Exploring the Multifaceted Driving Forces of Digital Economy on Regional Innovation: A Case Analysis of Changzhou, Jiangsu Province

Exploring the Multifaceted Driving Forces of Digital Economy on Regional Innovation: A Case Analysis of Changzhou, Jiangsu Province | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring the Multifaceted Driving Forces of Digital Economy on Regional Innovation: A Case Analysis of Changzhou, Jiangsu Province Miaomiao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7727238/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The digital economy, a key driver in the new tech era, is transforming the global economy and China's high-quality development strategy. It enhances regional innovation by optimizing resources, cutting costs, speeding knowledge sharing, and digitizing industries. This study, using fsQCA and VAR models with 2011–2022 data from Changzhou, Jiangsu, reveals that the digital economy initially spurs innovation through economic growth and foreign tech imports, while human capital and industrial clustering support long-term growth. Innovation platforms and economic development are vital for boosting innovation capacity, offering empirical insights crucial for policy development. Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences Business and commerce/Information systems and information technology Social science/Science technology and society Digital Economy Regional Innovation Changzhou Case Figures Figure 1 Figure 2 Introduction With the rapid growth of global digital technology, the digital economy has emerged as a key driver of economic and social transformation and progress. The 14th Five-Year Plan has raised inventive development to unprecedented strategic heights, and the digital economy has emerged as a leader in this process, playing an increasingly important catalytic role in regional innovation capabilities (Tuo and He 2021; Yali and Benwu 2023). The digital economy, which uses data as its primary production factor and relies on continuous innovation in digital technology and extensive coverage of modern information networks, is fundamentally altering the face of traditional industries and promoting their transformation towards digitalization and intelligence(MottaevaKhussainova,et al 2023; QiuLiu,et al 2023; WenfuXiao,et al 2024). This procedure not only speeds up the reconstruction of the economic development model, but it also encourages the modernization of government administration. The digital economy is critical to regional innovation as a whole. By fostering digital industrialization, which encompasses the rapid advancement of cutting-edge technologies like big data, cloud computing, the Internet of Things, and artificial intelligence, it establishes a strong technological base and opens up boundless opportunities for innovation within the regional innovation system(IsaksenTrippl,et al 2021). The extensive utilization of these technologies has not only significantly enhanced the effectiveness and excellence of innovation activities, but also consistently broadened the limits and domains of innovation. Simultaneously, the digital economy has profoundly reshaped conventional industries and fostered the innovative capacity of businesses through the process of industrial digitalization(Tang and Yang 2022). Driven by digital technology, organizations have experienced significant transformations in their production techniques, organizational structures, and management models, shifting towards a more digital, interconnected, and intelligent approach. This transition has not only increased the market competitiveness of businesses, but also provided a significant boost to the overall growth of the regional innovation ecosystem. An analysis is conducted on the dual-dimensional function of the digital economy in enhancing regional innovation capacities, considering both macro and micro viewpoints. From a broad view, the digital economy, with its distinct benefits, is increasingly becoming a significant driver in enhancing regional innovation capacities. Prior research has extensively demonstrated a significant and intricate non-linear relationship between the growth of the digital economy and the ability to innovate (Jun, Zhijun, ,et al 2019; WangGuan,et al 2024). This association is especially apparent in its ability to stimulate technical innovation. In contrast to pure product innovation, the digital economy has generated substantial advancements and growth in the technology sector due to its distinctive benefits, propelling the entire region towards a more advanced and efficient innovation system. At the micro level, the digital economy plays a crucial role in enhancing the innovation capacities of businesses, particularly in the context of small-scale organizations, where its impact is particularly noteworthy(Xu Hui and Chenguang 2022). The digital economy enhances resource allocation efficiency both within and outside the firm, thereby immediately stimulating the enterprise's innovative potential. Enterprises can get a competitive advantage in the intense market rivalry by employing sophisticated resource allocation strategies and efficient operation and management procedures. This enables them to expedite the conversion of innovation into market value(JianLexin,et al 2024; WuXue,et al 2024). When broken down into smaller parts, the digital economy has varying effects on various forms of corporate innovation activities. More precisely, it demonstrates a powerful impetus for revolutionary ideas that challenge the status quo, whereas its impact on gradual improvements is very modest (XiaoZhang,et al 2024). This discovery emphasizes the crucial role of the digital economy in propelling the evolution of business innovation models. When doing a thorough analysis of the inner workings of the digital economy to enhance corporate innovation performance, it becomes evident that the primary areas of concentration are the acquisition of talented individuals and the reduction of environmental uncertainties. The digital economy has enticed a significant number of exceptionally skilled individuals with its distinct allure and established a dynamic environment for innovative talent. This ecosystem not only boosts the capacity for innovation in organizations, but also efficiently mitigates the uncertainty resulting from external environmental changes, and establishes a more stable and efficient support structure for corporate innovation endeavors (Shen MH and YB 2024). Hence, the digital economy is crucial in enhancing regional innovation capacities. Its impact extends to both large-scale and small-scale aspects, fostering innovation and enhancing efficiency in the entire economic framework, while fundamentally transforming the innovation ecosystem and capacity structure of businesses. To maximize the positive impact, it is essential to consistently improve the application environment of digital technology, encourage the unrestricted movement of skilled individuals and knowledge, and minimize uncertainties in the innovation ecosystem. This will effectively enhance the driving force of the digital economy on regional innovation capabilities. Changzhou City, located in Jiangsu Province, stands out nationwide due to its innovative achievements. It is utilizing the momentum of the digital economy to fully exploit its capacity for innovation and strengthen the basis of urban growth. The regional innovation capabilities have undergone substantial enhancement, serving as a clear manifestation of the extensive integration and empowerment of the digital economy(BaisongChenyi,et al 2022). Changzhou is committed to being at the forefront of scientific and technical innovation. It focuses on building strong platforms, achieving technological breakthroughs, empowering enterprises, and cultivating talent. This approach effectively promotes innovation and contributes to high-quality development. Nevertheless, as Changzhou looks ahead, it must address obstacles such as the insufficient integration of the digital economy with regional innovation, the scarcity of innovative individuals, and the need for environmental optimization. Only by doing so can it consistently advance innovation and development to new levels. Therefore, this study is committed to deeply analyzing the internal mechanism and multi-driven effects of digital economy on the improvement of regional innovation capacity in Changzhou City, Jiangsu Province, and revealing the deep connection and interaction between digital economy and regional innovation, in order to provide strong theoretical support and practical guidance for regional innovation practice in Changzhou City and other regions in the country in the digital economy era. Methodology 2.1༎Data source and processing In order to study the path and driving effect of regional innovation multi-configuration in Changzhou, this study analyzes the data of Changzhou from 2011 to 2022, including (1) Human capital concentration (CH): location entropy is used to calculate the degree of human capital concentration. (2) Industrial agglomeration (IA): location entropy is used to calculate the degree of industrial agglomeration. (3) Innovation platform construction (IP): the number of science and technology enterprise incubators is used as a proxy variable for innovation platform construction. (4) Government funding support (GS): the proportion of government funding to internal R&D expenditure is used to measure the strength of government support. (5) Foreign technology introduction (TI): the proportion of foreign technology introduction contract amount to GDP is used to measure the strength of technology introduction. (6) Economic development level (ED): the per capita GDP of each region is used to measure the level of economic development. The data comes from statistical data such as “Jiangsu Province Statistical Yearbook” and “China City Statistical Yearbook”. 2.2 Research methods The study examines how the digital economy influences the diverse patterns of regional innovation in Changzhou. To investigate this, the researchers employ fsQCA (fuzzy set qualitative comparative analysis) and VAR (vector autoregression) models as research methodologies. FSQCA and VAR models are mutually supportive in research methodology. fsQCA mostly uncovers multivariate configuration paths in intricate causal linkages (Braumoeller 2017 ; KumarSahoo,et al 2022; CifciKahraman,et al 2023), whereas VAR models are primarily employed to examine dynamic relationships in time series data (Zhang and Jiang 2022; HXHL,et al 2024). By integrating the two, one can thoroughly examine the intricate connection between the digital economy and regional innovation. This analysis uncovers the various factors that drive this relationship and allows for an assessment of how these factors develop over time. Demonstration and Analysis 3.1༎Analysis of regional innovatio-driven paths 3.1.1༎Variable calibration Variable calibration refers to converting the original data into fuzzy set membership scores between 0 and 1 by setting three critical values, so as to be suitable for fsQCA software operation. The conditional variables and outcome variables are both the average values ​​of the data from 2011 to 2022. Referring to the research of FISS (Fiss 2011 ) the direct calibration method is adopted, and the 25th, 50th and 75th percentile values ​​of each continuous variable are counted as complete non-membership, conversion point and complete membership. The direct method of RAGIN is used to complete the data calibration processing. The objectivity of data calibration is guaranteed by the variable calibration table, which establishes the 25th, 50th, and 75th percentile values of each continuous variable to represent complete non-membership, transition point, and complete membership, respectively. The calibration values of the specific variables are shown in Table 1 . The transition point is 42.358, the complete membership degree is 71.821, and the complete non-submission degree of "regional innovation level" is 35.557. This indicates that the regional innovation level is entirely non-submission to the high innovation level when it is less than 35.557, and it is entirely submission to the high innovation level when it is greater than 71.821. Additional indicators, including "industrial agglomeration," "innovation platform construction," "government funding support," "foreign technology introduction," and "economic development level," also have precise calibration value ranges. Within distinct ranges, the influence of each indicator on the regional innovation level is inconsistent. The ensuing fuzzy set qualitative comparative analysis (fsQCA) was significantly influenced by these calibration values. The regional innovation level can be more precisely assessed by examining the impact of each variable and its combination through these calibration values. During the analysis process, this method guarantees the objectivity and consistency of data processing, thereby establishing a strong foundation for subsequent in-depth analysis. Table 1 Calibration variables Complete non-membership Conversion Point Complete membership Regional innovation level(RI) 35.557 42.358 71.821 Human capital concentration (CH) 0.010 0.010 0.011 Industrial agglomeration (IA) 217791.371 241626.042 262144.787 Innovation platform construction (IP) 3973.250 4269.500 4968.500 Government funding support (GS) 0.041 0.046 0.049 Foreign technology introduction (TI) 0.023 0.027 0.046 Economic development level (ED) 95852.000 131578.216 154611.250 3.1.2༎Analysis of necessary conditions It is imperative to conduct a necessity analysis of the antecedent conditions prior to conducting configuration analysis. This test determines whether a specific antecedent condition is a necessary condition for promoting regional high-level innovation and non-regional high-level innovation. Table 2 shows the outcomes of the examination. The calibration value of 0.1 is adjusted in this section of the necessary condition analysis, and the threshold of the necessary condition is typically set to 0.9. A condition variable can be approximated as a necessary condition of the result variable if it comprises more than 90% of the result variable(Rihoux and Ragin 2009). The necessary condition analysis table displays the influence of various conditional variables on the outcome variables Y (high innovation level) and non-Y (low innovation level), which includes the two indicators of consistency and coverage. The necessary condition analysis table displays the influence of various conditional variables on the outcome variables Y (high innovation level) and non-Y (low innovation level), which includes the two indicators of consistency and coverage(AfonsoSilva,et al 2018). According to the analysis results, Innovation platform construction (IP) and economic development level (ED) are critical conditions for the result Y. These conditions have high consistency (0.919414 and 0.868132) and coverage (0.801917 and 0.770732), respectively. This demonstrates that the regional innovation level is also high when the level of innovation platform construction and economic development is high. In the same vein, the consistency and coverage of result Y are high for non-innovation platform construction (~ IP) and non-economic development level (ED), with a consistency of 0.810398 and 0.784404, and a coverage of 0.923345 and 0.876923, respectively. Table 2 Analysis of necessary conditions “~” represents the “not” of logical operation. Condition variables Y ~Y Consistency Coverage Consistency Coverage Human capital concentration (CH) 0.531135 0.487395 0.539755 0.593277 ~ Human capital concentration (~ CH) 0.556777 0.502479 0.533639 0.576859 Industrial agglomeration (IA) 0.655678 0.632509 0.493884 0.570671 ~ Industrial agglomeration (~ IA) 0.554945 0.477918 0.681957 0.70347 Innovation platform construction IP 0.919414 0.801917 0.382263 0.399361 ~ Innovation platform construction (~ IP) 0.311355 0.296167 0.810398 0.923345 Government funding support GS 0.434066 0.398319 0.649847 0.714286 ~ Government funding support (~ GS) 0.688645 0.621488 0.452599 0.489256 Foreign technology introduction TI 0.298535 0.291592 0.712538 0.833631 ~ Foreign technology introduction (~ TI) 0.82967 0.706708 0.394495 0.402496 Economic development level ED 0.868132 0.770732 0.308868 0.328455 ~ Economic development level (~ ED) 0.24359 0.22735 0.784404 0.876923 In general, the construction of innovation platforms and the level of economic development are crucial to improving the level of regional innovation, while their absence may lead to a decline in the level of innovation. Through this analytical method, we can clearly identify which variables are the key necessary conditions for achieving a high level of innovation, thus providing an important basis for policy making and resource allocation. 3.1.3༎Conditional combination analysis The regional innovation level is selected as the outcome variable to investigate the configuration-driven path that results in its generation, as determined by variable calibration and necessity analysis. The case frequency threshold is set to 1 in accordance with the principle that the case frequency should retain more than 75% of the observed cases, and the original consistency threshold is set to 0.8 to avoid the simultaneous subset relationship(Ming 2020 ). In order to mitigate potential configuration inconsistencies, the PRI (Proportional Reduction in Inconsistency) consistency threshold is fixed at 0.75(Thomann and Maggetti 2020). The simplified, intermediate, and complex solutions were obtained through the use of fsQCA3.0 software, following the standardization of operations. The outcome was somewhat biased due to the absence of the logical remainder in the complex solution, which involved disregarding the unobserved cases. The configuration path was obtained by nesting and comparing the simplified solution and the intermediate solution in this article(Patrício and Ferreira 2023). If a variable is present exclusively in the intermediate solution, it is classified as a marginal condition and serves as an auxiliary driving force. Conversely, if it is present in both the intermediate solution and the simplified solution, it is classified as a core condition and serves as a significant driving force. The results of the configuration analysis are shown in Table 3 . Table 3 Analysis on the driving path of regional high-level innovation configuration Condition variables Regional high-level innovation Combination 1 Combination 2 Combination 3 Human capital concentration (CH) • • ● Industrial agglomeration (IA) ⊗ ⊗ • Innovation platform construction(IP) ⊗ • • Government funding support(GS) ⊗ ⊗ • Foreign technology introduction(TI) ⊗ ⊗ ⊗ Economic development level(ED) • • ● Consistency 0.886364 0.896373 0.858696 Original coverage 0.142857 0.31685 0.289377 Net coverage 0.0567766 0.254579 0.221612 Total consistency 0.897098 Total coverage 0.622711 * • indicates the existence of edge conditions ⊗ indicates the absence of edge conditions ● indicates the existence of core conditions The impact of various conditional combinations on the high level of regional innovation is revealed by the conditional combination analysis. Human capital agglomeration (CH) and economic development level (ED) are the core conditions of Combination 1, while industrial agglomeration strength (IA), innovation platform construction (IP), government funding support (GS), and foreign technology introduction (TI) are marginal conditions. The original coverage is 0.142857, the net coverage is 0.0567766, and the consistency of this combination is 0.886364. This indicates that combination 1 partially explains the high level of innovation, but the coverage is limited. The core conditions of Combination 2 are human capital agglomeration (CH), innovation platform construction (IP), and economic development level (ED), while industrial agglomeration strength (IA), government funding support (GS), and foreign technology introduction (TI) are marginal conditions. The original coverage is 0.316850, the net coverage is 0.254579, and the consistency of this combination is 0.896373. This demonstrates that combination 2 has a substantial explanatory power and a broad coverage in high-innovation levels. It is an effective combination of conditions, demonstrating the significance of these conditions in fostering regional innovation levels. Combination 3 contains the most core conditions, including human capital concentration (CH), industrial agglomeration (IA), innovation platform construction (IP), government funding support (GS), and economic development level (ED). The combination 3 has a strong explanatory power in high innovation level and a large number of core conditions, as evidenced by the consistency of 0.858696, the original coverage of 0.289377, and the net coverage of 0.221612. These findings demonstrate the critical role of these conditions in achieving high innovation levels. The total coverage is 0.622711, and the overall consistency is 0.897098, suggesting that these combinations have a strong explanatory power in explanation of high innovation levels. The case analysis also demonstrates that high innovation levels are the result of specific combinations of conditions in various years. By recognizing and prioritizing these critical conditions, policymakers can more effectively encourage the enhancement of regional innovation levels. 3.2༎Regional innovation driving effect empirical analysis The impact of random interference on regional innovation capabilities and related variables is further examined in this study based on the configuration analysis results. Additionally, the study looks at how each variable fluctuates and how stable their mutual relationship is when random interference is present, provides a deep understanding of how each variable interacts dynamically in the time dimension, and expands on the configuration analysis results. This study conducts an empirical investigation of the regional innovation driving effect using the Var (Vector Auto Regression) model. A multivariate time series model that can be used to examine the dynamic relationship between several variables is the VAR model. 3.2.1༎The stationarity test of the VAR model The ADF test result is shown in Table 4 . Time series data stationarity is tested using the ADF test. In the ADF test, a statistic is created and contrasted with the crucial value. The null hypothesis is rejected and the series is deemed stationary if the ADF value is less than the critical value or p < 0.05(Zhang and Jiang 2022). Table 4 shows that the result variable Y is stationary at the 5% significance level, with an ADF test t value of -4.257 and a p value of 0.004. The initial statistics on government financial assistance (GS), foreign technology introduction (TI), innovation platform construction (IP), industrial agglomeration intensity (IA), human capital concentration (CH), and economic development level (ED) are non-stationary. The study treated these factors differently in order to stabilize them. Agglomeration of human capital and government funding are stable following first-order differences; however, the establishment of innovation platforms and the import of foreign technology necessitate second-order differences before they can become stable. At a particular significance level, the differenced data becomes stationary, and these stationary processes serve as the basis for the further development and analysis of VAR models. Table 4 ADF Inspection Table Variable T statistic P-value Critical value 1% 5% 10% Regional innovation(Y) -4.257 0.004 -5.797 -4.189 -3.555 Human capital concentration(CH) 0 0.684 -3.039 -1.935 -1.531 d.CH -2.42 0.015 -2.902 -1.966 -1.576 Industrial agglomeration(IA) 0 0.684 -3.039 -1.935 -1.531 d.IA -1.857 0.06 -3.039 -1.935 -1.531 Innovation platform construction(IP) 8.748 1 -4.939 -3.478 -2.844 d.IP -2.045 0.267 -4.332 -3.233 -2.749 d2.IP -17.343 0 -5.354 -3.646 -2.901 Government funding support(GS) 0 0.684 -3.039 -1.935 -1.531 d.GS -3.45 0.001 -2.826 -1.97 -1.592 Foreign technology introduction(TI) -2.245 0.19 -4.332 -3.233 -2.749 d.TI -1.865 0.349 -4.332 -3.233 -2.749 d2.TI -3.206 0.02 -4.473 -3.29 -2.772 Economic development level(ED) -1.449 0.559 -4.665 -3.367 -2.803 d.ED -2.878 0.048 -4.332 -3.233 -2.749 3.2.2༎VAR model A VAR (vector autoregression) model of the first-order difference variable is built based on the ADF test findings, which demonstrate that the data reaches stability after the first-order difference. Afterwards, this model is applied to variance decomposition analysis and impulse response analysis to investigate the dynamic relationship and implications between the variables even more. A thorough analysis of the AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), FPE (Final Prediction Error Criterion), and HQIC (Hannan-Quinn Information Criterion) is required in order to determine the lag order of the VAR model. Because each of the four criteria obtains the least value at the first order, Table 5 's data demonstrate that the first order is the ideal lag order. Given the coherence of the aforementioned analysis, the lag order of the VAR model is ultimately determined to be the first order, and the VAR model is built in accordance with this selection. This decision upholds the statistical optimality principle and ensures the quality and dependability of the analysis results by serving as a strong model foundation for the impulse response and variance decomposition analyses that follow. Table 5 Model order determination Order AIC BIC FPE HQIC 0 -44.407 -44.226 0 -44.521 1 -54.942* -53.857* 0.000* -55.626* * Represents the number of steps for this item The VAR model results show the impact of the lag terms of different variables on the regional innovation level (Y). The results are shown in Table 6 and The VAR model formula is as follows: Y=-30.166 + 0.131*L1.Y-219.799*L1.CH-18.121*L1.GS + 2.996*L1.ED + 49.885*L1.TI. Table 6 shows that the constant term coefficient is -30.166 and the t value is -2.477. This indicates that when all lag components are zero, the baseline value of the regional innovation level is -30.166 with a significance level of 5%. To a considerable extent. The level of economic development at a single point in time (L1.ED) and the entry of foreign technology (L1.TI) have a notable and beneficial effect on the level of innovation inside a certain region. The coefficients are 2.996 and 49.885, while the t values are 2.976 and 2.772, both significant at the 1% level of significance. This demonstrates that the enhancement of economic development and the entry of foreign technology have a substantial impact on the level of regional creativity. Changzhou City should allocate additional resources towards innovation activities, such as increasing investment in research and development, enhancing talent development, and improving infrastructure, in order to further strengthen regional innovation capabilities. Furthermore, the speed of technological advancement should be expedited by the implementation of foreign cutting-edge technology and managerial expertise. In contrast, the lack of government financial support in the previous era (L1.GS) has a substantial detrimental effect on the degree of regional innovation. This effect is quantified by a coefficient of -18.121 and a t value of -2.340, which is statistically significant at the 5% significance level. This implies that while government financial assistance is a significant policy instrument, its consequences may require more assessment and fine-tuning to prevent adverse effects. The influence of Y and the concentration of human capital (L1.CH) in one previous time period on the present regional innovation level is not statistically significant, as indicated by the coefficients of 0.131 and − 219.799 respectively, and the corresponding t-values of 0.526 and − 0.696 respectively. This suggests that these factors do not have a substantial direct influence on regional innovation levels within a single time period. Collectively, these findings offer valuable understanding of the interaction between many factors over a period of time, aiding Changzhou City in developing more efficient strategies to foster regional innovation. Table 6 VAR model results Y(t-value) Constant -30.166** (-2.477) L1 Y 0.131 (0.526) L1 CH -219.799 (-0.696) L1 GS -18.121** (-2.340) L1 ED 2.996*** (2.976) L1 TI 49.885*** (2.772) nobs 11 llf 254.141 AIC value -40.753 SC value -39.668 HQIC value -41.437 * p < 0.1 ** p < 0.05 *** p < 0.01 The t value is in brackets The stability of the VAR model is assessed by examining the AR root diagram. If all eigenvalues lie within the unit circle, indicating that all points are within the circle, then the model is considered stable. By analyzing the AR characteristic root diagram (Fig. 1 ), it is evident that all eigenroot values fall within the unit circle, confirming the strong stability of the constructed VAR model. 3.2.3༎Impulse Response Impulse response analysis use the VAR model to precisely illustrate the dynamic interplay among variables in the economic system. When the system is affected, the variables exhibit a cascade effect, and their dynamic adjustment paths are visually shown by orthogonalized impulse response diagrams. These diagrams display both the immediate and long-term effects of the shock on itself and other factors. Additionally, they provide information on the direction (positive or negative), trajectory, and strength of the impact(Baek and Lee 2022). These intricate dynamics are intuitively depicted in Fig. 2 , where the direction of the impact is indicated by positive and negative values, and the intensity of the impact is measured by the absolute value. The impulse response diagram illustrates that the impact of various variables on the regional innovation level (Y) fluctuates over time. In the initial stages, the unit impact of economic development level (ED) has a substantial positive impact on regional innovation level. However, this influence gradually diminishes in subsequent periods. This demonstrates that regional innovation can be rapidly stimulated by economic development enhancement; however, its influence will gradually diminish. In the same vein, the unit impact of foreign technology introduction (TI) has a substantial positive impact on regional innovation levels during the early stages and subsequently stabilizes, indicating that foreign technology introduction has a robust promoting effect on regional innovation during the early stages. However, this effect will progressively diminish. In contrast, the initial negative impact of the unit shock of government financial support (GS) on regional innovation levels is progressively mitigated in subsequent periods. This demonstrates that, despite the potential negative impact of government financial support on regional innovation during the initial stages, this impact will progressively diminish over time. The early stage of regional innovation is not significantly influenced by the unit impact of industrial agglomeration intensity (IA) and human capital agglomeration (CH). Nevertheless, these factors progressively demonstrate positive effects as time progresses, suggesting that in the medium and long term, regional innovation is positively influenced by human capital and industrial agglomeration. Impulse response analysis exposes the dynamic impact of each variable on regional innovation levels when considered in conjunction. The economic development and the introduction of foreign technology will significantly promote regional innovation in the early stage, but their influence will weaken and level off. Government financial support may have a negative impact in the early stage, but its impact will gradually decrease. Human capital agglomeration and industrial agglomeration will have a minimal impact in the short term, but will have a positive effect on regional innovation in the medium and long term. 3.2.4༎Variance Decomposition While impulse response analysis can offer insights into the dynamic causal relationships between variables, it does not provide a quantitative measure of the extent of influence. This article presents the utilization of variance decomposition technology to precisely measure the degree of effect that each variable has in the VAR model. Variance decomposition quantifies the dependency and strength of effect by revealing the fraction of variance in forecast error that is explained by changes in other variables. High variance decomposition values imply a substantial impact, whilst low values suggest minimal impact. Table 7 displays the variance decomposition results. The variance decomposition analysis illustrates the extent to which various variables contribute to the fluctuation of the regional innovation level (Y). During the initial phase (Phase 1), the regional innovation level fluctuation is entirely self-explanatory, with a 100% contribution rate. This implies that the initial stage of Y's evolution is entirely determined by its own historical fluctuations. Table 7 VAR model variance decomposition results Lag order Variance Decomposition of S.E. Y(%) CH(%) GS(%) ED(%) TI(%) 1 0.126 100 0 0 0 0 2 0.161 62.346 6.134 1.762 29.498 0.26 3 0.214 58.319 3.747 3.901 33.679 0.354 4 0.243 63.253 3.097 6.525 26.812 0.313 5 0.262 59.034 5.648 7.367 27.678 0.274 6 0.293 47.617 13.767 6.259 32.103 0.254 7 0.322 39.371 20.35 5.203 34.829 0.246 8 0.341 35.107 23.8 4.636 36.213 0.245 9 0.352 33.04 25.389 4.362 36.964 0.245 10 0.358 32.068 26.097 4.235 37.353 0.247 The level of economic development (ED) begins to substantially influence the fluctuation of regional innovation level in the short-term stage (period 2 to period 4), with the contribution rate in period 2 reaching 29.498%. The fluctuation of Y is progressively influenced by human capital concentration (CH) and government financial support (GS), although their contribution is relatively minor. The contribution rate of economic development level (ED) continued to increase, reaching 33.679% and 26.812% respectively, in the third and fourth periods. Conversely, the contribution rate of Y itself progressively decreased. The level of economic development (ED) remains the primary influencing factor in the mid-term stage (from the 5th to the 7th period), with a contribution rate of 27.678% in the 5th period. The contribution rates of human capital agglomeration (CH) and government funding support (GS) have also increased, indicating that the level of economic development, human capital concentration, and government funding support have a significant impact on the fluctuation of regional innovation levels in the medium term. The contribution rate of human capital concentration (CH) increased considerably to 20.35% by the seventh period, while the contribution rate of economic development level (ED) further increased to 34.829%. During the period from period 8 to period 10, the economic development level (ED) reaches its greatest contribution rate of 37.353% in period 10. In the 10th period, the contribution rate of human capital concentration (CH) climbed even further, reaching 26.097%. This demonstrates that the concentration of human capital and the level of economic development have a lasting and substantial influence on the long-term variations in regional innovation levels. The government's financial support (GS) has a modest although consistent impact. In summary, the variance decomposition analysis reveals that economic development level and human capital concentration have increasingly become the primary drivers of fluctuations in regional innovation levels over time. On the other hand, government funding support and foreign technology introduction, although relatively minor, still make a certain contribution. Conclusions This paper uses fuzzy set qualitative comparative analysis (fsQCA) based on data of Changzhou City, Jiangsu Province from 2011 to 2022 to identify several configuration-driven paths that drive regional high-level innovation and further uses the Var model to conduct an empirical analysis of the regional innovation-driving effect, examining the dynamic interaction between many variables in the time series and the driving effect of the configuration path on regional innovation. The results of the necessity and condition combination analysis indicate that the regional innovation level is significantly enhanced by the construction of innovation platforms and the level of economic development. Conversely, their absence may result in a decrease in the innovation level, and the specific condition combinations in different years contribute to the high innovation level. This aligns with the findings of Previous research (ChenLei and DuBaogui 2022). The only way to accelerate the agglomeration of research and development capital is to build a multi-level science and technology financial service platform system, promote capital market financing actively, expand regional innovation indirect financing channels, give full play to the effectiveness and guidance of fiscal policies, and encourage enterprises and other innovation entities to increase research and development funds through targeted subsidies, tax incentives, and other incentives. The degree of economic development and the entry of foreign technology have greatly aided in the early phases of regional innovation activities, as indicated by the results of impulse response and variance decomposition. On the other hand, agglomerations of industries and human capital have less immediate benefits than other factors. However, over an extended period of time, these agglomerations' beneficial effects on regional innovation gradually become apparent and eventually become a vital source of power. A comprehensive assessment of Poland's innovation capabilities reaffirmed the crucial role of a strong economic foundation in driving innovation activities. It emphasized that continuous enhancement of economic development is essential for creating regional competitive advantages and achieving innovation leadership. The primary function of substitution is crucial (Brodny J and M 2022). Simultaneously, a separate scholarly investigation into the environmental shift of the industrial sector in the Yangtze River Economic Belt has distinctly demonstrated the notable impact of the concentration of educated individuals on this transformation. This discovery not only reinforced the notion that human capital is the primary catalyst for regional growth. The notion emphasizes its essential nature and extensive importance in advancing environmentally friendly change and achieving strategies for sustainable development (YuWei,et al 2023) (Flückiger and Ludwig 2018). These research findings highlight the fundamental worth and strategic significance of the economic base and human capital in driving regional innovation and transformation. Based to this, the subsequent recommendations are suggested in this investigation: (1) Enhance industrial upgrading and the innovation system. Changzhou should establish a comprehensive innovation system that is centered around innovation platforms, fosters collaboration among enterprises, universities, scientific research institutions, and other stakeholders, and establishes an ecological environment that is conducive to collaborative innovation. Accelerate industrial upgrading, concentrate on high-end manufacturing, smart technology, renewable energy, and other sectors, and foster the transition of the economy to high-quality development through policy and financial support. Simultaneously, establish a dual-wheel propulsion of technological innovation and industrial upgrading, enhance the technical level and innovation ability of enterprises, and deepen international cooperation. Additionally, introduce advanced technologies. (2) Optimize the talent and innovation environment. Changzhou needs to increase investment in education and training, improve the quality of talent, and optimize talent introduction policies to attract high-level and innovative talents. Build a multi-level and wide-ranging innovative education system to provide a solid talent base for innovation. Simultaneously, Changzhou should enhance the innovation ecosystem, enhance intellectual property safeguards, bolster financial backing for scientific and technological advancements, and foster an innovation culture that is open, inclusive, and conducive to research. To enhance the innovation capacity of society as a whole, it is important to implement policy incentives and foster a conducive social environment. This will facilitate the efficient conversion of scientific and technological advancements into tangible productivity, and encourage the seamless integration of the innovation and industrial sectors. Thus, Changzhou should prioritize the establishment of an innovation platform and the improvement of its economic development level. It should leverage its existing economic advantages and foreign technology integration, while also emphasizing long-term planning for the concentration of human capital and industries. Additionally, optimizing the policy environment and system construction, as well as fostering a robust culture of innovation, will be crucial in enhancing regional innovation capacity and driving high-quality economic growth. Simultaneously, it is crucial to consider the unique set of circumstances in various years and systematically elevate the level of innovation in different regions. This can be achieved by implementing measures such as establishing and refining mechanisms that encourage innovation, bolstering the cultivation and recruitment of innovative individuals, and facilitating the seamless integration of industry, academia, and research. Declarations Funding source: This study was funded by a grant (Project 22KJD630001) from the Natural Science Foundation of the Jiangsu Higher Education Institutions of China. Author Contribution Conceptualization: MIAOMIAO LI; Formal analysis: MIAOMIAO LI; Investigation: MIAOMIAO LI; Methodology: MIAOMIAO LI；Supervision: MIAOMIAO LI；Validation: MIAOMIAO LI；Writing - original draft: MIAOMIAO LI; Writing - review & editing: MIAOMIAO LI； Data Availability The survey data generated and analysed during this study are available in theNational Bureau of Statistics, （https://www.stats.gov.cn/sj/ndsj/） References Afonso CandSilva GM et al (2018) The role of motivations and involvement in wine tourists' intention to return: SEM and fsQCA findings. J Bus Res 89:313–321 Baek CLee B (2022) A guide to autoregressive distributed lag models for impulse response estimations. Oxf Bull Econ Stat 84:1101–1122 Baisong XandChenyi W et al (2022) ༲esearch and exploration on deepening the innovative practice of industry university research cooperation in Changzhou. Jiangsu Sci ༆ Technol Inform 39:6–8 Braumoeller BF (2017) Aggregation bias and the analysis of necessary and sufficient conditions in fsQCA. Sociol Methods Res 46:242–251 Brodny JMT (2022) Assessing the Level of Innovation of Poland from the Perspective of Regions between 2010 and 2020. J Open Innovation: Technol Market Complex 8:190 ChenLeiDuBaogui (2022) How Can the Science and Technology Service Industry Stimulate the Vitality Regional Innovation? An Analysis of fsQCA Basedon 31 Provincial Regionsin China's Mainland. Sci Technol Progress Policy 39:31–38 Cifci IandKahraman OC et al (2023) Demystifying meal-sharing experiences through a combination of PLS-SEM and fsQCA. J Hospitality Mark Manage 32:843–869 Fiss PC (2011) Building better causal theories: A fuzzy set approach to typologies in organization research. Acad Manag J 54:393–420 Flückiger MLudwig M (2018) Geography, human capital and urbanization: A regional analysis. Econ Lett 168:10–14 HX LandHL P et al (2024) Dynamic Relationship Between Agricultural Science and Technology Innovation, Eco-efficiency, and the Quality of Farmers' Income Increase: Empirical Analysis Based on Vector Autoregression (VAR) Model. Sci Technol Manage Res 44:214–221 Isaksen AandTrippl M et al (2021) Digital transformation of regional industries through asset modification. J Competitiveness Review: Int Bus J 31:130–144 Jian LandLexin Z et al (2024) Digital Economy and Firms’ Innovation Catering Behavior: An Empirical Study on Information Alleviating the Distortion Effects of Policies. J Quant Tech Econ 41:134–154 Jun, WandZhijun Y et al (2019) Digital Economy and Upgrading ༲egional Innovation Capacity. Inquiry Into Economic Issues ,112–124 Kumar SandSahoo S et al (2022) Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research: A contemporary overview. Technological Forecast 178:121599 Ming Z (2020) Configurational Paths and Performance of Cross-border Majority Acquisitions of Chinese Firms. South China University of Technology Mottaeva AandKhussainova Z,E3S Web of Conferences et al (eds) (2023) Patrício LDFerreira JJ (2023) University entrepreneurial performance: A fuzzy set qualitative comparative analysis. J High Educ Q 77:602–622 Qiu YandLiu W et al (2023) Digital economy and urban green innovation: from the perspective of environmental regulation. J Environ Plann Manage, 1–23 Rihoux BRagin CC (2009) Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques. Sage Shen MHYBP (2024) Digital Technology Innovation Empowers Inter-regional Capital Flows and Discussion on the Construction of a Unified National Market. Mod Finance Economics-Fournal Tianjin Universitj Finance Econ 44:3–21 Tang WYang S (2022) [Retracted] Digital Transformation and Firm Performance in the Context of Sustainability: Mediating Effects Based on Behavioral Integration. Journal of Environmental Public Health , 2022,8220940 Thomann EMaggetti M (2020) Designing research with qualitative comparative analysis (QCA): Approaches, challenges, and tools. J Sociol Methods Res 49:356–386 Tuo SHe H (2021) A study of multiregional economic correlation analysis based on big data—Taking the regional economy of cities in Shaanxi Province, China, as an example. J Sustain 13:5121 Wang QandGuan H et al (2024) Digital economy development and regional innovation resilience: mechanism and empirical analysis. J Appl Econ, 1–16 Wenfu PandXiao P et al (2024) Green Development and Digital Economy Enabling Regional Innovation. Ecol Econ 40:66–73 Wu JandXue Y et al (2024) Digital economy, financial development, and corporate green technology innovation. J Finance Res Lett 66:105552 Xiao DandZhang C et al (2024) Digital economy policy and enterprise digital transformation: Evidence from innovation and structural effect. J Managerial Decis Econ 45:2348–2359 Xu HuiChenguang Q (2022) Has Development of Digital Economy Enhanced Regional Innovation Capability:Based on Spatial Econometric Analysis of Yangtze River Economic Belt. Sci &Technology Progress Policy 39:43–53 Yali ZBenwu X (2023) The Impact of Digital Economy Development on Urban Economic Resilience. Econ Geogr 43:105–113 Yu JandWei Y et al (2023) The impact of human capital agglomeration on the green upgrading of the manufacturing industry in the Yangtze River Economic Belt. Appl Econ 55:5317–5329 Zhang HJiang W (2022) An Empirical Study on the Relationship between Economy and Finance in Underdeveloped Areas Based on the VAR Model. Journal of Sensors , 2022,2224239 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":123301,"visible":true,"origin":"","legend":"\u003cp\u003eAR root test results of VAR model\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7727238/v1/53a2863fa825166bf6a6d2b1.png"},{"id":98074899,"identity":"a362d6b6-f472-4a70-85c4-6db437b4dfd0","added_by":"auto","created_at":"2025-12-12 13:29:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":168066,"visible":true,"origin":"","legend":"\u003cp\u003eImpulse response figure\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7727238/v1/2c842129fd003c64169455c9.png"},{"id":101226852,"identity":"b7e21421-3c38-4d42-ab38-ae4ed8f26362","added_by":"auto","created_at":"2026-01-27 12:56:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1207247,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7727238/v1/514bea0e-486b-4b8e-a990-588a44c3f544.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Multifaceted Driving Forces of Digital Economy on Regional Innovation: A Case Analysis of Changzhou, Jiangsu Province","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the rapid growth of global digital technology, the digital economy has emerged as a key driver of economic and social transformation and progress. The 14th Five-Year Plan has raised inventive development to unprecedented strategic heights, and the digital economy has emerged as a leader in this process, playing an increasingly important catalytic role in regional innovation capabilities (Tuo and He 2021; Yali and Benwu 2023). The digital economy, which uses data as its primary production factor and relies on continuous innovation in digital technology and extensive coverage of modern information networks, is fundamentally altering the face of traditional industries and promoting their transformation towards digitalization and intelligence(MottaevaKhussainova,et al 2023; QiuLiu,et al 2023; WenfuXiao,et al 2024). This procedure not only speeds up the reconstruction of the economic development model, but it also encourages the modernization of government administration. The digital economy is critical to regional innovation as a whole. By fostering digital industrialization, which encompasses the rapid advancement of cutting-edge technologies like big data, cloud computing, the Internet of Things, and artificial intelligence, it establishes a strong technological base and opens up boundless opportunities for innovation within the regional innovation system(IsaksenTrippl,et al 2021). The extensive utilization of these technologies has not only significantly enhanced the effectiveness and excellence of innovation activities, but also consistently broadened the limits and domains of innovation. Simultaneously, the digital economy has profoundly reshaped conventional industries and fostered the innovative capacity of businesses through the process of industrial digitalization(Tang and Yang 2022). Driven by digital technology, organizations have experienced significant transformations in their production techniques, organizational structures, and management models, shifting towards a more digital, interconnected, and intelligent approach. This transition has not only increased the market competitiveness of businesses, but also provided a significant boost to the overall growth of the regional innovation ecosystem. An analysis is conducted on the dual-dimensional function of the digital economy in enhancing regional innovation capacities, considering both macro and micro viewpoints. From a broad view, the digital economy, with its distinct benefits, is increasingly becoming a significant driver in enhancing regional innovation capacities.\u003c/p\u003e\u003cp\u003ePrior research has extensively demonstrated a significant and intricate non-linear relationship between the growth of the digital economy and the ability to innovate (Jun, Zhijun, ,et al 2019; WangGuan,et al 2024). This association is especially apparent in its ability to stimulate technical innovation. In contrast to pure product innovation, the digital economy has generated substantial advancements and growth in the technology sector due to its distinctive benefits, propelling the entire region towards a more advanced and efficient innovation system. At the micro level, the digital economy plays a crucial role in enhancing the innovation capacities of businesses, particularly in the context of small-scale organizations, where its impact is particularly noteworthy(Xu Hui and Chenguang 2022). The digital economy enhances resource allocation efficiency both within and outside the firm, thereby immediately stimulating the enterprise's innovative potential. Enterprises can get a competitive advantage in the intense market rivalry by employing sophisticated resource allocation strategies and efficient operation and management procedures. This enables them to expedite the conversion of innovation into market value(JianLexin,et al 2024; WuXue,et al 2024). When broken down into smaller parts, the digital economy has varying effects on various forms of corporate innovation activities. More precisely, it demonstrates a powerful impetus for revolutionary ideas that challenge the status quo, whereas its impact on gradual improvements is very modest (XiaoZhang,et al 2024). This discovery emphasizes the crucial role of the digital economy in propelling the evolution of business innovation models. When doing a thorough analysis of the inner workings of the digital economy to enhance corporate innovation performance, it becomes evident that the primary areas of concentration are the acquisition of talented individuals and the reduction of environmental uncertainties. The digital economy has enticed a significant number of exceptionally skilled individuals with its distinct allure and established a dynamic environment for innovative talent. This ecosystem not only boosts the capacity for innovation in organizations, but also efficiently mitigates the uncertainty resulting from external environmental changes, and establishes a more stable and efficient support structure for corporate innovation endeavors (Shen MH and YB 2024). Hence, the digital economy is crucial in enhancing regional innovation capacities. Its impact extends to both large-scale and small-scale aspects, fostering innovation and enhancing efficiency in the entire economic framework, while fundamentally transforming the innovation ecosystem and capacity structure of businesses. To maximize the positive impact, it is essential to consistently improve the application environment of digital technology, encourage the unrestricted movement of skilled individuals and knowledge, and minimize uncertainties in the innovation ecosystem. This will effectively enhance the driving force of the digital economy on regional innovation capabilities.\u003c/p\u003e\u003cp\u003eChangzhou City, located in Jiangsu Province, stands out nationwide due to its innovative achievements. It is utilizing the momentum of the digital economy to fully exploit its capacity for innovation and strengthen the basis of urban growth. The regional innovation capabilities have undergone substantial enhancement, serving as a clear manifestation of the extensive integration and empowerment of the digital economy(BaisongChenyi,et al 2022). Changzhou is committed to being at the forefront of scientific and technical innovation. It focuses on building strong platforms, achieving technological breakthroughs, empowering enterprises, and cultivating talent. This approach effectively promotes innovation and contributes to high-quality development. Nevertheless, as Changzhou looks ahead, it must address obstacles such as the insufficient integration of the digital economy with regional innovation, the scarcity of innovative individuals, and the need for environmental optimization. Only by doing so can it consistently advance innovation and development to new levels.\u003c/p\u003e\u003cp\u003eTherefore, this study is committed to deeply analyzing the internal mechanism and multi-driven effects of digital economy on the improvement of regional innovation capacity in Changzhou City, Jiangsu Province, and revealing the deep connection and interaction between digital economy and regional innovation, in order to provide strong theoretical support and practical guidance for regional innovation practice in Changzhou City and other regions in the country in the digital economy era.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1༎Data source and processing\u003c/h2\u003e\u003cp\u003eIn order to study the path and driving effect of regional innovation multi-configuration in Changzhou, this study analyzes the data of Changzhou from 2011 to 2022, including (1) Human capital concentration (CH): location entropy is used to calculate the degree of human capital concentration. (2) Industrial agglomeration (IA): location entropy is used to calculate the degree of industrial agglomeration. (3) Innovation platform construction (IP): the number of science and technology enterprise incubators is used as a proxy variable for innovation platform construction. (4) Government funding support (GS): the proportion of government funding to internal R\u0026amp;D expenditure is used to measure the strength of government support. (5) Foreign technology introduction (TI): the proportion of foreign technology introduction contract amount to GDP is used to measure the strength of technology introduction. (6) Economic development level (ED): the per capita GDP of each region is used to measure the level of economic development. The data comes from statistical data such as \u0026ldquo;Jiangsu Province Statistical Yearbook\u0026rdquo; and \u0026ldquo;China City Statistical Yearbook\u0026rdquo;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Research methods\u003c/h2\u003e\u003cp\u003eThe study examines how the digital economy influences the diverse patterns of regional innovation in Changzhou. To investigate this, the researchers employ fsQCA (fuzzy set qualitative comparative analysis) and VAR (vector autoregression) models as research methodologies. FSQCA and VAR models are mutually supportive in research methodology. fsQCA mostly uncovers multivariate configuration paths in intricate causal linkages (Braumoeller \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; KumarSahoo,et al 2022; CifciKahraman,et al 2023), whereas VAR models are primarily employed to examine dynamic relationships in time series data (Zhang and Jiang 2022; HXHL,et al 2024). By integrating the two, one can thoroughly examine the intricate connection between the digital economy and regional innovation. This analysis uncovers the various factors that drive this relationship and allows for an assessment of how these factors develop over time.\u003c/p\u003e\u003c/div\u003e"},{"header":"Demonstration and Analysis","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1༎Analysis of regional innovatio-driven paths\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1༎Variable calibration\u003c/h2\u003e\u003cp\u003eVariable calibration refers to converting the original data into fuzzy set membership scores between 0 and 1 by setting three critical values, so as to be suitable for fsQCA software operation. The conditional variables and outcome variables are both the average values ​​of the data from 2011 to 2022. Referring to the research of FISS (Fiss \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) the direct calibration method is adopted, and the 25th, 50th and 75th percentile values ​​of each continuous variable are counted as complete non-membership, conversion point and complete membership. The direct method of RAGIN is used to complete the data calibration processing. The objectivity of data calibration is guaranteed by the variable calibration table, which establishes the 25th, 50th, and 75th percentile values of each continuous variable to represent complete non-membership, transition point, and complete membership, respectively. The calibration values of the specific variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The transition point is 42.358, the complete membership degree is 71.821, and the complete non-submission degree of \"regional innovation level\" is 35.557. This indicates that the regional innovation level is entirely non-submission to the high innovation level when it is less than 35.557, and it is entirely submission to the high innovation level when it is greater than 71.821. Additional indicators, including \"industrial agglomeration,\" \"innovation platform construction,\" \"government funding support,\" \"foreign technology introduction,\" and \"economic development level,\" also have precise calibration value ranges. Within distinct ranges, the influence of each indicator on the regional innovation level is inconsistent. The ensuing fuzzy set qualitative comparative analysis (fsQCA) was significantly influenced by these calibration values. The regional innovation level can be more precisely assessed by examining the impact of each variable and its combination through these calibration values. During the analysis process, this method guarantees the objectivity and consistency of data processing, thereby establishing a strong foundation for subsequent in-depth analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCalibration variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComplete non-membership\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConversion Point\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComplete membership\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional innovation level(RI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman capital concentration (CH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustrial agglomeration (IA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e217791.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e241626.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e262144.787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInnovation platform construction (IP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3973.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4269.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4968.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment funding support (GS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign technology introduction (TI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic development level (ED)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e95852.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131578.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e154611.250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2༎Analysis of necessary conditions\u003c/h2\u003e\u003cp\u003eIt is imperative to conduct a necessity analysis of the antecedent conditions prior to conducting configuration analysis. This test determines whether a specific antecedent condition is a necessary condition for promoting regional high-level innovation and non-regional high-level innovation. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the outcomes of the examination. The calibration value of 0.1 is adjusted in this section of the necessary condition analysis, and the threshold of the necessary condition is typically set to 0.9. A condition variable can be approximated as a necessary condition of the result variable if it comprises more than 90% of the result variable(Rihoux and Ragin 2009). The necessary condition analysis table displays the influence of various conditional variables on the outcome variables Y (high innovation level) and non-Y (low innovation level), which includes the two indicators of consistency and coverage. The necessary condition analysis table displays the influence of various conditional variables on the outcome variables Y (high innovation level) and non-Y (low innovation level), which includes the two indicators of consistency and coverage(AfonsoSilva,et al 2018). According to the analysis results, Innovation platform construction (IP) and economic development level (ED) are critical conditions for the result Y. These conditions have high consistency (0.919414 and 0.868132) and coverage (0.801917 and 0.770732), respectively. This demonstrates that the regional innovation level is also high when the level of innovation platform construction and economic development is high. In the same vein, the consistency and coverage of result Y are high for non-innovation platform construction (~\u0026thinsp;IP) and non-economic development level (ED), with a consistency of 0.810398 and 0.784404, and a coverage of 0.923345 and 0.876923, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of necessary conditions \u0026ldquo;~\u0026rdquo; represents the \u0026ldquo;not\u0026rdquo; of logical operation.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCondition variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e~Y\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsistency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoverage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConsistency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoverage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman capital concentration (CH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.531135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.487395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.539755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.593277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e~ Human capital concentration (~\u0026thinsp;CH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.556777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.502479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.533639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.576859\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustrial\u003c/p\u003e\u003cp\u003eagglomeration (IA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.655678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.632509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.493884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.570671\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e~ Industrial\u003c/p\u003e \u003cp\u003eagglomeration (~\u0026thinsp;IA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.554945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.477918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.681957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70347\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInnovation platform construction IP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.919414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.801917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.382263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.399361\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e~ Innovation platform construction (~\u0026thinsp;IP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.311355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.296167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.810398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.923345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment funding support GS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.434066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.398319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.649847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.714286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e~ Government funding support (~\u0026thinsp;GS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.688645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.621488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.452599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.489256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign technology introduction TI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.298535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.291592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.712538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.833631\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e~ Foreign technology introduction (~\u0026thinsp;TI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.82967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.706708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.394495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.402496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic development level ED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.868132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.770732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.308868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.328455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e~ Economic development level (~\u0026thinsp;ED)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.24359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.784404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.876923\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn general, the construction of innovation platforms and the level of economic development are crucial to improving the level of regional innovation, while their absence may lead to a decline in the level of innovation. Through this analytical method, we can clearly identify which variables are the key necessary conditions for achieving a high level of innovation, thus providing an important basis for policy making and resource allocation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3༎Conditional combination analysis\u003c/h2\u003e\u003cp\u003eThe regional innovation level is selected as the outcome variable to investigate the configuration-driven path that results in its generation, as determined by variable calibration and necessity analysis. The case frequency threshold is set to 1 in accordance with the principle that the case frequency should retain more than 75% of the observed cases, and the original consistency threshold is set to 0.8 to avoid the simultaneous subset relationship(Ming \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In order to mitigate potential configuration inconsistencies, the PRI (Proportional Reduction in Inconsistency) consistency threshold is fixed at 0.75(Thomann and Maggetti 2020). The simplified, intermediate, and complex solutions were obtained through the use of fsQCA3.0 software, following the standardization of operations. The outcome was somewhat biased due to the absence of the logical remainder in the complex solution, which involved disregarding the unobserved cases. The configuration path was obtained by nesting and comparing the simplified solution and the intermediate solution in this article(Patr\u0026iacute;cio and Ferreira 2023). If a variable is present exclusively in the intermediate solution, it is classified as a marginal condition and serves as an auxiliary driving force. Conversely, if it is present in both the intermediate solution and the simplified solution, it is classified as a core condition and serves as a significant driving force. The results of the configuration analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis on the driving path of regional high-level innovation configuration\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCondition variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eRegional high-level innovation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombination 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCombination 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCombination 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman capital concentration (CH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026bull;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e●\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustrial\u003c/p\u003e\u003cp\u003eagglomeration (IA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026otimes;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026otimes;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026bull;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInnovation platform construction(IP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026otimes;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026bull;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment funding support(GS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026otimes;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026otimes;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026bull;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign technology introduction(TI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026otimes;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026otimes;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026otimes;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic development level(ED)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026bull;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e●\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConsistency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.886364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.896373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.858696\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOriginal coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.142857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.289377\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0567766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.254579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.221612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal consistency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0.897098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0.622711\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e* \u0026bull; indicates the existence of edge conditions\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026otimes; indicates the absence of edge conditions\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e● indicates the existence of core conditions\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe impact of various conditional combinations on the high level of regional innovation is revealed by the conditional combination analysis. Human capital agglomeration (CH) and economic development level (ED) are the core conditions of Combination 1, while industrial agglomeration strength (IA), innovation platform construction (IP), government funding support (GS), and foreign technology introduction (TI) are marginal conditions. The original coverage is 0.142857, the net coverage is 0.0567766, and the consistency of this combination is 0.886364. This indicates that combination 1 partially explains the high level of innovation, but the coverage is limited.\u003c/p\u003e\u003cp\u003eThe core conditions of Combination 2 are human capital agglomeration (CH), innovation platform construction (IP), and economic development level (ED), while industrial agglomeration strength (IA), government funding support (GS), and foreign technology introduction (TI) are marginal conditions. The original coverage is 0.316850, the net coverage is 0.254579, and the consistency of this combination is 0.896373. This demonstrates that combination 2 has a substantial explanatory power and a broad coverage in high-innovation levels. It is an effective combination of conditions, demonstrating the significance of these conditions in fostering regional innovation levels.\u003c/p\u003e\u003cp\u003eCombination 3 contains the most core conditions, including human capital concentration (CH), industrial agglomeration (IA), innovation platform construction (IP), government funding support (GS), and economic development level (ED). The combination 3 has a strong explanatory power in high innovation level and a large number of core conditions, as evidenced by the consistency of 0.858696, the original coverage of 0.289377, and the net coverage of 0.221612. These findings demonstrate the critical role of these conditions in achieving high innovation levels.\u003c/p\u003e\u003cp\u003eThe total coverage is 0.622711, and the overall consistency is 0.897098, suggesting that these combinations have a strong explanatory power in explanation of high innovation levels. The case analysis also demonstrates that high innovation levels are the result of specific combinations of conditions in various years. By recognizing and prioritizing these critical conditions, policymakers can more effectively encourage the enhancement of regional innovation levels.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2༎Regional innovation driving effect empirical analysis\u003c/h2\u003e\u003cp\u003eThe impact of random interference on regional innovation capabilities and related variables is further examined in this study based on the configuration analysis results. Additionally, the study looks at how each variable fluctuates and how stable their mutual relationship is when random interference is present, provides a deep understanding of how each variable interacts dynamically in the time dimension, and expands on the configuration analysis results. This study conducts an empirical investigation of the regional innovation driving effect using the Var (Vector Auto Regression) model. A multivariate time series model that can be used to examine the dynamic relationship between several variables is the VAR model.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1༎The stationarity test of the VAR model\u003c/h2\u003e\u003cp\u003eThe ADF test result is shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Time series data stationarity is tested using the ADF test. In the ADF test, a statistic is created and contrasted with the crucial value. The null hypothesis is rejected and the series is deemed stationary if the ADF value is less than the critical value or p\u0026thinsp;\u0026lt;\u0026thinsp;0.05(Zhang and Jiang 2022). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the result variable Y is stationary at the 5% significance level, with an ADF test t value of -4.257 and a p value of 0.004. The initial statistics on government financial assistance (GS), foreign technology introduction (TI), innovation platform construction (IP), industrial agglomeration intensity (IA), human capital concentration (CH), and economic development level (ED) are non-stationary. The study treated these factors differently in order to stabilize them. Agglomeration of human capital and government funding are stable following first-order differences; however, the establishment of innovation platforms and the import of foreign technology necessitate second-order differences before they can become stable. At a particular significance level, the differenced data becomes stationary, and these stationary processes serve as the basis for the further development and analysis of VAR models.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eADF Inspection Table\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eT statistic\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eCritical value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegional innovation(Y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.555\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman capital concentration(CH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ed.CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.576\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustrial agglomeration(IA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ed.IA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInnovation platform construction(IP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.844\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ed.IP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ed2.IP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-17.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.901\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment funding support(GS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ed.GS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign technology introduction(TI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ed.TI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ed2.TI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic development level(ED)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.803\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ed.ED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2༎VAR model\u003c/h2\u003e\u003cp\u003eA VAR (vector autoregression) model of the first-order difference variable is built based on the ADF test findings, which demonstrate that the data reaches stability after the first-order difference. Afterwards, this model is applied to variance decomposition analysis and impulse response analysis to investigate the dynamic relationship and implications between the variables even more. A thorough analysis of the AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), FPE (Final Prediction Error Criterion), and HQIC (Hannan-Quinn Information Criterion) is required in order to determine the lag order of the VAR model. Because each of the four criteria obtains the least value at the first order, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e's data demonstrate that the first order is the ideal lag order. Given the coherence of the aforementioned analysis, the lag order of the VAR model is ultimately determined to be the first order, and the VAR model is built in accordance with this selection. This decision upholds the statistical optimality principle and ensures the quality and dependability of the analysis results by serving as a strong model foundation for the impulse response and variance decomposition analyses that follow.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel order determination\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\u003eOrder\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFPE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHQIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-44.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-44.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-44.521\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-54.942*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-53.857*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-55.626*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e* Represents the number of steps for this item\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe VAR model results show the impact of the lag terms of different variables on the regional innovation level (Y). The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and The VAR model formula is as follows: Y=-30.166\u0026thinsp;+\u0026thinsp;0.131*L1.Y-219.799*L1.CH-18.121*L1.GS\u0026thinsp;+\u0026thinsp;2.996*L1.ED\u0026thinsp;+\u0026thinsp;49.885*L1.TI. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the constant term coefficient is -30.166 and the t value is -2.477. This indicates that when all lag components are zero, the baseline value of the regional innovation level is -30.166 with a significance level of 5%. To a considerable extent. The level of economic development at a single point in time (L1.ED) and the entry of foreign technology (L1.TI) have a notable and beneficial effect on the level of innovation inside a certain region. The coefficients are 2.996 and 49.885, while the t values are 2.976 and 2.772, both significant at the 1% level of significance. This demonstrates that the enhancement of economic development and the entry of foreign technology have a substantial impact on the level of regional creativity. Changzhou City should allocate additional resources towards innovation activities, such as increasing investment in research and development, enhancing talent development, and improving infrastructure, in order to further strengthen regional innovation capabilities. Furthermore, the speed of technological advancement should be expedited by the implementation of foreign cutting-edge technology and managerial expertise. In contrast, the lack of government financial support in the previous era (L1.GS) has a substantial detrimental effect on the degree of regional innovation. This effect is quantified by a coefficient of -18.121 and a t value of -2.340, which is statistically significant at the 5% significance level. This implies that while government financial assistance is a significant policy instrument, its consequences may require more assessment and fine-tuning to prevent adverse effects. The influence of Y and the concentration of human capital (L1.CH) in one previous time period on the present regional innovation level is not statistically significant, as indicated by the coefficients of 0.131 and \u0026minus;\u0026thinsp;219.799 respectively, and the corresponding t-values of 0.526 and \u0026minus;\u0026thinsp;0.696 respectively. This suggests that these factors do not have a substantial direct influence on regional innovation levels within a single time period. Collectively, these findings offer valuable understanding of the interaction between many factors over a period of time, aiding Changzhou City in developing more efficient strategies to foster regional innovation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVAR model results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY(t-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-30.166**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.477)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL1 Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.526)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL1 CH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-219.799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-0.696)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL1 GS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-18.121**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(-2.340)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL1 ED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.996***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.976)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eL1 TI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.885***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2.772)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enobs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ellf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e254.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-40.753\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSC value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-39.668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHQIC value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-41.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e*\u0026nbsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 **\u0026nbsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ***\u0026nbsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003cp\u003eThe t value is in brackets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe stability of the VAR model is assessed by examining the AR root diagram. If all eigenvalues lie within the unit circle, indicating that all points are within the circle, then the model is considered stable. By analyzing the AR characteristic root diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), it is evident that all eigenroot values fall within the unit circle, confirming the strong stability of the constructed VAR model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3༎Impulse Response\u003c/h2\u003e\u003cp\u003eImpulse response analysis use the VAR model to precisely illustrate the dynamic interplay among variables in the economic system. When the system is affected, the variables exhibit a cascade effect, and their dynamic adjustment paths are visually shown by orthogonalized impulse response diagrams. These diagrams display both the immediate and long-term effects of the shock on itself and other factors. Additionally, they provide information on the direction (positive or negative), trajectory, and strength of the impact(Baek and Lee 2022). These intricate dynamics are intuitively depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, where the direction of the impact is indicated by positive and negative values, and the intensity of the impact is measured by the absolute value. The impulse response diagram illustrates that the impact of various variables on the regional innovation level (Y) fluctuates over time. In the initial stages, the unit impact of economic development level (ED) has a substantial positive impact on regional innovation level. However, this influence gradually diminishes in subsequent periods. This demonstrates that regional innovation can be rapidly stimulated by economic development enhancement; however, its influence will gradually diminish. In the same vein, the unit impact of foreign technology introduction (TI) has a substantial positive impact on regional innovation levels during the early stages and subsequently stabilizes, indicating that foreign technology introduction has a robust promoting effect on regional innovation during the early stages. However, this effect will progressively diminish.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn contrast, the initial negative impact of the unit shock of government financial support (GS) on regional innovation levels is progressively mitigated in subsequent periods. This demonstrates that, despite the potential negative impact of government financial support on regional innovation during the initial stages, this impact will progressively diminish over time. The early stage of regional innovation is not significantly influenced by the unit impact of industrial agglomeration intensity (IA) and human capital agglomeration (CH). Nevertheless, these factors progressively demonstrate positive effects as time progresses, suggesting that in the medium and long term, regional innovation is positively influenced by human capital and industrial agglomeration.\u003c/p\u003e\u003cp\u003eImpulse response analysis exposes the dynamic impact of each variable on regional innovation levels when considered in conjunction. The economic development and the introduction of foreign technology will significantly promote regional innovation in the early stage, but their influence will weaken and level off. Government financial support may have a negative impact in the early stage, but its impact will gradually decrease. Human capital agglomeration and industrial agglomeration will have a minimal impact in the short term, but will have a positive effect on regional innovation in the medium and long term.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4༎Variance Decomposition\u003c/h2\u003e\u003cp\u003eWhile impulse response analysis can offer insights into the dynamic causal relationships between variables, it does not provide a quantitative measure of the extent of influence. This article presents the utilization of variance decomposition technology to precisely measure the degree of effect that each variable has in the VAR model. Variance decomposition quantifies the dependency and strength of effect by revealing the fraction of variance in forecast error that is explained by changes in other variables. High variance decomposition values imply a substantial impact, whilst low values suggest minimal impact. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e displays the variance decomposition results. The variance decomposition analysis illustrates the extent to which various variables contribute to the fluctuation of the regional innovation level (Y). During the initial phase (Phase 1), the regional innovation level fluctuation is entirely self-explanatory, with a 100% contribution rate. This implies that the initial stage of Y's evolution is entirely determined by its own historical fluctuations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVAR model variance decomposition results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLag order\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariance Decomposition of S.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGS(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eED(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTI(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.354\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe level of economic development (ED) begins to substantially influence the fluctuation of regional innovation level in the short-term stage (period 2 to period 4), with the contribution rate in period 2 reaching 29.498%. The fluctuation of Y is progressively influenced by human capital concentration (CH) and government financial support (GS), although their contribution is relatively minor. The contribution rate of economic development level (ED) continued to increase, reaching 33.679% and 26.812% respectively, in the third and fourth periods. Conversely, the contribution rate of Y itself progressively decreased.\u003c/p\u003e\u003cp\u003eThe level of economic development (ED) remains the primary influencing factor in the mid-term stage (from the 5th to the 7th period), with a contribution rate of 27.678% in the 5th period. The contribution rates of human capital agglomeration (CH) and government funding support (GS) have also increased, indicating that the level of economic development, human capital concentration, and government funding support have a significant impact on the fluctuation of regional innovation levels in the medium term. The contribution rate of human capital concentration (CH) increased considerably to 20.35% by the seventh period, while the contribution rate of economic development level (ED) further increased to 34.829%.\u003c/p\u003e\u003cp\u003eDuring the period from period 8 to period 10, the economic development level (ED) reaches its greatest contribution rate of 37.353% in period 10. In the 10th period, the contribution rate of human capital concentration (CH) climbed even further, reaching 26.097%. This demonstrates that the concentration of human capital and the level of economic development have a lasting and substantial influence on the long-term variations in regional innovation levels. The government's financial support (GS) has a modest although consistent impact.\u003c/p\u003e\u003cp\u003eIn summary, the variance decomposition analysis reveals that economic development level and human capital concentration have increasingly become the primary drivers of fluctuations in regional innovation levels over time. On the other hand, government funding support and foreign technology introduction, although relatively minor, still make a certain contribution.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis paper uses fuzzy set qualitative comparative analysis (fsQCA) based on data of Changzhou City, Jiangsu Province from 2011 to 2022 to identify several configuration-driven paths that drive regional high-level innovation and further uses the Var model to conduct an empirical analysis of the regional innovation-driving effect, examining the dynamic interaction between many variables in the time series and the driving effect of the configuration path on regional innovation.\u003c/p\u003e\u003cp\u003eThe results of the necessity and condition combination analysis indicate that the regional innovation level is significantly enhanced by the construction of innovation platforms and the level of economic development. Conversely, their absence may result in a decrease in the innovation level, and the specific condition combinations in different years contribute to the high innovation level. This aligns with the findings of Previous research (ChenLei and DuBaogui 2022). The only way to accelerate the agglomeration of research and development capital is to build a multi-level science and technology financial service platform system, promote capital market financing actively, expand regional innovation indirect financing channels, give full play to the effectiveness and guidance of fiscal policies, and encourage enterprises and other innovation entities to increase research and development funds through targeted subsidies, tax incentives, and other incentives.\u003c/p\u003e\u003cp\u003eThe degree of economic development and the entry of foreign technology have greatly aided in the early phases of regional innovation activities, as indicated by the results of impulse response and variance decomposition. On the other hand, agglomerations of industries and human capital have less immediate benefits than other factors. However, over an extended period of time, these agglomerations' beneficial effects on regional innovation gradually become apparent and eventually become a vital source of power. A comprehensive assessment of Poland's innovation capabilities reaffirmed the crucial role of a strong economic foundation in driving innovation activities. It emphasized that continuous enhancement of economic development is essential for creating regional competitive advantages and achieving innovation leadership. The primary function of substitution is crucial (Brodny J and M 2022). Simultaneously, a separate scholarly investigation into the environmental shift of the industrial sector in the Yangtze River Economic Belt has distinctly demonstrated the notable impact of the concentration of educated individuals on this transformation. This discovery not only reinforced the notion that human capital is the primary catalyst for regional growth. The notion emphasizes its essential nature and extensive importance in advancing environmentally friendly change and achieving strategies for sustainable development (YuWei,et al 2023) (Fl\u0026uuml;ckiger and Ludwig 2018). These research findings highlight the fundamental worth and strategic significance of the economic base and human capital in driving regional innovation and transformation.\u003c/p\u003e\u003cp\u003eBased to this, the subsequent recommendations are suggested in this investigation: (1) Enhance industrial upgrading and the innovation system. Changzhou should establish a comprehensive innovation system that is centered around innovation platforms, fosters collaboration among enterprises, universities, scientific research institutions, and other stakeholders, and establishes an ecological environment that is conducive to collaborative innovation. Accelerate industrial upgrading, concentrate on high-end manufacturing, smart technology, renewable energy, and other sectors, and foster the transition of the economy to high-quality development through policy and financial support. Simultaneously, establish a dual-wheel propulsion of technological innovation and industrial upgrading, enhance the technical level and innovation ability of enterprises, and deepen international cooperation. Additionally, introduce advanced technologies. (2) Optimize the talent and innovation environment. Changzhou needs to increase investment in education and training, improve the quality of talent, and optimize talent introduction policies to attract high-level and innovative talents. Build a multi-level and wide-ranging innovative education system to provide a solid talent base for innovation. Simultaneously, Changzhou should enhance the innovation ecosystem, enhance intellectual property safeguards, bolster financial backing for scientific and technological advancements, and foster an innovation culture that is open, inclusive, and conducive to research. To enhance the innovation capacity of society as a whole, it is important to implement policy incentives and foster a conducive social environment. This will facilitate the efficient conversion of scientific and technological advancements into tangible productivity, and encourage the seamless integration of the innovation and industrial sectors. Thus, Changzhou should prioritize the establishment of an innovation platform and the improvement of its economic development level. It should leverage its existing economic advantages and foreign technology integration, while also emphasizing long-term planning for the concentration of human capital and industries. Additionally, optimizing the policy environment and system construction, as well as fostering a robust culture of innovation, will be crucial in enhancing regional innovation capacity and driving high-quality economic growth. Simultaneously, it is crucial to consider the unique set of circumstances in various years and systematically elevate the level of innovation in different regions. This can be achieved by implementing measures such as establishing and refining mechanisms that encourage innovation, bolstering the cultivation and recruitment of innovative individuals, and facilitating the seamless integration of industry, academia, and research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding source:\u003c/h2\u003e\u003cp\u003eThis study was funded by a grant (Project 22KJD630001) from the Natural Science Foundation of the Jiangsu Higher Education Institutions of China.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: MIAOMIAO LI; Formal analysis: MIAOMIAO LI; Investigation: MIAOMIAO LI; Methodology: MIAOMIAO LI；Supervision: MIAOMIAO LI；Validation: MIAOMIAO LI；Writing - original draft: MIAOMIAO LI; Writing - review \u0026amp; editing: MIAOMIAO LI；\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe survey data generated and analysed during this study are available in theNational Bureau of Statistics, （https://www.stats.gov.cn/sj/ndsj/）\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfonso CandSilva GM et al (2018) The role of motivations and involvement in wine tourists' intention to return: SEM and fsQCA findings. 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Appl Econ 55:5317\u0026ndash;5329\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang HJiang W (2022) An Empirical Study on the Relationship between Economy and Finance in Underdeveloped Areas Based on the VAR Model. \u003cem\u003eJournal of Sensors\u003c/em\u003e, 2022,2224239\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital Economy, Regional Innovation, Changzhou Case","lastPublishedDoi":"10.21203/rs.3.rs-7727238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7727238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe digital economy, a key driver in the new tech era, is transforming the global economy and China's high-quality development strategy. It enhances regional innovation by optimizing resources, cutting costs, speeding knowledge sharing, and digitizing industries. This study, using fsQCA and VAR models with 2011\u0026ndash;2022 data from Changzhou, Jiangsu, reveals that the digital economy initially spurs innovation through economic growth and foreign tech imports, while human capital and industrial clustering support long-term growth. Innovation platforms and economic development are vital for boosting innovation capacity, offering empirical insights crucial for policy development.\u003c/p\u003e","manuscriptTitle":"Exploring the Multifaceted Driving Forces of Digital Economy on Regional Innovation: A Case Analysis of Changzhou, Jiangsu Province","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 13:29:13","doi":"10.21203/rs.3.rs-7727238/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":"799e5243-2645-4cc9-bbdb-eee6edce54b2","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59480963,"name":"Business and commerce/Economics"},{"id":59480964,"name":"Social science/Economics"},{"id":59480965,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":59480966,"name":"Business and commerce/Information systems and information technology"},{"id":59480967,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-01-27T12:55:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 13:29:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7727238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7727238","identity":"rs-7727238","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}