Public Attention to Intellectual Property Rights Protection, Openness, and Regional Innovation Capability: An Empirical Analysis Based on Provincial Panel Data in China

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While prior research emphasizes formal IPR institutions, it overlooks the socio-cognitive dimension of public attention and its interaction with regional openness—particularly in geographically heterogeneous economies like China. Using provincial panel data (2011–2023), we measure public attention to IPR protection (PAIPRP) via Baidu search indices, regional innovation capability (RIC) through patent applications (APQ) and grants (AUQ), and regional openness (RO) by actual foreign direct investment utilization (AUFDIy). Fixed-effects models control for R&D expenditure, human capital, and institutional factors. The key findings are as follows: Public IPR attention significantly boosts RIC. Regional heterogeneity is pronounced—strongest in eastern China , insignificant in central regions, and negative in the west, where higher education expansion paradoxically reduces innovation. Openness amplifies this effect, with FDI-intensive regions showing stronger responses. Policy implications include region-specific IPR governance, while the study enriches theory by linking societal awareness, economic integration, and innovation across institutional contexts. Business and commerce/Economics Social science/Science technology and society Social science/Social policy Social science/Sociology public attention to intellectual property rights protection (PAIPRP) regional innovation capability (RIC) regional openness FDI regional heterogeneity Introduction In the context of a rapidly evolving knowledge economy, innovation has become the primary engine of high-quality economic development and national competitiveness. With the rise of intangible assets and technology-driven industries, intellectual property rights (IPR) have gained strategic importance in protecting innovative outputs, attracting investment, and incentivizing knowledge production. According to the World Intellectual Property Organization (WIPO), over 3.55 million patent applications were filed globally in 2023—2.7% increase over last year—applicants in China filed about 1.64 million patent applications, covering both domestic and foreign jurisdictions. This surge highlights not only the centrality of innovation in global development but also the growing relevance of intellectual property protection as an institutional safeguard. Intellectual property rights protection (IPRP) has emerged as a cornerstone of modern economic development, serving as both an accelerator for innovation ecosystems and a barometer of national competitiveness. A robust IPRP system not only mitigates the risks of innovation externalities, but also promotes industrial upgrading through optimized resource allocation (Song & Chen, 2023 ). World Bank research reveals that IPR-intensive industries contribute over 38% of GDP in advanced economies, dwarfing the mere 12% in least-developed countries—a statistic that underscores the critical role of IPR governance in bridging global innovation divides (Auriol et al., 2022 ). For China, this imperative takes on particular urgency as the nation navigates its economic transformation. While demonstrating remarkable progress in patent filings (covering inventions, utility models, and industrial designs) and trademark registrations, structural imbalances persist (Qayyum et al., 2022 ). Notably, the innovation-output gap manifests not only internationally but also intra-nationally, with pronounced regional disparities. As diminishing returns plague traditional growth models predicated on low-cost labor and capital-intensive inputs, strategic recalibration of IPR frameworks has become pivotal for catalyzing industrial upgrading and sustaining regional development momentum ( Song & Chen, 2023 ; Luo & Zhao, 2024 ; Neves et al., 2021 ). To supplement institutional metrics, this study introduces public attention to IPRP as a behavioral dimension in the analysis of regional innovation capability. Public attention reflects the extent to which IPR issues are visible, salient, and actively discussed in society—capturing collective perceptions of enforcement credibility and institutional legitimacy. While not a replacement for formal legal indicators, this perspective helps illuminate how societal engagement may shape innovation decisions, particularly in regions where institutional systems are still maturing. Existing studies have rarely integrated this cognitive aspect into quantitative models of regional innovation, especially in the Chinese context. Existing scholarship has predominantly centered on the macro-level correlation between intellectual property rights protection and innovation, frequently employing aggregated national indices or cross-country comparative frameworks to validate the robust positive correlation (Neves et al., 2021 ; Zhang & Chen, 2022 ). This body of research, however, exhibits three critical limitations in addressing China's context: First, it disproportionately emphasizes legislative completeness while underrepresenting enforcement heterogeneity across jurisdictions; second, it relies excessively on patent quantity metrics that inadequately capture qualitative innovation outcomes; third, it insufficiently addresses subnational disparities in institutional ecosystems. Empirical evidence reveals stark contrasts in IPR governance efficacy—eastern provinces like Jiangsu, Zhejiang, and Guangdong demonstrate superior performance through integrated legal frameworks, proactive enforcement mechanisms (e.g., specialized IP courts), and market-oriented innovation clusters (Yu & Zhao, 2020 ; Zhao & He, 2024 ). Conversely, central and western regions lag due to fragmented institutional coordination and resource constraints, resulting in weaker compliance monitoring and limited public IP literacy (Jie et al., 2025 ). Crucially, while macro studies confirm IPR' aggregate economic benefits, they largely neglect the micro-institutional dynamics—such as localized enforcement innovations in e-commerce governance or cross-regional technology spillovers—that underpin these disparities (Hou et al., 2021 ; Wippel, 2023 ). This analytical gap hinders the formulation of spatially differentiated policies essential for addressing China's multidimensional innovation divide (Rao et al., 2024 ). Another dimension that merits attention is regional openness, particularly the degree to which regions are integrated into global markets through foreign direct investment (FDI). Open regions not only receive external knowledge and capital but also tend to adopt stricter enforcement standards due to international pressure. This may reinforce public trust in the IPR system and encourage innovation by reducing perceived risk. However, the interplay between openness, public attention to IPRP, and innovation capacity has not been sufficiently investigated, especially in countries like China with significant institutional and regional diversity (Zhang & Chen, 2022 ). Given these gaps, this study aims to address three core questions:(1) Does public attention to IPRP influence regional innovation capability? (2) Does this relationship vary significantly across different regions in China? (3) Is this relationship moderated by the level of regional openness, as measured by actual FDI utilization? To answer these questions, we construct a panel dataset comprising 31 Chinese provinces from 2011 to 2023, allowing for a longitudinal analysis of spatial heterogeneity in the innovation effects of public IPR attention. This study contributes to the literature in three main ways. First, it incorporates public attention to IPRP into the analytical framework as a supplementary behavioral factor, providing new insights beyond conventional institutional indicators. Second, it empirically identifies regional heterogeneity in the effect of IPRP attention on innovation capability, revealing that the influence is strongest in eastern China and weakest in the west. Third, it highlights the moderating role of regional openness, offering policy-relevant findings on how institutional perception and international integration jointly shape regional innovation outcomes. The rest of this paper is organized as follows: Section 2 reviews the relevant literature and presents the research hypotheses. Section 3 outlines the empirical model, variables, and data sources. Section 4 discusses the regression results, followed by heterogeneity and moderating effect analyses. Section 5 conducts robustness checks. Section 6 concludes with theoretical and policy implications. Literature Review and Research Hypotheses Literature Review The theory of intellectual property protection is grounded in a multidimensional framework that spans economic, legal, and philosophical dimensions. Economically, intellectual property plays a pivotal role in promoting technological spillovers, enhancing innovation incentives, and facilitating industrial upgrading (Poyago-Theotoky & Tsai, 2023). Scholars generally agree that within a segmented global market, a partially strong IP system can be more socially optimal than a universally stringent one. At work are two effects: the market-penetrating effect (MPE), which evaluates the extent to which national welfare can be increased by IPR policy via firm investment.; And the business-stealing effect (BSE), which examines the direct impact on the level of competition (Poyago-Theotoky & Tsai, 2023; T. Han et al., 2021). From a legal standpoint, IPRP is anchored in the logic of private property rights, where laws grant inventors and creators exclusive control over their innovations to prevent unauthorized use or reproduction. While IP law is often discussed at the national level, sub-national differences in enforcement, institutional quality, and public awareness are also critical, particularly in large, regionally diverse countries like China (Gao, 2020). Philosophically, the justification for IPRP largely stems from utilitarianism, which supports the granting of exclusive rights as long as they contribute positively to overall social welfare. IP rights, from this view, are temporary tools designed to incentivize innovation and are maintained only as long as they remain necessary for generating desired levels of creative investment (Garcia et al., 2024). The link between IPRP and innovation remains one of the most debated topics in contemporary economic and legal scholarship. A large body of empirical research supports the notion that robust IP regimes can enhance innovation efficiency. For instance, Gmeiner et al. (2021) find that stronger adherence to international IP standards improves domestic innovation performance; Zhou et al. (2022) posit that strengthening intellectual property protection effectively stimulates regional technological innovation, thereby facilitating industrial upgrading. However, this relationship exhibits significant heterogeneity. For instance, Neves et al. argue that in developing countries, due to weak institutional environments, the incentive effect of IPRP on innovation is weaker than in developed countries (Neves et al., 2021) . Similarly, Luo and Zhao (2024) find that both excessively stringent and overly weak intellectual property protection can create adverse feedback mechanisms that hinder the optimization of regional technological innovation structures. Castaldi et al., (2023) suggest that innovation output tends to be higher under patent protection, especially when private and social returns are closely aligned. Uyar et al. (2021) find that a considerable trade-off between strengthening IPR and the country's economic activity, with both domestic and foreign innovation increasing under strong IPRP. Christopoulou et al. (2021) and Uyar et al. (2021) further argue that effective patent systems are critical for supporting firm-level R&D and long-term economic growth. Su et al. (2021) through their analysis of total factor productivity (TFP), reveal a nonlinear relationship: IPRP has a negative effect in the least-developed economies but follows an inverted U-shape in developing and developed countries. Song et al. (2024) empirically investigate the non-linear relationship between intellectual property protection and enterprise innovation performance, revealing an inverted U-shaped curve in the effect of IPP on EIP. In the Chinese context, Song and Chen (2023) find that IPRP serves as a crucial driver of green innovation among firms, highlighting the importance of aligning protection strategies with sustainable development goals. In the Chinese context, empirical studies have consistently highlighted regional heterogeneity in the innovation-enhancing effects of IPRP. Yi et al. (2024) demonstrate a bidirectional, reinforcing relationship between IPRP and regional innovation across provinces. However, this effect is mediated by regional openness. For example, Qayyum et al. (2022) find that trade openness and IPRP significantly promote innovation in eastern provinces, but not in central or western regions. Other studies explore the nuanced role of protection models. Zhang et al. (2024) note that overly rigid IP regimes may inhibit innovation spillovers in more advanced regions like the east. Deng et al. (2018) show that government R&D subsidies only complement IPRP effectively in the eastern and western regions—not the central region. Similarly, Luo et al. (2023) highlight the synergistic effect of internet development and IPRP on regional innovation efficiency, noting a U-shaped relationship between protection levels and innovation outcomes. Chi et al. (2024) provide further evidence that strong IPRP boosts domestic R&D and invention patent filings. However, they also caution that in less developed regions—particularly the west—foreign direct investment (FDI) can interact negatively with domestic innovation efforts, weakening the intended benefits of IPRP. Recent research extends the discussion into the domain of economic complexity and institutional quality. Luo and Zhao (2024) argue that moderate IPRP optimizes innovation structures, while extremes in either direction hinder knowledge diffusion. Studies in fintech and green innovation sectors similarly confirm that well-calibrated IP regimes act as effective innovation incentives(Cai and Zhang, 2023). Yet, two critical gaps remain in the literature: Overreliance on macro-level analysis has left the micro-mechanisms of public and organizational attention to IPRP underexplored. As a socio-cognitive driver, public awareness may significantly influence innovation behaviors but remains poorly theorized and measured. Oversimplified regional classifications (e.g., east–central–west) obscure institutional heterogeneity within regions, such as differences in legal enforcement, resource allocation, or local innovation ecosystems. Further complicating the picture, Chi et al. (2024) find that while heightened IPRP can deter FDI (due to stricter enforcement burdens), it can simultaneously enhance domestic innovation, highlighting the dual-edged nature of strong IP regimes. Nguyen et al. (2023) in a study of Vietnam, argue that open innovation ecosystems create informal IPRP via increased complexity and imitation barriers, adding another layer to how openness affects innovation dynamics. Despite the growing body of research examining the relationship between intellectual property protection and regional innovation, several critical gaps remain unaddressed (Cai et al., 2024). First，the primary issue lies in the excessive emphasis on formal institutional frameworks without adequately considering socio-cognitive dimensions such as public awareness regarding intellectual property protection. Second, Reliance on static macro-indicators (patents/R&D) that ignore dynamic public-institution feedback loops. Third, the literature frequently relies on coarse regional classifications, such as the east–central–west framework in China, which can mask important intra-regional differences (Yi et al., 2024). To address these gaps, the present study introduces “Public Attention to Intellectual Property Rights Protection (PAIPRP)” as a novel behavioral indicator, measured through web search behavior using the Baidu Index. This provides a real-time, region-specific proxy for public awareness and concern regarding IP issues. Moreover, the study incorporates regional openness—captured through actual foreign direct investment (FDI) utilization—as a moderating variable to examine how institutional perceptions interact with external economic integration. By combining cognitive, institutional, and economic factors in a panel data framework, this research seeks to offer a more nuanced and empirically grounded understanding of the mechanisms through which IPRP influences regional innovation in a complex and uneven national landscape. Research Hypotheses Existing studies suggest that IPRP influences regional innovation capability through dual mechanisms: institutional incentives and risk mitigation (Luo & Zhao, 2024). However, the efficacy of this relationship may systematically vary depending on socio-cognitive factors (e.g., public and corporate "attention"), regional institutional environments, and openness levels. Meanwhile, Strong IPRP frameworks encourage firms to invest in research and development by safeguarding their proprietary knowledge and reducing the likelihood of infringement. However, the effectiveness of this relationship is far from uniform across contexts. Socio-cognitive factors—such as public awareness, organizational perception, and media attention—can significantly influence how IP policies are internalized and acted upon. Moreover, the regional institutional environment and varying levels of economic openness further shape the extent to which IPRP translates into innovation outcomes.Building on these debates, we propose the following hypotheses: Hypothesis 1 (H1): Attention to IPRP exerts a significantly positive impact on regional innovation capability. Building on this theoretical foundation, the present study first hypothesizes that greater public attention to IPRP positively impacts regional innovation capability. Attention, in this context, serves as a proxy for societal expectations and perceptions regarding the enforcement and legitimacy of IP systems. When public awareness is high, firms are more likely to view the innovation ecosystem as credible and protective, thus increasing their willingness to engage in R&D activities. Moreover, heightened attention contributes to improved policy transparency and more efficient allocation of innovation-related resources. This study captures such attention by using web search index data, which reflects the intensity of public interest and engagement with IP-related topics in real time (Chen et al., 2023; Li et al., 2021; Yu & Zhao, 2020) . Hypothesis 2 (H2): The impact of PAIPRP on innovation capability exhibits significant regional heterogeneity, with the strongest effect observed in eastern China. The second hypothesis posits that the effect of PAIPRP on innovation is not homogeneous across regions. Due to vast differences in institutional quality, administrative efficiency, and resource endowments, the impact is expected to exhibit strong spatial heterogeneity. In eastern China—where the legal system is more developed, markets are more open, and industrial clusters are more advanced—the translation of PAIPRP into innovation output is likely to be most efficient. Conversely, in central and western regions, weaker institutional enforcement and the misallocation of R&D resources may dilute the effectiveness of such attention. Institutional inertia and limited policy execution capacity in these regions contribute to a gap between awareness and actionable innovation outcomes (Jin et al., 2024; Dang and Luo et al., 2021). Additionally, the scale effects of industrial clusters in eastern China amplify attention-driven innovation incentives(Cai and Zhang, 2023). Hypothesis 3 (H3): Openness to foreign investment positively moderates the relationship between PAIPRP and innovation capability, with stronger moderating effects in eastern China. Finally, the study hypothesizes that the level of regional openness—measured through actual foreign direct investment (FDI) utilization—positively moderates the relationship between PAIPRP and innovation capability. Openness facilitates the inflow of advanced technologies and managerial practices, which can intensify innovation pressure on domestic firms and reinforce the incentives provided by IPRP. Additionally, international cooperation raises IP governance standards and increases the global commercialization potential of locally developed innovations. However, there is also a risk of innovation inertia in highly open regions if reliance on external technologies leads to reduced domestic initiative. Nevertheless, in eastern China—where openness is highest and institutional flexibility is greatest—this moderating effect is expected to be particularly strong, as local firms are better positioned to absorb and respond to foreign innovation stimuli(Cai and Zhang, 2023; Ding & Xue, 2023; Yu & Zhao, 2020; Dang and Luo, 2021). Materials and methods Variable Design Independent Variable In this study, the independent variable is PAIPRP, which reflects the level of emphasis placed on intellectual property rights protection by each province. According to previous scholarly research, this study measures the level of attention to IPR using the search index for IPR-related terms from Baidu Index (Li et al., 2021 ; Hu et al., 2022 ; Li et al., 2023 ). The Intellectual Property Protection Search Index (IRP), provided by Baidu Index, is a metric that quantifies the search behavior of Baidu users regarding intellectual property protection, as well as the media exposure related to it. The index reflects the level of public attention to intellectual property protection, with higher values indicating stronger attention in a given region, and lower values suggesting weaker attention. In this study, the intellectual property protection attention level across different provinces is considered as the independent variable. Dependent Variable There are multiple indicators in the existing literature to assess RIC (Regional Innovation Capability). This study builds on the approaches of Zhang et al. and Zhao et al., adopting a regional innovation output perspective to measure innovation capacity across regions (Zhou et al., 2021 ). Patent-related indicators are frequently used to determine regional innovation levels, where a higher patent count indicates stronger innovation capability (Zhou et al., 2021 ). In this study, the number of patent applications (APQ, Applications for Patents Quantity) and patent grants (AUQ, Authorized Utility Patents Quantity) at the provincial level are used as key indicators of regional innovation capability (Cai and Zhang, 2023 ). Moderator Variable By drawing on Damien et al. research on foreign direct investment and intellectual property protection, this study introduces the actual utilization of foreign direct investment (AUFDIy) as a moderating variable (Viglioni et al., 2023 ; Gao & Zhao, 2023 ; Lv et al., 2021 ; Li et al., 2021 ; Dussaux et al. 2022 ). The data for actual FDI utilization is sourced from the Ministry of Commerce of China and National Bureau of Statistics, and its calculation method follows the formula: $$\:Actual\:FDI\:Utilization\:\left(Billion\:CNY\right)=FDI\:\left(Billion\:USD\right)\times\:CNY/USD\:Exchange\:Rate$$ The level of regional openness is expected to moderate the relationship between attention to IPP and regional innovation capability. Since actual FDI utilization is a key indicator of regional openness, this study selects AUFDIy as the moderator. Control Variables To minimize potential confounding factors and reduce endogeneity concerns in regression results, this study incorporates the following control variables based on prior literature: Gross Regional Domestic Product (GRDP): In this article, GRDP is calculated using the expenditure method, this method evaluates total spending on final goods and services, including household/government consumption, investments, and net foreign demand, it represents economic development levels across provinces(Fang et al., 2024 ). Number of Undergraduate Students Enrolled in Higher Education Institutions (NS): Represents the level of education in each province. Due to the absence of 2023 data from the National Bureau of Statistics (NBS), this study draws on the approach of Bigaignon et al. and uses linear interpolation to impute the missing values for 2023 (Jin et al., 2024 ; Bigaignon et al., 2020 ). Permanent Resident Population at Year-End (PRP) : Measures the total population in each province (Lv et al., 2023 ). Patent Case Closure Count (PCC): Reflects the governmental emphasis on IPRP enforcement across provinces (Luo & Zhao, 2024 ). Scientific and Technological Investment Expenditure (IST): Represents the level of provincial government investment in technological innovation (Cai and Zhang, 2023 ). Table 1 . provides a detailed description of the variables used in this study. Table 1 Variable Definitions and Data Sources. Variable Type Variable Name Variable Code Measurement Method Data Source Independent Variable X Search Volume for IPR Protection on Baidu PAIPRP Number of searches related to intellectual property protection by Baidu users Baidu Index Dependent Variable Y Patent Application Quantity (units) APQ Annual Provincial Aggregates of Patent Applications/Grants in China by Type: Inventions, Utility Models, and Designs incoPat (website: https://www.incopat.com/ ) Patent Authorization Quantity (units) AUQ Moderator Variable Z Actual Utilized Foreign Direct Investment (CNY billion) AUFDIy FDI (in USD, billion) converted to CNY using annual average exchange rates Ministry of Commerce and National Bureau of Statistics Control Variables M Gross Regional Domestic Product (CNY billion) GRDP GRDP = Final consumption expenditure + Gross capital formation + Net exports of goods/services National Bureau of Statistics Permanent Resident Population (10,000 persons) PRP De facto resident population (including registered and migrant populations) as of December 31 National Bureau of Statistics Patent Case Closures (units) PCC Annual count of administrative patent infringement dispute cases resolved by province National Intellectual Property Administration Undergraduate Enrollment in Regular Higher Education Institutions (10,000 students) NS Annual enrollment of undergraduate students in regular higher education institutions National Bureau of Statistics Science and Technology Expenditure (billion CNY) IST Total annual provincial expenditure on research and development (R&D) activities National Bureau of Statistics Notes: All monetary values are adjusted for inflation and reported in constant CNY.Exchange rates for FDI conversion are based on annual averages published by the People’s Bank of China.Data sources are official government agencies to ensure reliability and validity. Model Specification To examine the impact of PAIPRP on Regional Innovation Capability (APQ & AUQ) and explore the moderating role of Actual Utilization of Foreign Direct Investment (AUFDIy) in this relationship, this study constructs a panel data regression model. The model includes independent variables, dependent variables, moderating variables, and control variables. The control variables comprise Gross Regional Domestic Product (GRDP), year-end resident population (PRP), number of patent cases concluded (PCC), number of undergraduate students in general higher education institutions (NS), and expenditure on scientific and technological investment (IST). $$\:{APQ}_{it}={\beta\:}_{o}+{\beta\:}_{1}{\text{P}\text{A}\text{I}\text{P}\text{R}\text{P}}_{it}+{\beta\:}_{2}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{it}+{\mu\:}_{i}+{\lambda\:}_{t}+{ϵ}_{it}$$ (1) Where 𝐴𝑃𝑄𝑖𝑡 and 𝐴𝑈𝑄𝑖𝑡 represent the regional innovation capability of region ( i ) in year ( t ); 𝐼𝑅𝑃𝑠𝑖𝑡 denotes the level of intellectual property protection attention in region ( i ) during year ( t ); Μ𝑖𝑡 In this model, ( \(\:{APQ}_{it}\) ) represents the regional innovation capabilities of region ( i ) in year ( t ); ( \(\:{\text{P}\text{A}\text{I}\text{P}\text{R}\text{P}}_{it}\) ) denotes the level of intellectual property protection attention in region ( i ) in year ( t ); ( \(\:{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{it}\) ) is a set of control variables, including Gross Regional Domestic Product (GRDP), year-end resident population (PRP), number of patent cases closed (PCC), number of undergraduate students in general higher education institutions (NS), and science and technology investment (IST); ( \(\:{\mu\:}_{i}\) ) denotes the regional fixed effects, capturing unobserved region-specific characteristics; ( \(\:{\lambda\:}_{t}\) ) is the time fixed effect, capturing macro trends over time; ( \(\:{ϵ}_{it}\) ) is the error term. In order to test the hypothesis that the relationship between intellectual property protection attention and regional innovation capability is dynamically moderated by the level of openness, this study incorporates the Actual Utilization of Foreign Direct Investment (AUFIDy) to examine its moderating effect. $$\:{APQ}_{it}={\beta\:}_{o}+{\beta\:}_{1}{\text{P}\text{A}\text{I}\text{P}\text{R}\text{P}}_{it}+{\beta\:}_{2}{\text{A}\text{U}\text{F}\text{I}\text{D}\text{y}}_{it}+{\beta\:}_{3}({\text{P}\text{A}\text{I}\text{P}\text{R}\text{P}}_{it}\times\:{\text{A}\text{U}\text{F}\text{I}\text{D}\text{y}}_{it})+{\beta\:}_{4}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{it}+{\mu\:}_{i}+{\lambda\:}_{t}+{ϵ}_{it}$$ (2) Where \(\:{\text{A}\text{U}\text{F}\text{I}\text{D}\text{y}}_{it}\) represents the actual utilization of foreign direct investment, used to measure the level of openness; \(\:{\text{P}\text{A}\text{I}\text{P}\text{R}\text{P}}_{it}\times\:{\text{A}\text{U}\text{F}\text{I}\text{D}\text{y}}_{it}\) is the interaction term, employed to test the moderating effect of the level of openness on the relationship between intellectual property protection attention and regional innovation capability. Results and disscusion Descriptive Results An analysis of the descriptive statistics presented in Table 2 reveals significant regional disparities in both attention to intellectual property protection and regional innovation capability (RIC) across Chinese provinces during the period from 2011 to 2023. These disparities are also reflected in other key variables, including educational attainment, economic development levels, and population size. The Baidu Index—used to measure the frequency of searches related to IPR—along with the number of patent applications (APQ) and patent grants (AUQ), shows substantial variation across provinces. Specifically, the gap between the maximum and minimum PAIPRP values reaches 50,308, suggesting significant heterogeneity in public awareness and concern for IPRP. The difference in APQ reaches 929,231, while that in AUQ is also large, at 775,415, underscoring considerable variation in regional innovation output. Substantial differences are also observed in other explanatory and control variables, such as actual foreign direct investment utilization (AUFDIy), Gross Regional Domestic Product (GRDP), Permanent Resident Population (PRP), Patent Case Closure Count (PCC), Number of Undergraduate Students (NS), and Scientific and Technological Investment (IST). While the range of values for these variables suggests uneven development across regions, the relatively stable mean values indicate a broader trend toward convergence and more balanced development at the national level. These findings highlight both the challenges and opportunities of promoting innovation through differentiated regional policy strategies. Table 2 Descriptive Statistical Analysis of Key Variables. Variable Obs Mean Std. Dev. Min Max PAIPRP 403 16402.136 11253.522 171 50479 APQ 403 94298.285 138785.83 130 929361 AUFDIy 403 657.448 1392.435 .207 12234.895 GRDP 403 27422.368 24205.123 611.5 135673.2 PRP 403 4481.645 2919.671 309 12706 PCC 403 900.501 2359.628 0 18334 NS 403 54.141 31.795 1.99 141.46 IST 403 619.384 781.914 1.2 4802.6 Model validation Prior to constructing the panel data regression model, this study conducted a series of specification tests to determine the most appropriate modeling strategy. Using Stata 18, both the F-test and Hausman test were applied to provincial-level panel data spanning from 2011 to 2023. The purpose of these tests was to assess whether a pooled ordinary least squares (OLS) model, a fixed-effects model, or a random-effects model would be more appropriate for the analysis. The results of the F-test yielded a test statistic of 171.142 with a p-value of 0.000, indicating that the fixed-effects model is significantly preferred over the pooled OLS model. Furthermore, the Hausman test produced a chi-square statistic of 91.45 (degrees of freedom = 7) with a p-value of 0.000, confirming that the fixed-effects model is superior to the random-effects model. Therefore, based on both statistical criteria, the individual fixed-effects model was selected as the optimal specification for subsequent regression analysis. Analysis of the Main Regression Results Table 3 presents the regression results examining the effect of PAIPRP on regional innovation capability, using the number of patent applications (APQ) as the dependent variable. In the first model, only control variables are included, while the second model incorporates PAIPRP as the key independent variable. Both regressions are estimated using the fixed-effects model, as validated in the previous section. In both specifications, the coefficient on PAIPRP is 1.776 and is statistically significant at the 1% level, indicating a robust and positive relationship between public attention to IPRP and innovation output. This empirical finding provides strong support for Hypothesis 1 , which posits that heightened societal attention to IPRP enhances regional innovation capability. The results suggest that greater awareness and concern for IPRP may incentivize R&D investment, reduce perceived infringement risks, and contribute to a more innovation-conducive environment at the regional level. Table 3 Analysis of the Main Regression Results. (1) (2) APQ APQ PAIPRP 1.776*** (3.820) GRDP 7.937 5.533 (11.770) (8.451) PRP 367.743 364.673 (23.735) (23.943) pcc -9.570 -9.252 (-3.039) (-2.959) NS -3459.241 -3008.620 (-5.282) (-4.399) IST 14.045 27.201* (0.883) (1.701) _cons -7.49e + 05*** -7.59e + 05*** (-10.196) (-10.512) N 403 403 R 2 0.716 0.727 F 184.977 162.289 *** p < 0.01, ** p < 0.05, * p < 0.1 Heterogeneity Analysis Following the regional classification framework adopted by Lv et al., ( 2021 ) and Li et al. ( 2021 ), this study divides mainland China into three macro-regions—eastern, central, and western—encompassing a total of 31 provinces, autonomous regions, and municipalities directly under the central government. Hong Kong, Macao, and Taiwan are excluded from this analysis. Specifically, the eastern region includes 11 provinces/municipalities: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region comprises 8 provinces: Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan. The western region includes 12 provincial-level administrative divisions: Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. To explore the regional differences in the effect of PAIPRP on regional innovation capability, a categorical variable named “area” is introduced to classify the sample. The values of this variable are coded as follows: “2” for eastern regions, “1” for central regions, and “0” for western regions. Based on this classification, separate regression analyses are conducted for each group. The regression results are summarized in Table 4 . In the eastern region, the coefficient of PAIPRP is 2.893 and is statistically significant at the 1% level, indicating a strong positive association between public attention to IPRP and innovation performance. In the central region, the PAIPRP coefficient is 0.502, which is positive but statistically insignificant, suggesting that attention to IPRP does not have a clear impact on innovation capacity in this region. In the western region, the coefficient of PAIPRP is − 0.180, also statistically insignificant, implying that the effect of PAIPRP on innovation capability in western provinces is negligible or potentially negative. To explain the regional disparity, the study further examines the role of educational factors. In the western region, the coefficient for the number of enrolled undergraduate students is − 1818.102, a highly significant negative value, as shown in Table 6 . This suggests that the expansion of higher education enrollment may not effectively translate into innovation productivity in less developed regions. Possible explanations include the relatively poor quality of education, limited absorption capacity for high-level talent, and weak linkage between academic institutions and regional innovation systems. Moreover, the rapid expansion of undergraduate education in recent years has increased fiscal pressure on local governments in the western region, potentially undermining the targeted allocation of educational resources and innovation funding. These findings confirm the presence of significant regional heterogeneity in the effect of PAIPRP on innovation. The effect is strongest in the eastern region, weaker in the central region, and largely absent or even negative in the western region. Therefore, Hypothesis 2 —which posits regionally differentiated impacts of PAIPRP on innovation capability—is empirically supported. Table 4 Results of Heterogeneity Regression Analysis. (1)East = 2 (2)Middle = 1 (3)West = 0 APQ APQ APQ PAIPRP 2.893*** 0.502 -0.180 (3.328) (1.189) (-0.613) IST 15.653 -25.287 -15.690 (0.607) (-0.977) (-0.710) GRDP 2.427 4.658*** 3.812*** (1.639) (5.789) (3.740) PCC -8.368*** -3.660 4.556* (-3.029) (-0.519) (1.672) PRP 290.645*** 16.972 45.338** (9.205) (1.162) (2.446) NS -2358.137** -559.713 -1818.102*** (-2.350) (-1.448) (-3.200) _cons -1.39e + 06*** -1.03e + 05 -9.47e + 04* (-9.335) (-1.403) (-1.686) N 156 117 130 R 2 0.792 0.760 0.728 F 87.370 53.977 50.763 ***p < 0.01, **p < 0.05, *p < 0.10 Moderating Effect Analysis To assess whether regional openness moderates the relationship between attention to intellectual property protection and regional innovation capability, this study conducts a moderating effect analysis using the actual utilization of foreign direct investment (AUFDIy) as a proxy for openness. A higher level of FDI utilization indicates a greater degree of regional economic openness (Rao et al. 2024 ). As shown in Table 5 , an interaction term between Baidu search volume for PAIPRP and actual FDI utilization (AUFDIy, measured in 100 million RMB) is introduced into the regression model. The results show that the coefficient of PAIPRP remains positive and highly significant, reaffirming its direct positive effect on innovation. More importantly, the interaction term between PAIPRP and AUFDIy yields a coefficient of 0.001, which is also statistically significant at the 1% level. These findings indicate that the effect of attention to IPRP on innovation capability is positively moderated by the level of openness. In other words, regions with higher FDI inflows are more likely to translate public attention to IPRP into enhanced innovation performance. This supports Hypothesis 3 , confirming that openness plays an amplifying role in the IP–innovation relationship. Table 5 Results of Moderating Effect Analysis. (1) (2) APQ APQ PAIPRP 1.776*** 1.353*** (3.820) (3.216) IST 27.201* -19.466 (1.701) (-1.279) GRDP 2.766*** 3.907*** (3.226) (5.004) pcc -4.626** -0.251 (-2.480) (-0.144) PRP 182.337*** 88.374*** (10.972) (4.909) NS -1504.310*** -1225.133*** (-3.199) (-2.890) PAIPRP_AUFDIy 0.001*** (9.360) _cons -7.59e + 05*** -3.63e + 05*** (-10.512) (-4.676) N 403 403 R 2 0.727 0.780 F 162.289 184.539 ***p < 0.01, **p < 0.05, *p < 0.10 Robustness Test To further ensure the robustness of the regression results, This study draws on Garcia et al. and Han et al. research and it adopts two test methods: First, the control variable for science and technology investment (IST) is removed. After removing this control variable, as shown in the second column of Table 6 , the coefficient of PAIPRP is 1.606, which is statistically significant at the 1% level, and the result remains a highly significant positive value (Han et al., 2025 ). Second, the dependent variable is changed from the number of patent applications (APQ) to the number of patent grants (AUQ) (Garcia et al., 2024 ; Han et al., 2025 ; Yang & Wang, 2024 ). This approach follows the study of Guo & Zhong ( 2024 ), substituting the dependent variable with the number of patent grants (AUQ) to measure the regional innovation capability of provinces based on the number of patents granted. The results show that, under the condition of changing the dependent variable while keeping other variables unchanged, the coefficient of PAIPRP is 1.876, which is significant at the 1% level, and the result remains a highly significant positive value. Here's the model after replacing the dependent variable, ( \(\:{AUQ}_{it}\) ) represents the regional innovation capabilities of region ( i ) in year ( t ). $$\:{AUQ}_{it}={\beta\:}_{o}+{\beta\:}_{1}{\text{P}\text{A}\text{I}\text{P}\text{R}\text{P}}_{it}+{\beta\:}_{2}{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}}_{it}+{\mu\:}_{i}+{\lambda\:}_{t}+{ϵ}_{it}$$ (3) The results in the first column of Table 6 represent the original regression analysis, while the second and third columns present the results of regression analyses with reduced control variables and the changed dependent variable. The results indicate that, whether by reducing the control variables or changing the dependent variable, the level of intellectual property protection awareness has a significant positive impact on regional innovation capability. These results are consistent with the previous estimation results, confirming the robustness of the findings in this study. Table 6 Results of Robustness Test. (1) (2) (3) APQ APQ AUQ PAIPRP 1.776*** 1.606*** 1.876*** (3.820) (3.528) (4.394) GRDP 2.766*** 3.986*** 2.841*** (3.226) (8.447) (3.608) PRP 182.337*** 183.451*** 160.331*** (10.972) (11.019) (10.509) PCC -4.626** -4.535** -4.743*** (-2.480) (-2.425) (-2.769) NS -1504.310*** -1850.782*** -1280.742*** (-3.199) (-4.357) (-2.967) IST 27.201* -12.970 (1.701) (-0.883) _cons -7.59e + 05*** -7.59e + 05*** -6.73e + 05*** (-10.512) (-10.487) (-10.156) N 403 403 403 R 2 0.727 0.725 0.634 F 162.289 193.173 105.714 ***p < 0.01, **p < 0.05, *p < 0.10 Discussion The above results are very enlightening for this study. The results of the descriptive analysis in the first part, the innovation-driven strategy of China has played an important role in the quality of economic development (Xiao et al., 2022 ). With the deepening of economic reforms and the implementation of national innovation policies, public awareness and attention toward intellectual property protection have generally increased across provinces. This growing awareness has coincided with an overall strengthening of innovation capability at the national level, albeit with marked regional disparities. The descriptive statistics revealed considerable variation in key variables across provinces, particularly in actual utilization of foreign direct investment (AUFDIy), gross regional domestic product (GRDP), permanent resident population (PRP), patent case closure count (PCC), number of undergraduate students (NS), and scientific and technological investment (IST). These disparities are shaped by several structural and institutional factors. First, regional disparities in economic development play a critical role. Coastal provinces such as Jiangsu, Guangdong, and Zhejiang enjoy more advanced industrial bases, stronger market institutions, and more active engagement with global trade and investment. These regions tend to exhibit greater public awareness of IPRP, more efficient enforcement mechanisms, and higher levels of innovation output. In contrast, central and western provinces continue to face challenges related to lower economic density, weaker legal enforcement capacities, and limited institutional support for innovation (Zhao et al., 2022 ). Second, the uneven distribution of educational resources significantly affects regional innovation performance (Shkarlet et al., 2019 ; Wu & Liu, 2020 ). Provinces in the eastern region generally benefit from more prestigious universities, greater R&D funding, and a better alignment between higher education systems and industry demand. In contrast, many central and western provinces still lack high-quality educational infrastructure, which limits their capacity to generate, retain, and transform human capital into innovation productivity. The heterogeneity analysis in this study revealed that in western provinces, a negative and significant coefficient exists between the number of undergraduate students and innovation capability. This counterintuitive finding suggests that merely expanding enrollment without corresponding improvements in educational quality or policy coordination may inadvertently dilute educational effectiveness and place additional fiscal stress on local governments. The rapid expansion of higher education in recent years has indeed increased access but has also created new challenges. Resource constraints and administrative inefficiencies in the west have made it difficult to match enrollment expansion with adequate instructional quality and employment opportunities. As noted by Zhang et al., ( 2023 ) the Chinese government should not only emphasize the importance of educational expansion but also focus on rational allocation of educational resources, particularly in less-developed central and western provinces. Universities should serve as engines of entrepreneurship and innovation (E&I), providing both knowledge and institutional support to local economies. Third, regional openness to foreign investment emerges as a vital factor in shaping innovation outcomes (Arvin et al., 2021 ). As supported by the moderating effect analysis in this study, AUFDIy has a statistically significant and positive moderating effect on the relationship between public attention to IPRP and regional innovation capability. This finding is consistent with the broader literature that views FDI as a critical engine of economic growth and technological upgrading, particularly in developing countries (Martins et al., 2023 ). In the regulatory effect analysis part of this paper, it is concluded that AUFDIy has a very significant regulatory effect on the attention paid to intellectual property protection and regional innovation ability, which indicates that the level of openness to the outside world is very important and has a positive impact on regional innovation (Garcia et al., 2024 ). Foreign investment contributes to innovation not only through capital inflows but also by fostering technology transfer, competitive pressure, and international collaboration (Burinskas et al., 2021 ; Elgin, 2020 ). Provinces with higher FDI utilization often experience increased exposure to global standards in IP enforcement, which can incentivize domestic firms to improve their own innovation strategies. Furthermore, foreign firms often demand stronger IPRP frameworks as a precondition for establishing operations, thereby reinforcing the institutionalization of IP norms in host regions. However, the positive effects of openness are not automatic. Their realization depends on a region's absorptive capacity, institutional quality, and policy coherence. The eastern provinces, with their mature institutional systems and strong industrial bases, are better positioned to leverage openness and translate public IP awareness into innovation output (Guo et al., 2022 ). Meanwhile, the central and western provinces may require targeted policy support to upgrade their innovation ecosystems and better integrate into national and global innovation networks. The findings of this study highlight a clear regional stratification in how attention to intellectual property protection affects innovation. The eastern region benefits the most from increased IP awareness due to its favorable institutional, economic, and educational environments (Han et al., 2023 ). The central region exhibits a moderate, yet inconsistent, effect, while the western region struggles to convert attention into outcomes, largely due to structural constraints in education and innovation systems. Based on the above discussion, several implications can be drawn to optimize intellectual property protection policies and enhance regional innovation capacity in China. First, local governments may leverage regional intellectual property protection regimes to attract innovative talent, thereby enhancing local innovation productivity (Auriol et al., 2022 ; Xu et al. 2025 ). At the same time, they should strengthen the penalties for intellectual property infringements to encourage innovation. Second, local governments can increase investment in education, particularly by allocating more funding to universities, while also focusing on enhancing the innovation capacity of higher education institutions. This includes establishing stringent management of research and development funding, emphasizing the cultivation of innovative talent, and developing reward mechanisms for innovation outputs in universities to support the improvement of local innovation capacity. Only by establishing a robust intellectual property protection system can localities better promote the enhancement of regional innovation capacity, thereby improving the overall level of intellectual property protection in the country (Han et al., 2025 ). Third, local governments can further expand their openness to the outside world, increase the scale of investment attraction, and strictly protect the patent rights of foreign investors. By reducing local protectionism, this will foster market competition and stimulate the production of local patent rights, ultimately improving regional innovation capacity. Fourth, we should implement differentiated IPRP systems according to local conditions. In peripheral cities, cities with a relatively weak science and education foundation and cities with a relatively low innovation level, we should promote the successful experience of IPRP system construction and expand the construction scope of IPRP demonstration pilot cities (Yang et al., 2023 ). This study also has the following limitations: First, the data sample selected for this research is limited to the mainland provinces and municipalities of China from 2011 to 2023, without covering a broader geographical range or time period. Second, the control variables chosen in this study are limited, which restricts the ability to precisely control for the regression analysis results. Additionally, while the level of openness to the outside world is used as a moderating variable, there may be other variables that could dynamically moderate the relationship between intellectual property protection and regional innovation capacity. Third, when selecting the number of students in general undergraduate universities, the limited data from the National Bureau of Statistics prevented the inclusion of master's and doctoral students as part of the sample, which may ultimately affect the final research results. Conclusion and Implications This study empirically investigates the relationship between PAIPRP and RIC using provincial panel data from China (2011–2023), revealing three key findings. First, public attention to IPRP exerts a statistically significant positive effect on RIC, underscoring the critical role of societal awareness in fostering innovation-driven development. Second, this relationship exhibits pronounced regional heterogeneity: the effect is strongest in eastern China, moderate in central regions, and weakest in western China, which can be attributed to disparities in institutional quality, educational resources, and economic openness. Third, regional openness, measured by actual FDI utilization, positively moderates the link between PAIPRP and RIC, indicating that higher levels of economic integration amplify the innovation-inducing effects of public IPR awareness. While this study advances the understanding of PAIPRP and regional innovation, several avenues for future research warrant exploration. For instance, incorporating micro-level data (e.g., firm-level innovation activities) could further unpack the behavioral mechanisms underlying the observed relationships. Longitudinal studies with extended time horizons or cross-country comparative analyses would enhance the generalizability of these findings. Moreover, investigating other potential moderators—such as digital infrastructure or regional policy networks—could provide a more comprehensive understanding of the multifaceted drivers of innovation in heterogeneous contexts. Declarations Ethical approval This article does not contain any studies with human participants or their personal data performed by any of the authors. All data used are aggregated and publicly available from official sources. According to the Jianghan University Research Ethics Committee, this study is exempt from ethics approval. Informed consent This study does not involve human participants, their personal data, or biological material. All data analysed are aggregated and obtained from publicly accessible official sources. According to the guidelines of the Jianghan University Research Ethics Committee and relevant national regulations, studies based solely on publicly available, non-identifiable data do not require informed consent. Funding: This research is funded by Wuhan Intellectual Property Talent Cultivation Project–Enterprise Patent Technology Mining and Achievement Transformation and Utilization Ability Enhancement Training Program. Author Contribution L.X.Z and T.L.Z wrote the main manuscript text. J.K.Y and R.H.Y collected the data and provided idea. Z.Z provided suggestions for modification. All authors reviewed the manuscript. Data Availability The datasets generated and/or analysed during the current study are available in the figshare repository at https://doi.org/10.6084/m9.figshare.29918756.v2. The dataset includes all variables listed in Table 1, along with their definitions, measurement methods, and original data sources. All data were obtained from publicly accessible and authoritative sources, including the Baidu Index, incoPat, National Bureau of Statistics of China, Ministry of Commerce of the People’s Republic of China, and the National Intellectual Property Administration. Monetary values have been adjusted for inflation and are reported in constant CNY; foreign direct investment (FDI) values were converted from USD to CNY using annual average exchange rates from official statistics. 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(2023). Branding as a global phenomenon: from theory to practice and vice versa. In De Gruyter eBooks (pp. 7 – 54). https://doi.org/10.1515/9783110741100-005 Wu, N., & Liu, Z. (2020). Higher education development, technological innovation and industrial structure upgrade. Technological Forecasting and Social Change, 162 , 120400. https://doi.org/10.1016/j.techfore.2020.120400 Xiao, W., Kong, H., Shi, L., Boamah, V., & Tang, D. (2022). The Impact of Innovation-Driven Strategy on High-Quality Economic Development: Evidence from China. Sustainability, 14 (7), 4212. https://doi.org/10.3390/su14074212 Xu, L., Guo, K., & Yan, M. (2025). Study on the Influence Mechanism of Government Innovation Preference on the Development of Digital Economy. International Business & Economics Studies, 7 (1), p37. https://doi.org/10.22158/ibes.v7n1p37 Yang, S., Liu, F., Wang, G., & Feng, D. (2023). Intellectual property system and urban green innovation: Evidence from China. Growth and Change, 55 (1). https://doi.org/10.1111/grow.12689 Yang, W., & Wang, X. (2024). The impact of patent protection on technological innovation: A global value chain division of labor perspective. Technological Forecasting and Social Change, 203 , 123370. https://doi.org/10.1016/j.techfore.2024.123370 Yi, T., Dong, Y., & Cao, W. (2024). Examining the Bidirectional Mechanism between Intellectual Property Protection and Regional Innovation: Evidence from China. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-024-02540-5 Yu, H., & Zhao, J. (2020). The impact of environmental conditions on Urban Eco-Sustainable Total Factor Productivity: a case study of 21 cities in Guangdong Province, China. International Journal of Environmental Research and Public Health, 17 (4), 1329. https://doi.org/10.3390/ijerph17041329 Zhang, H., & Chen, X. (2022). Open Innovation and Sustainable Innovation Performance: The moderating role of IP Strategic Planning and IP Operation. Sustainability, 14 (14), 8693. https://doi.org/10.3390/su14148693 Zhang, H., Cai, C., Zhang, X., Tu, Y., & Zhu, Q. (2024). Relationship between business environment and regional innovation level: Examining the moderating role of digital finance. International Review of Financial Analysis, 96 , 103647. https://doi.org/10.1016/j.irfa.2024.103647 Zhang, H., Zhang, D., & Jin, Y. (2023). Does expansion of college education benefit urban entrepreneurship and innovation in China?. Heliyon, 9 (11). e21813-e21813. https://doi.org/10.1016/j.heliyon.2023.e21813 Zhao, R., & He, P. (2024). Government spending efficiency, fiscal decentralization and regional innovation capability: Evidence from China. Economic Analysis and Policy. 84 , 693-706. https://doi.org/10.1016/j.eap.2024.08.033 Zhao, X., Jiang, M., & Zhang, W. (2022). Decoupling between Economic Development and Carbon Emissions and Its Driving Factors: Evidence from China. International Journal of Environmental Research and Public Health, 19 (5), 2893. https://doi.org/10.3390/ijerph19052893 Zhou, X., Chen, Y. F., & Wang, W. T. (2022). Intellectual Property Rights Protection Level, Regional Innovation, and Industrial Upgrading. Statistics and Decision, 38 (16), 168-171. https://doi.org/10.13546/j.cnki.tjyjc.2022.16.033 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6910162","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":518701745,"identity":"673c7d2e-ed39-4170-af83-0b948959abc7","order_by":0,"name":"Lixin Zhao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lixin","middleName":"","lastName":"Zhao","suffix":""},{"id":518701746,"identity":"b08ebea0-900f-40e8-867c-e385d7ba0dcc","order_by":1,"name":"TaoLe 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18:36:28","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191062,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6910162/v1/4a295b7e070fca2e995f4d9d.html"},{"id":101943359,"identity":"3131aa4f-7579-4407-a768-a0edc02ca9bc","added_by":"auto","created_at":"2026-02-05 09:41:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":965979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6910162/v1/fe07b827-f009-499d-8655-7f42c09efd04.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Public Attention to Intellectual Property Rights Protection, Openness, and Regional Innovation Capability: An Empirical Analysis Based on Provincial Panel Data in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the context of a rapidly evolving knowledge economy, innovation has become the primary engine of high-quality economic development and national competitiveness. With the rise of intangible assets and technology-driven industries, intellectual property rights (IPR) have gained strategic importance in protecting innovative outputs, attracting investment, and incentivizing knowledge production. According to the World Intellectual Property Organization (WIPO), over 3.55\u0026nbsp;million patent applications were filed globally in 2023\u0026mdash;2.7% increase over last year\u0026mdash;applicants in China filed about 1.64\u0026nbsp;million patent applications, covering both domestic and foreign jurisdictions. This surge highlights not only the centrality of innovation in global development but also the growing relevance of intellectual property protection as an institutional safeguard.\u003c/p\u003e\u003cp\u003eIntellectual property rights protection (IPRP) has emerged as a cornerstone of modern economic development, serving as both an accelerator for innovation ecosystems and a barometer of national competitiveness. A robust IPRP system not only mitigates the risks of innovation externalities, but also promotes industrial upgrading through optimized resource allocation (Song \u0026amp; Chen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). World Bank research reveals that IPR-intensive industries contribute over 38% of GDP in advanced economies, dwarfing the mere 12% in least-developed countries\u0026mdash;a statistic that underscores the critical role of IPR governance in bridging global innovation divides (Auriol et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For China, this imperative takes on particular urgency as the nation navigates its economic transformation. While demonstrating remarkable progress in patent filings (covering inventions, utility models, and industrial designs) and trademark registrations, structural imbalances persist (Qayyum et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, the innovation-output gap manifests not only internationally but also intra-nationally, with pronounced regional disparities. As diminishing returns plague traditional growth models predicated on low-cost labor and capital-intensive inputs, strategic recalibration of IPR frameworks has become pivotal for catalyzing industrial upgrading and sustaining regional development momentum ( Song \u0026amp; Chen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Luo \u0026amp; Zhao, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Neves et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo supplement institutional metrics, this study introduces public attention to IPRP as a behavioral dimension in the analysis of regional innovation capability. Public attention reflects the extent to which IPR issues are visible, salient, and actively discussed in society\u0026mdash;capturing collective perceptions of enforcement credibility and institutional legitimacy. While not a replacement for formal legal indicators, this perspective helps illuminate how societal engagement may shape innovation decisions, particularly in regions where institutional systems are still maturing. Existing studies have rarely integrated this cognitive aspect into quantitative models of regional innovation, especially in the Chinese context. Existing scholarship has predominantly centered on the macro-level correlation between intellectual property rights protection and innovation, frequently employing aggregated national indices or cross-country comparative frameworks to validate the robust positive correlation (Neves et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang \u0026amp; Chen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This body of research, however, exhibits three critical limitations in addressing China's context: First, it disproportionately emphasizes legislative completeness while underrepresenting enforcement heterogeneity across jurisdictions; second, it relies excessively on patent quantity metrics that inadequately capture qualitative innovation outcomes; third, it insufficiently addresses subnational disparities in institutional ecosystems. Empirical evidence reveals stark contrasts in IPR governance efficacy\u0026mdash;eastern provinces like Jiangsu, Zhejiang, and Guangdong demonstrate superior performance through integrated legal frameworks, proactive enforcement mechanisms (e.g., specialized IP courts), and market-oriented innovation clusters (Yu \u0026amp; Zhao, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao \u0026amp; He, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, central and western regions lag due to fragmented institutional coordination and resource constraints, resulting in weaker compliance monitoring and limited public IP literacy (Jie et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Crucially, while macro studies confirm IPR' aggregate economic benefits, they largely neglect the micro-institutional dynamics\u0026mdash;such as localized enforcement innovations in e-commerce governance or cross-regional technology spillovers\u0026mdash;that underpin these disparities (Hou et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wippel, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This analytical gap hinders the formulation of spatially differentiated policies essential for addressing China's multidimensional innovation divide (Rao et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Another dimension that merits attention is regional openness, particularly the degree to which regions are integrated into global markets through foreign direct investment (FDI). Open regions not only receive external knowledge and capital but also tend to adopt stricter enforcement standards due to international pressure. This may reinforce public trust in the IPR system and encourage innovation by reducing perceived risk. However, the interplay between openness, public attention to IPRP, and innovation capacity has not been sufficiently investigated, especially in countries like China with significant institutional and regional diversity (Zhang \u0026amp; Chen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven these gaps, this study aims to address three core questions:(1) Does public attention to IPRP influence regional innovation capability? (2) Does this relationship vary significantly across different regions in China? (3) Is this relationship moderated by the level of regional openness, as measured by actual FDI utilization? To answer these questions, we construct a panel dataset comprising 31 Chinese provinces from 2011 to 2023, allowing for a longitudinal analysis of spatial heterogeneity in the innovation effects of public IPR attention.\u003c/p\u003e\u003cp\u003eThis study contributes to the literature in three main ways. First, it incorporates public attention to IPRP into the analytical framework as a supplementary behavioral factor, providing new insights beyond conventional institutional indicators. Second, it empirically identifies regional heterogeneity in the effect of IPRP attention on innovation capability, revealing that the influence is strongest in eastern China and weakest in the west. Third, it highlights the moderating role of regional openness, offering policy-relevant findings on how institutional perception and international integration jointly shape regional innovation outcomes. The rest of this paper is organized as follows: Section 2 reviews the relevant literature and presents the research hypotheses. Section 3 outlines the empirical model, variables, and data sources. Section 4 discusses the regression results, followed by heterogeneity and moderating effect analyses. Section 5 conducts robustness checks. Section 6 concludes with theoretical and policy implications.\u003c/p\u003e"},{"header":"Literature Review and Research Hypotheses","content":"\u003ch2\u003eLiterature Review\u003c/h2\u003e\n\u003cp\u003eThe theory of intellectual property protection is grounded in a multidimensional framework that spans economic, legal, and philosophical dimensions. Economically, intellectual property plays a pivotal role in promoting technological spillovers, enhancing innovation incentives, and facilitating industrial upgrading (Poyago-Theotoky \u0026amp; Tsai, 2023). Scholars generally agree that within a segmented global market, a partially strong IP system can be more socially optimal than a universally stringent one. At work are two effects: the market-penetrating effect (MPE), which evaluates the extent to which national welfare can be increased by IPR policy via firm investment.; And the business-stealing effect (BSE), which examines the direct impact on the level of competition (Poyago-Theotoky \u0026amp; Tsai, 2023; T. Han et al., 2021). From a legal standpoint, IPRP is anchored in the logic of private property rights, where laws grant inventors and creators exclusive control over their innovations to prevent unauthorized use or reproduction. While IP law is often discussed at the national level, sub-national differences in enforcement, institutional quality, and public awareness are also critical, particularly in large, regionally diverse countries like China (Gao, 2020). Philosophically, the justification for IPRP largely stems from utilitarianism, which supports the granting of exclusive rights as long as they contribute positively to overall social welfare. IP rights, from this view, are temporary tools designed to incentivize innovation and are maintained only as long as they remain necessary for generating desired levels of creative investment (Garcia et al., 2024).\u003c/p\u003e\n\u003cp\u003eThe link between IPRP and innovation remains one of the most debated topics in contemporary economic and legal scholarship. A large body of empirical research supports the notion that robust IP regimes can enhance innovation efficiency. For instance, Gmeiner et al. (2021) find that stronger adherence to international IP standards improves domestic innovation performance; Zhou et al. (2022) posit that strengthening intellectual property protection effectively stimulates regional technological innovation, thereby facilitating industrial upgrading. However, this relationship exhibits significant heterogeneity. For instance, Neves et al. argue that in developing countries, due to weak institutional environments, the incentive effect of IPRP on innovation is weaker than in developed countries (Neves et al., 2021) . Similarly, Luo and Zhao (2024) find that both excessively stringent and overly weak intellectual property protection can create adverse feedback mechanisms that hinder the optimization of regional technological innovation structures. Castaldi et al., (2023) suggest that innovation output tends to be higher under patent protection, especially when private and social returns are closely aligned. Uyar et al. (2021) find that a considerable trade-off between strengthening IPR and the country's economic activity, with both domestic and foreign innovation increasing under strong IPRP. Christopoulou et al. (2021) and Uyar et al. (2021) further argue that effective patent systems are critical for supporting firm-level R\u0026amp;D and long-term economic growth. Su et al. (2021) through their analysis of total factor productivity (TFP), reveal a nonlinear relationship: IPRP has a negative effect in the least-developed economies but follows an inverted U-shape in developing and developed countries. Song et al. (2024) empirically investigate the non-linear relationship between intellectual property protection and enterprise innovation performance, revealing an inverted U-shaped curve in the effect of IPP on EIP. In the Chinese context, Song and Chen (2023) find that IPRP serves as a crucial driver of green innovation among firms, highlighting the importance of aligning protection strategies with sustainable development goals.\u003c/p\u003e\n\u003cp\u003eIn the Chinese context, empirical studies have consistently highlighted regional heterogeneity in the innovation-enhancing effects of IPRP. Yi et al. (2024) demonstrate a bidirectional, reinforcing relationship between IPRP and regional innovation across provinces. However, this effect is mediated by regional openness. For example, Qayyum et al. (2022) find that trade openness and IPRP significantly promote innovation in eastern provinces, but not in central or western regions. Other studies explore the nuanced role of protection models. Zhang et al. (2024) note that overly rigid IP regimes may inhibit innovation spillovers in more advanced regions like the east. Deng et al. (2018) show that government R\u0026amp;D subsidies only complement IPRP effectively in the eastern and western regions—not the central region. Similarly, Luo et al. (2023) highlight the synergistic effect of internet development and IPRP on regional innovation efficiency, noting a U-shaped relationship between protection levels and innovation outcomes. Chi et al. (2024) provide further evidence that strong IPRP boosts domestic R\u0026amp;D and invention patent filings. However, they also caution that in less developed regions—particularly the west—foreign direct investment (FDI) can interact negatively with domestic innovation efforts, weakening the intended benefits of IPRP.\u003c/p\u003e\n\u003cp\u003eRecent research extends the discussion into the domain of economic complexity and institutional quality. Luo and Zhao (2024) argue that moderate IPRP optimizes innovation structures, while extremes in either direction hinder knowledge diffusion. Studies in fintech and green innovation sectors similarly confirm that well-calibrated IP regimes act as effective innovation incentives(Cai and Zhang, 2023). Yet, two critical gaps remain in the literature: Overreliance on macro-level analysis has left the micro-mechanisms of public and organizational attention to IPRP underexplored. As a socio-cognitive driver, public awareness may significantly influence innovation behaviors but remains poorly theorized and measured. Oversimplified regional classifications (e.g., east–central–west) obscure institutional heterogeneity within regions, such as differences in legal enforcement, resource allocation, or local innovation ecosystems. Further complicating the picture, Chi et al. (2024) find that while heightened IPRP can deter FDI (due to stricter enforcement burdens), it can simultaneously enhance domestic innovation, highlighting the dual-edged nature of strong IP regimes. Nguyen et al. (2023) in a study of Vietnam, argue that open innovation ecosystems create informal IPRP via increased complexity and imitation barriers, adding another layer to how openness affects innovation dynamics.\u003c/p\u003e\n\u003cp\u003eDespite the growing body of research examining the relationship between intellectual property protection and regional innovation, several critical gaps remain unaddressed (Cai et al., 2024). First，the primary issue lies in the excessive emphasis on formal institutional frameworks without adequately considering socio-cognitive dimensions such as public awareness regarding intellectual property protection. Second, Reliance on static macro-indicators (patents/R\u0026amp;D) that ignore dynamic public-institution feedback loops. Third, the literature frequently relies on coarse regional classifications, such as the east–central–west framework in China, which can mask important intra-regional differences (Yi et al., 2024). \u003c/p\u003e\n\u003cp\u003eTo address these gaps, the present study introduces “Public Attention to Intellectual Property Rights Protection (PAIPRP)” as a novel behavioral indicator, measured through web search behavior using the Baidu Index. This provides a real-time, region-specific proxy for public awareness and concern regarding IP issues. Moreover, the study incorporates regional openness—captured through actual foreign direct investment (FDI) utilization—as a moderating variable to examine how institutional perceptions interact with external economic integration. By combining cognitive, institutional, and economic factors in a panel data framework, this research seeks to offer a more nuanced and empirically grounded understanding of the mechanisms through which IPRP influences regional innovation in a complex and uneven national landscape.\u003c/p\u003e\n\u003ch2\u003eResearch Hypotheses\u003c/h2\u003e\n\u003cp\u003eExisting studies suggest that IPRP influences regional innovation capability through dual mechanisms: institutional incentives and risk mitigation (Luo \u0026amp; Zhao, 2024). However, the efficacy of this relationship may systematically vary depending on socio-cognitive factors (e.g., public and corporate \"attention\"), regional institutional environments, and openness levels. Meanwhile, Strong IPRP frameworks encourage firms to invest in research and development by safeguarding their proprietary knowledge and reducing the likelihood of infringement. However, the effectiveness of this relationship is far from uniform across contexts. Socio-cognitive factors—such as public awareness, organizational perception, and media attention—can significantly influence how IP policies are internalized and acted upon. Moreover, the regional institutional environment and varying levels of economic openness further shape the extent to which IPRP translates into innovation outcomes.Building on these debates, we propose the following hypotheses:\u003c/p\u003e\n\u003cp\u003eHypothesis 1 (H1): Attention to IPRP exerts a significantly positive impact on regional innovation capability.\u003c/p\u003e\n\u003cp\u003eBuilding on this theoretical foundation, the present study first hypothesizes that greater public attention to IPRP positively impacts regional innovation capability. Attention, in this context, serves as a proxy for societal expectations and perceptions regarding the enforcement and legitimacy of IP systems. When public awareness is high, firms are more likely to view the innovation ecosystem as credible and protective, thus increasing their willingness to engage in R\u0026amp;D activities. Moreover, heightened attention contributes to improved policy transparency and more efficient allocation of innovation-related resources. This study captures such attention by using web search index data, which reflects the intensity of public interest and engagement with IP-related topics in real time (Chen et al., 2023; Li et al., 2021; Yu \u0026amp; Zhao, 2020) .\u003c/p\u003e\n\u003cp\u003eHypothesis 2 (H2): The impact of PAIPRP on innovation capability exhibits significant regional heterogeneity, with the strongest effect observed in eastern China.\u003c/p\u003e\n\u003cp\u003eThe second hypothesis posits that the effect of PAIPRP on innovation is not homogeneous across regions. Due to vast differences in institutional quality, administrative efficiency, and resource endowments, the impact is expected to exhibit strong spatial heterogeneity. In eastern China—where the legal system is more developed, markets are more open, and industrial clusters are more advanced—the translation of PAIPRP into innovation output is likely to be most efficient. Conversely, in central and western regions, weaker institutional enforcement and the misallocation of R\u0026amp;D resources may dilute the effectiveness of such attention. Institutional inertia and limited policy execution capacity in these regions contribute to a gap between awareness and actionable innovation outcomes (Jin et al., 2024; Dang and Luo et al., 2021). Additionally, the scale effects of industrial clusters in eastern China amplify attention-driven innovation incentives(Cai and Zhang, 2023).\u003c/p\u003e\n\u003cp\u003eHypothesis 3 (H3): Openness to foreign investment positively moderates the relationship between PAIPRP and innovation capability, with stronger moderating effects in eastern China.\u003c/p\u003e\n\u003cp\u003eFinally, the study hypothesizes that the level of regional openness—measured through actual foreign direct investment (FDI) utilization—positively moderates the relationship between PAIPRP and innovation capability. Openness facilitates the inflow of advanced technologies and managerial practices, which can intensify innovation pressure on domestic firms and reinforce the incentives provided by IPRP. Additionally, international cooperation raises IP governance standards and increases the global commercialization potential of locally developed innovations. However, there is also a risk of innovation inertia in highly open regions if reliance on external technologies leads to reduced domestic initiative. Nevertheless, in eastern China—where openness is highest and institutional flexibility is greatest—this moderating effect is expected to be particularly strong, as local firms are better positioned to absorb and respond to foreign innovation stimuli(Cai and Zhang, 2023; Ding \u0026amp; Xue, 2023; Yu \u0026amp; Zhao, 2020; Dang and Luo, 2021).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eVariable Design\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003eIndependent Variable\u003c/h2\u003e\u003cp\u003eIn this study, the independent variable is PAIPRP, which reflects the level of emphasis placed on intellectual property rights protection by each province. According to previous scholarly research, this study measures the level of attention to IPR using the search index for IPR-related terms from Baidu Index (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Intellectual Property Protection Search Index (IRP), provided by Baidu Index, is a metric that quantifies the search behavior of Baidu users regarding intellectual property protection, as well as the media exposure related to it. The index reflects the level of public attention to intellectual property protection, with higher values indicating stronger attention in a given region, and lower values suggesting weaker attention. In this study, the intellectual property protection attention level across different provinces is considered as the independent variable.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eDependent Variable\u003c/h3\u003e\n\u003cp\u003eThere are multiple indicators in the existing literature to assess RIC (Regional Innovation Capability). This study builds on the approaches of Zhang et al. and Zhao et al., adopting a regional innovation output perspective to measure innovation capacity across regions (Zhou et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Patent-related indicators are frequently used to determine regional innovation levels, where a higher patent count indicates stronger innovation capability (Zhou et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, the number of patent applications (APQ, Applications for Patents Quantity) and patent grants (AUQ, Authorized Utility Patents Quantity) at the provincial level are used as key indicators of regional innovation capability (Cai and Zhang, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eModerator Variable\u003c/h3\u003e\n\u003cp\u003eBy drawing on Damien et al. research on foreign direct investment and intellectual property protection, this study introduces the actual utilization of foreign direct investment (AUFDIy) as a moderating variable (Viglioni et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gao \u0026amp; Zhao, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dussaux et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The data for actual FDI utilization is sourced from the Ministry of Commerce of China and National Bureau of Statistics, and its calculation method follows the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Actual\\:FDI\\:Utilization\\:\\left(Billion\\:CNY\\right)=FDI\\:\\left(Billion\\:USD\\right)\\times\\:CNY/USD\\:Exchange\\:Rate$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe level of regional openness is expected to moderate the relationship between attention to IPP and regional innovation capability. Since actual FDI utilization is a key indicator of regional openness, this study selects AUFDIy as the moderator.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eControl Variables\u003c/h2\u003e\u003cp\u003eTo minimize potential confounding factors and reduce endogeneity concerns in regression results, this study incorporates the following control variables based on prior literature:\u003c/p\u003e\u003cp\u003eGross Regional Domestic Product (GRDP): In this article, GRDP is calculated using the expenditure method, this method evaluates total spending on final goods and services, including household/government consumption, investments, and net foreign demand, it represents economic development levels across provinces(Fang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNumber of Undergraduate Students Enrolled in Higher Education Institutions (NS): Represents the level of education in each province. Due to the absence of 2023 data from the National Bureau of Statistics (NBS), this study draws on the approach of Bigaignon et al. and uses linear interpolation to impute the missing values for 2023 (Jin et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bigaignon et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePermanent Resident Population at Year-End (PRP) : Measures the total population in each province (Lv et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePatent Case Closure Count (PCC): Reflects the governmental emphasis on IPRP enforcement across provinces (Luo \u0026amp; Zhao, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eScientific and Technological Investment Expenditure (IST): Represents the level of provincial government investment in technological innovation (Cai and Zhang, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eprovides a detailed description of the variables used in this study.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable Definitions and Data Sources.\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\u003cp\u003eVariable Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariable Code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMeasurement Method\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eData Source\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndependent Variable X\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSearch Volume for IPR Protection on Baidu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePAIPRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of searches related to intellectual property protection by Baidu users\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBaidu Index\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eDependent Variable Y\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatent Application Quantity (units)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAnnual Provincial Aggregates of Patent Applications/Grants in China by Type: Inventions, Utility Models, and Designs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eincoPat (website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.incopat.com/\u003c/span\u003e\u003cspan address=\"https://www.incopat.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatent Authorization Quantity (units)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUQ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerator Variable Z\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eActual Utilized Foreign Direct Investment (CNY billion)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUFDIy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFDI (in USD, billion) converted to CNY using annual average exchange rates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinistry of Commerce and National Bureau of Statistics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eControl Variables M\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGross Regional Domestic Product (CNY billion)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGRDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGRDP\u0026thinsp;=\u0026thinsp;Final consumption expenditure\u0026thinsp;+\u0026thinsp;Gross capital formation\u0026thinsp;+\u0026thinsp;Net exports of goods/services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNational Bureau of Statistics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePermanent Resident Population (10,000 persons)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDe facto resident population (including registered and migrant populations) as of December 31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNational Bureau of Statistics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatent Case Closures (units)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnnual count of administrative patent infringement dispute cases resolved by province\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNational Intellectual Property Administration\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUndergraduate Enrollment in Regular Higher Education Institutions (10,000 students)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnnual enrollment of undergraduate students in regular higher education institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNational Bureau of Statistics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScience and Technology Expenditure (billion CNY)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal annual provincial expenditure on research and development (R\u0026amp;D) activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNational Bureau of Statistics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: All monetary values are adjusted for inflation and reported in constant CNY.Exchange rates for FDI conversion are based on annual averages published by the People\u0026rsquo;s Bank of China.Data sources are official government agencies to ensure reliability and validity.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel Specification\u003c/p\u003e\u003cp\u003eTo examine the impact of PAIPRP on Regional Innovation Capability (APQ \u0026amp; AUQ) and explore the moderating role of Actual Utilization of Foreign Direct Investment (AUFDIy) in this relationship, this study constructs a panel data regression model. The model includes independent variables, dependent variables, moderating variables, and control variables. The control variables comprise Gross Regional Domestic Product (GRDP), year-end resident population (PRP), number of patent cases concluded (PCC), number of undergraduate students in general higher education institutions (NS), and expenditure on scientific and technological investment (IST).\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{APQ}_{it}={\\beta\\:}_{o}+{\\beta\\:}_{1}{\\text{P}\\text{A}\\text{I}\\text{P}\\text{R}\\text{P}}_{it}+{\\beta\\:}_{2}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{it}+{\\mu\\:}_{i}+{\\lambda\\:}_{t}+{ϵ}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e(1)\u003c/h3\u003e\n\u003cp\u003eWhere \u0026#119860;\u0026#119875;\u0026#119876;\u0026#119894;\u0026#119905; and \u0026#119860;\u0026#119880;\u0026#119876;\u0026#119894;\u0026#119905; represent the regional innovation capability of region ( i ) in year ( t ); \u0026#119868;\u0026#119877;\u0026#119875;\u0026#119904;\u0026#119894;\u0026#119905; denotes the level of intellectual property protection attention in region ( i ) during year ( t ); Μ\u0026#119894;\u0026#119905; In this model, ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{APQ}_{it}\\)\u003c/span\u003e\u003c/span\u003e) represents the regional innovation capabilities of region ( i ) in year ( t ); ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}\\text{A}\\text{I}\\text{P}\\text{R}\\text{P}}_{it}\\)\u003c/span\u003e\u003c/span\u003e ) denotes the level of intellectual property protection attention in region ( i ) in year ( t ); ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{it}\\)\u003c/span\u003e\u003c/span\u003e ) is a set of control variables, including Gross Regional Domestic Product (GRDP), year-end resident population (PRP), number of patent cases closed (PCC), number of undergraduate students in general higher education institutions (NS), and science and technology investment (IST); (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e) denotes the regional fixed effects, capturing unobserved region-specific characteristics; (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e) is the time fixed effect, capturing macro trends over time; (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{it}\\)\u003c/span\u003e\u003c/span\u003e) is the error term.\u003c/p\u003e\u003cp\u003eIn order to test the hypothesis that the relationship between intellectual property protection attention and regional innovation capability is dynamically moderated by the level of openness, this study incorporates the Actual Utilization of Foreign Direct Investment (AUFIDy) to examine its moderating effect.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{APQ}_{it}={\\beta\\:}_{o}+{\\beta\\:}_{1}{\\text{P}\\text{A}\\text{I}\\text{P}\\text{R}\\text{P}}_{it}+{\\beta\\:}_{2}{\\text{A}\\text{U}\\text{F}\\text{I}\\text{D}\\text{y}}_{it}+{\\beta\\:}_{3}({\\text{P}\\text{A}\\text{I}\\text{P}\\text{R}\\text{P}}_{it}\\times\\:{\\text{A}\\text{U}\\text{F}\\text{I}\\text{D}\\text{y}}_{it})+{\\beta\\:}_{4}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{it}+{\\mu\\:}_{i}+{\\lambda\\:}_{t}+{ϵ}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e(2)\u003c/h3\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{A}\\text{U}\\text{F}\\text{I}\\text{D}\\text{y}}_{it}\\)\u003c/span\u003e\u003c/span\u003e represents the actual utilization of foreign direct investment, used to measure the level of openness; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}\\text{A}\\text{I}\\text{P}\\text{R}\\text{P}}_{it}\\times\\:{\\text{A}\\text{U}\\text{F}\\text{I}\\text{D}\\text{y}}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the interaction term, employed to test the moderating effect of the level of openness on the relationship between intellectual property protection attention and regional innovation capability.\u003c/p\u003e"},{"header":"Results and disscusion","content":"\u003cp\u003eDescriptive Results\u003c/p\u003e\n\u003cp\u003eAn analysis of the descriptive statistics presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reveals significant regional disparities in both attention to intellectual property protection and regional innovation capability (RIC) across Chinese provinces during the period from 2011 to 2023. These disparities are also reflected in other key variables, including educational attainment, economic development levels, and population size. The Baidu Index\u0026mdash;used to measure the frequency of searches related to IPR\u0026mdash;along with the number of patent applications (APQ) and patent grants (AUQ), shows substantial variation across provinces. Specifically, the gap between the maximum and minimum PAIPRP values reaches 50,308, suggesting significant heterogeneity in public awareness and concern for IPRP. The difference in APQ reaches 929,231, while that in AUQ is also large, at 775,415, underscoring considerable variation in regional innovation output.\u003c/p\u003e\n\u003cp\u003eSubstantial differences are also observed in other explanatory and control variables, such as actual foreign direct investment utilization (AUFDIy), Gross Regional Domestic Product (GRDP), Permanent Resident Population (PRP), Patent Case Closure Count (PCC), Number of Undergraduate Students (NS), and Scientific and Technological Investment (IST). While the range of values for these variables suggests uneven development across regions, the relatively stable mean values indicate a broader trend toward convergence and more balanced development at the national level. These findings highlight both the challenges and opportunities of promoting innovation through differentiated regional policy strategies.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistical Analysis of Key Variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAIPRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16402.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11253.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94298.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138785.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e929361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUFDIy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e657.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1392.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12234.895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27422.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24205.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e611.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135673.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4481.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2919.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e900.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2359.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e619.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e781.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4802.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eModel validation\u003c/p\u003e\n\u003cp\u003ePrior to constructing the panel data regression model, this study conducted a series of specification tests to determine the most appropriate modeling strategy. Using Stata 18, both the F-test and Hausman test were applied to provincial-level panel data spanning from 2011 to 2023. The purpose of these tests was to assess whether a pooled ordinary least squares (OLS) model, a fixed-effects model, or a random-effects model would be more appropriate for the analysis.\u003c/p\u003e\n\u003cp\u003eThe results of the F-test yielded a test statistic of 171.142 with a p-value of 0.000, indicating that the fixed-effects model is significantly preferred over the pooled OLS model. Furthermore, the Hausman test produced a chi-square statistic of 91.45 (degrees of freedom\u0026thinsp;=\u0026thinsp;7) with a p-value of 0.000, confirming that the fixed-effects model is superior to the random-effects model. Therefore, based on both statistical criteria, the individual fixed-effects model was selected as the optimal specification for subsequent regression analysis.\u003c/p\u003e\n\u003cp\u003eAnalysis of the Main Regression Results\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the regression results examining the effect of PAIPRP on regional innovation capability, using the number of patent applications (APQ) as the dependent variable. In the first model, only control variables are included, while the second model incorporates PAIPRP as the key independent variable. Both regressions are estimated using the fixed-effects model, as validated in the previous section.\u003c/p\u003e\n\u003cp\u003eIn both specifications, the coefficient on PAIPRP is 1.776 and is statistically significant at the 1% level, indicating a robust and positive relationship between public attention to IPRP and innovation output. This empirical finding provides strong support for Hypothesis\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, which posits that heightened societal attention to IPRP enhances regional innovation capability. The results suggest that greater awareness and concern for IPRP may incentivize R\u0026amp;D investment, reduce perceived infringement risks, and contribute to a more innovation-conducive environment at the regional level.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of the Main Regression Results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAIPRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.776***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(11.770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.451)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(23.735)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(23.943)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epcc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.959)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3459.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3008.620\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-5.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-4.399)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.201*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.883)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.701)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.49e\u0026thinsp;+\u0026thinsp;05***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.59e\u0026thinsp;+\u0026thinsp;05***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-10.196)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-10.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHeterogeneity Analysis\u003c/p\u003e\n\u003cp\u003eFollowing the regional classification framework adopted by Lv et al., (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Li et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), this study divides mainland China into three macro-regions\u0026mdash;eastern, central, and western\u0026mdash;encompassing a total of 31 provinces, autonomous regions, and municipalities directly under the central government. Hong Kong, Macao, and Taiwan are excluded from this analysis. Specifically, the eastern region includes 11 provinces/municipalities: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region comprises 8 provinces: Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan. The western region includes 12 provincial-level administrative divisions: Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang.\u003c/p\u003e\n\u003cp\u003eTo explore the regional differences in the effect of PAIPRP on regional innovation capability, a categorical variable named \u0026ldquo;area\u0026rdquo; is introduced to classify the sample. The values of this variable are coded as follows: \u0026ldquo;2\u0026rdquo; for eastern regions, \u0026ldquo;1\u0026rdquo; for central regions, and \u0026ldquo;0\u0026rdquo; for western regions. Based on this classification, separate regression analyses are conducted for each group. The regression results are summarized in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eIn the eastern region, the coefficient of PAIPRP is 2.893 and is statistically significant at the 1% level, indicating a strong positive association between public attention to IPRP and innovation performance. In the central region, the PAIPRP coefficient is 0.502, which is positive but statistically insignificant, suggesting that attention to IPRP does not have a clear impact on innovation capacity in this region. In the western region, the coefficient of PAIPRP is \u0026minus;\u0026thinsp;0.180, also statistically insignificant, implying that the effect of PAIPRP on innovation capability in western provinces is negligible or potentially negative.\u003c/p\u003e\n\u003cp\u003eTo explain the regional disparity, the study further examines the role of educational factors. In the western region, the coefficient for the number of enrolled undergraduate students is \u0026minus;\u0026thinsp;1818.102, a highly significant negative value, as shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. This suggests that the expansion of higher education enrollment may not effectively translate into innovation productivity in less developed regions. Possible explanations include the relatively poor quality of education, limited absorption capacity for high-level talent, and weak linkage between academic institutions and regional innovation systems. Moreover, the rapid expansion of undergraduate education in recent years has increased fiscal pressure on local governments in the western region, potentially undermining the targeted allocation of educational resources and innovation funding.\u003c/p\u003e\n\u003cp\u003eThese findings confirm the presence of significant regional heterogeneity in the effect of PAIPRP on innovation. The effect is strongest in the eastern region, weaker in the central region, and largely absent or even negative in the western region. Therefore, Hypothesis\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026mdash;which posits regionally differentiated impacts of PAIPRP on innovation capability\u0026mdash;is empirically supported.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Heterogeneity Regression Analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)East\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)Middle\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)West\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAIPRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.893***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.328)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.613)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-25.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.607)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.710)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.658***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.812***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.639)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.789)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.740)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.368***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.556*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.519)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.672)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290.645***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.338**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.446)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2358.137**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-559.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1818.102***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.448)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.39e\u0026thinsp;+\u0026thinsp;06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.03e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.47e\u0026thinsp;+\u0026thinsp;04*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-9.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.403)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.686)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eModerating Effect Analysis\u003c/p\u003e\n\u003cp\u003eTo assess whether regional openness moderates the relationship between attention to intellectual property protection and regional innovation capability, this study conducts a moderating effect analysis using the actual utilization of foreign direct investment (AUFDIy) as a proxy for openness. A higher level of FDI utilization indicates a greater degree of regional economic openness (Rao et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, an interaction term between Baidu search volume for PAIPRP and actual FDI utilization (AUFDIy, measured in 100\u0026nbsp;million RMB) is introduced into the regression model. The results show that the coefficient of PAIPRP remains positive and highly significant, reaffirming its direct positive effect on innovation. More importantly, the interaction term between PAIPRP and AUFDIy yields a coefficient of 0.001, which is also statistically significant at the 1% level.\u003c/p\u003e\n\u003cp\u003eThese findings indicate that the effect of attention to IPRP on innovation capability is positively moderated by the level of openness. In other words, regions with higher FDI inflows are more likely to translate public attention to IPRP into enhanced innovation performance. This supports Hypothesis\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, confirming that openness plays an amplifying role in the IP\u0026ndash;innovation relationship.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Moderating Effect Analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAIPRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.776***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.353***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.201*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.766***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.907***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epcc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.626**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.480)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182.337***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.374***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(10.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.909)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1504.310***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1225.133***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAIPRP_AUFDIy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.360)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.59e\u0026thinsp;+\u0026thinsp;05***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.63e\u0026thinsp;+\u0026thinsp;05***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-10.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-4.676)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eRobustness Test\u003c/h2\u003e\n \u003cp\u003eTo further ensure the robustness of the regression results, This study draws on Garcia et al. and Han et al. research and it adopts two test methods: First, the control variable for science and technology investment (IST) is removed. After removing this control variable, as shown in the second column of Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the coefficient of PAIPRP is 1.606, which is statistically significant at the 1% level, and the result remains a highly significant positive value (Han et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Second, the dependent variable is changed from the number of patent applications (APQ) to the number of patent grants (AUQ) (Garcia et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Han et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yang \u0026amp; Wang, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). This approach follows the study of Guo \u0026amp; Zhong (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), substituting the dependent variable with the number of patent grants (AUQ) to measure the regional innovation capability of provinces based on the number of patents granted. The results show that, under the condition of changing the dependent variable while keeping other variables unchanged, the coefficient of PAIPRP is 1.876, which is significant at the 1% level, and the result remains a highly significant positive value. Here\u0026apos;s the model after replacing the dependent variable, ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{AUQ}_{it}\\)\u003c/span\u003e\u003c/span\u003e) represents the regional innovation capabilities of region ( i ) in year ( t ).\u003c/p\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:{AUQ}_{it}={\\beta\\:}_{o}+{\\beta\\:}_{1}{\\text{P}\\text{A}\\text{I}\\text{P}\\text{R}\\text{P}}_{it}+{\\beta\\:}_{2}{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}}_{it}+{\\mu\\:}_{i}+{\\lambda\\:}_{t}+{ϵ}_{it}$$ (3)\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cp\u003eThe results in the first column of Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e represent the original regression analysis, while the second and third columns present the results of regression analyses with reduced control variables and the changed dependent variable. The results indicate that, whether by reducing the control variables or changing the dependent variable, the level of intellectual property protection awareness has a significant positive impact on regional innovation capability. These results are consistent with the previous estimation results, confirming the robustness of the findings in this study.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Robustness Test.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUQ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAIPRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.776***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.606***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.876***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.528)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGRDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.766***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.986***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.841***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.447)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.608)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182.337***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.451***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.331***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(10.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(11.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(10.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.626**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.535**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.743***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.480)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.425)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.769)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1504.310***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1850.782***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1280.742***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-4.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.967)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.201*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.883)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e_cons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.59e\u0026thinsp;+\u0026thinsp;05***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.59e\u0026thinsp;+\u0026thinsp;05***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.73e\u0026thinsp;+\u0026thinsp;05***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-10.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-10.487)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-10.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003e***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe above results are very enlightening for this study. The results of the descriptive analysis in the first part, the innovation-driven strategy of China has played an important role in the quality of economic development (Xiao et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With the deepening of economic reforms and the implementation of national innovation policies, public awareness and attention toward intellectual property protection have generally increased across provinces. This growing awareness has coincided with an overall strengthening of innovation capability at the national level, albeit with marked regional disparities. The descriptive statistics revealed considerable variation in key variables across provinces, particularly in actual utilization of foreign direct investment (AUFDIy), gross regional domestic product (GRDP), permanent resident population (PRP), patent case closure count (PCC), number of undergraduate students (NS), and scientific and technological investment (IST). These disparities are shaped by several structural and institutional factors.\u003c/p\u003e\u003cp\u003eFirst, regional disparities in economic development play a critical role. Coastal provinces such as Jiangsu, Guangdong, and Zhejiang enjoy more advanced industrial bases, stronger market institutions, and more active engagement with global trade and investment. These regions tend to exhibit greater public awareness of IPRP, more efficient enforcement mechanisms, and higher levels of innovation output. In contrast, central and western provinces continue to face challenges related to lower economic density, weaker legal enforcement capacities, and limited institutional support for innovation (Zhao et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, the uneven distribution of educational resources significantly affects regional innovation performance (Shkarlet et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wu \u0026amp; Liu, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Provinces in the eastern region generally benefit from more prestigious universities, greater R\u0026amp;D funding, and a better alignment between higher education systems and industry demand. In contrast, many central and western provinces still lack high-quality educational infrastructure, which limits their capacity to generate, retain, and transform human capital into innovation productivity. The heterogeneity analysis in this study revealed that in western provinces, a negative and significant coefficient exists between the number of undergraduate students and innovation capability. This counterintuitive finding suggests that merely expanding enrollment without corresponding improvements in educational quality or policy coordination may inadvertently dilute educational effectiveness and place additional fiscal stress on local governments. The rapid expansion of higher education in recent years has indeed increased access but has also created new challenges. Resource constraints and administrative inefficiencies in the west have made it difficult to match enrollment expansion with adequate instructional quality and employment opportunities. As noted by Zhang et al., (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) the Chinese government should not only emphasize the importance of educational expansion but also focus on rational allocation of educational resources, particularly in less-developed central and western provinces. Universities should serve as engines of entrepreneurship and innovation (E\u0026amp;I), providing both knowledge and institutional support to local economies.\u003c/p\u003e\u003cp\u003eThird, regional openness to foreign investment emerges as a vital factor in shaping innovation outcomes (Arvin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As supported by the moderating effect analysis in this study, AUFDIy has a statistically significant and positive moderating effect on the relationship between public attention to IPRP and regional innovation capability. This finding is consistent with the broader literature that views FDI as a critical engine of economic growth and technological upgrading, particularly in developing countries (Martins et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the regulatory effect analysis part of this paper, it is concluded that AUFDIy has a very significant regulatory effect on the attention paid to intellectual property protection and regional innovation ability, which indicates that the level of openness to the outside world is very important and has a positive impact on regional innovation (Garcia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eForeign investment contributes to innovation not only through capital inflows but also by fostering technology transfer, competitive pressure, and international collaboration (Burinskas et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Elgin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Provinces with higher FDI utilization often experience increased exposure to global standards in IP enforcement, which can incentivize domestic firms to improve their own innovation strategies. Furthermore, foreign firms often demand stronger IPRP frameworks as a precondition for establishing operations, thereby reinforcing the institutionalization of IP norms in host regions.\u003c/p\u003e\u003cp\u003eHowever, the positive effects of openness are not automatic. Their realization depends on a region's absorptive capacity, institutional quality, and policy coherence. The eastern provinces, with their mature institutional systems and strong industrial bases, are better positioned to leverage openness and translate public IP awareness into innovation output (Guo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, the central and western provinces may require targeted policy support to upgrade their innovation ecosystems and better integrate into national and global innovation networks.\u003c/p\u003e\u003cp\u003eThe findings of this study highlight a clear regional stratification in how attention to intellectual property protection affects innovation. The eastern region benefits the most from increased IP awareness due to its favorable institutional, economic, and educational environments (Han et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The central region exhibits a moderate, yet inconsistent, effect, while the western region struggles to convert attention into outcomes, largely due to structural constraints in education and innovation systems.\u003c/p\u003e\u003cp\u003eBased on the above discussion, several implications can be drawn to optimize intellectual property protection policies and enhance regional innovation capacity in China. First, local governments may leverage regional intellectual property protection regimes to attract innovative talent, thereby enhancing local innovation productivity (Auriol et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). At the same time, they should strengthen the penalties for intellectual property infringements to encourage innovation. Second, local governments can increase investment in education, particularly by allocating more funding to universities, while also focusing on enhancing the innovation capacity of higher education institutions. This includes establishing stringent management of research and development funding, emphasizing the cultivation of innovative talent, and developing reward mechanisms for innovation outputs in universities to support the improvement of local innovation capacity. Only by establishing a robust intellectual property protection system can localities better promote the enhancement of regional innovation capacity, thereby improving the overall level of intellectual property protection in the country (Han et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Third, local governments can further expand their openness to the outside world, increase the scale of investment attraction, and strictly protect the patent rights of foreign investors. By reducing local protectionism, this will foster market competition and stimulate the production of local patent rights, ultimately improving regional innovation capacity. Fourth, we should implement differentiated IPRP systems according to local conditions. In peripheral cities, cities with a relatively weak science and education foundation and cities with a relatively low innovation level, we should promote the successful experience of IPRP system construction and expand the construction scope of IPRP demonstration pilot cities (Yang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study also has the following limitations: First, the data sample selected for this research is limited to the mainland provinces and municipalities of China from 2011 to 2023, without covering a broader geographical range or time period. Second, the control variables chosen in this study are limited, which restricts the ability to precisely control for the regression analysis results. Additionally, while the level of openness to the outside world is used as a moderating variable, there may be other variables that could dynamically moderate the relationship between intellectual property protection and regional innovation capacity. Third, when selecting the number of students in general undergraduate universities, the limited data from the National Bureau of Statistics prevented the inclusion of master's and doctoral students as part of the sample, which may ultimately affect the final research results.\u003c/p\u003e"},{"header":"Conclusion and Implications","content":"\u003cp\u003eThis study empirically investigates the relationship between PAIPRP and RIC using provincial panel data from China (2011–2023), revealing three key findings. First, public attention to IPRP exerts a statistically significant positive effect on RIC, underscoring the critical role of societal awareness in fostering innovation-driven development. Second, this relationship exhibits pronounced regional heterogeneity: the effect is strongest in eastern China, moderate in central regions, and weakest in western China, which can be attributed to disparities in institutional quality, educational resources, and economic openness. Third, regional openness, measured by actual FDI utilization, positively moderates the link between PAIPRP and RIC, indicating that higher levels of economic integration amplify the innovation-inducing effects of public IPR awareness.\u003c/p\u003e\u003cp\u003eWhile this study advances the understanding of PAIPRP and regional innovation, several avenues for future research warrant exploration. For instance, incorporating micro-level data (e.g., firm-level innovation activities) could further unpack the behavioral mechanisms underlying the observed relationships. Longitudinal studies with extended time horizons or cross-country comparative analyses would enhance the generalizability of these findings. Moreover, investigating other potential moderators—such as digital infrastructure or regional policy networks—could provide a more comprehensive understanding of the multifaceted drivers of innovation in heterogeneous contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthical approval\u003c/h2\u003e\u003cp\u003eThis article does not contain any studies with human participants or their personal data performed by any of the authors. All data used are aggregated and publicly available from official sources. According to the Jianghan University Research Ethics Committee, this study is exempt from ethics approval.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cp\u003eThis study does not involve human participants, their personal data, or biological material. All data analysed are aggregated and obtained from publicly accessible official sources. According to the guidelines of the Jianghan University Research Ethics Committee and relevant national regulations, studies based solely on publicly available, non-identifiable data do not require informed consent.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research is funded by Wuhan Intellectual Property Talent Cultivation Project\u0026ndash;Enterprise Patent Technology Mining and Achievement Transformation and Utilization Ability Enhancement Training Program.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.X.Z and T.L.Z wrote the main manuscript text. J.K.Y and R.H.Y collected the data and provided idea. Z.Z provided suggestions for modification. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the figshare repository at https://doi.org/10.6084/m9.figshare.29918756.v2. The dataset includes all variables listed in Table 1, along with their definitions, measurement methods, and original data sources. All data were obtained from publicly accessible and authoritative sources, including the Baidu Index, incoPat, National Bureau of Statistics of China, Ministry of Commerce of the People\u0026rsquo;s Republic of China, and the National Intellectual Property Administration. Monetary values have been adjusted for inflation and are reported in constant CNY; foreign direct investment (FDI) values were converted from USD to CNY using annual average exchange rates from official statistics. For any questions regarding the data, please contact the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArvin, M. B., Pradhan, R. P., \u0026amp; Nair, M. (2021). 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Intellectual Property Rights Protection Level, Regional Innovation, and Industrial Upgrading. \u003cem\u003eStatistics and Decision, 38\u003c/em\u003e(16), 168-171. https://doi.org/10.13546/j.cnki.tjyjc.2022.16.033\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"public attention to intellectual property rights protection (PAIPRP), regional innovation capability (RIC), regional openness, FDI, regional heterogeneity","lastPublishedDoi":"10.21203/rs.3.rs-6910162/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6910162/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of China's innovation-driven development strategy and pronounced regional disparities in institutional quality, public attention to intellectual property rights (IPR) has emerged as a critical yet understudied factor shaping regional innovation ecosystems. While prior research emphasizes formal IPR institutions, it overlooks the socio-cognitive dimension of public attention and its interaction with regional openness—particularly in geographically heterogeneous economies like China. Using provincial panel data (2011–2023), we measure public attention to IPR protection (PAIPRP) via Baidu search indices, regional innovation capability (RIC) through patent applications (APQ) and grants (AUQ), and regional openness (RO) by actual foreign direct investment utilization (AUFDIy). Fixed-effects models control for R\u0026amp;D expenditure, human capital, and institutional factors. The key findings are as follows: Public IPR attention significantly boosts RIC. Regional heterogeneity is pronounced—strongest in eastern China , insignificant in central regions, and negative in the west, where higher education expansion paradoxically reduces innovation. Openness amplifies this effect, with FDI-intensive regions showing stronger responses. Policy implications include region-specific IPR governance, while the study enriches theory by linking societal awareness, economic integration, and innovation across institutional contexts.\u003c/p\u003e","manuscriptTitle":"Public Attention to Intellectual Property Rights Protection, Openness, and Regional Innovation Capability: An Empirical Analysis Based on Provincial Panel Data in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 18:36:23","doi":"10.21203/rs.3.rs-6910162/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":"f8e1df66-ac1c-480c-8c42-145ac24d4835","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55117029,"name":"Business and commerce/Economics"},{"id":55117030,"name":"Social science/Science technology and society"},{"id":55117031,"name":"Social science/Social policy"},{"id":55117032,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2026-02-04T21:54:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 18:36:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6910162","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6910162","identity":"rs-6910162","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}