Hindawi Complexity Volume 2021, Article ID 6665945, 12 pages https://doi.org/10.1155/2021/6665945

Research Article The Evolution and Determinants of Interorganizational Coinvention Networks in New Energy Vehicles: Evidence from , China

Jia Liu,1 Zhaohui Chong ,2 and Shijian Lu3,4

1School of Economics, University of Finance and Economics, 510320, China 2Business School, University, Shantou 515063, China 3School of Chemistry and Chemical Engineering, Liaocheng University, Liaocheng 252000, China 4Sinopec Petroleum Engineering Corporation, Dongying 257026, China

Correspondence should be addressed to Zhaohui Chong; [email protected]

Received 13 November 2020; Revised 28 December 2020; Accepted 16 January 2021; Published 31 January 2021

Academic Editor: Wei Zhang

Copyright © 2021 Jia Liu et al. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the increasing attention to climate change, air pollution, and related public health issues, China’s new energy vehicles (NEVs) industry has developed rapidly. However, few studies investigated the evolution of interorganizational collaborative innovation networks in the sector domain of NEVs and the influence of different drivers on the establishment of innovation relationships. In this context, this paper uses the joint invention patent of Shenzhen, a low-carbon pilot city of China, to investigate the dynamics of network influencing factors. *e social network analysis shows that the scale of coinvention network of NEVs is constantly increasing, which is featured with diversified cooperative entities, and collaboration depth (i.e., the intensity of the interactions with these partners) is also expanding. *e empirical results from the Exponential Random Graph Model (ERGM) demonstrate that, with the deepening of collaborative innovation, technological upgrading caused by knowledge exchange makes organizations in the network more inclined to cognitive proximity and less dependent on geographical proximity. In addition, organizational proximity and triadic closure contribute positively to the collaborative network, with their relevance remaining nearly the same, while the impeding effect of cultural/language difference is slightly decreasing with time.

1. Introduction seven strategic emerging industries. In the “13th five-year plan for the development of strategic emerging industries” *e promotion and development of new energy vehicles issued by the State Council in 2016, it pointed out to cultivate (NEVs) is not only a strategic choice to reduce emissions green and low-carbon industries such as NEVs into pillar containing atmospheric pollutants, but also an inevitable industries. A series of policies reveal that China’s NEVs need for the development of a low-carbon economy [1–3]. industry is facing unprecedented opportunities. Many countries have released preferable policies to support As a representative of the emerging industry, NEVs their application such as free vehicle plates, governmental adopt emerging technologies and the core of their industrial subsidies, rapid development of charging stations, and competitiveness depends on technological innovation [5, 6]. cheaper insurance fees [4]. *e development of China’s Due to the complexity of technology and the high cost and NEVs industry began in the early twenty-first century. Since uncertainty of innovation, innovation subjects are gradually the launch of the “863” major electric vehicle project in 2001, shifting from independent research and development (R&D) the national and local governments have constantly intro- to cooperative innovation, so as to realize the sharing of duced relevant policies to promote the development of innovation resources, cost reduction, and risk diversification NEVs. In 2010, the NEVs industry was listed in China’s [7]. As the main carrier of the transformation of scientific 2 Complexity and technological achievements in the process of coopera- *is paper expands the existing literature on two aspects. tive innovation, patent application, to a large extent, rep- First, it explores the evolutionary path of topological resents the innovation ability of a firm’s R&D capacity. A structure and spatial patterns of interorganizational inno- large amount of literature has explored patent cooperation vative collaboration in NEVs industry. Second, in contrast to in the high-tech industry. Lei et al. [8] studied the overall most literature on a static or certain type of influential situation of patent cooperation of Taiener battery by elab- factors [29, 30], the paper provides an explanation for the orating three different types of cooperation, and the evolutionary mechanism of interorganizational innovative changing trend of cooperation mode. Omelianenko [9] networks. For this purpose, we analyze the determining analyzed the necessity of establishing diplomatic relations factors employing the Exponential Random Graph Model and mutual relations between clusters and further proposed (ERGM) which can combine the endogenous structures and that international partnership development between clusters the exogenous factors [31]. Hence, we could distinguish is the most appropriate form for developing cooperation between different types of determinants to confirm how the between high-tech sectors. significance and magnitude of the influential factors change. With the acceleration of network research, the collab- In particular, Shenzhen is chosen as the study area to orative innovation network of high technology has attracted inspect and analyze interorganizational coinvention net- extensive attention. Choe et al. [10] studied the structure and work of NEVs. In an attempt to promote the imple- characteristics of knowledge flow among countries, insti- mentation of China’s greenhouse gas emissions control tutions, and technologies in the field of organic photovoltaic target, Shenzhen was offered as the pilot low-carbon city in cells. Zheng et al. [11] analyzed the development of inter- 2010 and has reduced carbon emissions by more than national cooperation in nanotechnology by using a patent 200,000 tones annually due to the implementation of low- network. Sun and Liu [12] established a multilayer network carbon transportation and construction projects [32]. In model of intraregional and interregional joint patent re- addition, the local government has set up a special fund for search and applied the model to China context. De Paulo the development of NEVs industry since low-carbon city et al. [13] studied photovoltaic technology patents and co- construction, making the focal area an archetypal location operation according to the structure and interaction among for interorganizational coinvention network of NEVs national clusters. Generally, studies on the outline of these research. collaborative research networks, especially at the interor- *e remainder of the paper is structured as follows: ganizational level, remain few and far between. Section 2 discusses the theoretical foundation of potential With regard to the development of the electric vehicle determinants of interorganizational knowledge collabora- industry, several studies focus on patents and technological tion and formulates several hypotheses. Section 3 describes innovation for NEVs. For instance, taking Japan as an ex- the data collection followed by introducing the theoretical ample, Ahman [14] discussed the relationship of govern- network model. Network characteristic evolution based on ment policy and the development path of the electric vehicle. respective network indicators is presented in Section 4, Based on a comparative analysis of the entire invention while, in Section 5, we report and interpret the empirical patents and joint patents, Wang and Zhu [15] examined results; and the final section concludes the paper. patents development in China’s NEVs industry from the perspective of industry-university-institute cooperation. 2. Research Hypotheses Christensen [16] argued that sharing components of power transmission systems, such as battery power systems, hybrid In this work, we mainly focus on four commonly identified power, and fuel systems, is of great help to implement the types of geographical and nongeographical proximity in the modular strategy. However, most of the existing research on development of NEVs networks, including geographical, patents of NEVs industry address the technology develop- cognitive, organizational, and cultural factors. ment trend [17] and the driving forces of industrial devel- Geographical proximity indicates the degree of prox- opment [18, 19]. Notable exceptions in this respect included imity of spatial distance between enterprises, and it is and the studies of Sun et al. [20] studied the characteristics of regarded as a prerequisite for enterprises in a cluster to patent cooperation network based on the top 38 organiza- pursue knowledge externality [33]. On the one hand, geo- tions with the most NEV-related patents as research objects, graphical proximity reduces the cost of transportation and and the study of Cao et al. [21] established technological communication between enterprises, thereby facilitating the cooperation innovation network and analyzed the invinci- transfer and learning of tacit knowledge. On the other hand, bility and optimization process of the network. *e role of it provides opportunities for frequent face-to-face com- proximity in favoring the transmission of knowledge in munication among enterprises and helps to enhance mutual innovation and interorganizational networks has also re- trust and understanding among enterprises, especially for ceived increasing attention recently [22–27]. Despite this, highly skilled workers [34]. However, despite the tendency existing studies find little evidence about the evolution of for spatial agglomeration of innovation activities, the geographical or nongeographical factors in the formation of number of interregional R&D collaborations has increased such networks [28]. markedly as well [35, 36]. *ey believe that permanent *e aim of this study is to investigate the impact of the colocation within a cluster is not necessary, and the excessive different relationships between partners for the establish- proximity of partners will bring certain negative effects such ment of innovation linkages in Shenzhen’s NEVs industry. as lock-in, a situation in which inventors successively create Complexity 3 technologies of the same type, industrial structures [37]. In between organizations, and its importance remains stable the early stage of the development of NEVs coinvention over time. network, companies benefit from geographical proximity in Apart from the above proximity affecting coinvention space and conduct extensive short-distance collaborations activities, the transmission of knowledge is easier when [38]. With the continuous development of technology and individuals and companies share a common language and industry, cooperation between companies is not limited to similar cultural and religious values [39]. Regarded as in- proximity in space. Long-distance collaboration is often formal institutional proximity, cultural/linguistic proximity more valuable and involves the exchange of knowledge in can increase trust and lower transactions costs, assisting in high-level technological fields. *erefore, we test the fol- the generation and diffusion of collaborative ideas. lowing hypothesis. In China, regional dialects are quite prevailing in their respective areas and act as an important factor of cultural Hypothesis 1. Geographical distances impede upon research identity [43, 44]. *e complexity of Chinese dialects is collaboration between Shenzhen and other cities, and the parallel in many respects with the Roman family in Europe effect is diminishing over time in importance. [45]. Different linguistic areas are therefore supposed to Cognitive proximity is a particularly important element intensify the fragmentation of the research networks be- for promoting innovation. Coinvention requires an ap- tween Shenzhen and other cities. However, as both the propriate capacity to absorb new knowledge, which requires Chinese central government and the local government in the establishment of a homogeneous cognitive basis [39]. Shenzhen have promulgated a set of policies aiming to Neither very small nor large cognitive distances result in strengthen the position of Shenzhen as a national scientific good outputs and learning effects in the interactions [40]. center and knowledge linkages to the globe, Shenzhen has On the one hand, the development of new technology is established branches of many renowned URIs at the national more and more based on the combination of remote and global scale, such as Peking University, Harbin Institute technological fields [41], as it helps generate new knowledge of Technology, and Moscow State University. In addition, it and radical innovations and avoid cognitive lock-in. On the has launched its “Peacock Plan” in 2011 to attract overseas other hand, the amount of knowledge grows exponentially. talent to work in the city. We assume that these projects Even with intensive and interdisciplinary education, the could facilitate cultural proximity in the long run. Hence, we proportion of everyone in the overall knowledge is declining, will test the following hypothesis. so it is necessary to work with collaborators who have minor cognitive distance [42]. In the embryonic development period of NEVs industry, most enterprises are making ex- Hypothesis 4. *e negative relationship of cultural/linguistic ploratory attempts. Firms and other organizations primarily differences with the probability of a coinvention link be- form ties with other actors that share the same knowledge tween two organizations decreases over time. base and competencies since establishing relations between different knowledge bases are more difficult. For these 3. Data and Methodology reasons, we assume the increased specialization outweighs the increased interdisciplinarity in the innovative collabo- 3.1. Data. *rough the patent information inquiry system of ration of NEVs and state. China National Intellectual Property Administration, the paper takes the applicant’s address and the new energy Hypothesis 2. *e positive relationship of cognitive prox- vehicle keywords as the screening conditions and uses imity, with the probability of a coinvention link between two custom Python scripts designed to crawl the specific coin- organizations, remains stable over time. vention patent data whose at least one of the coinventors is Organizational proximity refers to rules and procedures located at Shenzhen from 2006 to 2017. *e new energy that connect enterprises to the same organizational vehicle keywords include electric vehicles, new energy, new framework, reflecting the degree of sharing of relationships energy vehicles, charging piles, hybrid, intelligent vehicles, within and between organizations [31]. Similar rules and new energy batteries, electronic controls, automobile mo- incentive mechanisms among enterprises help to manage tors, and automobile safety. *e paper has extracted 1351 knowledge exchange and reduce transaction costs. In ad- NEVs coinvention patent data involving 2 or more inventors dition, organizations need strong control mechanisms to in the study period. ensure their ownership of new technologies and adequate returns. However, excessive organizational proximity may also limit interactive learning and flexibility between or- 3.2. Methods ganizations. Based on the constraints of the same organi- zational framework, the possibility of establishing 3.2.1. Network Index cooperative relations between parent corporations and subsidiaries is much higher than that of general enterprises. (1) Network Density. Network density reflects the overall *erefore, we will test the following hypothesis. connectivity level by dividing the number of actual ties by the number of maximum possible ties. A higher network Hypothesis 3. Organizational proximity plays an important density means a higher degree of connectivity between node role in the establishment of innovative collaborations cities in the network. Using V1 to denote the number of 4 Complexity actual ties in the network with N cities, network density DN 4. The Network Characteristic and can be expressed as Evolutionary Pattern V1 DN � . (1) 4.1. StructuralEvolutionAnalysis. To illustrate the dynamics (N ×(N − 1)/2) of the collaborative network, we divide the sample into three periods of roughly equal length: 2006–2009, (2) Centrality. Centrality measures the importance of a node 2010–2013, and 2014–2017. In addition, R software is used city in the network. Although there are as many as thirty to draw the topology evolution diagram of the collabora- centrality indicators, the degree of centrality is considered to tion network. As shown in Figure 1, the connection line be the indicator that most directly reflects the location of the represents the cooperative relationship between the patent node network by focusing on the number of ties an orga- applicants in the coinvention network of NEVs. *e larger nization has [46, 47]. In equation (2), Ci denote the degree node suggests the more collaborators it has, and the thicker centrality of organization i and lij denote the ties from the connection line, the more frequent cooperation be- organization i to organization j: tween the nodes. In the first period, from 2006 to 2009, the network is in N � lij the initial stage of formation and the amount of patent C � j�1,j≠i . (2) i (N − 1) cooperation is relatively small. With the development of NEVs industry, universities and enterprises begin to ex- Eigenvector centrality is to find the node with the most change knowledge, technology, and resources and gradually central position on the basis of the overall network structure. develop a cooperative relationship with each other. At this Its basic principle is to identify the dimension of the distance stage, patent cooperation mainly occurs between enterprises. between network nodes. Each node has a position on each For example, Hongfujin Precision Industry (Shenzhen) Co., dimension, which is an eigenvalue, and all eigenvalue sets are Ltd. establishes a stable partnership with HonHi Precision eigenvectors. Industry. Fugang Electronic (Dongguan) Co., Ltd. and Cheng Uei Precision Industry Co., Ltd. are also frequent collaborators. 3.2.2. 4e ERGM Specification. ERGM is a statistical model *e cooperative network develops into the expansion to analyze the factors affecting network formation [48]. By stage during the period of 2010–2013, reflecting an in- abstracting and modeling social networks, ERGM can ex- creasing number of organizations carrying out collab- press the influence of network links and character attributes orative innovation activities carry out innovative on social networks. *e main advantage of ERGM lies in its activities. It is notable that cooperation between uni- capacity to analyze the dependence among nodes on the versities and enterprises is on the rise, which entails the formation of a network [49]. frequent flow of knowledge, technology, and other re- *e specification of ERGM can be set as sources in the network. For instance, Tsinghua Uni- versity and Hongfujin Precision Industry (Shenzhen) 1 ⎧⎨ ⎫⎬ Co., Ltd. establishes a close cooperative relationship and Pr(Y � y) �� �exp �η g (y) , (3) ⎩ A A ⎭ conducts exchanges, which weakens the limitation of k A spatial distance on knowledge transmission. In addition, where A denotes all possible network configurations and ηA Tsinghua University occupies a relatively central position is its corresponding parameter; gA(y) � �(i,j)∈Ayij repre- in the cooperative network. *is shows that universities sents the network statistic corresponding configuration represented by Tsinghua University possess critical which obtains a value of 1 when it is observed in y. k is a knowledge resources, which plays an important role in normalizing constant which ensures the distribution of promoting the evolution of the coinvention network for equation (3). NEVs. Another advantage of ERGM is that it can simulta- In the first two stages, the networks are relatively sparse. neously consider structural network level factors, as well as From the third period, however, the network begins to node and dyad level factors, which correspond to the three develop towards multipolarization. In the meanwhile, the parts of the network configuration [50]. *e general form of partnerships between the nodes of the cooperative network ERGM that includes the three kinds of factors can be have also been established substantially. Among the nodes, specified as follows: Dongguan Tafel New Energy Technology Co., Ltd. and 1 Shenzhen Tafel New Energy Technology Co., Ltd. have Pr(Y � y) �� �exp�η g (y) + η g (y) + η g (y)�. established a constant and close relationship with each other k α α β β c c and also maintain cooperation with multiple nodes. ( ) 4 Moreover, Tsinghua University is still in the dominant *e parameters (α, β, c) signify the significance and position of the network for the diffusion of R&D related importance of selected factors. *e estimation of the pa- resources. It signifies that Shenzhen has reinforced the ex- rameters is done with the Markov Chain Monte Carlo change of knowledge between different geographical method using R software. regions. Complexity 5

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Shenzhen cimc intelligent technology Shenzhen Dazu CNC technology Tsinghua University Shenzhen National Innovation Energy Research Institute Guangdong mei Chen communications Shenzhen Woer Heat - Shrinkable Material. Shenzhen institute of tsinghua university

Guangdong Tengbiao technology Institute of automation, Chinese Academy of Sciences Shenzhen Haopeng technology Shanghai lane precision machine tool accessories Champ Tech Optical (Foshan) Corporation Xinfeng Kelick technology

Shenzhen Yihua times technology Nanjing Yuexun Electronic Technology Huadian Zhongguang new energy technology

Shenzhen Kstar Science and Technology. Dongguan maike new energy CGNPC nuclear power operation China guangdong nuclear group Shenzhen @ network Guangdong boyu group Shenzhen Kangtuopu information technology Changsha futian automobile technology Shenzhen Click Technology. Shenzhen Hanxing Xiang Technology

Kingsignal Technology. Jingmen Gelinmei new materials Shenzhen Woer New Energy Electric Technology Beiqi Foton Automobile

Shenzhen Auto Electric network Dongguan AnDeFeng battery Shenzhen Inovance Technology Nitto electric Hongtu east technology (shenzhen) Shenzhen power supply bureau Hongfujin (shenzhen) Shenzhen dragon for power supply service Shenzhen Tianlong wireless technology Chongqing university

China Guangdong Nuclear Engineering Shenzhen @ network technology of guangdong province quality measurement supervision and inspection

Shenzhen THZ system equipment Zhejiang university, Dongguan maike technology Shenzhen Yihua Computer China Kehua Nuclear Power Technology Research Institute Shenzhen Meidong power technology. Nanjing branch Huizhou Haineng new material technology Harbin Institute of Technology shenzhen graduate school Daya bay nuclear power operation management Shenzhen Tianlong mobile technology Heyuan City Meichen Joint Intelligent Hardware Electronics Research Institute

Dongguan McNair technology Beiqi Foton Motor Co.,Ltd.Shenzhen Clou Electronics. New of pump valve

Shenzhen Hai Neng Power Holding Shenzhen Tianlong Wireless Technology Jingmen Gem. Wuhan Gem Resources Recycling. Shenzhen Capchem Technology.

China petroleum and chemical technology., shengli oilfield branch testing center Ridong Electrician (Shanghai Songjiang) Shenzhen graduate school of tsinghua university Suzhou Huichuan technology Shenyang northeast battery Shenzhen east public transportation. New energy vehicles industry double gen base operatingShanghai company KaiHua power supply complete sets of equipment Shenzhen Zhongke Ruineng Industrial ShenzhenYihua times technology Jiangsu Tianlong Electronic Technology

Calling from huaxun ark technology Shenzhen Glimay high-tech Huaxun Ark Technology Shandong weifang fukuda mould Midea Smart Home Technology Jilin province huadian power automation engineering Jianrong Semiconductor (Shenzhen) Mechanical research and design institute of sichuan province Shengli oilfield sunchon energy saving technology Shenzhen power grid technology TCL air-conditioner (zhongshan) Dongguan Hengliang New Energy Technology

Dongguan Mcnair New Power. Midea Group. Build glory integrated circuit technology (zhuhai) Shenzhen National innovative energy research institute Shenzhen Terahertz system equipment Shenzhen Juhuatai Technology Shenzhen Guochuang Power System Shenzhen research institute of advanced technology

Zhongshang Haibeirui intelligence soware technology Shenzhen carbon Xi biological technology Shenzhen Meineng power technology

Xinwangda Electric Vehicle Battery TCL air conditioner (zhongshan) Sunwoda Electronic

(c)

Figure 1: Topological evolution of NEVs coinvention network. (a) 2006–2009, (b) 2010–2013, (c) 2014–2017. Complexity 7

4.2. Network Structure Analysis. We show the evolutionary It can be seen from Table 2 that, in the first stage, en- process of the interorganizational coinvention network of terprises occupy the core position of Shenzhen’s NEVs NEVs by calculating multiple network indicators to illustrate coinvention network. Hongfujin Precision Industry the structural characteristics of the overall network, which is (Shenzhen) Co., Ltd. has the highest degree of degree depicted in Table 1. centrality and eigenvector centrality. It signifies that Hon- Network size means the number of organizations par- gfujin Precision Industry (Shenzhen) Co., Ltd. has the most ticipating in the interorganizational network. As shown in patent cooperation with other nodes in the NEVs field and is Table 1, with the gradual development of NEVs coinvention the center of knowledge diffusion in the network. activities, the network scale is expanding enormously. *e In the second stage, the degree centrality and eigenvector number of nodes in the network increases from 52 in period centrality of Tsinghua University is increasing, which in- 2006–2009 to 285 in period 2014–2017, and the network dicates that it has become another important node in the scale expands nearly 5 times. Network edges refer to the total network. In addition, Hongfujin Precision Industry number of relationships generated by the cooperation be- (Shenzhen) Co., Ltd. is still a key node in the cooperative tween nodes. As depicted in the above table, similar to network with strong control ability. It has the largest number network size, the number of network edges increases sub- of partners and some nodes connected with it are also stantially, suggesting a continuously growing trend of the important nodes in the network, which means that there are establishment of cooperative relationship in the network. more resources such as knowledge and information at its Network density reflects the overall connectivity level by disposal. dividing the number of actual ties by the number of max- For the 2006–2017 period, Tsinghua University is in a imum possible ties. A higher network density means a higher relatively central position, whereas Hongfujin Precision degree of connectivity between node cities in the network. Industry (Shenzhen) Co., Ltd.’s position in the coinvention As shown in Table 1, the increase speed of the network edge network is declining. In addition, it can be observed that in number is less than the expansion speed of the network scale, the third stage, Dongguan Tafel New Energy Technology resulting in the decline of the network density from 0.0128 in Co., Ltd. and Shenzhen Tafel New Energy Technology Co., the first stage to 0.0024 in the third stage. *is reveals that Ltd. also occupy the core positions. *ey have established the relationship between innovation entities is not partic- close cooperative relations and carried out patent collabo- ularly close. rations frequently. It signifies that Shenzhen has strength- Network diameter is the length of the largest geodesic ened its geographical proximity with organizations within path in the network. *e smaller network diameter indicates and beyond the city. that the cooperative network has higher transmission per- formance and efficiency. *e average path length is the average value of the geodesic path length between any pair of 5. Evolution of Determinants of nodes in the network. It can be observed that the cooperative Coinvention Network network has a small average path length, and this indicator 5.1. ERGM Specification. Links(dependent variable) is given fluctuates between 1.1 and 1.3 over these three stages, in- by the absolute number of connections in the node. *is dicating that cooperation and interaction between innova- variable is constructed from the total number of collabo- tive organizations of the NEVs coinvention network are rations between institutions a and b during the process of relatively frequent. technological production, in which the patent application Average weighting is the average weighted value of all has more than one inventor. nodes. *e value of this indicator is declining constantly over In order to verify the four hypotheses proposed in this the whole period. *e network’s average clustering coeffi- paper about how coinnovation networks evolve, the fol- cient is the average of the clustering coefficients for all of the lowing variables are given as follows: nodes. *e small value of this indicator means that there are still some obstacles to establish cooperative relations be- (1) Geographical distance is measured as the logarithm tween adjacent nodes. *e common evaluation standard for of the physical distance between two organizations. the quality of community detection is the maximization of a *is variable is introduced to test Hypothesis 1. benefit function called network modularity Q; when Q (2) Cognitive proximity is computed based on institu- approaches 1, the community connectivity of a given net- tions’ technology profiles. Each firm and organiza- work is strong. Remarkably, modularity increases from 0.713 tion’s technology profile has been classified based on in the first stage to 0.965, and the value is continuously its IPC codes. *e cognitive proximity between two increasing in the past decade. *is shows that Shenzhen’s institutions is obtained by calculating Pearson’s NEVs coinvention network is becoming more advanced and correlation coefficients between their technology complex. vectors, after which the values are min–max scaled. In order to further analyze the evolution of key nodes in Two institutions with the highest similarity in Shenzhen’s NEVs coinvention network, we use degree technology profiles have a value equal to 1, while the centrality and eigenvector centrality to reflect the impor- two institutions with the most different technology tance of nodes in the network structure. *e results are listed profiles have a value equal to 0. *is estimate is in Table 2. applied to test Hypothesis 2. 8 Complexity

Table 1: Topological structure indicators of NEVs coinvention network. Network Number of Network Network Average path Average Average clustering Periods Modularity size edges density diameter length weighting degree coefficient 2006–2009 52 215 0.0128 2 1.15 8.27 0 0.7130 2010–2013 154 470 0.0045 3 1.24 6.10 0 0.8140 2014–2017 285 657 0.0024 3 1.16 4.61 0.0396 0.9655

Table 2: Key nodes of the NEVs coinvention network. *e name of the node Periods Sorted by degree centrality Sorted by eigenvector centrality Hongfujin Precision Industry (Shenzhen) Co., Ltd. Hongfujin Precision Industry (Shenzhen) Co., Ltd. Hon Hi Precision Industry Co., Ltd. Hon hi Precision Industry Co., Ltd. Phase one (2006–2009) Cheng Uei Precision Industry Co., Ltd. Tsinghua University Fugang Electronic (Dongguan) Co., Ltd. Cheng Uei Precision Industry Co., Ltd. Tsinghua University Chi Mei Communication Co., Ltd. Hongfujin Precision Industry (Shenzhen) Co., Ltd. Hongfujin Precision Industry (Shenzhen) Co., Ltd. Tsinghua University Tsinghua University Phase two (2010–2013) Hon Hi Precision Industry Co., Ld. Hon Hi Precision Industry Co., Ltd. Rui Acoustics Technology (Shenzhen) Co., Ltd. Futaihua Industry (Shenzhen) Co., Ltd. Rui Aoustics Science and Technology (Changzhou) Co., Ltd. Huahan Technology Co., Ltd. Dongguan Tafel New Energy Technology Co., Ltd. Dongguan Tafel New Energy Technology Co., Ltd. Shenzhen Tafel New Energy Technology Co., Ltd. Shenzhen Tafel New Energy Technology Co., Ltd. Phase three (2014–2017) Hongfujin Precision Industry (Shenzhen) Co., Ltd. Hongfujin Precision Industry (Shenzhen) Co., Ltd. Tsinghua University Tsinghua University Han’s Laser Technology Industry Group Co., Ltd. Han’s Laser Technology Industry Group Co., Ltd.

(3) Organizational proximity is usually measured by two *e above variables are categorized into three types methods. *e first one is whether enterprises belong following ERGM specification: Firmscale and Uniscale are to the same group company or parent company, that node covariates. Geographical distance, is, constrained by organizational structure and hi- Cognitive proximity, Linguistic difference, and erarchy [29]. *e second measurement is by the organizational proximity are exogenous relational cova- degree of similar routine and incentive mechanism riates. Mutual and Edges are an endogenous structure. among enterprises [51]. Considering the limited number of cooperation between scientific research institutes in the NEVs industry and the amount 5.2. ERGM Results. *e results of ERGM from 2006 to 2017 number of cooperation between the parent company are presented in Table 3. Geographical distance is found to and its subsidiaries, head office, and branch com- exhibit statistically negative impacts over the whole period pany, we use the first method to construct the or- and shows a decreasing effect. *ese results corroborate ganizational proximity matrix. If two organizations Hypothesis 1, signifying that geographical proximity plays an belong to the parent-subsidiary relationship or are increasingly weaker role in the evolution of the collaborative between the head office and the branch company, the network. In the meantime, with the deepening of collabo- value is 1; otherwise, it is 0. *is variable is intro- rative innovation, the technological upgrading caused by duced to test Hypothesis 3. knowledge exchange makes the organizations in the network more inclined to cognitive proximity and less dependent on (4) Linguistic difference is set up to measure the cultural geographical proximity. A possible explanation is that due to differences. *is variable for cultural/linguistic dif- the construction of high-speed railways and airports since ference is set to 1 when two institutions are located in 2010, Shenzhen has strengthened its geographical proximity different dialect areas. *e partition of dialect areas with cities within and beyond Guangdong province. Figure 1 of China is indexed from the 2010 Atlas of Chinese also shows, in the second and third stage of the innovation Dialects [52]. network, that Rui Acoustics Technology (Shenzhen) Co., Ltd. Other variables are also included in the ERGM analysis. has carried out extensive cooperation with Aac Microtech Firmscale represents the size of firms, which is measured by (Changzhou). Big companies such as Hongfujin Precision the organizations’ number of employees. Larger firms may Industry (Shenzhen) Co., Ltd. have also established part- be more likely to be tied to other organizations. Uniscale nerships with Tsinghua University. represents the size of universities, which is measured by the Concerning cognitive proximity, we find a positive number of students in the school. overall trend, suggesting that closer cognitive base and Complexity 9

Table 3: Cross-sectional ERGM results for interorganization coinvention network from 2006 to 2017. Dependent variable 2006–2009 (1) 2010–2013 (2) 2014–2017 (3) −1.820∗∗∗ −3.098∗∗∗ −3.269∗∗∗ Edges (0.045) (0.068) (0.098) 4.773∗∗∗ 4.503∗∗∗ 5.369∗∗∗ Mutual (0.084) (0.065) (0.075) 0.001∗∗∗ 0.011∗∗∗ 0.005∗∗ Firmscale (0.0003) (0.001) (0.002) 0.018∗∗∗ 0.028∗∗∗ 0.036∗∗∗ Uniscale (0.005) (0.006) (0.004) 0.082∗∗∗ 0.091∗∗∗ 0.109∗∗∗ Cognitive proximity (0.002) (0.001) (0.001) −0.182∗∗∗ −0.090∗∗∗ −0.061∗∗∗ Geographical distance (0.005) (0.004) (0.005) 0.086∗∗∗ 0.146∗∗∗ 0.100∗∗∗ Organizational proximity (0.020) (0.025) (0.031) −0.050∗∗∗ −0.025∗∗∗ −0.023∗∗∗ Cultural/linguistic difference (0.008) (0.007) (0.008) Akaike Inf. Crit. 34,743.710 46,832.720 40,778.240 Bayesian Inf. Crit. 34,891.500 46,980.520 40,926.030 ∗∗∗, ∗∗, and ∗ represent significance at the levels of 1%, 5%, and 10%, respectively. values generate more links among the inventors of different distant and culturally diversified, while those close to each organizations. In other words, it appears that increasing other are inherently low tech. On the other hand, cultural specialization outweighs interdisciplinarity, confirming difference is to some extent complemented by the increasing Hypothesis 2. It demonstrates that the trend goes rather number of overseas returnees who have gained their degrees towards specialization in the NEVs coinvention activities in in foreign universities. Shenzhen. *is result is consistent with Broekel and For node-level variables, the coefficients of Firmscale and Boschma’s [23] investigation in the Dutch aviation industry Uniscale stay positive and significant over time, meaning and also corroborates the majority of literature that they do that firms with larger size are prone to have more links as not find a change in the negative impact of cognitive distance anticipated. *e same applies to big universities. Besides, the over time on a global or national scale [42]. It is worth noting significant coefficients of Edge and Mutual at the level of that, in recent years, as a result of the construction of high- structural networks empirically confirm the correlation at speed railways and airports, as well as the establishment of this level. *e negative coefficient of Edges is a common cross-city branches of many renowned universities and feature of social processes established networks, indicating research institutes (URIs), the strengthening of geographical that such networks exhibit less density than exponential proximity of Shenzhen with other cities has also facilitated random networks. Besides, the coefficient of Mutual is also cognitive proximity, such as knowledge interactions between positive and significant. It means that organizations tend to researchers from different URIs. form connections with patterners that have similar degree With regard to organizational proximity, it is positively centrality values. *e result also indicates that triadic closure associated with the intensity of interorganizational scientific is a driving force in promoting the evolution of the inter- collaboration, indicating that there are many collaborations organization coinvention networks. between parent and subsidiary companies or subsidiaries in NEVs coinvention network, such as Shenzhen Tafel New 6. Conclusion and Discussion Energy Technology Co., Ltd. and Dongguan Tafel New Energy Technology Co., Ltd. *is result corroborates In order to study the structure and evolution of interor- Broekel and Boschma’s [23] empirical result that organi- ganizational scientific collaborations in the NEVs, this paper zational proximity is a driver behind the formation of uses the coinvention data of Shenzhen, China, to construct knowledge network relationships. *erefore, Hypothesis 3 is the coinvention network of NEVs. *e evolution pattern of supported. coinvention activities from 2006 to 2017 is analyzed by using In terms of dialect, similar to geographical distance, the social network analysis. *en, we test our proposed evo- impeding effect of cultural/linguistic difference is statistically lutionary hypothesis with ERGM to investigate the dynamics significant and loses slight importance over time, providing of network influencing factors. *e main conclusions of this evidence for Hypothesis 4. On the one hand, long-distance paper are as follows. collaboration is often more valuable and involves the ex- First, the scale of coinvention network keeps expanding, change of knowledge in high-level technological fields. and the cooperation depth between nodes has remarkably Teixeira et al.’s [53] research demonstrated that projects that improved, which is featured with diversified cooperative are more technologically advanced are geographically entities. In addition, geographical distance shows a 10 Complexity statistically negative but diminishing effect over the whole Conflicts of Interest period. *e ERGM results confirm our hypothesis that geographic proximity plays a weaker role in the evolution of *e authors declare that they have no conflicts of interest. cooperative networks. In the meanwhile, with regard to cognitive proximity, it presents a positive overall trend, Authors’ Contributions indicating that closer cognitive bases and values generate more connections between inventors in different organi- Jia liu and Zhaohui Chong’s contributions include the study zations. It demonstrates that, with the deepening of col- design, data analysis and writing. Shijian Lu participated in laborative innovation, technological upgrading caused by the revision of the article. knowledge exchange makes organizations in the network more inclined to cognitive proximity and less dependent on Acknowledgments geographical proximity, which is consistent with most of the existing studies. Due to the construction of high-speed *is work is supported by Science and Technology Program railways and airports, as well as the establishment of cross- of Guangdong Province (Grant No. 2020A1010020048), city branches of many famous universities and research Science and Technology Program of Guangdong Province institutions (URIs), the reduction of Shenzhen’s travel time (Grant No. 2019A101002064), Natural Science Foundation to other cities also promotes cognitive proximity. of Guangdong (Grant No. 2018A030313453), Guangzhou Secondly, in terms of organizational proximity, it is Philosophy and Social Science Project (Grant No. positively correlated with the intensity of interorganizational 2019GZQN35), the Humanities and Social Science Fund of scientific cooperation. As far as dialects are concerned, similar Ministry of Education of China (20YJC790189), National to geographical distances, the impeding effect of cultural/ Statistical Science Research Program (2019510), Social Sci- linguistic differences is statistically significant and has de- ence Planning Project of Guangdong Province (Grant No. clined slightly in importance over time. Moreover, cultural GD19YGL17), Science and Technology Program of differences are to some extent complemented by a growing Guangdong Province (2019-45;2019B101003021), *e 13th number of returnees from overseas who have earned degrees Five-Year Plan of Education science in Guangdong Province at foreign universities. In addition, the result indicates that (Grant No.2020GXJK213) and the Sci- triadic closure is a driving force in promoting the evolution of entific Research Foundation Project (Grant No. STF18010). the interorganizational coinvention networks. *e paper is also supported by the Natural Science Foun- For policy implications, firstly, the potential cooperation dation of Basic and Applied Basic Research Foundation of between innovation organizations in the field of NEVs needs Guangdong Province in 2021. to be fully explored. *e establishment of a technology information sharing platform and the design of a cooper- References ation innovation incentive mechanism would effectively [1] X. Zhang and X. Bai, “Incentive policies from 2006 to 2016 shorten the technological distance and improve the effi- and new energy vehicle adoption in 2010-2020 in China,” ciency of information transmission. 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