Connected knowledge spillovers, technological cluster innovation and efficient industrial structure
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Xu, Ye; Li, Xinyi; Tao, Changqi; Zhou, Xuan Article Connected knowledge spillovers, technological cluster innovation and efficient industrial structure Journal of Innovation & Knowledge (JIK) Provided in Cooperation with: Elsevier Suggested Citation: Xu, Ye; Li, Xinyi; Tao, Changqi; Zhou, Xuan (2022) : Connected knowledge spillovers, technological cluster innovation and efficient industrial structure, Journal of Innovation & Knowledge (JIK), ISSN 2444-569X, Elsevier, Amsterdam, Vol. 7, Iss. 3, pp. 1-11, https://doi.org/10.1016/j.jik.2022.100195 This Version is available at: https://hdl.handle.net/10419/327165 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/
Connected knowledge spillovers, technological cluster innovation and efficient industrial structure Ye Xu a , Xinyi Li b, * ,1 , Changqi Tao a , Xuan Zhou a a School of Statistics, Jiangxi University of Finance and Economics, Nanchang, China b School of Public Finance and Public Administration, Jiangxi University of Finance and Economics, Nanchang, China ARTICLE INFO Article History: Received 23 November 2021 Accepted 20 April 2022 Available online 10 May 2022 ABSTRACT In the context of China’s knowledge economy, cluster-based technological innovation can promote the communication of knowledge among industries, advancing the industrial structure. This study constructs the Ricardian model with the differences in productivity between different sectors to explore the mechanism of connected knowledge spillovers of technological cluster innovation driving efficient industrial structure, applying a spatial Durbin model to Chinese provincial panel data from 2003 to 2017 to analyze spatial effects. The results demonstrate a significant positive correlation between regional efficient industrial structure and technological cluster innovation under connected knowledge spillovers. These effects increase sequentially from the perspectives of the technology introduction, technology upgrade and independent innovation. Moreover, technological cluster innovation exhibits a significant backwash effect, negatively influencing the efficient industrial structure of neighboring areas. Finally, the study discusses some policy recommendations on optimizing the regional technological innovation environment. © 2022 The Author(s). Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Keywords: Technological cluster innovation Efficient industrial structure Connected knowledge spillovers Spatial Durbin model O32 O33 C31 R12 Introduction Driven by the accelerated adjustment of the global industrial structure and unprecedented development of economic globalization, the global economy has entered a path of rapid growth. The status of industrial structure upgrade and optimization continues to rise and is increasingly becoming the driving force of a new era of global economic development. The innovation-driven is the foundation of economic transformation and upgrade, development of industries toward high- and mid-grade positions in the global value chain, and the sustainable development of economic society. With the accelerated process of global informatization and networking, severe challenges are hindering economic development in China and new modes of development are urgently needed (He et al., 2021). Technological cluster innovation has an important influence on the transformation of economic growth and sustainable economic development (Kihombo et al., 2021). In the context of China’s knowledge economy, technological cluster innovation can effectively organize and apply existing knowledge to promote knowledge spillovers among industries to achieve efficient industrial structure. This study focuses on analyzing the spatial effect of technological cluster innovation to promote efficient industrial structure from the perspective of dynamic changes, and mainly verifies the existence of the spatial effect by constructing spatial Durbin model under connected knowledge spillovers. The relationship between knowledge spillovers and the sustainable development of regional economies is demonstrated, presenting a new theory of economic growth that emphasizes the profound effect of knowledge spillovers (Huber, 2012). There is currently no unified scholarly consensus regarding the definition or the nature of the effect of knowledge spillovers. Marshall (1920) was one of the pioneers in studying the external characteristics of knowledge acquisition. Arrow (1962) used the effect of knowledge spillovers on economic growth to measure the externality of knowledge. Knowledge externalities were divided into “Marshall−Arrow−Romer (MAR) externalities”within the same industry and “Jacobs’externalities” between different industries by Glaeser et al. (1992). Multiplexity and heteromorphism in knowledge exchanges are confirmed by Giusti et al. (2020). According to the direction of knowledge spillover, scholars divide it into horizontal and vertical knowledge spillover (Le & Pomfret, 2011;Rojec & Knell, 2018). Under the economic branch of new economic geography and endogenous growth theory, knowledge spillover is an important variable in explaining regional economies, technological innovation, and industrial agglomeration (Altunba¸s et al., 2013;Serrano-Domingo & Cabrer-Borr as, 2017). Technological innovation has the characteristics of giving real events * Corresponding author. E-mail address: [email protected] (X. Li). 1 Present address: Changbei National Economic and Technological Development Zone, No.169, Shuanggang East Street, Nanchang, Jiangxi, China https://doi.org/10.1016/j.jik.2022.100195 2444-569X/© 2022 The Author(s). Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Journal of Innovation & Knowledge 7 (2022) 100195 Journal of Innovation &Knowledge https://www.journals.elsevier.com/journal-of-innovation-and-knowledge
the novelty of conception and successful realization. The complexity of development process of the technological innovation makes it difficult to define it at home and abroad, but many literatures revealed the internal mechanism of technological innovation in detail (Choi et al., 2016). More scholars explained the development trajectory of technological innovation from the aspects of technological innovation capability, technological innovation efficiency, its influencing factors (Adamides & Karacapilidis, 2020;Cruz-C azares et al., 2013;Talukder, 2012) and its measure indicators (Taques et al., 2021). Moretti & Biancardi (2020) focused on the effect of open innovation on firm performance. Previous research on technological innovation and industrial structure optimization and upgrade primarily focused on aspects such as the internal mechanism of technological innovation (Zhou & Li, 2012), the effect of technological innovation (Karltorp et al., 2017), the factors influencing the optimization and upgrade of industrial structure (Li & Lin, 2017), and the relationship between industrial structure transformation and economic growth (Dong et al., 2020). Romer (1986) and Lucas (1988) endogenized technological progress in the theory of endogenous economic growth, thus proposing that technological progress could promote the adjustment of industrial structure. From an empirical point of view, Wu & Liu (2021) found that technological innovation has a positive effect on the local industrial structure upgrade, but its indirect effect is not significant. Although existing research has investigated knowledge spillovers and technological innovation as drivers of industrial structure upgrade, the issues above remain understudied. To address this deficiency, this study aims to analyze the following questions: how does technological cluster innovation affect efficient industrial structure from the perspective of connected knowledge spillovers? Does technological cluster innovation have a positive or negative spatial spillover effect on efficient industrial structure of neighboring areas? What are the differences in the impact of technological cluster innovation driving efficient industrial structure from the perspective of different types of connected knowledge spillovers? Based on the questions above and previous research approaches, this study uses Chinese provincial data from 2003 to 2017 to analyze the spatial effect of technological cluster innovation driving the efficient industrial structure through connected knowledge spillovers. The contributions of this study are threefold. First, the internal mechanism of technological cluster innovation is investigated from a new perspective, focusing on the formation of a complex interactive mode of technological innovation, wherein the innovation chain embeds and fully integrates into the industrial chain, followed by a quantitative matching approach. Second, referencing previous research, a Ricardian model with the differences in productivity between different sectors is constructed to analyze the mechanism of technological cluster innovation driving efficient industrial structure. Third, a geographical and technological distance spatial weight matrix integrating various knowledge spillover variables is constructed to measure its spatial effect on efficient industrial structure. Existing literature predominantly focuses on spatial weight matrices based on geographical distance, ignoring the effect of knowledge spillovers (Feng et al., 2021;Wu & Liu, 2021). According to the directionality characteristics of knowledge spillovers, we construct a spatial weight matrix based on both geographical and technological distance, build a spatial Durbin model, and explore the spatial effect of technological cluster innovation as a driver of efficient industrial structure. This study is organized into six sections. Section 1 introduces the background and significance. Section 2 defines the main variables of the study. Section 3 introduces the relevant literature and proposes hypotheses. Section 4 describes the research materials and methods. Section 5 describes the data analyses and related discussion. Section 6 presents the conclusions and makes policy recommendations. Definition Connected knowledge spillovers The concept of knowledge spillover refers to a reengineering of knowledge and a method of pursuing knowledge diffusion, which measures the spillover effects when it is unconsciously leaked. In this way, enterprises obtain benefits without paying a price. Knowledge spillover has been demonstrated among domestic enterprises (Audretsch & Belitski, 2020;Paci & Usai, 2009) and domestic and foreign enterprises (Guo et al., 2021;Wang & Wu, 2016). Franco & Esteves (2020) tried to understand the cluster network, the mechanism for the share of knowledge. Altunba¸s et al. (2013) discovered the differential effects of knowledge spillovers within and between industries on urban development. This study presents a novel perspective regarding the study of directional knowledge spillovers and proposes the concept of connected knowledge spillovers. As shown in Fig. 1, connected knowledge spillovers refer to the knowledge spillover multiplier effect that is generated by multiple bodies or organizations, considering both horizontal knowledge spillovers (between different industries within the same profession) and vertical knowledge spillovers (between upstream and downstream industries). Technological cluster innovation Technological cluster innovation is a new mode of innovation that is derived through enterprise or industry clusters. Ruoslahti (2020) Fig. 1. Connected knowledge spillovers. Y. Xu, X. Li, C. Tao et al. Journal of Innovation & Knowledge 7 (2022) 100195 2
explored the complexity of collaboration for innovation. Overall, technological cluster innovation is a complex and sophisticated form of cooperative innovation. It is a process in which an industrial chain is embedded in the front, middle, and back ends of the industrial chain, and industrial and innovation chains are integrated and transformed into a form of technological convergence and diffusion. Through embedding and integration, emerging industries cluster in space through complex network interaction. Regional technological cluster innovation refers to the organic unity of technological and industrial organization innovation. Assume that there is a “craft innovation (C), product innovation (P), and service innovation (S)”innovation chain within an enterprise, and an “upstream enterprise (U), midstream enterprise (M) and downstream enterprise (D)”industrial chain within an enterprise (Nonaka et al., 2000). With increasing specialization in the social division of labor, a complex technological innovation network interaction model emerges to achieve technological cluster innovation when an innovation chain is embedding and integrating with the related industry chain (Fig. 2). Efficient industrial structure The main goal of the optimization and upgrading of the industrial structure is to optimize the national economic benefits, improving the efficiency of the industrial structure and the level of the industrial structure through industrial adjustment. The optimization of the industrial structure has been in a state of change. At different stages, the content of the optimization and upgrading of the industrial structure is different. This is also the basic feature of the evolution law of the industrial structure, which lays the foundation for the sustained and rapid development of the national economy (Peneder, 2003). Efficient industrial structure refers to the industrial structure of a country or region with high processing and high technology. It is the existence of an industrial alliance system with significant economic benefits, coordinated industries, and intricately linked industrial structures that reflects such efficiency. With the development of efficient industrial structure, society will establish higher resource allocation efficiency. Literature review Extant studies have primarily focused on several relevant considerations. The first stream of literature has focused on the impact of knowledge spillovers on technological innovation (Philipson, 2020). From the perspective of the internal logic of knowledge spillovers and technological innovation, the spatial attenuation of knowledge spillover efficiency caused by the locality of knowledge spillover will promote the innovation activity within a cluster geographically, increasing innovation output through the formation of a cluster-type innovation network development model (Anselin et al., 1997; Audretsch & Feldman, 1996). In the process of mutual exchanges and contacts between economic entities in different locations, geographical knowledge diffusion occurs unconsciously (Migu elez & Moreno, 2015). Most literature has analyzed the topic from a regional perspective. Jaffe et al. (1993) introduced the mechanism of knowledge spillover to improve regional technological innovation in the context of patent citations. From the perspective of absorptive capacity, Fu & Díez (2010) investigated its role in promoting the transformation of technology flows generated by the network and labor mobility in regional innovation. Some studies have analyzed spillover effects at the industrial or enterprise levels. Ribeiro- Navarrete et al. (2021) explored factors influenced business performance of KIBS. Ode & Ayavoo (2020) found that knowledge management practices could have positive effect on firm innovation. Enterprises’technological innovation capabilities were also found to be affected by local knowledge spillover effects (Grillitsch & Nilsson, 2015). Qiu et al. (2017) research determined that both local and global knowledge spillovers in relatively developed regions could enhance regional technological innovation capabilities. Lin (2015) demonstrated that firms located near a knowledge center experienced higher volatility, suggesting that knowledge spillovers contribute to corporate innovation. Whether at the micro enterprise level or the macro regional level, the existence and significance of knowledge spillover is irrefutable. Regional technological innovation increases through mutual learning and information exchange between innovation subjects both within and between regions (Cani€ els & Verspagen, 2001). Another stream of research has investigated the effect of technological innovation on industrial structure upgrade. Changes in the demand structure and labor productivity generated by technological innovation are principal driving forces for regional industrial structure upgrade (Antonelli, 2014;Ngai & Pissarides, 2007; Peneder, 2003). Altenburg et al. (2008) examined China’s economic development, asserting that China has improved its technological imitation and original innovation capabilities through the establishment of a national industrial innovation system and technological innovation system, thereby achieving the upgrade of its industrial structure. Wu & Liu (2021) found technological innovation to have a significant positive spatial spillover effect on industrial structure upgrade. Xia et al. (2020) focused on the technological advancements achieved through outward foreign direct investment (OFDI), which can significantly promote industrial structure upgrade. Furthermore, some scholars have explored the spatial effect of technological innovation on industrial structure upgrade, indicating that technological progress could significantly promote the industrial structure upgrade of a region; however, it has no significant impact on surrounding areas (Feng et al., 2021). Research on the joint analysis of the relationships between knowledge spillovers, technological innovation, and industrial Fig. 2. Technological cluster innovation. Y. Xu, X. Li, C. Tao et al. Journal of Innovation & Knowledge 7 (2022) 100195 3
structure upgrades is limited. Some scholars have explored and verified the positive role of technological innovation in promoting industrial structure upgrade under the influence of knowledge spillovers from the perspectives of higher education, financial development, and digital economy (Jiang et al., 2020;Su et al., 2021;Wu & Liu, 2021). A few scholars have conducted research on the impact of spillover effects, such as Xia et al. (2020), who constructed a fixed effects model, asserting that technology spillovers brought by OFDI promote industrial structure upgrade through technical structure and technological innovation. Nevertheless, the study did not specifically distinguish the differential effects based on the directions of knowledge spillovers. In summary, previous literature has largely focused on the mechanism and empirical data between two factors in knowledge spillovers, namely, technological innovation and industrial structure upgrade, and studies investigating the three factors are relatively scarce. Some scholars have attempted to analyze them by proposing theoretical frameworks and fixed effects models, providing policy recommendations for promoting industrial structure upgrade through technological innovation (Xia et al., 2020). However, these studies are insufficient in three ways. First, existing literature lacks the definition of connected knowledge spillovers and technological cluster innovation and fails to acknowledge innovation chain embedding and integration within industry chains. This perspective is helpful for interpreting the driving effect of technological cluster innovation on efficient industrial structure. Second, the application of mechanism model is lacked. Most research analyzes these constructs using a theoretical framework, and mechanism model analysis of technological innovation on industrial structure upgrade is limited. Third, previous empirical research is insufficient for capturing spatial pertinent relationships. Studies have predominantly explored these relationships based on the mediation effect and fixed effects models; therefore, there is a lack of analysis applying spatial effect models. Subsequently, this study constructs a spatial weight matrix of connected knowledge spillovers to examine the direction of knowledge spillovers from the perspective of both geographical and technological distance, which will improve, supplement, and expand upon existing research. Based on previous research status and real-world circumstances, this study proposes the following hypotheses: Hypothesis 1 (H1): There is a significant positive relationship between technological cluster innovation and efficient industrial structure with connected knowledge spillovers. Hypothesis 2 (H2): From the perspective of connected knowledge spillovers, technological cluster innovation inhibits the efficient industrial structure in neighboring areas. Materials and methods Theoretical model In the context of connected knowledge spillovers, technological cluster innovation promotes the cooperation of regional innovation bodies through full agglomeration of technological factors, such as capital, hightech talents, and institutional factors in the region. It drives enterprises or industries developing towards becoming technology-intensive, hightech, and high-productivity, and the efficiency of industrial structure will be improved through internal promotion and pulling effect. Ricardo’s (1817) theory of comparative advantage is an extension and revision of Adam Smith’s theory of international trade division. This theory proposes the principle of relative advantage in international trade, laying a foundation for the theoretical development of international trade and division of labor. With the extension of commodity transportation distance and the development and update of various trade policy measures, the assumption of perfect competition in the standard Ricardian model has been objectively broken down. Scholars have incorporated the hypothesis of imperfect competition into the model to enhance the model’s ability to anticipate trade flows. According to the proportional relationship between global demand and the production capacity of countries with comparative advantages in supplying products, Dornbusch et al. (1977) studied the conditions of complete and partial divisions of labor. Costinot (2009) considered the differences in productivity between different countries and industries and analyzed the impact of the tradeoff between the benefits of specialization and transaction costs on countries’degree of labor division across sectors. The above expansion model from Costinot carefully considers different enterprises or industries with different levels of productivity. This perspective is very suitable for this study, as the approach is based on regional differences in technological cluster innovation capabilities that lead to variations in the production efficiency of various enterprises or industries. This study combines the research of Dornbusch et al. (1977) and Costinot (2009), constructing a Ricardian model with the differences in productivity between different sectors to explore the mechanism of technological cluster innovation as a driver of efficient industrial structure. In the technological cluster innovation network, factors such as technology, knowledge, high-tech talent, and institutional factors will be fully agglomerated under the comprehensive effects of technique acquisition, technological transformation and upgrade, and independent innovation. The agglomeration will ultimately promote efficient industrial structure. There are differences in production efficiency between enterprises or industries due to differences in regional capabilities in technological cluster innovation, which generates production factor flows to high-tech industries with great production efficiency. The number of high-tech industries and the proportion of value added for industrial enterprises are continuously rising, which achieves efficient industrial structure. Suppose that each enterprise produces only one product, which includes a total of atasks. We can obtain the following: q¼Zþ1 0 mink20;aðÞ qk;lðÞ½dl ð1Þ where qðk;lÞ¼0，1probðqÞ¼1eð1=uÞand e1 uis the level of technological cluster innovation, the salary of each employee in the enterprise is M, and the number of fixed teams in the enterprise is R; thus, the number of work tasks for each employee is a=R. Before production, each employee needs to be trained. The learning cost that each employee must pay is also a=R. Therefore, the labor consumed by each task in production is given as follows: M a1 R ¢g¢eR uð2Þ It is also assumed that the number of workers employed by the enterprise for production is g, then the expected output can be shown as follows: EðqÞ¼ Ma R ¢g¢eR uð3Þ thus, the profit function of the enterprise can be written as follows: P¼p¢EðqÞw¢g¼p¢Ma R ¢eR uw hi ¢gð4Þ where the price of the product is pand wage is w.Tofind the firstorder derivative of Rin the above formula, the optimal division of labor of an enterprise isR¼aþffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi a2þ4¢a¢M p 2M, and the demand for labor per unit product can be derived asx¼M a1 R ¢eR u hi 1. Based on the Ricardian model, the ratio of labor demand per unit product in Region 1 and Region 2 can be expressed as follows: B¼x2 x1¼R2¢ðM1R1aiÞ¢e R2 u2 R1¢ðM2R2aiÞ¢e R1 u1ð5Þ Y. Xu, X. Li, C. Tao et al. Journal of Innovation & Knowledge 7 (2022) 100195 4
When two regions have the same level of human capital and products, the higher the level of technological cluster innovation is, the greater is the value of B.Ifu1>u2, Region 1 has a comparative advantage in producing high-complexity products. The market gradually approaches equilibrium under the adjustment of invisible forces. When the market is in equilibrium, region c requires the following: Zi2Sc biðw1g1þw2g2Þdi ¼wcgc;Sc20;1½ ð6Þ Furthermore, according to the Walrasian theory of general equilibrium, we know that the labor market in Region 2 exists as follows: Zi2Sc biðw1g1þw2g2Þdi ¼w2g2;Sc20;1½ ð7Þ where ~ aexists, while w¼Cð~ aÞand w>Cð~ aÞif and only if ai<~ a. Then, we obtain the following: w¼g2 g1 ¢1&ð~ aÞ &ð~ aÞ¼Dð~ aÞð8Þ where&ð~ aÞ¼Ri2S2bidi and w represents the relative salary of two regions. Let Cð~ aÞ¼Dð~ aÞ; therefore, there is ~ a¼fðuÞ, that is faptaradummyentityaptaradummyx27;ðuÞ>0. Free trade between regions determines the stability of the relative wages; therefore, an increase of u1leads to a right shift of DðaiÞand the scope of S2is expanded. The destination of product production will shift accordingly. Region 1 concentrates on producing technology-intensive high-tech products and developing high-tech industries. This further indicates that a high level of technological cluster innovation will drive efficient industrial structure (as shown in Fig. 3). Establishment of spatial weight matrix Horizontal and vertical knowledge spillovers will occur within an industry, among industries, and among upstream and downstream industries, wherein connected knowledge spillovers will also appear in the form of a network. Spillovers are realized through technical knowledge sharing among regional enterprises or industries; therefore, a geographical and technological distance spatial weight matrix is constructed combining geographical distance measures and knowledge spillover measures (Aiello & Cardamone, 2008). The spatial weight matrix can be expressed as follows: WLDI ¼Wd¢diag H01 H0;H02 H0;⋯;H0n H0 ¢diag S1 S;S2 S;⋯;Sn S ¢diag I1 I;I2 I;⋯;In I ¢diag Z1 Z;Z2 Z;⋯;Zn Z ð9Þ WLDR ¼Wd¢diag H;1 H;H2 H;⋯;Hn H ! ¢diag S1 S;S2 S;⋯;Sn S ! ¢diag R1 R;R2 R;⋯;Rn R ! ¢diag Z1 Z;Z2 Z;⋯;Zn Z ! ð10Þ WLDZ ¼Wd¢diag Z1 Z;Z2 Z;⋯;Zn Z ! ð11Þ where qðk;lÞ¼0，1probðqÞ¼1eð1=uÞ Hi¼1=ðti1tiþ2ÞPti ti1Hit; H¼1=ðti1tiþ1ÞXn i¼1Xti ti1Hit; Si¼1=ðti1tiþ2ÞXti ti1Sit; S¼1=ðti1tiþ1ÞXn i¼1Xti ti1 Sit; Ii¼1=ðti1tiþ2ÞXti ti1 RDit IOit ; I¼1=ðti1tiþ1ÞXn i¼1Xti ti1 RDit IOit ; Ri¼1=ðti1tiþ2ÞXti ti1 ISit IOit ; R¼1=ðti1tiþ1ÞXn i¼1Xti ti1 ISit IOit ; Zi¼1=ðti1tiþ2ÞXti ti1 Zit; Z¼1=ðti1tiþ1ÞXn i¼1Xti ti1Zit Wdis geographical distance matrix. 1 WLDI;WLDRandWLDZ are the spatial weight matrices for connected technology introduction, connected technology upgrade, and independent innovation, respectively. Hirefers to the stock of human capital in the province, expressed in terms of the per capita years of education. 2 Hrepresents the total stock of human capital. Sirepresents the total assets of the three kinds of investment enterprises in provincial region i. Srefers to the sum of the total assets of the three kinds of investment enterprises.Iirepresents total R&D activities in provincial region i, expressed in foreign funds of internal expenditures of R&D expenditures. Imeasures the total R&D activities.Riis the total R&D activities in provincial region i, expressed in the weighted sum of contract technology fees of foreign technology import and technological transformation, digestion, and absorption of industrial enterprises above designated size. Rmeasures the total R&D activities.IO is the total industrial output value.Ziis the independent innovation capability in provincial region i, expressed in terms of the weighted value of Fig. 3. Equilibrium map of the two markets. 1 The spatial weight matrix of the provincial geographical distance is expressed as Wij ¼1=d2;i6¼ j 0;i¼j , where dis the straight-line distance between the capital cities. 2 The calculation method of human capital stock is as follows:E¼16e1þ12e2þ9e3þ6e4þ2e5, wheree1;e2;e3;e4and e5represent the proportion of the population of junior college and above, high school, junior high school, elementary school and illiterate and semi-illiterate cultural population in the total population respectively, 16, 12, 9, 6 and 2 are the corresponding number of years of education. Y. Xu, X. Li, C. Tao et al. Journal of Innovation & Knowledge 7 (2022) 100195 5
the added value of high-tech industry, total high-tech industry import and export, number of authorized patent applications, number of scientific and technological papers, and value of contract turnover in the technology market. 3 Variable description Technological cluster innovation (JI) Technological cluster innovation refers to a coordinated process of technology innovation in which technology is clustered between industries. Such cooperation improves industrial production efficiency in a win−win situation. In this study, technological innovation efficiency is used to represent technological cluster innovation (JI) and measure it through input−output indicators. The input indicators represent the market benefits of new technologies, expressed in the new product output value and new product sales revenue of the high-tech industry. The output indicators are measured from the perspective of technological innovation achievements, expressed in the number of new product development projects and patent applications of high-tech industry (Cruz-C azares et al., 2013). In this study, we use the provincial industrial producer exfactory price index to deflate input the variables of technological cluster innovation and use the Data Envelopment Analysis (DEA) Malmquist index to measure the efficiency of provincial technological cluster innovation the base period 2003. Efficient industrial structure (EF) Efficient industrial structure is a dynamic process, wherein the proportion of high-efficiency industries is continuously increasing and the proportion of poor-efficiency industries is constantly reducing, resulting in gradual industrial intensive development. This study uses the proportion of high-tech industries to represent efficient industrial structure. Therefore, we can construct the following variable, efficient industrial structure (EF). EF ¼ Heit=P n i¼1 Heit Areait=P n i¼1 Areait ð12Þ Where Heit reflects the number of high-tech industry enterprises of provincial region iin the year t.Areait indicates the administrative area of provincial region i in the year t. Pattern of ownership (OS) The efficiency of industrial structure will be affected by the existing pattern of ownership through incentive mechanisms and governance structure (Thomsen & Pedersen, 1998). The newly increased fixed asset investment promotes enterprises’technological innovation activities. Sufficient investment funds can enhance research motivation and enthusiasm of scientific and technological researchers, increase the output of innovation achievements, stimulate the supply-and-demand structure of the market, and ultimately realize efficient industrial structure. This research uses the newly increased fixed asset investment, accounting for the proportion of the nation’s total investment in high-tech industries to measure the pattern of ownership. Innovative talents gathering (TG) The effects of technological cluster innovation are primarily exerted through independent innovation and technological knowledge accumulation, which drive efficient industrial structure. Improvements in independent innovation ability and the accumulation of technological knowledge are ultimately attributable to the accumulation of innovative talent, which accelerates the process of regional specialization in scientific research and achieves the refinement and specialization of high-end talent (Jiang et al., 2020). Cooperation between industries will be strengthened, affecting the structure of industrial demand and promoting efficient industrial structure. This study selects the proportion of R&D personnel accounted for in the nation’s total high-tech industries to measure the innovative talents gathering. Foreign investment factor (FI) Independent innovation can significantly promote efficient industrial structure. If an enterprise or industry endeavors to stand firm within the national economy, it must obtain intellectual property rights through establishing independent branding and ultimately achieving the goal of new industrialization. To improve the usage efficiency of advanced global scientific and technological elements in dynamic circulation and to enhance enterprises’cluster innovation capability, it is helpful to digest, absorb, and reinnovate foreign advanced technologies (Stiebale & Reize, 2011). This study uses the proportion of total investment in foreign-invested enterprises to estimate the FI factor. Spatial Durbin model construction Under the influence of connected knowledge spillover, there is a positive effect on efficient industrial structure with the accumulation of technical knowledge and the cooperative innovation of enterprises or industries in the region. The region attracts high-tech enterprises or emerging enterprises through accumulating factors, such as technology, talent, and innovative elements, which promotes industry agglomeration, enhances the cooperative competition effect of enterprises or industries, and ultimately achieves efficient industrial structure. Spatial evolution maps of regional technological cluster innovation and efficient industrial structure indicate that the hierarchical accumulation of technologies or industries leads to the hierarchical agglomeration of emerging technologies and advanced knowledge. The “polarization”phenomenon appears in efficient industrial structure. The characteristics of spatial dependence and spatial lag of the explanatory variables improve the equation’s estimation accuracy, which is increasingly favored by scholars (Elhorst & Fr eret, 2009; Greene, 2005). This study constructs a spatial Durbin model to explore the spatial effects of technological cluster innovation driving efficient industrial structure. yit ¼dX N j¼1 Wijyit þcþXitbþX N j¼1 WijXijtuþmiþλtþeit ð13Þ where dis the spatial association coefficient, reflecting the spatial dependence between the dependent variables. Wij measures the row-standardized spatial weight matrix, and c is the constant term. b refers to the unknown parameter vector of the independent variable, primarily explaining the degree and direction of the influence of the explanatory variable on the explained variable. urepresents the parameter vector of order K1, indicating the degree of influence of the explanatory variables on the explained variable in the neighboring area. mirefers to the individual spatial effect, capturing the individual heterogeneity between the regions. λtis the time effect, indicating the amount of unobservable information received by an individual during a specific period. This study builds the geographical distance spatial weight matrices from the perspective of technology acquisition, technological transformation and upgrade, and independent innovation ability. We then apply the spatial Durbin model constructed to explore the spatial effects of technological cluster innovation driving efficient industrial structure. 3 First, select the factor analysis method for maximizing variance rotation to calculate the weight of each index of independent innovation capability, and then calculate the index value of independent innovation capability. Y. Xu, X. Li, C. Tao et al. Journal of Innovation & Knowledge 7 (2022) 100195 6
Model 1, 2, and 3 can be established as follows: EFit ¼dWLDI EFit þb1JIit þb2OSit þb3TGit þb4FIit þu1WLDI JIit þu2WLDI OSit þu3WLDI TGit þu4WLDI FIit þcþmiþλt þeit ð14Þ EFit ¼dWLDREFit þb1JIit þb2OSit þb3TGit þb4FIit þu1WLDRJIit þu2WLDROSit þu3WLDRTGit þu4WLDRFIit þcþmiþλtþeit ð15Þ EFit ¼dWLDZ EFit þb1JIit þb2OSit þb3TGit þb4FIit þu1WLDZ JIit þu2WLDZ OSit þu3WLDZ TGit þu4WLDZ FIit þcþmiþλtþeit ð16Þ The data of 30 provinces in China (data from Tibet Province are severely lacking, and hence, were discarded) from 2003 to 2017 were selected for an empirical analysis of technological cluster innovation driving efficient industrial structure. 4 Data are from the China Science and Technology Statistical Yearbook, China High-Tech Industry Statistical Yearbook, China Statistical Yearbook, China Urban Database, and EPS Database. Missing data are complemented by interpolation of data from the previous and following years. Results and discussion Results of empirical analysis We establish a spatial Durbin model from the perspective of technology acquisition, technological transformation and upgrade, and independent innovation ability, exploring the spatial effects of technological cluster innovation driving efficient industrial structure. From the results, the Hausman test value is 28.87 and the p-value is 0.00, which is statistically significant at the 0.01 level. We then construct an individual fixed effect spatial Durbin model. See Table 1 for the descriptive statistics of each variable. Here, we apply the maximum likelihood estimation method to estimate Models 1, 2, and 3, obtaining the following empirical results in the China’s nationwide and subregions (Tables 2−4). 5 According to the estimation results of the individual fixed effects spatial Durbin models, we can observe two notable findings. First, the coefficients of technological cluster innovation, pattern of ownership, innovative talents gathering, and FI are predominantly and significantly positive, indicating a significant positive relationship among these variables and efficient industrial structure under connected knowledge spillovers. This is consistent with the results regarding technological innovation and FI of Wu & Liu (2021). Take Model 1 as an example, when technological cluster innovation changed by 1%, the efficient industrial structure in the nationwide changed by 1.12% in the same direction. Technological cluster innovation improves industrial efficiency by changing the innovation model of enterprises or industries and promotes efficient industrial structure. At a 1% significance level, for every 1% increase in the pattern of ownership, the efficient industrial structure in the Central China increased by 2.99%, and the rest of the regions showed positive changes. Promotion of the pattern of ownership accelerates the development of regional high-tech industries, increases the proportion of high-tech industries in the region, and promotes the accumulation of enterprises in hightech industry regions. When innovative talent gathering changed by 1%, the efficient industrial structure in the nationwide increased by 1.41% at a 1% significance level. Innovative talents gathering increases the proportion of technical innovation talent, the collective potential of which can be better tapped into and released. The efficient transformation of knowledge to technology can be realized and enterprises will gradually improve their competitiveness, both of which promote efficient industrial structure. When foreign investment changed by 1%, the efficient industrial structure in the nationwide increased. The increase of FI factors will help regional enterprises or industries to better introduce, digest, and absorb advanced foreign technologies and management methods. A large amount of professional talent will be cultivated to service enterprises, strengthening regional independent innovation ability. Regional industrial efficiency will be improved to achieve efficient industrial structure. The abovementioned measures improve the demand for regional technological innovation, transform the regional industrial structure, and advance the achievement of efficient industrial structure. Second, under the impact of technological cluster innovation, pattern of ownership, innovative talents gathering, and FI factors in the three models, an overall phenomenon emerges, which is that the effect of Model 1 is smaller than that of Model 2, which is smaller than that of Model 3. This indicates that the spatial effect from technology introduction is smaller than that from technical transformation and upgrade, which is smaller than the effect from independent innovation capability. This phenomenon indicates that the process of introduction, digestion, absorption, and reinnovation of foreign advanced technology is the most effective approach for driving efficient industrial structure. In the everchanging state of science and technology transformation, the constraints of resources and environment are increasingly strengthened. Enterprises in a region must face the dual international competitive pressure of continuous technological upgrade and technological innovation. The problem of weak independent innovation has become the bottleneck in China’s regional development; therefore, commitment toward independent innovation is the focus of China’s future efforts. The process of digesting, absorbing, and reinnovating advanced technologies will help to fully leverage existing global technological stock and strengthen the internal driving force of technological innovation in provinces. Subsequently, the motivation for technological innovation in China will be enhanced, and efficient industrial structure in the region will be promoted. Additionally, this study explains the effect of independent variables on dependent variables. We unravel the spatial effects of technological cluster innovation driving efficient industrial structure to understand the differences among different variables. From the Table 1 Statistical characteristics of each variable of the model. Variables Sign N Max Min Mean SD Technological cluster innovation JI 390 1.00 0.04 0.56 0.32 Efficient industrial structure EF 390 51.75 0.02 3.20 7.83 Pattern of ownership OS 390 0.32 0.00 0.03 0.05 Innovative talents gathering TG 390 0.14 0.00 0.03 0.03 Foreign investment factor FI 390 0.38 0.00 0.06 0.05 4 With the in-depth advancement of a new round of technological revolution, innovation plays an increasingly important role in industrial transformation. Since 2017, China has further clarified the important role of innovation in leading economic and social development, which indicates that the innovation drive will play a significant strategic supporting role. Using the data from 2003 to 2017, this paper focuses on the effect between technological cluster innovation and efficient industrial structure with connected knowledge spillovers, which has a certain reference value for the current industrial development with innovation-driven development strategy. 5 This study analyzes the empirical results of 30 provinces and cities in China and divides it into three regions: Eastern, Western, and Central China. Among them, the eastern part includes 11 provinces and cities, including Beijing, Tianjin, Hebei, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central part includes eight provinces and cities including Shanxi, Liaoning, Jilin, Anhui, Jiangxi, Henan, Hubei, and Hunan. The western part includes 11 provinces and cities including Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. Y. Xu, X. Li, C. Tao et al. Journal of Innovation & Knowledge 7 (2022) 100195 7
perspective of the direct and indirect effects, we examine the effect of each indicator on efficient industrial structure, taking the nationwide case as an example. This is shown in Table 5. The coefficients of the direct and indirect effects of technological cluster innovation driving efficient industrial structure are predominantly negative nationwide as well as in eastern, central, and western China. This indicates that technological cluster innovation will weaken the spatial spillover effect of efficient industrial structure in the region and inhibit the efficiency of industrial structures in other regions. The main reason for this weakness is that technological cluster innovation drives efficient industrial structure, revealing a significant backwash effect. A basic feature of technological cluster innovation is the accumulation of technical knowledge, whereas its core is the accumulation of talent. A region’s technology accumulation has significant effects on economic scale, constantly attracting technology and talent from surrounding underdeveloped areas and generating an inhibitory effect on industrial structure optimization and upgrade in the less-developed surrounding regions, leading to the emergence of negative spillover effects. Additionally, there is a diffusion effect of technological knowledge in developed regions and less-developed regions; that is, developed regions and the surrounding less-developed regions can produce spillovers of technological knowledge through economic and technological exchanges between enterprises. Less-developed regions will be affected by driving and demonstration effects from developed regions and then the positive spillover effects of knowledge and technology will appear; however, Table 2 Empirical results of model 1. Variables Nationwide Eastern China Central China Western China W LDI *EF 0.20 * (1.05) 0.34 *** (3.19) 0.27 *** (2.88) 0.03(0.35) JI 1.12 *** (2.89) 0.48 (0.55) 0.21 *** (4.7) 0.05 ** (1.66) OS 3.96 (0.61) 1.15 (0.92) 2.99 *** (3.04) 1.35 ** (2.31) TG 1.41 *** (8.03) 1.66 *** (5.45) 1.19 *** (6.81) 0.51(0.67) FI 1.41 (0.645) 1.02 * (1.05) 0.10 (0.41) 0.01 * (1.06) W LDI *JI 3.09 ** (2.75) 1.85 * (1.17) 0.13 ** (1.46) 0.08 * (1.16) W LDI *OS 4.68 ** (2.86) 2.57 *** (4.31) 0.49 (0.26) 2.82 (0.22) W LDI *TG 1.35 ** (2.91) 1.89 (0.86) 1.50 *** (5.56) 1.30 ** (1.37) W LDI *FI 1.51 (0.32) 2.75 ** (2.69) 0.01 (0.01) 0.09 ** (1.62) logL 636.77 278.73 131.12 208.38 R 2 0.97 0.97 0.95 0.96 s22.19 4.24 0.00 0.00 Note: * Significance at the p<0.1 level; ** Significance at the 0.05 level; *** Significance at the 0.01 level. The t-value is in parentheses. Table 3 Empirical results of Model 2. Variables Nationwide Eastern China Central China Western China W LDR *EF 0.07 (0.867) 0.31 *** (3.21) 0.12 (0.97) 0.13 (0.25) JI 0.99 ** (2.645) 1.00 * (1.13) 0.21 *** (4.49) 0.06 ** (1.67) OS 1.42 * (1.07) 1.40 * (1.08) 3.34 *** (3.61) 1.38 ** (2.35) TG 1.67 *** (9.38) 2.04 *** (6.50) 1.92 *** (7.43) 1.13 (0.78) FI 2.01 (1.01) 4.50 (0.89) 2.07 ** (2.30) 0.08 (0.70) W LDR *JI 2.32 ** (2.67) 0.43 (0.35) 2.21 ** (2.38) 0.08 ** (1.56) W LDR *OS 3.67 *** (3.92) 3.07 *** (4.42) 0.62 (0.32) 2.29 * (1.12) W LDR *TG 6.28 *** (3.86) 1.47 ** (1.96) 3.47 *** (6.43) 1.05 (0.69) W LDR *FI 1.59 * (1.656) 3.445 *** (3.63) 1.22 (0.55) 1.35 * (1.25) logL 484.06 280.20 136.25 208.47 R 2 0.97 0.97 0.95 0.96 s22.08 4.32 0.00 0.00 Note: * Significance at the p<0.1 level. ** Significance at the 0.05 level. *** Significance at the 0.01 level. The t-value is in parentheses. Table 4 Empirical results of Model 3. Variables Nationwide Eastern China Central China Western China W LDZ *EF 0.13 ** (1.64) 0.13 * (1.27) 0.28 ** (2.47) 0.05(0.43) JI 1.82 ** (2.53) 2.60 (0.63) 0.22 *** (4.83) 0.07 ** (1.96) OS 1.03 ** (1.81) 1.54 (1.06) 3.69 *** (3.83) 2.99 ** (1.87) TG 1.74 *** (9.99) 2.89 *** (5.58) 1.71 *** (6.67) 1.50 (0.75) FI 2.76 (0.87) 5.72 (0.56) 3.23 * (1.06) 0.10 (1.03) W LDZ *JI 2.08 *** (3.22) 1.40 (0.94) 0.22 ** (2.35) 0.15 ***(3.60) W LDZ *OS 3.80 * (1.11) 5.80 (0.81) 2.31 * (1.05) 1.87 *** (5.38) W LDZ *TG 6.51 * (1.04) 4.65 (0.62) 2.81 *** (5.44) 1.71 * (1.19) W LDZ *FI 7.45 ** (2.32) 3.09 *** (3.02) 0.13 (0.38) 0.17 * (1.12) logL 581.36 288.62 133.91 224.10 R 2 0.98 0.97 0.95 0.97 s21.61 5.02 0.00 0.00 Note: * Significance at the p<0.1 level. ** Significance at the 0.05 level. *** Significance at the 0.01 level. The t-value is in parentheses. Y. Xu, X. Li, C. Tao et al. Journal of Innovation & Knowledge 7 (2022) 100195 8