework. Technology Analysis & Strategic Management , 34 (12), 1432–1448. https://doi.org/10.1080/09537325.2021.1963427 Yang, X., Zhang, H., Lin, S., Zhang, J., & Zeng, J. (2021). Does high-speed railway promote regional innovation growth or innovation convergence? Technology in Society , 64 , 101472. https://doi.org/10.1016/j.techsoc.2020.101472 Yao, L., Li, J., Chen, K., & Yu, R. (2024). Winning the second race of technology standardization: Strategic maneuvers in SEP follow-on innovations. Research Policy , 53 (6), 105023. https://doi.org/10.1016/j.respol.2024.105023 Zhang, T., Ma, Z., & Shang, Y. (2023). Higher education, technological innovation, and green development—Analysis based on China’s provincial panel data. Sustainability , 15 (5), 4311. https://doi.org/10.3390/su15054311 Zhao, L., Sun, J., Zhang, L., He, P., & Yi, Q. (2021). Effects of technology lock-in on enterprise innovation performance. European Journal of Innovation Management , 24 (5), 1782–1805. https://doi.org/10.1108/EJIM-06-2020-0206 Zhou, X., Shan, M., & Li, J. (2018). R&D strategy and innovation performance: The role of standardization. Technology Analysis & Strategic Management , 30 (7), 778–792. https://doi.org/10.1080/09537325.2017.1378319 Zi, A., & Blind, K. (2014). Researchers' participation in standardisation: A case study from a public research institute in Germany. The Journal of Technology Transfer , 39 (5), 687–706. https://doi.org/10.1007/s10961-014-9370-y Zong, P. (2022). The art and science of econometrics (1st ed.). Routledge. https://doi.org/10.4324/9781003273905 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Feb, 2026 Read the published version in The Journal of Technology Transfer → 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. 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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-7720883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522935707,"identity":"656384d9-8cfa-4b20-aabe-71c5b560e3de","order_by":0,"name":"Yuxiao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIie3PsWrDMBCAYQmBu5zRKpXQvoKCwW2JqV/FpuDJdMws16CMWVPoc3SW8ZAlTVZBpuKxHuytS0udMUPkjIXoG4/7OQ4hx/nHACFcND+/EVAqz05IKcDLJnylzz50pRh4dSRkYt8T64/6y1f1hC4KxRjsQCCNuz63JJvnbOarDNimKu8F28MdkYS/vp9OQp2Hga8iQCYtTCL28CC1R3xbsmsPCYNbk0qmky0InYwkJg+awxVh0hcutR5PYtOG+G2bwXT4JcDyCfiqKq2/8GUedO28jm/Wi88Gy8eY0rLqeksy8K7heICldX9A+u+xFcdxnMv2B5oiU06T5xHXAAAAAElFTkSuQmCC","orcid":"","institution":"Xi'an Technological University","correspondingAuthor":true,"prefix":"","firstName":"Yuxiao","middleName":"","lastName":"Wang","suffix":""},{"id":522935708,"identity":"8cedef08-15b8-4ecd-9948-c6a6b95320b1","order_by":1,"name":"Meiyu Qin","email":"","orcid":"","institution":"Xi'an Technological University","correspondingAuthor":false,"prefix":"","firstName":"Meiyu","middleName":"","lastName":"Qin","suffix":""},{"id":522935709,"identity":"c89cff69-e56a-4990-8f7a-3840020e3a2e","order_by":2,"name":"Xingyu Yan","email":"","orcid":"","institution":"Xi'an Technological University","correspondingAuthor":false,"prefix":"","firstName":"Xingyu","middleName":"","lastName":"Yan","suffix":""},{"id":522935710,"identity":"3ce8b477-170a-4a1e-a275-f278668d2224","order_by":3,"name":"Lige Liu","email":"","orcid":"","institution":"Xi'an Technological University","correspondingAuthor":false,"prefix":"","firstName":"Lige","middleName":"","lastName":"Liu","suffix":""},{"id":522935711,"identity":"64a33191-6461-4366-af86-4d1ac815f481","order_by":4,"name":"Tiantian Shang","email":"","orcid":"","institution":"Xi'an Technological University","correspondingAuthor":false,"prefix":"","firstName":"Tiantian","middleName":"","lastName":"Shang","suffix":""}],"badges":[],"createdAt":"2025-09-26 10:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7720883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7720883/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10961-026-10322-1","type":"published","date":"2026-02-21T15:58:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96084535,"identity":"ff1212e4-b881-4deb-bc16-efda199584d2","added_by":"auto","created_at":"2025-11-17 12:13:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTheoretical Model Diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7720883/v1/6264c61fdbd89dfbb971921f.jpg"},{"id":96084534,"identity":"9ca48d41-3ea7-4f78-8915-4498ca93f9f3","added_by":"auto","created_at":"2025-11-17 12:13:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModerating Effect of Institutional Development Level on Government-Led Technical Standardization and Regional Innovation Performance\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7720883/v1/5b89331db0e485da1ae76fa9.jpg"},{"id":103251398,"identity":"e26477c0-c56f-4116-bc31-cedc8fb6a8d5","added_by":"auto","created_at":"2026-02-23 16:08:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1198174,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7720883/v1/9e48c365-7c5c-41d9-af2f-df8b519a6855.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Impact of Government-Led Technical Standardization on Regional Innovation Performance","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eInnovationInnovation is widely recognized as the core driver of regional economic growth and industrial upgrading (Pierre-Alexandre et al., 2022). Against this backdrop, as the gap in economic development between regions continues to widen, regional innovation performance has increasingly become a crucial indicator for evaluating regional development quality and policy effectiveness (Grillitsch \u0026amp; Sotarauta, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although existing studies have extensively discussed innovation mechanisms at the enterprise and national levels, systematic investigations into the institutional and policy factors affecting regional innovation performance\u0026mdash;especially the role of government-led technical standardization policies\u0026mdash;remain limited (Ortega \u0026amp; Serna, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, government-led technical standardization has become a key policy tool for promoting innovation and industrial transformation in emerging economies such as China (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies by Blind, Ramel, \u0026amp; Rochell (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) on the impact of standards and patents on long-term economic growth, as well as Tassey\u0026rsquo;s (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) systematic analysis of the economic role of standards, have highlighted the strategic significance of standardization activities at the macroeconomic level. Unlike market-driven or industry-led standardization paths, government-led standardization features stronger goal orientation and resource mobilization capabilities. By setting unified norms, reducing institutional frictions, and clarifying technical directions, it can effectively guide innovation resources to concentrate on policy-prioritized fields within regions (Huppenbauer, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mazzucato, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Existing studies have confirmed that participation in standardization can improve enterprise performance (Wakke, Blind, \u0026amp; Ramel, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oh et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, most of these literatures focus on the micro or industry level, ignoring the macro-regulatory role of the regional institutional environment and failing to fully reveal the process mechanism through which government-led standardization affects innovation performance (Hamidi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is particularly worth noting that existing theories have obvious limitations in explaining the role of government-led standardization in the Chinese context. While the technology trajectory theory profoundly reveals the evolutionary logic of technology itself, it overemphasizes the inherent law of technological development and underestimates the governments ability to proactively shape technology trajectories as a powerful institutional force in transition economies. Although the institutional theory emphasizes the constraints and guiding effects of the institutional environment on economic activities, it fails to deeply analyze how specific institutional tools such as \"government-led standardization\" interact with technology trajectories at different development stages, thereby generating differentiated innovation effects. This theoretical \"fragmentation\" makes it difficult for existing studies to systematically explain the internal mechanism through which government-led standardization affects regional innovation performance.\u003c/p\u003e\u003cp\u003eTo fill this research gap, this study integrates the technology trajectory theory (Dosi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) and institutional theory (North, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) to construct a moderated mediation model. This model aims to explain how government-led standardization affects regional innovation performance through technology trajectory evolution (including trajectory extension and transformation) (Geels, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Juliao-Rossi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and to examine the boundary role of institutional development level in the above path (Rodriguez-Pose \u0026amp; Di Cataldo, 2015).\u003c/p\u003e\u003cp\u003eThe contributions of this study are as follows: First, it introduces a regional policy perspective, enriching the theoretical discussion on the relationship between standardization and innovation. Second, it identifies \"technology trajectory evolution\" as a mediating mechanism, clarifying the process through which policy tools affect innovation performance. Third, it proposes and empirically tests the moderating role of institutional development level, providing practical implications for improving the effectiveness of standardization policies in regional innovation governance.\u003c/p\u003e"},{"header":"2. Theoretical Background and Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Government-Led Technical Standardization and Regional Innovation Performance\u003c/h2\u003e\u003cp\u003eGovernment-led technical standardization has transcended mere technical specifications and evolved into a critical strategic policy tool. Its role in innovation governance has become increasingly prominent, particularly in transition economies like China that can effectively coordinate institutional resources (Hu \u0026amp; Liu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Unlike the gradual and spontaneous nature of market-led paths, the core advantage of the government-led path lies in its ability to leverage public authority to proactively shape and define emerging technical paradigms, thereby systematically guiding and accelerating the evolution of regional innovation systems.\u003c/p\u003e\u003cp\u003eFrom the perspective of macroeconomic growth, standards and patents have been identified as key drivers of long-term economic performance. Research by Blind, Ramel, \u0026amp; Rochell (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reveals the profound impact of standards and patents on economic growth, emphasizing their foundational role in national innovation systems. This macro-level effect is supported by solid empirical evidence: early pioneering studies, such as the cross-country panel analysis by Blind \u0026amp; Jungmittag (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)有 covering twelve sectors across four countries, have already confirmed that standards make a significant positive contribution to sector-level productivity growth. This provides a logical starting point for cross-level verification in exploring their impact at the regional level in this study.\u003c/p\u003e\u003cp\u003eFrom the perspective of micro-behavioral mechanisms, the realization of the aforementioned macro-level effects ultimately depends on the active response and capacity improvement of market entities. The findings of Wakke, Blind, \u0026amp; Ramel (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) are crucial: they confirm that enterprises\u0026rsquo;participation in formal standardization activities can significantly improve their performance. Furthermore, the empirical analysis by Blind, Pohlisch, \u0026amp; Rainville (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) shows that standardization and innovation together constitute the key driving forces for enterprises to achieve success in public procurement.This clarifies at the micro level why government-led standardization policies can aggregate into regional performance\u0026mdash;by enhancing individual enterprises\u0026rsquo; technical capabilities, market reputation, and collaboration efficiency, it thereby drives regional innovation at the aggregated level. The latest theoretical advancements further position standards as the core architecture of modern innovation ecosystems, noting that they provide critical coordination functions for innovation activities by reducing technical uncertainty, guiding R\u0026amp;D resource allocation, and promoting knowledge diffusion (Blind, Kenney, Leiponen, et al., 2023).\u003c/p\u003e\u003cp\u003eHowever, a rigorous theoretical framework must include a dialectical examination of potential policy risks. Academic research clearly indicates that while standards offer numerous advantages, their inherent institutional rigidity may also trigger countervailing forces such as \"path dependence\" and \"technology lock-in\" (Tassey, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Swann, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For instance, when government-led standards become disconnected from rapidly changing market demands or form a closed system that excludes external competition, they may instead inhibit disruptive innovation and increase compliance burdens for small and medium-sized enterprises (Zhao et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent research by Blind \u0026amp; M\u0026uuml;nch (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) provides new evidence for this, revealing the complex interactive relationship between standards, innovation, and regulation, and pointing out that under specific conditions, such interactions may produce inhibitory effects.Blind et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) further corroborates this by demonstrating how standards and regulation can impact innovation differently across uncertain market environments.\u003c/p\u003e\u003cp\u003eNevertheless, these potential negative impacts precisely highlight the critical role of policy design and institutional environment. Against the backdrop of an underdeveloped institutional environment and flawed market coordination mechanisms, a well-designed, forward-looking, and open government-led standardization policy is likely to generate positive effects that far outweigh its potential costs in avoiding market fragmentation, providing clear technical signals, and guiding scarce resources\u0026mdash;resulting in a significant net positive effect Based on the above discussion, this paper proposes the following hypothesis:\u003c/p\u003e\u003cp\u003eH1: Government-led technical standardization promotes regional innovation performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 The Mediating Role of Technological Trajectory Evolution\u003c/h2\u003e\u003cp\u003eThe technology trajectory theory provides a classic framework for understanding the laws of technological change. This theory argues that technological innovation is not a random process but evolves cumulatively in a predetermined direction under the guidance of specific \"technical paradigms\" (Dosi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Geels\u0026rsquo;(2002) multi-level perspective further deepens this understanding, clarifying that the transformation of socio-technical systems is typically accompanied by two processes: the continuous strengthening (i.e., extension) of existing technical trajectories and the fundamental breakthrough (i.e., transformation) of new technical trajectories. In this study, following this theoretical vein, regional technology trajectory evolution is specifically operationalized into two dimensions: technology trajectory extension (corresponding to incremental innovation) and technology trajectory transformation (corresponding to radical innovation) (Schilling, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGovernment-led technical standardization can act on both dimensions simultaneously, thereby driving the evolution of technology trajectories. First, in terms of the technology trajectory extension path, the government creates a predictable environment for enterprises to accumulate knowledge and improve efficiency within a relatively stable paradigm by providing clear technical specifications and innovation guidelines (Zhou et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This deepening and expansion of existing technical paths aligns closely with the mechanism emphasized by Blind et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u0026mdash;whereby standards promote knowledge diffusion and adoption by \"reducing uncertainty\"\u0026mdash;and primarily supports the cumulative effects of incremental innovation.In particular, in complex technology fields such as biotechnology, studies have shown that standards play a core coordinating role at the interface of product and process development, which is precisely the micro-level manifestation of the systematic extension of technological trajectories (Blind, Lorenz, \u0026amp; Raven, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, in the more critical technology trajectory transformation path, government-led standardization also plays a vital role. It does not always lead to lock-in; on the contrary, by issuing forward-looking cutting-edge technical standards, the government can clarify the strategic direction of technological breakthroughs for enterprises and research institutions. More importantly, through supporting policy resources such as R\u0026amp;D subsidies and market access, the government can effectively share the high risks faced by enterprises in exploring disruptive technologies, thereby encouraging them to break free from dependence on existing technical paths and achieve leaps in technical paradigms (Geels, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This mechanism is closely linked to the \"resource allocation guidance\" attribute inherent in standards (Blind et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResearch by Aldieri, Barra, Vinci, \u0026amp; Zotti (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)有 on the combined impact of different types of innovation (e.g., product innovation and process innovation) on enterprise productivity indirectly supports the importance of technology trajectory extension and transformation as parallel and complementary paths. It indicates that the synergy of multiple innovation models is key to improving overall performance. To summarize, government-led technical standardization constitutes a critical mediating mechanism for enhancing regional innovation performance by driving both the extension (incremental path) and transformation (radical path) of technology trajectories.\u003c/p\u003e\u003cp\u003eBased on the above analysis, this paper proposes the following research hypotheses:\u003c/p\u003e\u003cp\u003eH2: Government-led technical standardization can improve regional innovation performance by promoting technological trajectory evolution, i.e., technological trajectory evolution plays a mediating role.\u003c/p\u003e\u003cp\u003eH2a: Government-led technical standardization promotes technological trajectory extension.\u003c/p\u003e\u003cp\u003eH2b: Government-led technical standardization promotes technological trajectory transition.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 The Moderating Role of Institutional Development Level\u003c/h2\u003e\u003cp\u003ehigh level of institutional development serves as the cornerstone for any policy tool to exert its effectiveness. It provides a stable, transparent, and predictable macro-environment for various innovation activities by optimizing the allocation efficiency of innovation resources, safeguarding a fair competitive market environment, and improving legal protection (especially in terms of intellectual property rights) (Wang et al., 2017; Papageorgiadis \u0026amp; Sharma, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe institutional environment plays a decisive role in either constraining or promoting the effectiveness of technical elements. In their research on the impact of standards and patents on long-term economic growth, Blind, Ramel, \u0026amp; Rochell (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) explicitly emphasize that a sound institutional framework is a prerequisite for ensuring these technical elements generate positive economic benefits. This view resonates in the broader field of technical policy. Research by Blind \u0026amp; Schubert (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) on open-source software reveals a highly relevant conclusion: the complementary effect between open-source software and R\u0026amp;D investment strongly depends on national-level institutional and environmental factors. This provides strong cross-field evidence for the hypothesis regarding the moderating role of institutions in this study, from another important dimension of technical governance.\u003c/p\u003e\u003cp\u003eSpecifically, in regions with a high level of institutional development: an efficient policy implementation system ensures that standardization strategies are accurately communicated and implemented; a sound market mechanism facilitates the rapid diffusion and efficient integration of standard-related knowledge and technologies; and strict intellectual property protection encourages enterprises to engage in bold re-innovation based on standards without excessive fear of innovation outcomes misappropriation. All these factors work together to ensure that the signals of standardization policies are clearly received, supporting resources are accurately allocated, and ultimately maximizing their innovation-promoting effects. Conversely, in regions with weak institutions, even well-designed policies may see their effectiveness greatly diminished\u0026mdash;or even produce counterproductive results\u0026mdash;due to distorted implementation, inefficient resource allocation, or inadequate property rights protection.\u003c/p\u003e\u003cp\u003eTherefore, the level of institutional development essentially sets a critical \"effectiveness boundary\" for the effectiveness of government-led standardization policies. This study proposes the following hypothesis:\u003c/p\u003e\u003cp\u003eH3: A higher level of institutional development positively moderates the relationship between government-led technical standardization and regional innovation performance, i.e., the higher the institutional development, the more significant the promoting effect of standardization policies on regional innovation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Theoretical Model\u003c/h2\u003e\u003cp\u003eBased on the above analysis, this paper proposes the relevant theoretical model, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data Sources and Sample Selection\u003c/h2\u003e\u003cp\u003eThis paper uses samples of industrial enterprises from 30 Chinese provinces from 2013 to 2022 to test the hypotheses. The sample data are obtained from databases such as CSMAR, Wind, and the Statistical Yearbooks of various provinces. The data for this period are selected because China\u0026rsquo;s government-led technical standardization work was comprehensively promoted during this stage, and the relevant policy system was gradually improved, providing a typical scenario for studying its impact on regional innovation performance. The sample covers industrial enterprises in 30 provinces. Initially, we selected normally operating industrial enterprises (excluding ST companies and those with shareholders\u0026rsquo;equity less than zero). Next, we excluded samples with missing key variables. Finally, we winsorized the number of enterprise patent authorizations at the 1% and 99% percentiles (adding 1 and taking the natural logarithm) to eliminate the impact of extreme values. Eventually, an effective sample of 4996 observations was obtained.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Variable Definition and Measurement\u003c/h2\u003e\u003cp\u003e(1) Regional Innovation Performance\u003c/p\u003e\u003cp\u003eRegional innovation performance measures the output of regional technological innovation activities. The number of patent authorizations, due to their legally confirmed validity, can well reflect the actual level of a region in knowledge output, R\u0026amp;D capabilities, and innovation efficiency. Compared with the number of applications, authorized patents can more accurately reflect the efficiency of intellectual property transformation and R\u0026amp;D quality ((Hegde et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e;Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This paper uses the total number of annual authorized patents (including inventions, utility models, and designs) in each province as the measure of regional innovation performance.\u003c/p\u003e\u003cp\u003e(2) Government-Led Technical Standardization\u003c/p\u003e\u003cp\u003eGovernment-led technical standardization reflects the government\u0026rsquo;s leading role in standard formulation, promotion, and implementation, especially in the diffusion of emerging industry technologies and institutional supplementation. This process helps guide industrial technical directions, reduce institutional frictions, and improve the efficiency of collaborative innovation (Hu \u0026amp; Liu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Referring to their approach, this paper uses the number of new technical standards in the industrial field issued by each provincial government every year to measure the level of government-led technical standardization.\u003c/p\u003e\u003cp\u003e(3) Technological Trajectory Evolution\u003c/p\u003e\u003cp\u003eTechnological trajectory evolution describes the path changes of regional innovation activities, divided into incremental \"trajectory extension\" and breakthrough \"trajectory transition\"\u003c/p\u003e\u003cp\u003e(Aghion et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This paper uses invention patents to represent breakthrough innovation and utility model and design patents to represent incremental innovation. Taking enterprises as the unit, the number of patents is winsorized at the 1% and 99% percentiles, then added 1 and taken the natural logarithm. The realization of technological transition or extension is judged based on industry averages.\u003c/p\u003e\u003cp\u003e(4) Institutional Development Level\u003c/p\u003e\u003cp\u003eInstitutional development level is a core indicator for measuring the maturity of regional market mechanisms, government governance capabilities, and policy implementation efficiency. A higher degree of marketization and more institutions can provide a stable environment for standardization policies(Long \u0026amp; Liao, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This paper uses the \"marketization index\" from the Report on China\u0026rsquo;s Provincial Marketization Index to measure the institutional development level of each province.\u003c/p\u003e\u003cp\u003eControl Variables\u003c/p\u003e\u003cp\u003e(5) Per Capita GDP\u003c/p\u003e\u003cp\u003ePer capita GDP can reflect the regional economic development level and resource carrying capacity, and is an important variable for measuring regional innovation potential, which has been widely used in regional innovation research (Yang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This paper calculates it as the ratio of each province\u0026rsquo;s GDP to its population.\u003c/p\u003e\u003cp\u003e(6) Proportion of the Tertiary Industry\u003c/p\u003e\u003cp\u003eThe service industry has a high knowledge intensity and can promote the effective flow of innovation resources. A higher proportion of the tertiary industry indicates a stronger regional capacity to carry high-value-added industries and innovation factors(Lemstra \u0026amp; de Mesquita, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This paper uses the proportion of the added value of the tertiary industry in GDP to measure the optimization of the industrial structure.\u003c/p\u003e\u003cp\u003e(7) Education Level\u003c/p\u003e\u003cp\u003eEducation provides talents and knowledge for innovation and is an important indicator for measuring regional human capital accumulation. Higher education, in particular, has a significant promoting effect on innovation activities(Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This paper uses the number of students in higher education per 100,000 people to measure education level.\u003c/p\u003e\u003cp\u003e(8) Total Population and Population Density\u003c/p\u003e\u003cp\u003eTotal population and population density reflect the regional social foundation and the agglomeration capacity of innovation resources. A high population density can enhance knowledge spillover and collaborative innovation effects(Gordon \u0026amp; McCann, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This paper uses the total permanent population and population density (number of people per square kilometer) to control for urbanization and scale effects, respectively.\u003c/p\u003e\u003cp\u003eIn the econometric analysis, we referred to the panel data model processing principles elaborated by Zong (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to ensure the validity and robustness of the estimation results.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThis paper uses StataMP 18 for regression analysis to test the theoretical framework based on a panel data model with provincial and year fixed effects to control for time trends and regional heterogeneity. The correlation between variables is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the main regression analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Correlation Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(7)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(8)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(10)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment-Led Technical Standardization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.693***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitutional Development Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.659***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.467***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnological Trajectory Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.660***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.457***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.520***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnological Trajectory Extension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.680***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.489***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.417***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.651***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer Capita GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.512***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.300***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.691***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.443***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.353***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of Tertiary Industry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.240***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.388***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.302***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.241***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.692***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.220***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.150***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.571***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.130*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.097*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.651***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.618***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.617***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.613***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.484***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.382***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.377***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.173***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.233***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.547***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.214***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.167***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.669***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.609***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.414***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.000\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***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Direct Effect of Government-Led Technical Standardization on Regional Innovation Performance\u003c/h2\u003e\u003cp\u003eTo test the direct impact of government-led technical standardization on regional innovation performance, this paper conducts regression analysis based on a panel data model with provincial and year fixed effects to control for time trends and regional heterogeneity. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the results of the benchmark model (Model 1) indicate that government-led technical standardization has a significantly positive impact on regional innovation performance (β\u0026thinsp;=\u0026thinsp;0.384, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), verifying Hypothesis 1. Models 2 to 5 gradually introduce control variables such as economic scale, infrastructure, education level, population size, and density. The coefficients of the core variable remain stable and significant, and the R\u0026sup2;value gradually increases, indicating that the model fits well and the positive effect of government-led standardization on innovation performance is robust.\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\u003eDirect Effect Test of Government-Led Technical Standardization on Regional Innovation Performance\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\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment-Led Technical Standardization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.384***\u003c/p\u003e\u003cp\u003e(4.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.373***\u003c/p\u003e\u003cp\u003e(4.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.378***\u003c/p\u003e\u003cp\u003e(4.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.378***(4.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.251***\u003c/p\u003e\u003cp\u003e(3.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer Capita GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.379***\u003c/p\u003e\u003cp\u003e(6.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.410***\u003c/p\u003e\u003cp\u003e(6.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.412***\u003c/p\u003e\u003cp\u003e(5.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003cp\u003e(0.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of Tertiary Industry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003cp\u003e(1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003cp\u003e(1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003cp\u003e(1.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003cp\u003e(0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003cp\u003e(-0.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.672***\u003c/p\u003e\u003cp\u003e(6.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.250**\u003c/p\u003e\u003cp\u003e(2.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.330***\u003c/p\u003e\u003cp\u003e(3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.393***\u003c/p\u003e\u003cp\u003e(-2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.764***\u003c/p\u003e\u003cp\u003e(-2.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.793*\u003c/p\u003e\u003cp\u003e(-1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.629\u003c/p\u003e\u003cp\u003e(0.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince Fixed Effects\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\u003eTime Fixed Effects\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Test of the Mediating Effect of Technological Trajectory Evolution\u003c/h2\u003e\u003cp\u003eFurther analysis of the mediating effect of technological trajectory evolution examines the mechanisms of technological trajectory transition and extension respectively. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, government-led technical standardization has a significantly positive impact on technological trajectory transition (β\u0026thinsp;=\u0026thinsp;0.163, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and trajectory transition also has a significant impact on regional innovation performance (β\u0026thinsp;=\u0026thinsp;0.244, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). After introducing trajectory transition, the direct effect of standardization remains significant (β\u0026thinsp;=\u0026thinsp;0.232, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating the existence of a partial mediating effect, which verifies Hypotheses H2 and H2a.\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\u003eMediating Effect Test of Technological Trajectory Transition\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\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTechnological Trajectory Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment-Led Technical Standardization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.251***\u003c/p\u003e\u003cp\u003e(3.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.163**\u003c/p\u003e\u003cp\u003e(2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.232***\u003c/p\u003e\u003cp\u003e(3.85)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnological Trajectory Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.121***\u003c/p\u003e\u003cp\u003e(2.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer Capita GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003cp\u003e(0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.176***\u003c/p\u003e\u003cp\u003e(1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003cp\u003e(0.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of Tertiary Industry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003cp\u003e(1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003cp\u003e(0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003cp\u003e(0.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.026(-0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.144\u003c/p\u003e\u003cp\u003e(-1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003cp\u003e(-0.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.672***\u003c/p\u003e\u003cp\u003e(6.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.455***\u003c/p\u003e\u003cp\u003e(3.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.254***\u003c/p\u003e\u003cp\u003e(6.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.250**\u003c/p\u003e\u003cp\u003e(2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.713\u003c/p\u003e\u003cp\u003e(1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.510**\u003c/p\u003e\u003cp\u003e(2.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.629\u003c/p\u003e\u003cp\u003e(0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.564\u003c/p\u003e\u003cp\u003e(-0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.819\u003c/p\u003e\u003cp\u003e(1.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince Fixed Effects\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\u003eTime Fixed Effects\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics.\u003c/p\u003e\u003cp\u003eSimilarly, the results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that government-led technical standardization also has a significantly positive impact on technological trajectory extension (β\u0026thinsp;=\u0026thinsp;0.148, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and extension has a significant impact on innovation performance (β\u0026thinsp;=\u0026thinsp;0.193, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The direct effect of the standardization variable remains positive and significant in the model (β\u0026thinsp;=\u0026thinsp;0.215, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), verifying Hypotheses H2 and H2b.\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\u003eMediating Effect Test of Technological Trajectory Extension\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\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTechnological Trajectory Extension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment-Led Technical Standardization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.251***(3.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.148**(2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.215***(4.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnological Trajectory Extension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.214***(3.87)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer Capita GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.039(0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.046(0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.027(0.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of Tertiary Industry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.067(1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.193(-1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.114(1.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.026(-0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.330***(-2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.054(0.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.672***(6.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.458***(3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.078***(5.84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.250**(2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.538(0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.587**(2.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.629(0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.554(0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.254(0.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince Fixed Effects\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 Effects\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics.\u003c/p\u003e\u003cp\u003eIn summary, government-led technical standardization can not only directly promote regional innovation performance but also have an indirect positive impact through the transition and extension of technological trajectories.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Test of the Moderating Effect of Institutional Development Level\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the test results of the moderating effect of institutional development level. After adding the interaction term between institutional development level and government-led standardization into the model, the results show that the interaction term is significantly positive (β\u0026thinsp;=\u0026thinsp;0.336, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and institutional development level itself has a significant impact on innovation performance (β\u0026thinsp;=\u0026thinsp;0.169, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), verifying Hypothesis H3. This indicates that in regions with more institutional foundations, the positive impact of government-led standardization on innovation performance is stronger, suggesting that the institutional environment plays a key role in improving the effectiveness of policy tools.\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\u003eModerating Effect Test of Institutional Development Level\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\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment-Led Technical Standardization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.251***\u003c/p\u003e\u003cp\u003e(3.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.068**\u003c/p\u003e\u003cp\u003e(2.46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitutional Development Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.169***\u003c/p\u003e\u003cp\u003e(2.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment-Led Technical Standardization \u0026times; Institutional Development Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.336***\u003c/p\u003e\u003cp\u003e(9.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer Capita GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003cp\u003e(0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003cp\u003e(0.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of Tertiary Industry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003cp\u003e(1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003cp\u003e(-0.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003cp\u003e(-0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.134***\u003c/p\u003e\u003cp\u003e(3.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.672***\u003c/p\u003e\u003cp\u003e(6.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.858***\u003c/p\u003e\u003cp\u003e(5.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.250**(2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.389(1.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.629\u003c/p\u003e\u003cp\u003e(0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.630\u003c/p\u003e\u003cp\u003e(1.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince Fixed Effects\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\u003eTime Fixed Effects\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics.\u003c/p\u003e\u003cp\u003eThe slope depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates that under conditions of a high institutional development level, the positive correlation between government-led technical standardization and regional innovation performance exhibits a greater magnitude compared to scenarios characterized by a low institutional development level. This observation serves to validate the positive moderating effect of institutional development level on the association between government-led technical standardization and regional innovation performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Robustness Test\u003c/h2\u003e\u003cp\u003eTo test the stability of the research results, this study uses an alternative measure of the dependent variable for verification. Borrowing from Pan et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), this study employs provincial patent application counts as a proxy for regional innovation performance, consistent with previous empirical frameworks in Chinese innovation research. The empirical results of the impact of government-led technical standardization on the alternative variable of regional innovation performance are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, which are consistent with the previous results, passing the robustness test.\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\u003eDirect Effect Test of Government-Led Technical Standardization on Alternative Variables of Regional Innovation Performance\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\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRegional Innovation Performance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment-Led Technical Standardization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.129***(5.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.111***(4.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.108***(4.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.040**(1.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnological Trajectory Transition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.108***(4.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eTechnological Trajectory Extension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.140***(3.74)\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\u003eInstitutional Development Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002(0.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment-Led Technical Standardization \u0026times; Institutional Development Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.136***(4.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer Capita GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.041(0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022(0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.035(0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007(0.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of Tertiary Industry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.006(-0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.022(-0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021(0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.070(-1.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.032(-0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.047(0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.079(1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.050(0.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.703***(6.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.328***(6.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.360***(6.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.858***(5.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.729**(2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.327**(2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.514**(2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.079***(6.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant Term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.137(0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.307(0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.920(0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.911**(2.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e127.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince Fixed Effects\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\u003eTime Fixed Effects\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with values in parentheses being t-statistics.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eBased on Chinese provincial panel data, this study systematically examines the impact mechanism of government-led technical standardization on regional innovation performance.\u003c/p\u003e\u003cp\u003eFirst, this study confirms the direct promoting effect of government-led technical standardization on regional innovation performance (H1), which supplements the previous research perspective that mainly focuses on traditional policy tools such as fiscal subsidies.\u003c/p\u003e\u003cp\u003eSecond, the mediation effect analysis shows that technical standardization improves innovation performance through the dual paths of technology trajectory extension and transformation (H2). This finding, especially regarding the trajectory transformation path, forms an interesting dialogue with Sandrini\u0026rsquo;s (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) research on \"innovation, competition, and incomplete adoption of superior technology\". This study shows that government-led standardization can serve as a coordination mechanism in specific contexts to alleviate market failures, promote more complete adoption and diffusion of new technologies, and thus drive a fundamental leap in technology trajectories.\u003c/p\u003e\u003cp\u003eFinally, the moderation effect analysis reveals that the level of institutional development positively strengthens the effect of standardization policies (H3). (This is consistent with the conclusion of Blind, Ramel, \u0026amp; Rochell (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that emphasizes the institutional framework for exerting the long-term growth effect of standards, jointly highlighting the key role of the institutional foundation in the success of technical policies.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Theoretical Contributions\u003c/h2\u003e\u003cp\u003eAt the theoretical level, this paper mainly expands and deepens the theoretical system of research on government-led technical standardization and regional innovation performance from the following aspects:\u003c/p\u003e\u003cp\u003eFirst, this paper broadens the research perspective and theoretical boundaries of technical standardization. Previous studies have mainly focused on market-led or industry self-governed technical standardization models, with insufficient attention to government-led technical standardization, especially in terms of how it affects regional innovation performance(Hu \u0026amp; Liu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Based on a summary of previous studies, this paper explicitly takes government-led technical standardization as an independent type of policy tool, constructs an analytical framework of \"government-led technical standardization\u0026mdash;technological trajectory evolution\u0026mdash;regional innovation performance\", and responds to the practical needs of theoretical construction for policy-oriented technical standardization mechanisms in current research(Lehr, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).In particular, this study echoes the findings of Zi \u0026amp; Blind (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which, through a case analysis of public research institutions in Germany, reveals the key role of researchers as participants in the source of standardization knowledge. Building on this, this paper extends the analytical perspective to the starting point of the innovation value chain, emphasizing the leading role of the government in integrating various elements including public R\u0026amp;D resources to promote the standardization process, thereby enriching the theoretical connotation of the government-led model.\u003c/p\u003e\u003cp\u003eSecond, this paper opens the \"black box\" of the intermediate mechanism through which government-led technical standardization affects regional innovation performance. Existing literature mostly discusses the impact of standardization on innovation from the perspectives of knowledge diffusion or technical lock-in (Allen \u0026amp; Sriram, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e;Uyarra et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but lacks a systematic description of meso-level mechanisms. This paper introduces the concept of \"technological trajectory evolution\" and divides it into two paths: \"trajectory extension\" and \"trajectory transition\". On this basis, it explores its mediating role in the process of government-led technical standardization affecting innovation performance, helping to clarify key processes such as how policy-oriented technical standards guide enterprises\u0026rsquo;innovation paradigm selection, path evolution, and performance changes (Dosi, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Juliao-Rossi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e;Sun et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).Third, this paper introduces \"institutional development level\" as a moderating variable, enriching the application scenarios of the institutional environment in regional innovation research. Although existing studies have paid attention to the indirect role of institutional level in regional innovation (Rodr\u0026iacute;guez-Pose \u0026amp; Di Cataldo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).there is still a lack of empirical tests analyzing it in the chain of policy tool\u0026mdash;innovation performance mechanisms. This paper finds that institutional development level can significantly enhance the effect of government-led technical standardization policies, providing a sound institutional foundation for regional innovation performance and expanding the theoretical application boundaries of the institutional perspective in innovation research.\u003c/p\u003e\u003cp\u003eFinally, this paper sinks the scale of innovation performance research to provincial panel data analysis, strengthening the meso-level empirical basis for policy mechanism research. Currently, academic circles mostly analyze the impact of policies on innovation at the national or industry level(Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but ignore the differentiated role of the government as a technical standard setter at the regional level. This paper conducts multi-dimensional empirical tests using panel data of 30 provinces over ten years, further verifying the applicability and robustness of the policy\u0026mdash;mechanism\u0026mdash;performance chain at the meso level.This approach aligns with recent calls in the literature for more granular analyses of standardization dynamics, as exemplified by Yao et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) work on strategic maneuvers in SEP follow-on innovations.\u003c/p\u003e\u003cp\u003eIn summary, this paper not only responds to the academic concerns about deepening the mechanism of government-led technical standardization and expanding empirical paths but also provides theoretically relevant support for regional innovation governance practices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Management Implications\u003c/h2\u003e\u003cp\u003e(1) Government Level: The government should take technical standardization as a policy tool, formulate forward-looking strategies, clarify key fields and development paths to promote regional innovation. Meanwhile, it should improve the legal and regulatory system, enhance the authority and coordination of policies, and encourage enterprises to participate in standard formulation through incentives such as fiscal subsidies and tax preferences.\u003c/p\u003e\u003cp\u003e(2) Enterprise Level: Enterprises should actively participate in government-led technical standardization, combine their own technical advantages to enhance industry influence. In addition, they should build a multi-dimensional innovation system focusing on both basic and applied research, pay attention to emerging technologies such as artificial intelligence and big data promote technological upgrading of products and services, and explore new technological trajectories.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Research Limitations and Future Research\u003c/h2\u003e\u003cp\u003eThis study still has the following limitations: First, although using the number of patent authorizations to measure regional innovation performance is representative, it is not comprehensive. Future studies can introduce supplementary indicators such as new product sales revenue. Second, the data mainly come from industrial technical standards, with limited industry coverage. Future studies can expand to other industries or refine industry types. Third, it does not discuss the possibility of failure of a single standard project. Future studies can further explore the different role orientations of the government in the standardization process and their adaptive mechanisms. Attention should be paid to emerging technologies such as artificial intelligence, big data, the Internet of Things, and blockchain, explore their applications in enterprise products and services, and open up new technological trajectories.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWYX critically reviewed and revised the entire manuscript. QMY developed the research framework, led data collection and empirical analysis, and completed the initial draft of the manuscript. YXY contributed to data processing and result visualization. LLG assisted with the literature review and methodology section. STT participated in the discussion of results and provided policy implications. All authors contributed to the revision and finalization of the manuscript and approved the version submitted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAghion, P., Hepburn, C., Teytelboym, A., \u0026amp; Zenghelis, D. (2019). Path dependence, innovation and the economics of climate change. In \u003cem\u003eHandbook on Green Growth\u003c/em\u003e (pp. 67\u0026ndash;83). Edw- ard Elgar Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4337/9781788110686.00011\u003c/span\u003e\u003cspan address=\"10.4337/9781788110686.00011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAldieri, L., Barra, C., Vinci, C. P., \u0026amp; Zotti, R. (2021). 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Routledge. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9781003273905\u003c/span\u003e\u003cspan address=\"10.4324/9781003273905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Government-led Technical Standardization, Regional Innovation Performance, Technological Trajectory Evolution, Level of Institutional Development","lastPublishedDoi":"10.21203/rs.3.rs-7720883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7720883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on the technology trajectory theory and institutional theory, this study develops a moderated mediation model to systematically investigate the mechanism and boundary conditions through which government-led technical standardization influences regional innovation performance in China. Utilizing a comprehensive provincial-level panel dataset from 2013 to 2022 and employing a two-way fixed effects approach, we provide robust empirical evidence demonstrating that government-led standardization significantly enhances regional innovation performance. Specifically, this positive impact operates through dual mediating pathways: facilitating technology trajectory extension and enabling technology trajectory transformation. Furthermore, our analysis reveals that institutional development serves as a critical moderator, where regions with more robust institutional frameworks experience substantially stronger innovation effects from standardization policies. By elucidating the complete \"policy-trajectory-performance\" transmission mechanism, this research advances theoretical understanding of standardization-innovation dynamics and offers valuable insights for designing effective innovation policies in emerging economies.\u003c/p\u003e","manuscriptTitle":"Research on the Impact of Government-Led Technical Standardization on Regional Innovation Performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 12:13:48","doi":"10.21203/rs.3.rs-7720883/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":"1fa238af-0bd7-43c5-80f3-8a5a04bf9c6e","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:05:30+00:00","versionOfRecord":{"articleIdentity":"rs-7720883","link":"https://doi.org/10.1007/s10961-026-10322-1","journal":{"identity":"the-journal-of-technology-transfer","isVorOnly":false,"title":"The Journal of Technology Transfer"},"publishedOn":"2026-02-21 15:58:42","publishedOnDateReadable":"February 21st, 2026"},"versionCreatedAt":"2025-11-17 12:13:48","video":"","vorDoi":"10.1007/s10961-026-10322-1","vorDoiUrl":"https://doi.org/10.1007/s10961-026-10322-1","workflowStages":[]},"version":"v1","identity":"rs-7720883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7720883","identity":"rs-7720883","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}