Research ArticleInnovation Efficiency and the Spatial Correlation NetworkCharacteristics of Intelligent-Manufacturing Enterprises

Ningning Fu 12

1Harbin Engineering University Harbin City Heilongjiang Province 150000 China2Harbin Finance University Harbin City Heilongjiang Province 150000 China

Correspondence should be addressed to Ningning Fu 249922516qqcom

Received 4 August 2021 Revised 27 September 2021 Accepted 21 October 2021 Published 11 November 2021

Academic Editor M De Aguiar

Copyright copy 2021Ningning Fuis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

A data envelopment analysis cross-efficiency model was used to measure the innovation efficiency of Chinese intelligent-manufacturing (IM) enterprises is paper took as samples the number of granted patents and RampD investments of IM en-terprises listed from 2015 to 2020 is research used the modified gravity model to determine the innovation efficiency and thespatial correlation of IM enterprises in China and used UCINET software to reveal the innovation efficiency and spatial networkcharacteristics of IM enterprises through a social network analysis e study found that the relationship was significant andfrequently close between innovation efficiency and the spatial correlation network of IM enterprisese distribution of the spatialassociation network was ldquocore-edgerdquo and IM enterprises in Eastern China were at the network core and mostly played anintermediary rolee spatial correlation network had four modulese distribution of the enterprise innovation correlation wasuneven within each module amalgamation was poor among the subgroups and characteristics of highly cohesive subgroupswere present

1 Introduction

In January 2021 the Ministry of Industry and InformationTechnology issued the Report on Intelligent-Manufacturing(IM) Development Index (2020) which pointed out that IMhas become an important channel for promoting thetransformation and upgrading of the manufacturing in-dustry and accelerating its high-quality development as anew round of scientific and technological and industrialrevolutions is constantly deepening e Manufactured inChina 2025 report issued by the State Council pointed outthat IM should be taken as the development core to realizeindustrial transformation and to upgrade China from amanufacturer of quantity to one of quality In this criticalphase the Ministry of Industry and Information Technologycarried out the special action of an IM pilot demonstration toimplement the overall requirements of Manufactured inChina 2025 At present Chinarsquos IM industry is still in thedevelopment stage and is at one remove from that in thedeveloped countries As an emerging industry Chinarsquos IMindustry is relatively weak in innovative applications and is

subject to sensor and control equipment technology Chi-nese companies have difficulty developing new products interms of technology research and development (RampD) Atthe same time Chinarsquos intelligent equipment companies aresmall in scale and weak in competitiveness e operation ofChinarsquos IM of equipment started late it has not formed abackbone with strong competitiveness and its industrialorganization is small In the international competitivelandscape enterprises in developed countries still dominateIn addition Chinarsquos IM of equipment lacks support and itsfoundation is relatively weak IM has no consistent defini-tion at home or abroad but China has offered a somewhatcomprehensive descriptive definition in IM DevelopmentPlanning (2016ndash2020) is definition states that IM is a newproduction mode based on the deep integration of new-generation information as well as communication and ad-vanced manufacturing technology It runs through all as-pects of manufacturing activitiesmdashsuch as designproduction management and servicemdashand has the char-acteristics of being self-sensing self-learning self-decision-making self-executing self-adaption etc Intelligent

HindawiComplexityVolume 2021 Article ID 4299045 15 pageshttpsdoiorg10115520214299045

upgrading refers to the innovation and transformation of theproduction and development modes It is also an effectiveinnovation and development path for realizing the trans-formation of themanufacturing industryWe thus formulatethe following research questions What is the specific in-novation efficiency of the upgrading of IM enterprisesWhatare the spatial network characteristics of the innovationefficiency of Chinarsquos IM enterprises is article has a novelresearch angle Because Chinarsquos current IM statistics lack aunified caliber and related macrolevel statistics are scarcethis article selects as the source microlevel IM enterpriseupgrade data Selected as the sample were the 2015ndash2020National Pilot Enterprises of IM which has high credibilityand availability and was officially announced by theMinistryof Industry and Information Technology e efficiencychanges are measured of Chinarsquos IM pilot enterprises inusing such technology to promote the innovation processfrom 2015 to 2020 Furthermore the overflow path is an-alyzed of innovation efficiency in the spatial network of theIM pilot enterprises and the role and function of eachenterprise are studied in the spatial correlation network ofIM innovation efficiency e latter is of great significancefor promoting the integration of informatization and in-dustrialization building a collaborative innovation mecha-nism of IM and driving regional economic development

2 Literature Review

Wright and Bourne first proposed the concept [1] of IM in1988 and a research on IM by domestic scholars has shownldquoblowoutrdquo growth in recent years [2ndash4] e IM researchfocus is its connotation [5] transformation and upgrade oftraditional manufacturing industries [6] production service[7] development strategy [8] performance and influencingfactors [9] and other related fields [10ndash13] Generally thecurrent IM research is relatively fragmented [14] In terms ofthe innovation efficiency of IM enterprises Zhao [15]pointed out that it is a means rather than an objective so itmust be evaluated Li et al [16] pointed out that thisevaluation should focus not only on the technical methods ofIM enterprises but also on their innovation efficiency Tomeasure the innovation efficiency of enterprises foreignscholars have mainly used a data envelopment analysis(DEA) [17] the technique for order of preference by sim-ilarity to ideal solution (TOPSIS) [18] and a stochasticfrontier analysis [19] Lu et al [20] pointed out thatquantitative methods are seldom used in existing domesticresearch for this measure Liu and Ning [21] used the DEAmodel to measure the technological innovation efficiency ofIM enterprises in China rough a theoretical analysis Qin[22] believed that the IM industry in China needs to improveits industrial standards and promote the capability for in-dependent innovation For example Liao [23] pointed outthat the gap of the overall technological innovation capa-bility of the IM industry in China is still large compared withthat of the developed regions abroad Xiao et al [24] tookcase study media and concluded that China could furtherimprove the innovation capability of IM enterprises only bybreaking through information technology barriers

Regarding the construction and influencing factors of IMnetworks Zhou et al [25] pointed out that IM is amanufacturing network integrated with a human-ndashphysicalndashinformation system rough an analysis fromthe perspective of technical applications Li et al [16] foundthat IM is a complex network that includes applicationresource network and cloud service platform layersJovanovic et al [26] suggested that the technologicalprogress of IM enterprises would interact with the envi-ronment which is a system undergoing constant develop-ment Zhang [27] analyzed the combination of 5Gtechnology and IM networks to promote regional innova-tion capability in China Chen [28] pointed out that IM cannot only help enterprises improve production efficiency butalso promote the coordinated development of regional in-dustries As the research has deepened on the innovation ofIM enterprises a few scholars have analyzed the networkproblems of IM enterprises from the spatial correlation [29]perspective For example Zhang et al [30] took as the re-search object the IM cluster of small- and medium-sizedenterprises and used a complex network model roughdynamic evolution they concluded that the high centralitynode in the cluster plays a leading role in the development ofits cluster

Innovation efficiency network structure characteristicscan be described from four network aspects scale opennessstructure holes and links Network scale refers to thenumber of subjects in the network [31 32] It represents themost basic characteristics of the network structure and iscomposed of network nodes ese nodes are mainlycharacterized by innovation entities such as enterprisesuniversities scientific research institutions and govern-ments Generally when the network is larger there are moreopportunities for communication between nodes and forinnovation and the ability to innovate is stronger [33 34]Network openness is mainly manifested in the autonomouscontrol of the network connection by the actors that is theestablishment and interruption of the network connectionits strengthening and weakening and its communicationwith other networks [35ndash37]

e existing literature has laid the research foundationfor this paper but the following aspects are still worthy offurther exploration① Although a small number of scholarshave studied the innovation efficiency of IM enterprises inChina most of these studies have used qualitative researchmethods such as case studies or theoretical analysesHowever the quantitative evaluation method is relativelysingular and the DEA model is applied for most mea-surements but a ranking evaluation cannot be carried outsince the efficiency value of the DEA model applies tomultiple units at the same time②e existing research ismainly focused on the construction of IM networks andtheir influencing factors from the resource logistics andtechnology layers Research is lacking on the spatial networkcorrelation of the innovation efficiency of IM enterprisesand the scarce existing research lacks pertinence ereforethe marginal contributions of this paper lie in the followingfirst the innovation efficiencies of 45 IM enterprises weremeasured by using a DEA cross-efficiency model and

2 Complexity

exploiting the special action of IM demonstration pilotprojects Furthermore the model was combined with thedata for listed companies to compensate for the lack of aDEA model that cannot rank the units with efficiency toenrich existing research methods and to evaluate the in-novation efficiency of IM enterprises Second this paperused the social network analysis method to construct thespatial correlation network of the innovation efficiency ofIM enterprises studied the influences of and relationshipsamong the subsystems in the spatial correlation networkand filled the theoretical gap of research on the spatialcorrelation network of the innovation efficiency of IM en-terprises ird the spatial correlation of the innovationefficiency of IM enterprises is examined from a larger spatialscope while the existing literature mainly examines theperspective of geographic proximity Fourth while theexisting literature builds a measurement model based onldquoattributerdquo data the network structure is difficult to describeis study relies on ldquorelationalrdquo data to describe the spatialcorrelation network structure of the innovation efficiency ofIM enterprises

3 Research Design

31 Construction of theDEACross-EfficiencyModel In 1986Silkman first proposed the DEA cross-efficiency evaluationmethod [38] used in evaluation ranking to evaluate theinnovation efficiency of K enterprises Each enterprise(decision-making unit DMU) had n input variables and Moutput variables in which Xik was the total amount of the i-th input of the k-th enterprise Ysk was the s-th outputvariable of the k-th enterprise and the input and output ofthe k-th enterprise were expressed as Xk (x1k x2k xnk)Tand Yk (y1k y2k ymk)T respectively e weight coef-ficient vector of input vector X was Q (q1 q2 qn)T andthe weight coefficient vector of output vector Y was G (g1g2 gm)

T

maxYTk g Eik

YTk gleX

Tk q 1le kle n

XTk q 1

gge 0 qge 0 Eik ge 0

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(1)

e optimal solutions of the above linear programmingwere set as glowastk and qlowastk and then optimal Eik YkTlowastgk was theefficiency value When the value of DMUk was from DMU1to DMUm m cross-efficiencies could be obtained from anyDMU and the cross-evaluation matrix was finally obtained

E

E11 middot middot middot E1m

⋮ ⋱ ⋮

Em1 middot middot middot Emm

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ (2)

where the main diagonal element was the efficiency value ofself-evaluation and the other elements were the efficiencyvalues of other evaluations e average value of each col-umn in the matrix was the final cross-efficiency value of theDMU When the value was larger the DMU was better

32 Measurement of the Relationship Efficiency of the Inno-vation Spatial Network of IM Enterprises e spatial cor-relation network of IM enterprises is determined throughspatial relationships Currently scholars mainly use thevector autoregressive (VAR) Granger causality and gravitymodels to test the spatial relationship According to Liu et al[39] the VAR Granger causality method is too sensitive tothe choice of the end of lag which weakens the accuracy ofthe characteristic description of the network structure to acertain extenterefore by referring to the modified gravitymodel of Shao et al [40] the spatial correlation network ofthe innovation efficiency of the IM enterprises was con-structed in this paper e modified gravity model was asfollows [41]

Xij RFi lowastFj

E2ij

(3)

where Xij was the spatial correlation strength of the inno-vation efficiency of the IM enterprises Fi and Fj were theirinnovation efficiency and Eijwas the linear distance betweentwo enterprises is paper used ArcGIS software to cal-culate and obtain RKi(KI+Kj) in which ki and kjwere thegross domestic products of the cities where the IM enter-prises were located e spatial correlation binary matrix ofthe innovation efficiency of the IM enterprises was calcu-lated by a modified gravity model To further describe thespatial correlation of their innovation efficiency the averagevalue of each row of the matrix was taken as critical value Pof the correlation degree between the IM enterprise in therow and the other IM enterprises If Xijge P the value was 1which indicated that those in the row were correlated withthe innovation efficiencies of those in the column Other-wise the value was 0 indicating that there was no correlationamong the enterprises

33 Social Network Analysis A social network analysis cantransform attribute data into relational data By measuringnetwork indices the paper analyzed the whole networkindividual network and module characteristics and theintermediary role of the spatial correlation network of theinnovation efficiency of IM enterprises in China

331 Characteristic Index of the Whole NetworkNetwork correlation refers to an index that can reflect thedegree to which two IM enterprises can establish relation-ships It can measure the independence and dependencebetween IM enterprises e calculation formula was asfollows [42]

C 1 minus U

[N(N minus 1)2] (4)

where C represented network relevance N was the numberof the IM enterprises in the network and U was the numberof those whose innovation efficiency was not discoverable inthe correlation network

Network density refers to the index that measures thedensity degree of relationships of innovation efficiency

Complexity 3

correlation among research objects in social networks It isthe ratio of the actual number of relationships in the networkto the maximum possible number of relationships in thewhole network When it is higher the spatial correlationdegree is closer to the innovation efficiency of IM enter-prises e calculation formula was as follows

D V

[N(N minus 1)] (5)

where D represented the network density V referred to theactual number of innovation efficiency relationships in thenetwork and N represented the number of nodes in thewhole network

Network efficiency refers to the extent to which thenetwork has a redundant relationship when the number ofresearch objects is known It canmeasure the extent to whichthe IM enterprises in the spatial correlation network ofinnovation efficiency have redundant relationships thatreduce network stability Its formula was as follows

GE 1 minus G

max(G) (6)

where G was the redundant relationship in the correlationnetwork of innovation efficiency and max (G) was themaximum number of possible redundant relationships inthe network

332 Characteristic Index of the Individual NetworkIndividual networks can reveal the power of IM enterprisesin the spatial correlation network of innovation efficiencyand whether one such enterprise is in the core positionSocial network scholars use degree closeness and inter-mediary centrality to quantify individual networks from theperspective of ldquorelationshipsrdquo Degree centrality canmeasurethe node number directly connected with other points It isapplied to the innovation efficiency network of the IMenterprises and can describe the impact of innovation ef-ficiency in each IM enterprise on that of the othersCloseness centrality can measure the distance between twopoints and can be applied to the innovation efficiencynetwork of IM to describe the stability and independence ofthe impact of each IM enterprise on the innovation efficiencyof the others Intermediary centrality can measure the abilityof a point between two points to control the communicationbetween the two points It can be applied to the innovationefficiency network of IM enterprises and can describe theability of IM enterprises to control others Among the threemeasurement indices degree centrality is relatively simpleand closeness and intermediary centrality need to bemeasured according to formulas

e calculation formula of closeness centrality was asfollows

Di 1113944n

j1Cij (7)

whereDiwas the betweenness centrality of node iCijwas theshortcut distance between point i and point j and ine j

e calculation formula of the intermediary centralitywas as follows

Fij 1113936

nj 1113936

nk Mjk(i)

Mjk

(8)

where Fijwas the betweenness centrality of node iMjk(i) wasthe number of shortcuts passing through point i betweenpoints k and j andMjk was the number of shortcuts betweenpoints k and j where kne jne i and jlt k

333 Module-Modeling Analysis A module-modelinganalysis classifies each node into a module according tosimilar characteristics through a cluster analysis in a socialnetwork analysis and reacts to the relationship character-istics among modules With reference to the networkmodule division method of Wasserman and Faust [43] theefficiency spatial correlation network of the IM enterprisescould be divided into four modules two-way benefit netincome net overflow and broker e transmitndashreceiverelationship was generally analyzed between modules andthe image matrix was used to react to the internal structuralchanges of the network and the correlation relationshipchanges among modules [44]

334 Intermediary Analysis Intermediaries master theldquosecretsrdquo among multiple groups regardless of whether thesecomprise the whole network of the innovation efficiencycorrelation of IM enterprises or individual modules Withreference to the intermediary classification of Gould andFernandez [45] the IM enterprises were divided into fivecategories according to the intermediary role coordinatorgatekeeper agent consultant and contact person e co-ordinator agglomerates the internal innovation efficiencycorrelation of its own module e agent gives the inno-vation efficiency of its own module to other modules econsultant produces the innovation efficiency correlationrelationship of different IM enterprises in a certain modulee gatekeeper incorporates the innovation efficiency ofother modules into its own modulee contact person bothincorporates the innovation efficiency of other modules intoits ownmodule and gives the innovation efficiency of its ownmodule to another module which can drive differentmodules to build the correlation relationship of innovationefficiency

34Variable andDataDeclaration For reasons of authoritythis paper selected as the data samples the demonstrationpilot project of IM enterprises officially announced by theMinistry of Industry and Information Technology Since2015 the Ministry of Industry and Information Technologyhas selected intelligent-upgrading projects of manufacturingenterprises as demonstration pilot projects according to therelevant requirements of the Element Conditions of the PilotDemonstration Project of IM e pilot demonstrationproject must have operationalized the transformation of IMenterprises must have achieved remarkable results inshortening the product development cycle reducing

4 Complexity

operating costs and improving production efficiency andmust continue to improve in future development and exhibitfavorable growth erefore the sample selection met thestudy objective and requirements of this paper From 2015 to2018 the Ministry of Industry and Information Technologyannounced 306 IM demonstration pilot projects Becauseobtaining enterprise microdata is difficult and financial datadisclosed by listed companies are highly reliable andavailable domestic A-share listed enterprises among thepilot enterprises were selected as typical samples Moreoverthe listed companies experiencing bankruptcy mergers andacquisitions or missing important information were elimi-nated leaving 103 listed companies To ensure the datavalidity and integrity extreme enterprises whose patentauthorization number was 0 were eliminated Ultimately 45effective target enterprises remained e annual report datawere all from the Guotai database and the number of patentauthorizations for each enterprise was searched with theapplicant as the search condition yearly on the NationalIntellectual Property Administration website

According to the innovation input and output process ofIM enterprises investment in RampD was selected as the inputindex in this papere practice of Su and Li [46] was used inthe input stage when measuring the innovation efficiencyindex by considering the principle of the availability andoperability of index selection e main reasons for usingthis method are as follows the enterprisersquos RampD activi-tiesmdashincluding personnel funds and equipmentmdashincludemany factors that are difficult to measure and RampD fundinvestment is the most direct response to the enterprisersquosRampD investment is paper used annual RampD investmentoperating income to measure innovation investment Re-garding the output index from the perspective of existingstudies all scholars measure innovation achievements bypatent application quantity and the number of grantedpatents [47 48] e quantity of patent applications rep-resents innovation efforts rather than scientific and tech-nological innovation capabilities e number of grantedpatents is the number of patents authorized only after strictexamination procedures which can more directly reflect theregional capability of scientific and technological innovation[49] erefore this paper used the number of grantedpatents rather than the number of applications e eval-uation index system of the innovation efficiency of the IMenterprises is shown in Table 1

4 Empirical Analyses

41 Spatial Distribution of the Innovation Efficiency of the IMEnterprises is paper used the DEA cross-efficiency modeland MaxDEA software to measure the innovation effi-ciencies of 45 IM enterprises from 2015 to 2020 As shown inTable 2 the development was relatively stable of the inno-vation efficiency of Chinarsquos IM enterprises innovation ef-ficiency has been increasing slowly in the past six years withfluctuations and the average innovation efficiency was 0515In these six years the average innovation efficiency ofJiangsu CMG Construction Machinery was 0957 rankingfirst and the lowest innovation efficiency was of Shaanxi

Xiagu Power in 2015 which was only 0109 IM enterprisesexhibited a large gap in innovation efficiency which indi-cated that some of Chinarsquos IM enterprises made somebreakthroughs in recent years but some of them had lowinnovation efficiency

42 Analysis of the Characteristics and Evolutionary Process oftheWholeNetwork Based on themodified gravity model thissection establishes the spatial network of the innovation effi-ciency of the IM enterprises draws a topological map of theirspatial network by using Netdraw in UCINET and selectssections of 2015 and 2020 for a comparative analysis (seeFigures 1 and 2) e nodes in the figure represent the 45 IMenterprises and the connections between the nodes representthe correlation relationships between them As shown inFigures 1 and 2 compared with 2015 the spatial correlationrelationships of innovation efficiency network density andtwo-way correlation represented by thick lines increased in2020 and the relationship network of innovation efficiencybecame more complex then e innovation efficiencies ofmany of the IM enterprises not only affected the enterprises inthe same provinces but also broke the geographical restric-tions permitting spatial correlation relationships to occur withneighboring and nonneighboring provinces To further analyzethe overall network characteristics of the spatial correlation ofthe innovation efficiency of the IM enterprises this papercalculated the spatial correlation network correlation degreedensity grade and efficiency of their innovation efficiencybased on the spatial correlation matrix A diagram comparingthe network density and relationship number is shown inFigure 3 and one comparing the network grade and networkefficiency is shown in Figure 4

e measurement results showed that the networkcorrelation degree from 2015 to 2020 was 1 which indicatedthat the network accessibility was good and the innovationefficiency network of the IM enterprises was closely asso-ciated among regions Figure 3 shows that the networkdensity and the number of relationships decreased slightly in2020 but the overall trend was still an increase equantitative analysis was consistent with the results of theprevious topological graph which indicated that the rela-tionships tended to be close among the spatial correlationnetwork of the innovation efficiency of the IM enterprisesFigure 4 indicates that the network grade showed adownward trend from 07928 in 2015 to 0686 in 2020indicating that the spatial correlation network structure ofthe innovation efficiency of the IM enterprises was brokenthe relationship between regional innovation efficiencies wasgradually strengthened and an obvious trend emerged ofcross-regional collaborative innovation e network effi-ciency decreased from 051 in 2015 to 04632 in 2020 whichindicated that the innovation efficiencies of the IM enter-prises were gradually close in the network and the stabilityof the spatial network has gradually strengthened

43 Individual Network Characteristics By measuring de-gree closeness and intermediary centrality this paper an-alyzed the individual network characteristics of the spatial

Complexity 5

network of the innovation efficiency of the IM enterprises todetermine their positions and rolesis paper measured thein- and outdegrees of each IM enterprise to study the in-fluence and affected situations of the spatial correlationnetwork of certain IM enterprises and the measurementresults are shown in Table 3

431 Degree Centrality Degree centrality can reflect thestatus of each IM enterprise in the spatial network of in-novation efficiency e five IM enterprises with the highestoutdegrees were XCMG Construction Machinery JiangsuHTGD Zhejiang Wynca Hubei Accelink and AnhuiHuamao Group which are located in Eastern and Central

Table 2 Innovation efficiency of 45 IM enterprises from 2015 to 2020

Enterprise abbreviation 2015 2016 2017 2018 2019 2020 Average valueHenei Heis Company Limited 0726 0781 0831 0884 0851 0846 0820Shandong Goertek Inc 0582 0559 0602 0643 0685 0685 0626Hunan Zoomlion 0531 0564 0587 0571 0639 0624 0586Liaoning Ansteel 0891 0784 0863 0831 0783 0961 0852Shanghai Tiandi Science amp Technology 0761 0963 0874 0767 0778 0934 0846Zhengjiang Robam 0873 0731 0783 0731 0791 0683 0765Henan Yutong Bus 0741 0739 0846 0883 0821 0857 0815Hebei CRRC 0835 0874 0887 0859 0983 0973 0902Jiangxi Copper 0783 0657 0674 0895 0856 0785 0775XCMG Construction Machinery 0928 0954 0961 0959 0972 0968 0957Jiangsu HTGD 0765 0875 0931 0976 0951 0874 0895Beijing Foton 0745 0784 0734 0861 0987 0943 0842China XD Group 0693 0646 0685 0699 0751 0631 0684Guangxi Liugong 0687 0691 0682 0691 0723 0703 0696Guodian Nanjing Automation 0735 0758 0876 0831 0941 0872 0836Chongqing Chuanyi Automation 0646 0763 0735 0772 0841 0864 0770Zhejiang Jushi 0695 0685 0821 0867 0841 0871 0797Xinjiang TBEA 0571 0645 0682 0631 0788 0793 0685Xinjiang West-Construction 0473 0482 0741 0783 0732 0778 0665Shandong Sailun Jinyu Group 0974 0861 0967 0983 0741 0865 0899Chongqing Yahua 0651 0731 0631 0689 0731 0786 0703North China Pharmaceutical 0573 0581 0542 0514 0631 0645 0581Gansu Great Wall Electrical 0463 0441 0485 0463 0431 0521 0467Xinjiang Goldwind Science amp Technology 0342 0314 0381 0321 0384 0351 0349Jiangsu Victory Precision 0351 0351 0431 0463 0471 0442 0418Jiangsu Tongding Connection 0319 0373 0379 0365 0383 0381 0367HT-SAAE 0341 0364 0373 0361 0371 0379 0365Hubei Accelink 0286 0281 0265 0365 0393 0345 0323CITIC Heavy Industries 0311 0382 0362 0374 0375 0321 0354Rapoo Technology 0331 0285 0231 0351 0341 0358 0316Guangxi Hytera 0221 0252 0241 0283 0261 0251 0252Zhejiang Eastcom 0156 0121 0241 0271 0263 0298 0225Fujian Changelight 0154 0173 0196 0141 0144 0131 0157Guizhou Space Appliance 0231 0274 0221 0251 0267 0271 0253Shanghai Brightdairy 0151 0182 0164 0171 0162 0153 0164Jiangxi Changhong Huayi 0291 0272 0263 0231 0285 0221 0261Jiangxi JZJT 0253 0262 0241 0235 0281 0284 0259Shangdong Doublestar 0186 0171 0184 0191 0121 0132 0164Shandong Tianrun Crankshaft 0257 0207 0231 0221 0267 0238 0237Zhejiang Wynca 0178 0211 0251 0268 0281 0252 0240Jiangsu Hengshun Vinegar Industry 0191 0137 0181 0189 0237 0173 0185Zhengjiang Saint Angelo 0219 0231 0298 0281 0231 0235 0249Anhui Huamao Group 0182 0175 0238 0247 0219 0183 0207Jiangsu Kanion Pharmaceutical 0178 0121 0243 0189 0182 0231 0191Shaanxi Xiagu Power 0109 0159 0194 0114 0184 0176 0156

Table 1 Evaluation index system of the innovation efficiency of the IM enterprises

First-grade index Second-grade index MeasurementInput Investment in RampD Annual RampD investmentoperating incomeOutput Number of granted patents Direct use of the index data

6 Complexity

China is finding showed that these five IM enterpriseshad more innovation efficiency overflow which promotedthe innovation efficiency of the other IM enterprisese fiveIM enterprises with the poorest outdegrees were XinjiangGoldwind Science amp Technology Xinjiang West-Con-struction Gansu Great Wall Electrical Xinjiang TBEA andGuangxi Liugong which were located in NorthwesternChina and had little correlation with the other IM enter-prises e five IM enterprises with leading indegrees wereXCMG Construction Machinery Guodian Nanjing Auto-mation Shandong Goertek Inc Shandong Sailun JinyuGroup and Zhejiang Jushi which were all located in EasternChina e higher outdegrees indicated that the innovationefficiencies of the other IM enterprises overflowed to thesefive enterprises e five IM enterprises with the lowestindegrees were Xinjiang Goldwind Science amp TechnologyGuangxi Liugong Shaanxi Xiagu Power Xinjiang TBEAand Xinjiang West-Construction which were located in

Northwestern China indicating that these enterprises un-dertook fewer network relationships A further analysisshowed that the degree centrality was the highest for the IMenterprises in Eastern China indicating that these enter-prises had a higher degree of external innovation efficiencyIn contrast the degree centrality of the IM enterprises inWestern China ranked lower indicating that these enter-prises had a lower degree of external innovation efficiencyAccording to the Report on the IM Development Index(2020) the IM enterprises in China are unevenly distributedand more IM enterprises are concentrated in Jiangsu andZhejiang provinces and other parts of Eastern China einnovation efficiencies in Eastern China produced a mutualaid effect with a high density which caused the spatialnetwork relationships to become increasingly closerHowever few IM enterprises were located inWestern Chinaand these were far from the correlation degree of the in-novation efficiency of other enterprises in the network

Figure 1 Spatial correlation network of the innovation efficiency of the IM enterprises in 2015

Figure 2 Spatial correlation network of the innovation efficiency of the IM enterprises in 2020

Complexity 7

2015 2016 2017 2018 2019 2020Years

052

05

048

046

044

042

08

06

04

02

0

Net

wor

k gr

ade

Net

wor

k ef

ficie

ncy

Linear (network efficiency)

Network gradeNetwork efficiency

Figure 4 Network grade and efficiency

Table 3 Analysis of the centrality of the spatial correlation network of the innovation efficiency of the IM enterprises

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Henei Heis CompanyLimited 5 37 15 7 22222 25 61972 4 4526 11

Shandong GoertekInc 8 27 19 3 30000 9 61111 9 1604 25

Hunan Zoomlion 10 23 10 22 22222 25 58667 15 7071 4Liaoning Ansteel 6 36 9 26 16666 33 50575 35 1009 34Shanghai TiandiScience ampTechnology

13 13 16 6 32222 4 57895 17 3079 17

Zhengjiang Robam 13 13 13 14 28889 13 57895 17 1802 24Henan Yutong Bus 12 18 15 7 30000 9 61972 4 10161 2Hebei CRRC 8 27 14 12 24444 22 58667 15 2332 22Jiangxi Copper 10 23 8 29 20000 27 55000 26 2568 21XCMG ConstructionMachinery 19 1 20 1 43333 1 68750 1 8573 3

Jiangsu HTGD 17 2 15 7 35555 3 65672 2 3415 13Beijing Foton 11 20 12 18 25555 19 57895 17 3242 15China XD Group 7 32 11 20 20000 27 56410 21 12609 1

500

450

400

350

300

250

200

150

100

50

0

Rela

tions

hip

num

ber

2015 2016 2017 2018 2019 2020Year

Net

wor

k de

nsity

Relationship numberNetwork densityLinear (relationship number)

03

025

02

015

01

005

0

Figure 3 Network density and relationship number

8 Complexity

432 Closeness Centrality Closeness centrality can be usedas an index to judge the degree of the correlation difficulty ofeach IM enterprise in the spatial association network In2020 the average closeness degree of the IM enterprises was54891 e top five enterprises in this regard were XCMGConstruction Machinery Jiangsu HTGD Shandong SailunJinyu Group Henan Yutong Bus and Jiangxi ChanghongHuayi which were located in Mideastern China where IM

enterprises were concentrated and had a short networkdistance from the other IM enterprises in the networkerefore they could more easily spatially correlate with theother IM enterprises in the spatial correlation network ebottom five enterprises were Xinjiang TBEA Guizhou SpaceAppliance Guangxi Hytera Xinjiang West-Constructionand Xinjiang Goldwind Science amp Technology which werelocated in Western China at the edge of the network

Table 3 Continued

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Guangxi Liugong 5 37 2 43 77775 44 45361 40 0559 39Guodian NanjingAutomation 13 13 20 1 36666 2 60274 11 2671 18

Chongqing ChuanyiAutomation 8 27 9 26 18889 30 56410 21 6018 5

Zhejiang Jushi 7 32 17 5 26667 16 53012 32 0435 41Xinjiang TBEA 4 40 5 37 10000 41 43564 41 338 14Xinjiang West-Construction 3 43 4 41 7778 42 37931 44 1468 27

Shandong SailunJinyu Group 11 20 18 4 32222 5 64706 3 4894 9

Chongqing Yahua 4 40 9 26 14444 36 47312 38 2009 23North ChinaPharmaceutical 7 32 5 37 13333 37 51163 34 087 36

Gansu Great WallElectrical 3 43 7 30 11111 39 48889 37 1112 33

Xinjiang GoldwindScience ampTechnology

2 45 2 43 4444 45 30769 45 0 45

Jiangsu VictoryPrecision 14 10 15 7 32222 5 60274 11 132 30

Jiangsu TongdingConnection 15 6 12 18 30000 9 55000 26 0643 38

HT-SAAE 15 6 13 14 31111 8 60274 11 1593 26Hubei Accelink 16 4 7 30 25556 18 61972 4 5366 6CITIC HeavyIndustries 12 18 5 37 18889 30 54321 28 1377 28

Rapoo Technology 7 32 7 30 15556 35 53659 31 522 8Guangxi Hytera 4 40 3 42 7778 42 40000 43 0872 35Zhejiang Eastcom 10 23 14 12 26666 17 55696 25 0518 40Fujian Changelight 8 27 13 14 25555 19 56410 21 1208 31Guizhou SpaceAppliance 5 37 6 35 12222 38 43564 41 2582 20

Shanghai Brightdairy 10 23 5 37 16666 33 50575 35 0123 44Jiangxi ChanghongHuayi 14 10 15 7 32222 5 61972 4 4709 10

Jiangxi JZJT 13 13 10 22 25555 19 60274 11 3701 12ShangdongDoublestar 11 20 11 20 24444 23 57895 17 1341 29

Shandong TianrunCrankshaft 13 13 13 14 28889 13 61111 9 3212 16

Zhejiang Wynca 17 2 10 22 30000 9 56410 21 1126 32Jiangsu HengshunVinegar Industry 15 6 7 30 20000 27 54321 28 0169 42

Zhengjiang SaintAngelo 15 6 6 35 18888 32 52381 33 0129 43

Anhui HuamaoGroup 16 4 10 22 28889 13 61972 4 5302 7

Jiangsu KanionPharmaceutical 14 10 7 30 23333 24 54321 28 2653 19

Shaanxi Xiagu Power 8 27 2 43 11111 40 45833 39 0658 37

Complexity 9

erefore these enterprises had difficulty obtaining inno-vation efficiency from the other IM enterprises and they hadno obvious impact and driving effect on the other IM en-terprises erefore their independence was stronger in theinnovation efficiency correlation

433 Intermediary Centrality Intermediary centrality canbe used to measure the ability of IM enterprises to controlresources in a spatial correlation network In 2020 theaverage intermediary centrality of the IM enterprises was2872e top five enterprises were China XDGroup HenanYutong Bus XCMG Construction Machinery HunanZoomlion and Chongqing Chuanyi Automation whichshowed that these IM enterprises were in the middle of theinnovation efficiency correlation network and controlled theconstruction and development of the correlation relation-ships of the innovation efficiency of the other IM enterprisesContrary to expectations the index was insufficient of theIM enterprises located in the economically developed areasof Eastern China which indicated that the IM enterpriseslocated in economically developed areas had no drivingeffect on the other IM enterprises

44 Module-Modeling Analysis To better show the networkrole and functional rule of the IM enterprises a socialnetwork analysis was introduced in this paper to perform aclustering analysis In this paper the CONCOR method inUCINET software was used to study the network structureand 45 IM enterprises were divided into four modules with amaximum segmentation depth of 2 and a concentrationstandard of 02 as shown in Table 4 Module I had 11 IMenterprises which were mainly located in Northeastern andEastern China Module II had 21 IM enterprises which weremainly located in Central and Eastern China Module III had8 IM enterprises which were mainly located in Central andWestern China Module IV had 5 IM enterprises whichwere mainly located in Western China

Table 5 shows that the entrepreneurial performance ofthe IM enterprises in 2020 had 465 spatial network rela-tionships Of these 338 were within the module accountingfor 7269 of the total and 127 were outside the moduleaccounting for 2731 of the total As a result intramoduleoverflow was the primary factor in the spatial networkoverflow of the innovation efficiency of the IM enterprisese network module nature was judged by using theWasserman evaluation method e number of sending-outrelationships inModule I was 104 among which the numberof relationships inside the module was 78 that receivedoutside the module was 58 and that overflowing outside was26 the proportion of expected internal relationships was2273 and the proportion of actual internal relationshipswas 75 As a result Module I had more overflow rela-tionships with the IM enterprises both inside and outsidethe module so it was a ldquotwo-way overflow modulerdquo enumber of sending-out relationships in Module II was 282among which the number of relationships inside the modulewas 220 that received outside the module was 34 and thatoverflowing outside the module was 62 the proportion of

expected internal relationships was 4545 and the pro-portion of actual internal relationships was 7801 eseresults indicated that Module II could improve its owninnovation efficiency while spilling innovation efficiency tomembers of other modules us Module II was designatedthe ldquonet overflow modulerdquo e number of sending-outrelationships in Module III was 60 among which thenumber of relationships inside the module was 27 thatreceived outside the module was 19 and that overflowingoutside the module was 33 the proportion of expectedinternal relationships was 1591 and the proportion ofactual internal relationships was 4500 us the con-nection between the module and the outside was similar tothat within the module which played the role of a ldquobrokerrdquoso Module III was designated the ldquobroker modulerdquo enumber of sending-out relationships in Module IV was 19among which the number of relationships inside the modulewas 13 that received outside the module was 16 and thatoverflowing outside the module was 6 the proportion ofexpected internal relationships was 909 and the pro-portion of actual internal relationships was 6842 enumber of external relationships received by the module wasfar greater than that overflowing from the module so themodule was a typical ldquonet income modulerdquo erefore in2020 Chinarsquos IM enterprises were unevenly distributed byregion and each module was composed of enterprises fromthe same or adjacent provinces e module regions weregeographically concentrated and when they were closer toeach other they were more likely to have innovation cor-relation Module II showed that the Yangtze River Delta andits surrounding economically developed regions accountedfor more innovation correlation and the internal correlationwas strong so they could meet the development needs oftheir own innovation efficiencies However the enterprisesin Module IV located in Western China needed to absorbthe overflow effect of the other modules to develop inno-vation efficiency because the module had a small number ofenterprises

To further study the overflow relationships and pathamong the modules UCINET software was used to cal-culate the network density and image matrices of eachmodule (see Table 6) Figure 4 indicates that the density ofthe whole network in 2020 was 02383 which was taken asthe critical value A module density greater than 02383indicated a concentration trend in the module e densityvalue was assigned to 1 and a density value less than 02383was assigned to 0 to convert the density matrix of eachmodule into an image matrix e evolutionary processshowed that the spatial correlation network of the inno-vation efficiency of the IM enterprises did not show theexpected core-edge structure but presented a significantcohesive subgroup characteristic e IM enterpriseswithin the module were closely connected while there werefewer external relations Only Module III sent relationshipsto Module IV that is the innovation efficiency of BrokerModule III flowed to Net IncomeModule IVerefore theIM enterprises in Module IV had to absorb the overfloweffects of the other modules to meet the development needsof their own innovation efficiency However they could

10 Complexity

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

exploiting the special action of IM demonstration pilotprojects Furthermore the model was combined with thedata for listed companies to compensate for the lack of aDEA model that cannot rank the units with efficiency toenrich existing research methods and to evaluate the in-novation efficiency of IM enterprises Second this paperused the social network analysis method to construct thespatial correlation network of the innovation efficiency ofIM enterprises studied the influences of and relationshipsamong the subsystems in the spatial correlation networkand filled the theoretical gap of research on the spatialcorrelation network of the innovation efficiency of IM en-terprises ird the spatial correlation of the innovationefficiency of IM enterprises is examined from a larger spatialscope while the existing literature mainly examines theperspective of geographic proximity Fourth while theexisting literature builds a measurement model based onldquoattributerdquo data the network structure is difficult to describeis study relies on ldquorelationalrdquo data to describe the spatialcorrelation network structure of the innovation efficiency ofIM enterprises

3 Research Design

31 Construction of theDEACross-EfficiencyModel In 1986Silkman first proposed the DEA cross-efficiency evaluationmethod [38] used in evaluation ranking to evaluate theinnovation efficiency of K enterprises Each enterprise(decision-making unit DMU) had n input variables and Moutput variables in which Xik was the total amount of the i-th input of the k-th enterprise Ysk was the s-th outputvariable of the k-th enterprise and the input and output ofthe k-th enterprise were expressed as Xk (x1k x2k xnk)Tand Yk (y1k y2k ymk)T respectively e weight coef-ficient vector of input vector X was Q (q1 q2 qn)T andthe weight coefficient vector of output vector Y was G (g1g2 gm)

T

maxYTk g Eik

YTk gleX

Tk q 1le kle n

XTk q 1

gge 0 qge 0 Eik ge 0

⎧⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎩

(1)

e optimal solutions of the above linear programmingwere set as glowastk and qlowastk and then optimal Eik YkTlowastgk was theefficiency value When the value of DMUk was from DMU1to DMUm m cross-efficiencies could be obtained from anyDMU and the cross-evaluation matrix was finally obtained

E

E11 middot middot middot E1m

⋮ ⋱ ⋮

Em1 middot middot middot Emm

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ (2)

where the main diagonal element was the efficiency value ofself-evaluation and the other elements were the efficiencyvalues of other evaluations e average value of each col-umn in the matrix was the final cross-efficiency value of theDMU When the value was larger the DMU was better

32 Measurement of the Relationship Efficiency of the Inno-vation Spatial Network of IM Enterprises e spatial cor-relation network of IM enterprises is determined throughspatial relationships Currently scholars mainly use thevector autoregressive (VAR) Granger causality and gravitymodels to test the spatial relationship According to Liu et al[39] the VAR Granger causality method is too sensitive tothe choice of the end of lag which weakens the accuracy ofthe characteristic description of the network structure to acertain extenterefore by referring to the modified gravitymodel of Shao et al [40] the spatial correlation network ofthe innovation efficiency of the IM enterprises was con-structed in this paper e modified gravity model was asfollows [41]

Xij RFi lowastFj

E2ij

(3)

where Xij was the spatial correlation strength of the inno-vation efficiency of the IM enterprises Fi and Fj were theirinnovation efficiency and Eijwas the linear distance betweentwo enterprises is paper used ArcGIS software to cal-culate and obtain RKi(KI+Kj) in which ki and kjwere thegross domestic products of the cities where the IM enter-prises were located e spatial correlation binary matrix ofthe innovation efficiency of the IM enterprises was calcu-lated by a modified gravity model To further describe thespatial correlation of their innovation efficiency the averagevalue of each row of the matrix was taken as critical value Pof the correlation degree between the IM enterprise in therow and the other IM enterprises If Xijge P the value was 1which indicated that those in the row were correlated withthe innovation efficiencies of those in the column Other-wise the value was 0 indicating that there was no correlationamong the enterprises

33 Social Network Analysis A social network analysis cantransform attribute data into relational data By measuringnetwork indices the paper analyzed the whole networkindividual network and module characteristics and theintermediary role of the spatial correlation network of theinnovation efficiency of IM enterprises in China

331 Characteristic Index of the Whole NetworkNetwork correlation refers to an index that can reflect thedegree to which two IM enterprises can establish relation-ships It can measure the independence and dependencebetween IM enterprises e calculation formula was asfollows [42]

C 1 minus U

[N(N minus 1)2] (4)

where C represented network relevance N was the numberof the IM enterprises in the network and U was the numberof those whose innovation efficiency was not discoverable inthe correlation network

Network density refers to the index that measures thedensity degree of relationships of innovation efficiency

Complexity 3

correlation among research objects in social networks It isthe ratio of the actual number of relationships in the networkto the maximum possible number of relationships in thewhole network When it is higher the spatial correlationdegree is closer to the innovation efficiency of IM enter-prises e calculation formula was as follows

D V

[N(N minus 1)] (5)

where D represented the network density V referred to theactual number of innovation efficiency relationships in thenetwork and N represented the number of nodes in thewhole network

Network efficiency refers to the extent to which thenetwork has a redundant relationship when the number ofresearch objects is known It canmeasure the extent to whichthe IM enterprises in the spatial correlation network ofinnovation efficiency have redundant relationships thatreduce network stability Its formula was as follows

GE 1 minus G

max(G) (6)

where G was the redundant relationship in the correlationnetwork of innovation efficiency and max (G) was themaximum number of possible redundant relationships inthe network

332 Characteristic Index of the Individual NetworkIndividual networks can reveal the power of IM enterprisesin the spatial correlation network of innovation efficiencyand whether one such enterprise is in the core positionSocial network scholars use degree closeness and inter-mediary centrality to quantify individual networks from theperspective of ldquorelationshipsrdquo Degree centrality canmeasurethe node number directly connected with other points It isapplied to the innovation efficiency network of the IMenterprises and can describe the impact of innovation ef-ficiency in each IM enterprise on that of the othersCloseness centrality can measure the distance between twopoints and can be applied to the innovation efficiencynetwork of IM to describe the stability and independence ofthe impact of each IM enterprise on the innovation efficiencyof the others Intermediary centrality can measure the abilityof a point between two points to control the communicationbetween the two points It can be applied to the innovationefficiency network of IM enterprises and can describe theability of IM enterprises to control others Among the threemeasurement indices degree centrality is relatively simpleand closeness and intermediary centrality need to bemeasured according to formulas

e calculation formula of closeness centrality was asfollows

Di 1113944n

j1Cij (7)

whereDiwas the betweenness centrality of node iCijwas theshortcut distance between point i and point j and ine j

e calculation formula of the intermediary centralitywas as follows

Fij 1113936

nj 1113936

nk Mjk(i)

Mjk

(8)

where Fijwas the betweenness centrality of node iMjk(i) wasthe number of shortcuts passing through point i betweenpoints k and j andMjk was the number of shortcuts betweenpoints k and j where kne jne i and jlt k

333 Module-Modeling Analysis A module-modelinganalysis classifies each node into a module according tosimilar characteristics through a cluster analysis in a socialnetwork analysis and reacts to the relationship character-istics among modules With reference to the networkmodule division method of Wasserman and Faust [43] theefficiency spatial correlation network of the IM enterprisescould be divided into four modules two-way benefit netincome net overflow and broker e transmitndashreceiverelationship was generally analyzed between modules andthe image matrix was used to react to the internal structuralchanges of the network and the correlation relationshipchanges among modules [44]

334 Intermediary Analysis Intermediaries master theldquosecretsrdquo among multiple groups regardless of whether thesecomprise the whole network of the innovation efficiencycorrelation of IM enterprises or individual modules Withreference to the intermediary classification of Gould andFernandez [45] the IM enterprises were divided into fivecategories according to the intermediary role coordinatorgatekeeper agent consultant and contact person e co-ordinator agglomerates the internal innovation efficiencycorrelation of its own module e agent gives the inno-vation efficiency of its own module to other modules econsultant produces the innovation efficiency correlationrelationship of different IM enterprises in a certain modulee gatekeeper incorporates the innovation efficiency ofother modules into its own modulee contact person bothincorporates the innovation efficiency of other modules intoits ownmodule and gives the innovation efficiency of its ownmodule to another module which can drive differentmodules to build the correlation relationship of innovationefficiency

34Variable andDataDeclaration For reasons of authoritythis paper selected as the data samples the demonstrationpilot project of IM enterprises officially announced by theMinistry of Industry and Information Technology Since2015 the Ministry of Industry and Information Technologyhas selected intelligent-upgrading projects of manufacturingenterprises as demonstration pilot projects according to therelevant requirements of the Element Conditions of the PilotDemonstration Project of IM e pilot demonstrationproject must have operationalized the transformation of IMenterprises must have achieved remarkable results inshortening the product development cycle reducing

4 Complexity

operating costs and improving production efficiency andmust continue to improve in future development and exhibitfavorable growth erefore the sample selection met thestudy objective and requirements of this paper From 2015 to2018 the Ministry of Industry and Information Technologyannounced 306 IM demonstration pilot projects Becauseobtaining enterprise microdata is difficult and financial datadisclosed by listed companies are highly reliable andavailable domestic A-share listed enterprises among thepilot enterprises were selected as typical samples Moreoverthe listed companies experiencing bankruptcy mergers andacquisitions or missing important information were elimi-nated leaving 103 listed companies To ensure the datavalidity and integrity extreme enterprises whose patentauthorization number was 0 were eliminated Ultimately 45effective target enterprises remained e annual report datawere all from the Guotai database and the number of patentauthorizations for each enterprise was searched with theapplicant as the search condition yearly on the NationalIntellectual Property Administration website

According to the innovation input and output process ofIM enterprises investment in RampD was selected as the inputindex in this papere practice of Su and Li [46] was used inthe input stage when measuring the innovation efficiencyindex by considering the principle of the availability andoperability of index selection e main reasons for usingthis method are as follows the enterprisersquos RampD activi-tiesmdashincluding personnel funds and equipmentmdashincludemany factors that are difficult to measure and RampD fundinvestment is the most direct response to the enterprisersquosRampD investment is paper used annual RampD investmentoperating income to measure innovation investment Re-garding the output index from the perspective of existingstudies all scholars measure innovation achievements bypatent application quantity and the number of grantedpatents [47 48] e quantity of patent applications rep-resents innovation efforts rather than scientific and tech-nological innovation capabilities e number of grantedpatents is the number of patents authorized only after strictexamination procedures which can more directly reflect theregional capability of scientific and technological innovation[49] erefore this paper used the number of grantedpatents rather than the number of applications e eval-uation index system of the innovation efficiency of the IMenterprises is shown in Table 1

4 Empirical Analyses

41 Spatial Distribution of the Innovation Efficiency of the IMEnterprises is paper used the DEA cross-efficiency modeland MaxDEA software to measure the innovation effi-ciencies of 45 IM enterprises from 2015 to 2020 As shown inTable 2 the development was relatively stable of the inno-vation efficiency of Chinarsquos IM enterprises innovation ef-ficiency has been increasing slowly in the past six years withfluctuations and the average innovation efficiency was 0515In these six years the average innovation efficiency ofJiangsu CMG Construction Machinery was 0957 rankingfirst and the lowest innovation efficiency was of Shaanxi

Xiagu Power in 2015 which was only 0109 IM enterprisesexhibited a large gap in innovation efficiency which indi-cated that some of Chinarsquos IM enterprises made somebreakthroughs in recent years but some of them had lowinnovation efficiency

42 Analysis of the Characteristics and Evolutionary Process oftheWholeNetwork Based on themodified gravity model thissection establishes the spatial network of the innovation effi-ciency of the IM enterprises draws a topological map of theirspatial network by using Netdraw in UCINET and selectssections of 2015 and 2020 for a comparative analysis (seeFigures 1 and 2) e nodes in the figure represent the 45 IMenterprises and the connections between the nodes representthe correlation relationships between them As shown inFigures 1 and 2 compared with 2015 the spatial correlationrelationships of innovation efficiency network density andtwo-way correlation represented by thick lines increased in2020 and the relationship network of innovation efficiencybecame more complex then e innovation efficiencies ofmany of the IM enterprises not only affected the enterprises inthe same provinces but also broke the geographical restric-tions permitting spatial correlation relationships to occur withneighboring and nonneighboring provinces To further analyzethe overall network characteristics of the spatial correlation ofthe innovation efficiency of the IM enterprises this papercalculated the spatial correlation network correlation degreedensity grade and efficiency of their innovation efficiencybased on the spatial correlation matrix A diagram comparingthe network density and relationship number is shown inFigure 3 and one comparing the network grade and networkefficiency is shown in Figure 4

e measurement results showed that the networkcorrelation degree from 2015 to 2020 was 1 which indicatedthat the network accessibility was good and the innovationefficiency network of the IM enterprises was closely asso-ciated among regions Figure 3 shows that the networkdensity and the number of relationships decreased slightly in2020 but the overall trend was still an increase equantitative analysis was consistent with the results of theprevious topological graph which indicated that the rela-tionships tended to be close among the spatial correlationnetwork of the innovation efficiency of the IM enterprisesFigure 4 indicates that the network grade showed adownward trend from 07928 in 2015 to 0686 in 2020indicating that the spatial correlation network structure ofthe innovation efficiency of the IM enterprises was brokenthe relationship between regional innovation efficiencies wasgradually strengthened and an obvious trend emerged ofcross-regional collaborative innovation e network effi-ciency decreased from 051 in 2015 to 04632 in 2020 whichindicated that the innovation efficiencies of the IM enter-prises were gradually close in the network and the stabilityof the spatial network has gradually strengthened

43 Individual Network Characteristics By measuring de-gree closeness and intermediary centrality this paper an-alyzed the individual network characteristics of the spatial

Complexity 5

network of the innovation efficiency of the IM enterprises todetermine their positions and rolesis paper measured thein- and outdegrees of each IM enterprise to study the in-fluence and affected situations of the spatial correlationnetwork of certain IM enterprises and the measurementresults are shown in Table 3

431 Degree Centrality Degree centrality can reflect thestatus of each IM enterprise in the spatial network of in-novation efficiency e five IM enterprises with the highestoutdegrees were XCMG Construction Machinery JiangsuHTGD Zhejiang Wynca Hubei Accelink and AnhuiHuamao Group which are located in Eastern and Central

Table 2 Innovation efficiency of 45 IM enterprises from 2015 to 2020

Enterprise abbreviation 2015 2016 2017 2018 2019 2020 Average valueHenei Heis Company Limited 0726 0781 0831 0884 0851 0846 0820Shandong Goertek Inc 0582 0559 0602 0643 0685 0685 0626Hunan Zoomlion 0531 0564 0587 0571 0639 0624 0586Liaoning Ansteel 0891 0784 0863 0831 0783 0961 0852Shanghai Tiandi Science amp Technology 0761 0963 0874 0767 0778 0934 0846Zhengjiang Robam 0873 0731 0783 0731 0791 0683 0765Henan Yutong Bus 0741 0739 0846 0883 0821 0857 0815Hebei CRRC 0835 0874 0887 0859 0983 0973 0902Jiangxi Copper 0783 0657 0674 0895 0856 0785 0775XCMG Construction Machinery 0928 0954 0961 0959 0972 0968 0957Jiangsu HTGD 0765 0875 0931 0976 0951 0874 0895Beijing Foton 0745 0784 0734 0861 0987 0943 0842China XD Group 0693 0646 0685 0699 0751 0631 0684Guangxi Liugong 0687 0691 0682 0691 0723 0703 0696Guodian Nanjing Automation 0735 0758 0876 0831 0941 0872 0836Chongqing Chuanyi Automation 0646 0763 0735 0772 0841 0864 0770Zhejiang Jushi 0695 0685 0821 0867 0841 0871 0797Xinjiang TBEA 0571 0645 0682 0631 0788 0793 0685Xinjiang West-Construction 0473 0482 0741 0783 0732 0778 0665Shandong Sailun Jinyu Group 0974 0861 0967 0983 0741 0865 0899Chongqing Yahua 0651 0731 0631 0689 0731 0786 0703North China Pharmaceutical 0573 0581 0542 0514 0631 0645 0581Gansu Great Wall Electrical 0463 0441 0485 0463 0431 0521 0467Xinjiang Goldwind Science amp Technology 0342 0314 0381 0321 0384 0351 0349Jiangsu Victory Precision 0351 0351 0431 0463 0471 0442 0418Jiangsu Tongding Connection 0319 0373 0379 0365 0383 0381 0367HT-SAAE 0341 0364 0373 0361 0371 0379 0365Hubei Accelink 0286 0281 0265 0365 0393 0345 0323CITIC Heavy Industries 0311 0382 0362 0374 0375 0321 0354Rapoo Technology 0331 0285 0231 0351 0341 0358 0316Guangxi Hytera 0221 0252 0241 0283 0261 0251 0252Zhejiang Eastcom 0156 0121 0241 0271 0263 0298 0225Fujian Changelight 0154 0173 0196 0141 0144 0131 0157Guizhou Space Appliance 0231 0274 0221 0251 0267 0271 0253Shanghai Brightdairy 0151 0182 0164 0171 0162 0153 0164Jiangxi Changhong Huayi 0291 0272 0263 0231 0285 0221 0261Jiangxi JZJT 0253 0262 0241 0235 0281 0284 0259Shangdong Doublestar 0186 0171 0184 0191 0121 0132 0164Shandong Tianrun Crankshaft 0257 0207 0231 0221 0267 0238 0237Zhejiang Wynca 0178 0211 0251 0268 0281 0252 0240Jiangsu Hengshun Vinegar Industry 0191 0137 0181 0189 0237 0173 0185Zhengjiang Saint Angelo 0219 0231 0298 0281 0231 0235 0249Anhui Huamao Group 0182 0175 0238 0247 0219 0183 0207Jiangsu Kanion Pharmaceutical 0178 0121 0243 0189 0182 0231 0191Shaanxi Xiagu Power 0109 0159 0194 0114 0184 0176 0156

Table 1 Evaluation index system of the innovation efficiency of the IM enterprises

First-grade index Second-grade index MeasurementInput Investment in RampD Annual RampD investmentoperating incomeOutput Number of granted patents Direct use of the index data

6 Complexity

China is finding showed that these five IM enterpriseshad more innovation efficiency overflow which promotedthe innovation efficiency of the other IM enterprisese fiveIM enterprises with the poorest outdegrees were XinjiangGoldwind Science amp Technology Xinjiang West-Con-struction Gansu Great Wall Electrical Xinjiang TBEA andGuangxi Liugong which were located in NorthwesternChina and had little correlation with the other IM enter-prises e five IM enterprises with leading indegrees wereXCMG Construction Machinery Guodian Nanjing Auto-mation Shandong Goertek Inc Shandong Sailun JinyuGroup and Zhejiang Jushi which were all located in EasternChina e higher outdegrees indicated that the innovationefficiencies of the other IM enterprises overflowed to thesefive enterprises e five IM enterprises with the lowestindegrees were Xinjiang Goldwind Science amp TechnologyGuangxi Liugong Shaanxi Xiagu Power Xinjiang TBEAand Xinjiang West-Construction which were located in

Northwestern China indicating that these enterprises un-dertook fewer network relationships A further analysisshowed that the degree centrality was the highest for the IMenterprises in Eastern China indicating that these enter-prises had a higher degree of external innovation efficiencyIn contrast the degree centrality of the IM enterprises inWestern China ranked lower indicating that these enter-prises had a lower degree of external innovation efficiencyAccording to the Report on the IM Development Index(2020) the IM enterprises in China are unevenly distributedand more IM enterprises are concentrated in Jiangsu andZhejiang provinces and other parts of Eastern China einnovation efficiencies in Eastern China produced a mutualaid effect with a high density which caused the spatialnetwork relationships to become increasingly closerHowever few IM enterprises were located inWestern Chinaand these were far from the correlation degree of the in-novation efficiency of other enterprises in the network

Figure 1 Spatial correlation network of the innovation efficiency of the IM enterprises in 2015

Figure 2 Spatial correlation network of the innovation efficiency of the IM enterprises in 2020

Complexity 7

2015 2016 2017 2018 2019 2020Years

052

05

048

046

044

042

08

06

04

02

0

Net

wor

k gr

ade

Net

wor

k ef

ficie

ncy

Linear (network efficiency)

Network gradeNetwork efficiency

Figure 4 Network grade and efficiency

Table 3 Analysis of the centrality of the spatial correlation network of the innovation efficiency of the IM enterprises

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Henei Heis CompanyLimited 5 37 15 7 22222 25 61972 4 4526 11

Shandong GoertekInc 8 27 19 3 30000 9 61111 9 1604 25

Hunan Zoomlion 10 23 10 22 22222 25 58667 15 7071 4Liaoning Ansteel 6 36 9 26 16666 33 50575 35 1009 34Shanghai TiandiScience ampTechnology

13 13 16 6 32222 4 57895 17 3079 17

Zhengjiang Robam 13 13 13 14 28889 13 57895 17 1802 24Henan Yutong Bus 12 18 15 7 30000 9 61972 4 10161 2Hebei CRRC 8 27 14 12 24444 22 58667 15 2332 22Jiangxi Copper 10 23 8 29 20000 27 55000 26 2568 21XCMG ConstructionMachinery 19 1 20 1 43333 1 68750 1 8573 3

Jiangsu HTGD 17 2 15 7 35555 3 65672 2 3415 13Beijing Foton 11 20 12 18 25555 19 57895 17 3242 15China XD Group 7 32 11 20 20000 27 56410 21 12609 1

500

450

400

350

300

250

200

150

100

50

0

Rela

tions

hip

num

ber

2015 2016 2017 2018 2019 2020Year

Net

wor

k de

nsity

Relationship numberNetwork densityLinear (relationship number)

03

025

02

015

01

005

0

Figure 3 Network density and relationship number

8 Complexity

432 Closeness Centrality Closeness centrality can be usedas an index to judge the degree of the correlation difficulty ofeach IM enterprise in the spatial association network In2020 the average closeness degree of the IM enterprises was54891 e top five enterprises in this regard were XCMGConstruction Machinery Jiangsu HTGD Shandong SailunJinyu Group Henan Yutong Bus and Jiangxi ChanghongHuayi which were located in Mideastern China where IM

enterprises were concentrated and had a short networkdistance from the other IM enterprises in the networkerefore they could more easily spatially correlate with theother IM enterprises in the spatial correlation network ebottom five enterprises were Xinjiang TBEA Guizhou SpaceAppliance Guangxi Hytera Xinjiang West-Constructionand Xinjiang Goldwind Science amp Technology which werelocated in Western China at the edge of the network

Table 3 Continued

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Guangxi Liugong 5 37 2 43 77775 44 45361 40 0559 39Guodian NanjingAutomation 13 13 20 1 36666 2 60274 11 2671 18

Chongqing ChuanyiAutomation 8 27 9 26 18889 30 56410 21 6018 5

Zhejiang Jushi 7 32 17 5 26667 16 53012 32 0435 41Xinjiang TBEA 4 40 5 37 10000 41 43564 41 338 14Xinjiang West-Construction 3 43 4 41 7778 42 37931 44 1468 27

Shandong SailunJinyu Group 11 20 18 4 32222 5 64706 3 4894 9

Chongqing Yahua 4 40 9 26 14444 36 47312 38 2009 23North ChinaPharmaceutical 7 32 5 37 13333 37 51163 34 087 36

Gansu Great WallElectrical 3 43 7 30 11111 39 48889 37 1112 33

Xinjiang GoldwindScience ampTechnology

2 45 2 43 4444 45 30769 45 0 45

Jiangsu VictoryPrecision 14 10 15 7 32222 5 60274 11 132 30

Jiangsu TongdingConnection 15 6 12 18 30000 9 55000 26 0643 38

HT-SAAE 15 6 13 14 31111 8 60274 11 1593 26Hubei Accelink 16 4 7 30 25556 18 61972 4 5366 6CITIC HeavyIndustries 12 18 5 37 18889 30 54321 28 1377 28

Rapoo Technology 7 32 7 30 15556 35 53659 31 522 8Guangxi Hytera 4 40 3 42 7778 42 40000 43 0872 35Zhejiang Eastcom 10 23 14 12 26666 17 55696 25 0518 40Fujian Changelight 8 27 13 14 25555 19 56410 21 1208 31Guizhou SpaceAppliance 5 37 6 35 12222 38 43564 41 2582 20

Shanghai Brightdairy 10 23 5 37 16666 33 50575 35 0123 44Jiangxi ChanghongHuayi 14 10 15 7 32222 5 61972 4 4709 10

Jiangxi JZJT 13 13 10 22 25555 19 60274 11 3701 12ShangdongDoublestar 11 20 11 20 24444 23 57895 17 1341 29

Shandong TianrunCrankshaft 13 13 13 14 28889 13 61111 9 3212 16

Zhejiang Wynca 17 2 10 22 30000 9 56410 21 1126 32Jiangsu HengshunVinegar Industry 15 6 7 30 20000 27 54321 28 0169 42

Zhengjiang SaintAngelo 15 6 6 35 18888 32 52381 33 0129 43

Anhui HuamaoGroup 16 4 10 22 28889 13 61972 4 5302 7

Jiangsu KanionPharmaceutical 14 10 7 30 23333 24 54321 28 2653 19

Shaanxi Xiagu Power 8 27 2 43 11111 40 45833 39 0658 37

Complexity 9

erefore these enterprises had difficulty obtaining inno-vation efficiency from the other IM enterprises and they hadno obvious impact and driving effect on the other IM en-terprises erefore their independence was stronger in theinnovation efficiency correlation

433 Intermediary Centrality Intermediary centrality canbe used to measure the ability of IM enterprises to controlresources in a spatial correlation network In 2020 theaverage intermediary centrality of the IM enterprises was2872e top five enterprises were China XDGroup HenanYutong Bus XCMG Construction Machinery HunanZoomlion and Chongqing Chuanyi Automation whichshowed that these IM enterprises were in the middle of theinnovation efficiency correlation network and controlled theconstruction and development of the correlation relation-ships of the innovation efficiency of the other IM enterprisesContrary to expectations the index was insufficient of theIM enterprises located in the economically developed areasof Eastern China which indicated that the IM enterpriseslocated in economically developed areas had no drivingeffect on the other IM enterprises

44 Module-Modeling Analysis To better show the networkrole and functional rule of the IM enterprises a socialnetwork analysis was introduced in this paper to perform aclustering analysis In this paper the CONCOR method inUCINET software was used to study the network structureand 45 IM enterprises were divided into four modules with amaximum segmentation depth of 2 and a concentrationstandard of 02 as shown in Table 4 Module I had 11 IMenterprises which were mainly located in Northeastern andEastern China Module II had 21 IM enterprises which weremainly located in Central and Eastern China Module III had8 IM enterprises which were mainly located in Central andWestern China Module IV had 5 IM enterprises whichwere mainly located in Western China

Table 5 shows that the entrepreneurial performance ofthe IM enterprises in 2020 had 465 spatial network rela-tionships Of these 338 were within the module accountingfor 7269 of the total and 127 were outside the moduleaccounting for 2731 of the total As a result intramoduleoverflow was the primary factor in the spatial networkoverflow of the innovation efficiency of the IM enterprisese network module nature was judged by using theWasserman evaluation method e number of sending-outrelationships inModule I was 104 among which the numberof relationships inside the module was 78 that receivedoutside the module was 58 and that overflowing outside was26 the proportion of expected internal relationships was2273 and the proportion of actual internal relationshipswas 75 As a result Module I had more overflow rela-tionships with the IM enterprises both inside and outsidethe module so it was a ldquotwo-way overflow modulerdquo enumber of sending-out relationships in Module II was 282among which the number of relationships inside the modulewas 220 that received outside the module was 34 and thatoverflowing outside the module was 62 the proportion of

expected internal relationships was 4545 and the pro-portion of actual internal relationships was 7801 eseresults indicated that Module II could improve its owninnovation efficiency while spilling innovation efficiency tomembers of other modules us Module II was designatedthe ldquonet overflow modulerdquo e number of sending-outrelationships in Module III was 60 among which thenumber of relationships inside the module was 27 thatreceived outside the module was 19 and that overflowingoutside the module was 33 the proportion of expectedinternal relationships was 1591 and the proportion ofactual internal relationships was 4500 us the con-nection between the module and the outside was similar tothat within the module which played the role of a ldquobrokerrdquoso Module III was designated the ldquobroker modulerdquo enumber of sending-out relationships in Module IV was 19among which the number of relationships inside the modulewas 13 that received outside the module was 16 and thatoverflowing outside the module was 6 the proportion ofexpected internal relationships was 909 and the pro-portion of actual internal relationships was 6842 enumber of external relationships received by the module wasfar greater than that overflowing from the module so themodule was a typical ldquonet income modulerdquo erefore in2020 Chinarsquos IM enterprises were unevenly distributed byregion and each module was composed of enterprises fromthe same or adjacent provinces e module regions weregeographically concentrated and when they were closer toeach other they were more likely to have innovation cor-relation Module II showed that the Yangtze River Delta andits surrounding economically developed regions accountedfor more innovation correlation and the internal correlationwas strong so they could meet the development needs oftheir own innovation efficiencies However the enterprisesin Module IV located in Western China needed to absorbthe overflow effect of the other modules to develop inno-vation efficiency because the module had a small number ofenterprises

To further study the overflow relationships and pathamong the modules UCINET software was used to cal-culate the network density and image matrices of eachmodule (see Table 6) Figure 4 indicates that the density ofthe whole network in 2020 was 02383 which was taken asthe critical value A module density greater than 02383indicated a concentration trend in the module e densityvalue was assigned to 1 and a density value less than 02383was assigned to 0 to convert the density matrix of eachmodule into an image matrix e evolutionary processshowed that the spatial correlation network of the inno-vation efficiency of the IM enterprises did not show theexpected core-edge structure but presented a significantcohesive subgroup characteristic e IM enterpriseswithin the module were closely connected while there werefewer external relations Only Module III sent relationshipsto Module IV that is the innovation efficiency of BrokerModule III flowed to Net IncomeModule IVerefore theIM enterprises in Module IV had to absorb the overfloweffects of the other modules to meet the development needsof their own innovation efficiency However they could

10 Complexity

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

correlation among research objects in social networks It isthe ratio of the actual number of relationships in the networkto the maximum possible number of relationships in thewhole network When it is higher the spatial correlationdegree is closer to the innovation efficiency of IM enter-prises e calculation formula was as follows

D V

[N(N minus 1)] (5)

where D represented the network density V referred to theactual number of innovation efficiency relationships in thenetwork and N represented the number of nodes in thewhole network

Network efficiency refers to the extent to which thenetwork has a redundant relationship when the number ofresearch objects is known It canmeasure the extent to whichthe IM enterprises in the spatial correlation network ofinnovation efficiency have redundant relationships thatreduce network stability Its formula was as follows

GE 1 minus G

max(G) (6)

where G was the redundant relationship in the correlationnetwork of innovation efficiency and max (G) was themaximum number of possible redundant relationships inthe network

332 Characteristic Index of the Individual NetworkIndividual networks can reveal the power of IM enterprisesin the spatial correlation network of innovation efficiencyand whether one such enterprise is in the core positionSocial network scholars use degree closeness and inter-mediary centrality to quantify individual networks from theperspective of ldquorelationshipsrdquo Degree centrality canmeasurethe node number directly connected with other points It isapplied to the innovation efficiency network of the IMenterprises and can describe the impact of innovation ef-ficiency in each IM enterprise on that of the othersCloseness centrality can measure the distance between twopoints and can be applied to the innovation efficiencynetwork of IM to describe the stability and independence ofthe impact of each IM enterprise on the innovation efficiencyof the others Intermediary centrality can measure the abilityof a point between two points to control the communicationbetween the two points It can be applied to the innovationefficiency network of IM enterprises and can describe theability of IM enterprises to control others Among the threemeasurement indices degree centrality is relatively simpleand closeness and intermediary centrality need to bemeasured according to formulas

e calculation formula of closeness centrality was asfollows

Di 1113944n

j1Cij (7)

whereDiwas the betweenness centrality of node iCijwas theshortcut distance between point i and point j and ine j

e calculation formula of the intermediary centralitywas as follows

Fij 1113936

nj 1113936

nk Mjk(i)

Mjk

(8)

where Fijwas the betweenness centrality of node iMjk(i) wasthe number of shortcuts passing through point i betweenpoints k and j andMjk was the number of shortcuts betweenpoints k and j where kne jne i and jlt k

333 Module-Modeling Analysis A module-modelinganalysis classifies each node into a module according tosimilar characteristics through a cluster analysis in a socialnetwork analysis and reacts to the relationship character-istics among modules With reference to the networkmodule division method of Wasserman and Faust [43] theefficiency spatial correlation network of the IM enterprisescould be divided into four modules two-way benefit netincome net overflow and broker e transmitndashreceiverelationship was generally analyzed between modules andthe image matrix was used to react to the internal structuralchanges of the network and the correlation relationshipchanges among modules [44]

334 Intermediary Analysis Intermediaries master theldquosecretsrdquo among multiple groups regardless of whether thesecomprise the whole network of the innovation efficiencycorrelation of IM enterprises or individual modules Withreference to the intermediary classification of Gould andFernandez [45] the IM enterprises were divided into fivecategories according to the intermediary role coordinatorgatekeeper agent consultant and contact person e co-ordinator agglomerates the internal innovation efficiencycorrelation of its own module e agent gives the inno-vation efficiency of its own module to other modules econsultant produces the innovation efficiency correlationrelationship of different IM enterprises in a certain modulee gatekeeper incorporates the innovation efficiency ofother modules into its own modulee contact person bothincorporates the innovation efficiency of other modules intoits ownmodule and gives the innovation efficiency of its ownmodule to another module which can drive differentmodules to build the correlation relationship of innovationefficiency

34Variable andDataDeclaration For reasons of authoritythis paper selected as the data samples the demonstrationpilot project of IM enterprises officially announced by theMinistry of Industry and Information Technology Since2015 the Ministry of Industry and Information Technologyhas selected intelligent-upgrading projects of manufacturingenterprises as demonstration pilot projects according to therelevant requirements of the Element Conditions of the PilotDemonstration Project of IM e pilot demonstrationproject must have operationalized the transformation of IMenterprises must have achieved remarkable results inshortening the product development cycle reducing

4 Complexity

operating costs and improving production efficiency andmust continue to improve in future development and exhibitfavorable growth erefore the sample selection met thestudy objective and requirements of this paper From 2015 to2018 the Ministry of Industry and Information Technologyannounced 306 IM demonstration pilot projects Becauseobtaining enterprise microdata is difficult and financial datadisclosed by listed companies are highly reliable andavailable domestic A-share listed enterprises among thepilot enterprises were selected as typical samples Moreoverthe listed companies experiencing bankruptcy mergers andacquisitions or missing important information were elimi-nated leaving 103 listed companies To ensure the datavalidity and integrity extreme enterprises whose patentauthorization number was 0 were eliminated Ultimately 45effective target enterprises remained e annual report datawere all from the Guotai database and the number of patentauthorizations for each enterprise was searched with theapplicant as the search condition yearly on the NationalIntellectual Property Administration website

According to the innovation input and output process ofIM enterprises investment in RampD was selected as the inputindex in this papere practice of Su and Li [46] was used inthe input stage when measuring the innovation efficiencyindex by considering the principle of the availability andoperability of index selection e main reasons for usingthis method are as follows the enterprisersquos RampD activi-tiesmdashincluding personnel funds and equipmentmdashincludemany factors that are difficult to measure and RampD fundinvestment is the most direct response to the enterprisersquosRampD investment is paper used annual RampD investmentoperating income to measure innovation investment Re-garding the output index from the perspective of existingstudies all scholars measure innovation achievements bypatent application quantity and the number of grantedpatents [47 48] e quantity of patent applications rep-resents innovation efforts rather than scientific and tech-nological innovation capabilities e number of grantedpatents is the number of patents authorized only after strictexamination procedures which can more directly reflect theregional capability of scientific and technological innovation[49] erefore this paper used the number of grantedpatents rather than the number of applications e eval-uation index system of the innovation efficiency of the IMenterprises is shown in Table 1

4 Empirical Analyses

41 Spatial Distribution of the Innovation Efficiency of the IMEnterprises is paper used the DEA cross-efficiency modeland MaxDEA software to measure the innovation effi-ciencies of 45 IM enterprises from 2015 to 2020 As shown inTable 2 the development was relatively stable of the inno-vation efficiency of Chinarsquos IM enterprises innovation ef-ficiency has been increasing slowly in the past six years withfluctuations and the average innovation efficiency was 0515In these six years the average innovation efficiency ofJiangsu CMG Construction Machinery was 0957 rankingfirst and the lowest innovation efficiency was of Shaanxi

Xiagu Power in 2015 which was only 0109 IM enterprisesexhibited a large gap in innovation efficiency which indi-cated that some of Chinarsquos IM enterprises made somebreakthroughs in recent years but some of them had lowinnovation efficiency

42 Analysis of the Characteristics and Evolutionary Process oftheWholeNetwork Based on themodified gravity model thissection establishes the spatial network of the innovation effi-ciency of the IM enterprises draws a topological map of theirspatial network by using Netdraw in UCINET and selectssections of 2015 and 2020 for a comparative analysis (seeFigures 1 and 2) e nodes in the figure represent the 45 IMenterprises and the connections between the nodes representthe correlation relationships between them As shown inFigures 1 and 2 compared with 2015 the spatial correlationrelationships of innovation efficiency network density andtwo-way correlation represented by thick lines increased in2020 and the relationship network of innovation efficiencybecame more complex then e innovation efficiencies ofmany of the IM enterprises not only affected the enterprises inthe same provinces but also broke the geographical restric-tions permitting spatial correlation relationships to occur withneighboring and nonneighboring provinces To further analyzethe overall network characteristics of the spatial correlation ofthe innovation efficiency of the IM enterprises this papercalculated the spatial correlation network correlation degreedensity grade and efficiency of their innovation efficiencybased on the spatial correlation matrix A diagram comparingthe network density and relationship number is shown inFigure 3 and one comparing the network grade and networkefficiency is shown in Figure 4

e measurement results showed that the networkcorrelation degree from 2015 to 2020 was 1 which indicatedthat the network accessibility was good and the innovationefficiency network of the IM enterprises was closely asso-ciated among regions Figure 3 shows that the networkdensity and the number of relationships decreased slightly in2020 but the overall trend was still an increase equantitative analysis was consistent with the results of theprevious topological graph which indicated that the rela-tionships tended to be close among the spatial correlationnetwork of the innovation efficiency of the IM enterprisesFigure 4 indicates that the network grade showed adownward trend from 07928 in 2015 to 0686 in 2020indicating that the spatial correlation network structure ofthe innovation efficiency of the IM enterprises was brokenthe relationship between regional innovation efficiencies wasgradually strengthened and an obvious trend emerged ofcross-regional collaborative innovation e network effi-ciency decreased from 051 in 2015 to 04632 in 2020 whichindicated that the innovation efficiencies of the IM enter-prises were gradually close in the network and the stabilityof the spatial network has gradually strengthened

43 Individual Network Characteristics By measuring de-gree closeness and intermediary centrality this paper an-alyzed the individual network characteristics of the spatial

Complexity 5

network of the innovation efficiency of the IM enterprises todetermine their positions and rolesis paper measured thein- and outdegrees of each IM enterprise to study the in-fluence and affected situations of the spatial correlationnetwork of certain IM enterprises and the measurementresults are shown in Table 3

431 Degree Centrality Degree centrality can reflect thestatus of each IM enterprise in the spatial network of in-novation efficiency e five IM enterprises with the highestoutdegrees were XCMG Construction Machinery JiangsuHTGD Zhejiang Wynca Hubei Accelink and AnhuiHuamao Group which are located in Eastern and Central

Table 2 Innovation efficiency of 45 IM enterprises from 2015 to 2020

Enterprise abbreviation 2015 2016 2017 2018 2019 2020 Average valueHenei Heis Company Limited 0726 0781 0831 0884 0851 0846 0820Shandong Goertek Inc 0582 0559 0602 0643 0685 0685 0626Hunan Zoomlion 0531 0564 0587 0571 0639 0624 0586Liaoning Ansteel 0891 0784 0863 0831 0783 0961 0852Shanghai Tiandi Science amp Technology 0761 0963 0874 0767 0778 0934 0846Zhengjiang Robam 0873 0731 0783 0731 0791 0683 0765Henan Yutong Bus 0741 0739 0846 0883 0821 0857 0815Hebei CRRC 0835 0874 0887 0859 0983 0973 0902Jiangxi Copper 0783 0657 0674 0895 0856 0785 0775XCMG Construction Machinery 0928 0954 0961 0959 0972 0968 0957Jiangsu HTGD 0765 0875 0931 0976 0951 0874 0895Beijing Foton 0745 0784 0734 0861 0987 0943 0842China XD Group 0693 0646 0685 0699 0751 0631 0684Guangxi Liugong 0687 0691 0682 0691 0723 0703 0696Guodian Nanjing Automation 0735 0758 0876 0831 0941 0872 0836Chongqing Chuanyi Automation 0646 0763 0735 0772 0841 0864 0770Zhejiang Jushi 0695 0685 0821 0867 0841 0871 0797Xinjiang TBEA 0571 0645 0682 0631 0788 0793 0685Xinjiang West-Construction 0473 0482 0741 0783 0732 0778 0665Shandong Sailun Jinyu Group 0974 0861 0967 0983 0741 0865 0899Chongqing Yahua 0651 0731 0631 0689 0731 0786 0703North China Pharmaceutical 0573 0581 0542 0514 0631 0645 0581Gansu Great Wall Electrical 0463 0441 0485 0463 0431 0521 0467Xinjiang Goldwind Science amp Technology 0342 0314 0381 0321 0384 0351 0349Jiangsu Victory Precision 0351 0351 0431 0463 0471 0442 0418Jiangsu Tongding Connection 0319 0373 0379 0365 0383 0381 0367HT-SAAE 0341 0364 0373 0361 0371 0379 0365Hubei Accelink 0286 0281 0265 0365 0393 0345 0323CITIC Heavy Industries 0311 0382 0362 0374 0375 0321 0354Rapoo Technology 0331 0285 0231 0351 0341 0358 0316Guangxi Hytera 0221 0252 0241 0283 0261 0251 0252Zhejiang Eastcom 0156 0121 0241 0271 0263 0298 0225Fujian Changelight 0154 0173 0196 0141 0144 0131 0157Guizhou Space Appliance 0231 0274 0221 0251 0267 0271 0253Shanghai Brightdairy 0151 0182 0164 0171 0162 0153 0164Jiangxi Changhong Huayi 0291 0272 0263 0231 0285 0221 0261Jiangxi JZJT 0253 0262 0241 0235 0281 0284 0259Shangdong Doublestar 0186 0171 0184 0191 0121 0132 0164Shandong Tianrun Crankshaft 0257 0207 0231 0221 0267 0238 0237Zhejiang Wynca 0178 0211 0251 0268 0281 0252 0240Jiangsu Hengshun Vinegar Industry 0191 0137 0181 0189 0237 0173 0185Zhengjiang Saint Angelo 0219 0231 0298 0281 0231 0235 0249Anhui Huamao Group 0182 0175 0238 0247 0219 0183 0207Jiangsu Kanion Pharmaceutical 0178 0121 0243 0189 0182 0231 0191Shaanxi Xiagu Power 0109 0159 0194 0114 0184 0176 0156

Table 1 Evaluation index system of the innovation efficiency of the IM enterprises

First-grade index Second-grade index MeasurementInput Investment in RampD Annual RampD investmentoperating incomeOutput Number of granted patents Direct use of the index data

6 Complexity

China is finding showed that these five IM enterpriseshad more innovation efficiency overflow which promotedthe innovation efficiency of the other IM enterprisese fiveIM enterprises with the poorest outdegrees were XinjiangGoldwind Science amp Technology Xinjiang West-Con-struction Gansu Great Wall Electrical Xinjiang TBEA andGuangxi Liugong which were located in NorthwesternChina and had little correlation with the other IM enter-prises e five IM enterprises with leading indegrees wereXCMG Construction Machinery Guodian Nanjing Auto-mation Shandong Goertek Inc Shandong Sailun JinyuGroup and Zhejiang Jushi which were all located in EasternChina e higher outdegrees indicated that the innovationefficiencies of the other IM enterprises overflowed to thesefive enterprises e five IM enterprises with the lowestindegrees were Xinjiang Goldwind Science amp TechnologyGuangxi Liugong Shaanxi Xiagu Power Xinjiang TBEAand Xinjiang West-Construction which were located in

Northwestern China indicating that these enterprises un-dertook fewer network relationships A further analysisshowed that the degree centrality was the highest for the IMenterprises in Eastern China indicating that these enter-prises had a higher degree of external innovation efficiencyIn contrast the degree centrality of the IM enterprises inWestern China ranked lower indicating that these enter-prises had a lower degree of external innovation efficiencyAccording to the Report on the IM Development Index(2020) the IM enterprises in China are unevenly distributedand more IM enterprises are concentrated in Jiangsu andZhejiang provinces and other parts of Eastern China einnovation efficiencies in Eastern China produced a mutualaid effect with a high density which caused the spatialnetwork relationships to become increasingly closerHowever few IM enterprises were located inWestern Chinaand these were far from the correlation degree of the in-novation efficiency of other enterprises in the network

Figure 1 Spatial correlation network of the innovation efficiency of the IM enterprises in 2015

Figure 2 Spatial correlation network of the innovation efficiency of the IM enterprises in 2020

Complexity 7

2015 2016 2017 2018 2019 2020Years

052

05

048

046

044

042

08

06

04

02

0

Net

wor

k gr

ade

Net

wor

k ef

ficie

ncy

Linear (network efficiency)

Network gradeNetwork efficiency

Figure 4 Network grade and efficiency

Table 3 Analysis of the centrality of the spatial correlation network of the innovation efficiency of the IM enterprises

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Henei Heis CompanyLimited 5 37 15 7 22222 25 61972 4 4526 11

Shandong GoertekInc 8 27 19 3 30000 9 61111 9 1604 25

Hunan Zoomlion 10 23 10 22 22222 25 58667 15 7071 4Liaoning Ansteel 6 36 9 26 16666 33 50575 35 1009 34Shanghai TiandiScience ampTechnology

13 13 16 6 32222 4 57895 17 3079 17

Zhengjiang Robam 13 13 13 14 28889 13 57895 17 1802 24Henan Yutong Bus 12 18 15 7 30000 9 61972 4 10161 2Hebei CRRC 8 27 14 12 24444 22 58667 15 2332 22Jiangxi Copper 10 23 8 29 20000 27 55000 26 2568 21XCMG ConstructionMachinery 19 1 20 1 43333 1 68750 1 8573 3

Jiangsu HTGD 17 2 15 7 35555 3 65672 2 3415 13Beijing Foton 11 20 12 18 25555 19 57895 17 3242 15China XD Group 7 32 11 20 20000 27 56410 21 12609 1

500

450

400

350

300

250

200

150

100

50

0

Rela

tions

hip

num

ber

2015 2016 2017 2018 2019 2020Year

Net

wor

k de

nsity

Relationship numberNetwork densityLinear (relationship number)

03

025

02

015

01

005

0

Figure 3 Network density and relationship number

8 Complexity

432 Closeness Centrality Closeness centrality can be usedas an index to judge the degree of the correlation difficulty ofeach IM enterprise in the spatial association network In2020 the average closeness degree of the IM enterprises was54891 e top five enterprises in this regard were XCMGConstruction Machinery Jiangsu HTGD Shandong SailunJinyu Group Henan Yutong Bus and Jiangxi ChanghongHuayi which were located in Mideastern China where IM

enterprises were concentrated and had a short networkdistance from the other IM enterprises in the networkerefore they could more easily spatially correlate with theother IM enterprises in the spatial correlation network ebottom five enterprises were Xinjiang TBEA Guizhou SpaceAppliance Guangxi Hytera Xinjiang West-Constructionand Xinjiang Goldwind Science amp Technology which werelocated in Western China at the edge of the network

Table 3 Continued

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Guangxi Liugong 5 37 2 43 77775 44 45361 40 0559 39Guodian NanjingAutomation 13 13 20 1 36666 2 60274 11 2671 18

Chongqing ChuanyiAutomation 8 27 9 26 18889 30 56410 21 6018 5

Zhejiang Jushi 7 32 17 5 26667 16 53012 32 0435 41Xinjiang TBEA 4 40 5 37 10000 41 43564 41 338 14Xinjiang West-Construction 3 43 4 41 7778 42 37931 44 1468 27

Shandong SailunJinyu Group 11 20 18 4 32222 5 64706 3 4894 9

Chongqing Yahua 4 40 9 26 14444 36 47312 38 2009 23North ChinaPharmaceutical 7 32 5 37 13333 37 51163 34 087 36

Gansu Great WallElectrical 3 43 7 30 11111 39 48889 37 1112 33

Xinjiang GoldwindScience ampTechnology

2 45 2 43 4444 45 30769 45 0 45

Jiangsu VictoryPrecision 14 10 15 7 32222 5 60274 11 132 30

Jiangsu TongdingConnection 15 6 12 18 30000 9 55000 26 0643 38

HT-SAAE 15 6 13 14 31111 8 60274 11 1593 26Hubei Accelink 16 4 7 30 25556 18 61972 4 5366 6CITIC HeavyIndustries 12 18 5 37 18889 30 54321 28 1377 28

Rapoo Technology 7 32 7 30 15556 35 53659 31 522 8Guangxi Hytera 4 40 3 42 7778 42 40000 43 0872 35Zhejiang Eastcom 10 23 14 12 26666 17 55696 25 0518 40Fujian Changelight 8 27 13 14 25555 19 56410 21 1208 31Guizhou SpaceAppliance 5 37 6 35 12222 38 43564 41 2582 20

Shanghai Brightdairy 10 23 5 37 16666 33 50575 35 0123 44Jiangxi ChanghongHuayi 14 10 15 7 32222 5 61972 4 4709 10

Jiangxi JZJT 13 13 10 22 25555 19 60274 11 3701 12ShangdongDoublestar 11 20 11 20 24444 23 57895 17 1341 29

Shandong TianrunCrankshaft 13 13 13 14 28889 13 61111 9 3212 16

Zhejiang Wynca 17 2 10 22 30000 9 56410 21 1126 32Jiangsu HengshunVinegar Industry 15 6 7 30 20000 27 54321 28 0169 42

Zhengjiang SaintAngelo 15 6 6 35 18888 32 52381 33 0129 43

Anhui HuamaoGroup 16 4 10 22 28889 13 61972 4 5302 7

Jiangsu KanionPharmaceutical 14 10 7 30 23333 24 54321 28 2653 19

Shaanxi Xiagu Power 8 27 2 43 11111 40 45833 39 0658 37

Complexity 9

erefore these enterprises had difficulty obtaining inno-vation efficiency from the other IM enterprises and they hadno obvious impact and driving effect on the other IM en-terprises erefore their independence was stronger in theinnovation efficiency correlation

433 Intermediary Centrality Intermediary centrality canbe used to measure the ability of IM enterprises to controlresources in a spatial correlation network In 2020 theaverage intermediary centrality of the IM enterprises was2872e top five enterprises were China XDGroup HenanYutong Bus XCMG Construction Machinery HunanZoomlion and Chongqing Chuanyi Automation whichshowed that these IM enterprises were in the middle of theinnovation efficiency correlation network and controlled theconstruction and development of the correlation relation-ships of the innovation efficiency of the other IM enterprisesContrary to expectations the index was insufficient of theIM enterprises located in the economically developed areasof Eastern China which indicated that the IM enterpriseslocated in economically developed areas had no drivingeffect on the other IM enterprises

44 Module-Modeling Analysis To better show the networkrole and functional rule of the IM enterprises a socialnetwork analysis was introduced in this paper to perform aclustering analysis In this paper the CONCOR method inUCINET software was used to study the network structureand 45 IM enterprises were divided into four modules with amaximum segmentation depth of 2 and a concentrationstandard of 02 as shown in Table 4 Module I had 11 IMenterprises which were mainly located in Northeastern andEastern China Module II had 21 IM enterprises which weremainly located in Central and Eastern China Module III had8 IM enterprises which were mainly located in Central andWestern China Module IV had 5 IM enterprises whichwere mainly located in Western China

Table 5 shows that the entrepreneurial performance ofthe IM enterprises in 2020 had 465 spatial network rela-tionships Of these 338 were within the module accountingfor 7269 of the total and 127 were outside the moduleaccounting for 2731 of the total As a result intramoduleoverflow was the primary factor in the spatial networkoverflow of the innovation efficiency of the IM enterprisese network module nature was judged by using theWasserman evaluation method e number of sending-outrelationships inModule I was 104 among which the numberof relationships inside the module was 78 that receivedoutside the module was 58 and that overflowing outside was26 the proportion of expected internal relationships was2273 and the proportion of actual internal relationshipswas 75 As a result Module I had more overflow rela-tionships with the IM enterprises both inside and outsidethe module so it was a ldquotwo-way overflow modulerdquo enumber of sending-out relationships in Module II was 282among which the number of relationships inside the modulewas 220 that received outside the module was 34 and thatoverflowing outside the module was 62 the proportion of

expected internal relationships was 4545 and the pro-portion of actual internal relationships was 7801 eseresults indicated that Module II could improve its owninnovation efficiency while spilling innovation efficiency tomembers of other modules us Module II was designatedthe ldquonet overflow modulerdquo e number of sending-outrelationships in Module III was 60 among which thenumber of relationships inside the module was 27 thatreceived outside the module was 19 and that overflowingoutside the module was 33 the proportion of expectedinternal relationships was 1591 and the proportion ofactual internal relationships was 4500 us the con-nection between the module and the outside was similar tothat within the module which played the role of a ldquobrokerrdquoso Module III was designated the ldquobroker modulerdquo enumber of sending-out relationships in Module IV was 19among which the number of relationships inside the modulewas 13 that received outside the module was 16 and thatoverflowing outside the module was 6 the proportion ofexpected internal relationships was 909 and the pro-portion of actual internal relationships was 6842 enumber of external relationships received by the module wasfar greater than that overflowing from the module so themodule was a typical ldquonet income modulerdquo erefore in2020 Chinarsquos IM enterprises were unevenly distributed byregion and each module was composed of enterprises fromthe same or adjacent provinces e module regions weregeographically concentrated and when they were closer toeach other they were more likely to have innovation cor-relation Module II showed that the Yangtze River Delta andits surrounding economically developed regions accountedfor more innovation correlation and the internal correlationwas strong so they could meet the development needs oftheir own innovation efficiencies However the enterprisesin Module IV located in Western China needed to absorbthe overflow effect of the other modules to develop inno-vation efficiency because the module had a small number ofenterprises

To further study the overflow relationships and pathamong the modules UCINET software was used to cal-culate the network density and image matrices of eachmodule (see Table 6) Figure 4 indicates that the density ofthe whole network in 2020 was 02383 which was taken asthe critical value A module density greater than 02383indicated a concentration trend in the module e densityvalue was assigned to 1 and a density value less than 02383was assigned to 0 to convert the density matrix of eachmodule into an image matrix e evolutionary processshowed that the spatial correlation network of the inno-vation efficiency of the IM enterprises did not show theexpected core-edge structure but presented a significantcohesive subgroup characteristic e IM enterpriseswithin the module were closely connected while there werefewer external relations Only Module III sent relationshipsto Module IV that is the innovation efficiency of BrokerModule III flowed to Net IncomeModule IVerefore theIM enterprises in Module IV had to absorb the overfloweffects of the other modules to meet the development needsof their own innovation efficiency However they could

10 Complexity

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

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[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

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[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

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[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

operating costs and improving production efficiency andmust continue to improve in future development and exhibitfavorable growth erefore the sample selection met thestudy objective and requirements of this paper From 2015 to2018 the Ministry of Industry and Information Technologyannounced 306 IM demonstration pilot projects Becauseobtaining enterprise microdata is difficult and financial datadisclosed by listed companies are highly reliable andavailable domestic A-share listed enterprises among thepilot enterprises were selected as typical samples Moreoverthe listed companies experiencing bankruptcy mergers andacquisitions or missing important information were elimi-nated leaving 103 listed companies To ensure the datavalidity and integrity extreme enterprises whose patentauthorization number was 0 were eliminated Ultimately 45effective target enterprises remained e annual report datawere all from the Guotai database and the number of patentauthorizations for each enterprise was searched with theapplicant as the search condition yearly on the NationalIntellectual Property Administration website

According to the innovation input and output process ofIM enterprises investment in RampD was selected as the inputindex in this papere practice of Su and Li [46] was used inthe input stage when measuring the innovation efficiencyindex by considering the principle of the availability andoperability of index selection e main reasons for usingthis method are as follows the enterprisersquos RampD activi-tiesmdashincluding personnel funds and equipmentmdashincludemany factors that are difficult to measure and RampD fundinvestment is the most direct response to the enterprisersquosRampD investment is paper used annual RampD investmentoperating income to measure innovation investment Re-garding the output index from the perspective of existingstudies all scholars measure innovation achievements bypatent application quantity and the number of grantedpatents [47 48] e quantity of patent applications rep-resents innovation efforts rather than scientific and tech-nological innovation capabilities e number of grantedpatents is the number of patents authorized only after strictexamination procedures which can more directly reflect theregional capability of scientific and technological innovation[49] erefore this paper used the number of grantedpatents rather than the number of applications e eval-uation index system of the innovation efficiency of the IMenterprises is shown in Table 1

4 Empirical Analyses

41 Spatial Distribution of the Innovation Efficiency of the IMEnterprises is paper used the DEA cross-efficiency modeland MaxDEA software to measure the innovation effi-ciencies of 45 IM enterprises from 2015 to 2020 As shown inTable 2 the development was relatively stable of the inno-vation efficiency of Chinarsquos IM enterprises innovation ef-ficiency has been increasing slowly in the past six years withfluctuations and the average innovation efficiency was 0515In these six years the average innovation efficiency ofJiangsu CMG Construction Machinery was 0957 rankingfirst and the lowest innovation efficiency was of Shaanxi

Xiagu Power in 2015 which was only 0109 IM enterprisesexhibited a large gap in innovation efficiency which indi-cated that some of Chinarsquos IM enterprises made somebreakthroughs in recent years but some of them had lowinnovation efficiency

42 Analysis of the Characteristics and Evolutionary Process oftheWholeNetwork Based on themodified gravity model thissection establishes the spatial network of the innovation effi-ciency of the IM enterprises draws a topological map of theirspatial network by using Netdraw in UCINET and selectssections of 2015 and 2020 for a comparative analysis (seeFigures 1 and 2) e nodes in the figure represent the 45 IMenterprises and the connections between the nodes representthe correlation relationships between them As shown inFigures 1 and 2 compared with 2015 the spatial correlationrelationships of innovation efficiency network density andtwo-way correlation represented by thick lines increased in2020 and the relationship network of innovation efficiencybecame more complex then e innovation efficiencies ofmany of the IM enterprises not only affected the enterprises inthe same provinces but also broke the geographical restric-tions permitting spatial correlation relationships to occur withneighboring and nonneighboring provinces To further analyzethe overall network characteristics of the spatial correlation ofthe innovation efficiency of the IM enterprises this papercalculated the spatial correlation network correlation degreedensity grade and efficiency of their innovation efficiencybased on the spatial correlation matrix A diagram comparingthe network density and relationship number is shown inFigure 3 and one comparing the network grade and networkefficiency is shown in Figure 4

e measurement results showed that the networkcorrelation degree from 2015 to 2020 was 1 which indicatedthat the network accessibility was good and the innovationefficiency network of the IM enterprises was closely asso-ciated among regions Figure 3 shows that the networkdensity and the number of relationships decreased slightly in2020 but the overall trend was still an increase equantitative analysis was consistent with the results of theprevious topological graph which indicated that the rela-tionships tended to be close among the spatial correlationnetwork of the innovation efficiency of the IM enterprisesFigure 4 indicates that the network grade showed adownward trend from 07928 in 2015 to 0686 in 2020indicating that the spatial correlation network structure ofthe innovation efficiency of the IM enterprises was brokenthe relationship between regional innovation efficiencies wasgradually strengthened and an obvious trend emerged ofcross-regional collaborative innovation e network effi-ciency decreased from 051 in 2015 to 04632 in 2020 whichindicated that the innovation efficiencies of the IM enter-prises were gradually close in the network and the stabilityof the spatial network has gradually strengthened

43 Individual Network Characteristics By measuring de-gree closeness and intermediary centrality this paper an-alyzed the individual network characteristics of the spatial

Complexity 5

network of the innovation efficiency of the IM enterprises todetermine their positions and rolesis paper measured thein- and outdegrees of each IM enterprise to study the in-fluence and affected situations of the spatial correlationnetwork of certain IM enterprises and the measurementresults are shown in Table 3

431 Degree Centrality Degree centrality can reflect thestatus of each IM enterprise in the spatial network of in-novation efficiency e five IM enterprises with the highestoutdegrees were XCMG Construction Machinery JiangsuHTGD Zhejiang Wynca Hubei Accelink and AnhuiHuamao Group which are located in Eastern and Central

Table 2 Innovation efficiency of 45 IM enterprises from 2015 to 2020

Enterprise abbreviation 2015 2016 2017 2018 2019 2020 Average valueHenei Heis Company Limited 0726 0781 0831 0884 0851 0846 0820Shandong Goertek Inc 0582 0559 0602 0643 0685 0685 0626Hunan Zoomlion 0531 0564 0587 0571 0639 0624 0586Liaoning Ansteel 0891 0784 0863 0831 0783 0961 0852Shanghai Tiandi Science amp Technology 0761 0963 0874 0767 0778 0934 0846Zhengjiang Robam 0873 0731 0783 0731 0791 0683 0765Henan Yutong Bus 0741 0739 0846 0883 0821 0857 0815Hebei CRRC 0835 0874 0887 0859 0983 0973 0902Jiangxi Copper 0783 0657 0674 0895 0856 0785 0775XCMG Construction Machinery 0928 0954 0961 0959 0972 0968 0957Jiangsu HTGD 0765 0875 0931 0976 0951 0874 0895Beijing Foton 0745 0784 0734 0861 0987 0943 0842China XD Group 0693 0646 0685 0699 0751 0631 0684Guangxi Liugong 0687 0691 0682 0691 0723 0703 0696Guodian Nanjing Automation 0735 0758 0876 0831 0941 0872 0836Chongqing Chuanyi Automation 0646 0763 0735 0772 0841 0864 0770Zhejiang Jushi 0695 0685 0821 0867 0841 0871 0797Xinjiang TBEA 0571 0645 0682 0631 0788 0793 0685Xinjiang West-Construction 0473 0482 0741 0783 0732 0778 0665Shandong Sailun Jinyu Group 0974 0861 0967 0983 0741 0865 0899Chongqing Yahua 0651 0731 0631 0689 0731 0786 0703North China Pharmaceutical 0573 0581 0542 0514 0631 0645 0581Gansu Great Wall Electrical 0463 0441 0485 0463 0431 0521 0467Xinjiang Goldwind Science amp Technology 0342 0314 0381 0321 0384 0351 0349Jiangsu Victory Precision 0351 0351 0431 0463 0471 0442 0418Jiangsu Tongding Connection 0319 0373 0379 0365 0383 0381 0367HT-SAAE 0341 0364 0373 0361 0371 0379 0365Hubei Accelink 0286 0281 0265 0365 0393 0345 0323CITIC Heavy Industries 0311 0382 0362 0374 0375 0321 0354Rapoo Technology 0331 0285 0231 0351 0341 0358 0316Guangxi Hytera 0221 0252 0241 0283 0261 0251 0252Zhejiang Eastcom 0156 0121 0241 0271 0263 0298 0225Fujian Changelight 0154 0173 0196 0141 0144 0131 0157Guizhou Space Appliance 0231 0274 0221 0251 0267 0271 0253Shanghai Brightdairy 0151 0182 0164 0171 0162 0153 0164Jiangxi Changhong Huayi 0291 0272 0263 0231 0285 0221 0261Jiangxi JZJT 0253 0262 0241 0235 0281 0284 0259Shangdong Doublestar 0186 0171 0184 0191 0121 0132 0164Shandong Tianrun Crankshaft 0257 0207 0231 0221 0267 0238 0237Zhejiang Wynca 0178 0211 0251 0268 0281 0252 0240Jiangsu Hengshun Vinegar Industry 0191 0137 0181 0189 0237 0173 0185Zhengjiang Saint Angelo 0219 0231 0298 0281 0231 0235 0249Anhui Huamao Group 0182 0175 0238 0247 0219 0183 0207Jiangsu Kanion Pharmaceutical 0178 0121 0243 0189 0182 0231 0191Shaanxi Xiagu Power 0109 0159 0194 0114 0184 0176 0156

Table 1 Evaluation index system of the innovation efficiency of the IM enterprises

First-grade index Second-grade index MeasurementInput Investment in RampD Annual RampD investmentoperating incomeOutput Number of granted patents Direct use of the index data

6 Complexity

China is finding showed that these five IM enterpriseshad more innovation efficiency overflow which promotedthe innovation efficiency of the other IM enterprisese fiveIM enterprises with the poorest outdegrees were XinjiangGoldwind Science amp Technology Xinjiang West-Con-struction Gansu Great Wall Electrical Xinjiang TBEA andGuangxi Liugong which were located in NorthwesternChina and had little correlation with the other IM enter-prises e five IM enterprises with leading indegrees wereXCMG Construction Machinery Guodian Nanjing Auto-mation Shandong Goertek Inc Shandong Sailun JinyuGroup and Zhejiang Jushi which were all located in EasternChina e higher outdegrees indicated that the innovationefficiencies of the other IM enterprises overflowed to thesefive enterprises e five IM enterprises with the lowestindegrees were Xinjiang Goldwind Science amp TechnologyGuangxi Liugong Shaanxi Xiagu Power Xinjiang TBEAand Xinjiang West-Construction which were located in

Northwestern China indicating that these enterprises un-dertook fewer network relationships A further analysisshowed that the degree centrality was the highest for the IMenterprises in Eastern China indicating that these enter-prises had a higher degree of external innovation efficiencyIn contrast the degree centrality of the IM enterprises inWestern China ranked lower indicating that these enter-prises had a lower degree of external innovation efficiencyAccording to the Report on the IM Development Index(2020) the IM enterprises in China are unevenly distributedand more IM enterprises are concentrated in Jiangsu andZhejiang provinces and other parts of Eastern China einnovation efficiencies in Eastern China produced a mutualaid effect with a high density which caused the spatialnetwork relationships to become increasingly closerHowever few IM enterprises were located inWestern Chinaand these were far from the correlation degree of the in-novation efficiency of other enterprises in the network

Figure 1 Spatial correlation network of the innovation efficiency of the IM enterprises in 2015

Figure 2 Spatial correlation network of the innovation efficiency of the IM enterprises in 2020

Complexity 7

2015 2016 2017 2018 2019 2020Years

052

05

048

046

044

042

08

06

04

02

0

Net

wor

k gr

ade

Net

wor

k ef

ficie

ncy

Linear (network efficiency)

Network gradeNetwork efficiency

Figure 4 Network grade and efficiency

Table 3 Analysis of the centrality of the spatial correlation network of the innovation efficiency of the IM enterprises

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Henei Heis CompanyLimited 5 37 15 7 22222 25 61972 4 4526 11

Shandong GoertekInc 8 27 19 3 30000 9 61111 9 1604 25

Hunan Zoomlion 10 23 10 22 22222 25 58667 15 7071 4Liaoning Ansteel 6 36 9 26 16666 33 50575 35 1009 34Shanghai TiandiScience ampTechnology

13 13 16 6 32222 4 57895 17 3079 17

Zhengjiang Robam 13 13 13 14 28889 13 57895 17 1802 24Henan Yutong Bus 12 18 15 7 30000 9 61972 4 10161 2Hebei CRRC 8 27 14 12 24444 22 58667 15 2332 22Jiangxi Copper 10 23 8 29 20000 27 55000 26 2568 21XCMG ConstructionMachinery 19 1 20 1 43333 1 68750 1 8573 3

Jiangsu HTGD 17 2 15 7 35555 3 65672 2 3415 13Beijing Foton 11 20 12 18 25555 19 57895 17 3242 15China XD Group 7 32 11 20 20000 27 56410 21 12609 1

500

450

400

350

300

250

200

150

100

50

0

Rela

tions

hip

num

ber

2015 2016 2017 2018 2019 2020Year

Net

wor

k de

nsity

Relationship numberNetwork densityLinear (relationship number)

03

025

02

015

01

005

0

Figure 3 Network density and relationship number

8 Complexity

432 Closeness Centrality Closeness centrality can be usedas an index to judge the degree of the correlation difficulty ofeach IM enterprise in the spatial association network In2020 the average closeness degree of the IM enterprises was54891 e top five enterprises in this regard were XCMGConstruction Machinery Jiangsu HTGD Shandong SailunJinyu Group Henan Yutong Bus and Jiangxi ChanghongHuayi which were located in Mideastern China where IM

enterprises were concentrated and had a short networkdistance from the other IM enterprises in the networkerefore they could more easily spatially correlate with theother IM enterprises in the spatial correlation network ebottom five enterprises were Xinjiang TBEA Guizhou SpaceAppliance Guangxi Hytera Xinjiang West-Constructionand Xinjiang Goldwind Science amp Technology which werelocated in Western China at the edge of the network

Table 3 Continued

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Guangxi Liugong 5 37 2 43 77775 44 45361 40 0559 39Guodian NanjingAutomation 13 13 20 1 36666 2 60274 11 2671 18

Chongqing ChuanyiAutomation 8 27 9 26 18889 30 56410 21 6018 5

Zhejiang Jushi 7 32 17 5 26667 16 53012 32 0435 41Xinjiang TBEA 4 40 5 37 10000 41 43564 41 338 14Xinjiang West-Construction 3 43 4 41 7778 42 37931 44 1468 27

Shandong SailunJinyu Group 11 20 18 4 32222 5 64706 3 4894 9

Chongqing Yahua 4 40 9 26 14444 36 47312 38 2009 23North ChinaPharmaceutical 7 32 5 37 13333 37 51163 34 087 36

Gansu Great WallElectrical 3 43 7 30 11111 39 48889 37 1112 33

Xinjiang GoldwindScience ampTechnology

2 45 2 43 4444 45 30769 45 0 45

Jiangsu VictoryPrecision 14 10 15 7 32222 5 60274 11 132 30

Jiangsu TongdingConnection 15 6 12 18 30000 9 55000 26 0643 38

HT-SAAE 15 6 13 14 31111 8 60274 11 1593 26Hubei Accelink 16 4 7 30 25556 18 61972 4 5366 6CITIC HeavyIndustries 12 18 5 37 18889 30 54321 28 1377 28

Rapoo Technology 7 32 7 30 15556 35 53659 31 522 8Guangxi Hytera 4 40 3 42 7778 42 40000 43 0872 35Zhejiang Eastcom 10 23 14 12 26666 17 55696 25 0518 40Fujian Changelight 8 27 13 14 25555 19 56410 21 1208 31Guizhou SpaceAppliance 5 37 6 35 12222 38 43564 41 2582 20

Shanghai Brightdairy 10 23 5 37 16666 33 50575 35 0123 44Jiangxi ChanghongHuayi 14 10 15 7 32222 5 61972 4 4709 10

Jiangxi JZJT 13 13 10 22 25555 19 60274 11 3701 12ShangdongDoublestar 11 20 11 20 24444 23 57895 17 1341 29

Shandong TianrunCrankshaft 13 13 13 14 28889 13 61111 9 3212 16

Zhejiang Wynca 17 2 10 22 30000 9 56410 21 1126 32Jiangsu HengshunVinegar Industry 15 6 7 30 20000 27 54321 28 0169 42

Zhengjiang SaintAngelo 15 6 6 35 18888 32 52381 33 0129 43

Anhui HuamaoGroup 16 4 10 22 28889 13 61972 4 5302 7

Jiangsu KanionPharmaceutical 14 10 7 30 23333 24 54321 28 2653 19

Shaanxi Xiagu Power 8 27 2 43 11111 40 45833 39 0658 37

Complexity 9

erefore these enterprises had difficulty obtaining inno-vation efficiency from the other IM enterprises and they hadno obvious impact and driving effect on the other IM en-terprises erefore their independence was stronger in theinnovation efficiency correlation

433 Intermediary Centrality Intermediary centrality canbe used to measure the ability of IM enterprises to controlresources in a spatial correlation network In 2020 theaverage intermediary centrality of the IM enterprises was2872e top five enterprises were China XDGroup HenanYutong Bus XCMG Construction Machinery HunanZoomlion and Chongqing Chuanyi Automation whichshowed that these IM enterprises were in the middle of theinnovation efficiency correlation network and controlled theconstruction and development of the correlation relation-ships of the innovation efficiency of the other IM enterprisesContrary to expectations the index was insufficient of theIM enterprises located in the economically developed areasof Eastern China which indicated that the IM enterpriseslocated in economically developed areas had no drivingeffect on the other IM enterprises

44 Module-Modeling Analysis To better show the networkrole and functional rule of the IM enterprises a socialnetwork analysis was introduced in this paper to perform aclustering analysis In this paper the CONCOR method inUCINET software was used to study the network structureand 45 IM enterprises were divided into four modules with amaximum segmentation depth of 2 and a concentrationstandard of 02 as shown in Table 4 Module I had 11 IMenterprises which were mainly located in Northeastern andEastern China Module II had 21 IM enterprises which weremainly located in Central and Eastern China Module III had8 IM enterprises which were mainly located in Central andWestern China Module IV had 5 IM enterprises whichwere mainly located in Western China

Table 5 shows that the entrepreneurial performance ofthe IM enterprises in 2020 had 465 spatial network rela-tionships Of these 338 were within the module accountingfor 7269 of the total and 127 were outside the moduleaccounting for 2731 of the total As a result intramoduleoverflow was the primary factor in the spatial networkoverflow of the innovation efficiency of the IM enterprisese network module nature was judged by using theWasserman evaluation method e number of sending-outrelationships inModule I was 104 among which the numberof relationships inside the module was 78 that receivedoutside the module was 58 and that overflowing outside was26 the proportion of expected internal relationships was2273 and the proportion of actual internal relationshipswas 75 As a result Module I had more overflow rela-tionships with the IM enterprises both inside and outsidethe module so it was a ldquotwo-way overflow modulerdquo enumber of sending-out relationships in Module II was 282among which the number of relationships inside the modulewas 220 that received outside the module was 34 and thatoverflowing outside the module was 62 the proportion of

expected internal relationships was 4545 and the pro-portion of actual internal relationships was 7801 eseresults indicated that Module II could improve its owninnovation efficiency while spilling innovation efficiency tomembers of other modules us Module II was designatedthe ldquonet overflow modulerdquo e number of sending-outrelationships in Module III was 60 among which thenumber of relationships inside the module was 27 thatreceived outside the module was 19 and that overflowingoutside the module was 33 the proportion of expectedinternal relationships was 1591 and the proportion ofactual internal relationships was 4500 us the con-nection between the module and the outside was similar tothat within the module which played the role of a ldquobrokerrdquoso Module III was designated the ldquobroker modulerdquo enumber of sending-out relationships in Module IV was 19among which the number of relationships inside the modulewas 13 that received outside the module was 16 and thatoverflowing outside the module was 6 the proportion ofexpected internal relationships was 909 and the pro-portion of actual internal relationships was 6842 enumber of external relationships received by the module wasfar greater than that overflowing from the module so themodule was a typical ldquonet income modulerdquo erefore in2020 Chinarsquos IM enterprises were unevenly distributed byregion and each module was composed of enterprises fromthe same or adjacent provinces e module regions weregeographically concentrated and when they were closer toeach other they were more likely to have innovation cor-relation Module II showed that the Yangtze River Delta andits surrounding economically developed regions accountedfor more innovation correlation and the internal correlationwas strong so they could meet the development needs oftheir own innovation efficiencies However the enterprisesin Module IV located in Western China needed to absorbthe overflow effect of the other modules to develop inno-vation efficiency because the module had a small number ofenterprises

To further study the overflow relationships and pathamong the modules UCINET software was used to cal-culate the network density and image matrices of eachmodule (see Table 6) Figure 4 indicates that the density ofthe whole network in 2020 was 02383 which was taken asthe critical value A module density greater than 02383indicated a concentration trend in the module e densityvalue was assigned to 1 and a density value less than 02383was assigned to 0 to convert the density matrix of eachmodule into an image matrix e evolutionary processshowed that the spatial correlation network of the inno-vation efficiency of the IM enterprises did not show theexpected core-edge structure but presented a significantcohesive subgroup characteristic e IM enterpriseswithin the module were closely connected while there werefewer external relations Only Module III sent relationshipsto Module IV that is the innovation efficiency of BrokerModule III flowed to Net IncomeModule IVerefore theIM enterprises in Module IV had to absorb the overfloweffects of the other modules to meet the development needsof their own innovation efficiency However they could

10 Complexity

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

network of the innovation efficiency of the IM enterprises todetermine their positions and rolesis paper measured thein- and outdegrees of each IM enterprise to study the in-fluence and affected situations of the spatial correlationnetwork of certain IM enterprises and the measurementresults are shown in Table 3

431 Degree Centrality Degree centrality can reflect thestatus of each IM enterprise in the spatial network of in-novation efficiency e five IM enterprises with the highestoutdegrees were XCMG Construction Machinery JiangsuHTGD Zhejiang Wynca Hubei Accelink and AnhuiHuamao Group which are located in Eastern and Central

Table 2 Innovation efficiency of 45 IM enterprises from 2015 to 2020

Enterprise abbreviation 2015 2016 2017 2018 2019 2020 Average valueHenei Heis Company Limited 0726 0781 0831 0884 0851 0846 0820Shandong Goertek Inc 0582 0559 0602 0643 0685 0685 0626Hunan Zoomlion 0531 0564 0587 0571 0639 0624 0586Liaoning Ansteel 0891 0784 0863 0831 0783 0961 0852Shanghai Tiandi Science amp Technology 0761 0963 0874 0767 0778 0934 0846Zhengjiang Robam 0873 0731 0783 0731 0791 0683 0765Henan Yutong Bus 0741 0739 0846 0883 0821 0857 0815Hebei CRRC 0835 0874 0887 0859 0983 0973 0902Jiangxi Copper 0783 0657 0674 0895 0856 0785 0775XCMG Construction Machinery 0928 0954 0961 0959 0972 0968 0957Jiangsu HTGD 0765 0875 0931 0976 0951 0874 0895Beijing Foton 0745 0784 0734 0861 0987 0943 0842China XD Group 0693 0646 0685 0699 0751 0631 0684Guangxi Liugong 0687 0691 0682 0691 0723 0703 0696Guodian Nanjing Automation 0735 0758 0876 0831 0941 0872 0836Chongqing Chuanyi Automation 0646 0763 0735 0772 0841 0864 0770Zhejiang Jushi 0695 0685 0821 0867 0841 0871 0797Xinjiang TBEA 0571 0645 0682 0631 0788 0793 0685Xinjiang West-Construction 0473 0482 0741 0783 0732 0778 0665Shandong Sailun Jinyu Group 0974 0861 0967 0983 0741 0865 0899Chongqing Yahua 0651 0731 0631 0689 0731 0786 0703North China Pharmaceutical 0573 0581 0542 0514 0631 0645 0581Gansu Great Wall Electrical 0463 0441 0485 0463 0431 0521 0467Xinjiang Goldwind Science amp Technology 0342 0314 0381 0321 0384 0351 0349Jiangsu Victory Precision 0351 0351 0431 0463 0471 0442 0418Jiangsu Tongding Connection 0319 0373 0379 0365 0383 0381 0367HT-SAAE 0341 0364 0373 0361 0371 0379 0365Hubei Accelink 0286 0281 0265 0365 0393 0345 0323CITIC Heavy Industries 0311 0382 0362 0374 0375 0321 0354Rapoo Technology 0331 0285 0231 0351 0341 0358 0316Guangxi Hytera 0221 0252 0241 0283 0261 0251 0252Zhejiang Eastcom 0156 0121 0241 0271 0263 0298 0225Fujian Changelight 0154 0173 0196 0141 0144 0131 0157Guizhou Space Appliance 0231 0274 0221 0251 0267 0271 0253Shanghai Brightdairy 0151 0182 0164 0171 0162 0153 0164Jiangxi Changhong Huayi 0291 0272 0263 0231 0285 0221 0261Jiangxi JZJT 0253 0262 0241 0235 0281 0284 0259Shangdong Doublestar 0186 0171 0184 0191 0121 0132 0164Shandong Tianrun Crankshaft 0257 0207 0231 0221 0267 0238 0237Zhejiang Wynca 0178 0211 0251 0268 0281 0252 0240Jiangsu Hengshun Vinegar Industry 0191 0137 0181 0189 0237 0173 0185Zhengjiang Saint Angelo 0219 0231 0298 0281 0231 0235 0249Anhui Huamao Group 0182 0175 0238 0247 0219 0183 0207Jiangsu Kanion Pharmaceutical 0178 0121 0243 0189 0182 0231 0191Shaanxi Xiagu Power 0109 0159 0194 0114 0184 0176 0156

Table 1 Evaluation index system of the innovation efficiency of the IM enterprises

First-grade index Second-grade index MeasurementInput Investment in RampD Annual RampD investmentoperating incomeOutput Number of granted patents Direct use of the index data

6 Complexity

China is finding showed that these five IM enterpriseshad more innovation efficiency overflow which promotedthe innovation efficiency of the other IM enterprisese fiveIM enterprises with the poorest outdegrees were XinjiangGoldwind Science amp Technology Xinjiang West-Con-struction Gansu Great Wall Electrical Xinjiang TBEA andGuangxi Liugong which were located in NorthwesternChina and had little correlation with the other IM enter-prises e five IM enterprises with leading indegrees wereXCMG Construction Machinery Guodian Nanjing Auto-mation Shandong Goertek Inc Shandong Sailun JinyuGroup and Zhejiang Jushi which were all located in EasternChina e higher outdegrees indicated that the innovationefficiencies of the other IM enterprises overflowed to thesefive enterprises e five IM enterprises with the lowestindegrees were Xinjiang Goldwind Science amp TechnologyGuangxi Liugong Shaanxi Xiagu Power Xinjiang TBEAand Xinjiang West-Construction which were located in

Northwestern China indicating that these enterprises un-dertook fewer network relationships A further analysisshowed that the degree centrality was the highest for the IMenterprises in Eastern China indicating that these enter-prises had a higher degree of external innovation efficiencyIn contrast the degree centrality of the IM enterprises inWestern China ranked lower indicating that these enter-prises had a lower degree of external innovation efficiencyAccording to the Report on the IM Development Index(2020) the IM enterprises in China are unevenly distributedand more IM enterprises are concentrated in Jiangsu andZhejiang provinces and other parts of Eastern China einnovation efficiencies in Eastern China produced a mutualaid effect with a high density which caused the spatialnetwork relationships to become increasingly closerHowever few IM enterprises were located inWestern Chinaand these were far from the correlation degree of the in-novation efficiency of other enterprises in the network

Figure 1 Spatial correlation network of the innovation efficiency of the IM enterprises in 2015

Figure 2 Spatial correlation network of the innovation efficiency of the IM enterprises in 2020

Complexity 7

2015 2016 2017 2018 2019 2020Years

052

05

048

046

044

042

08

06

04

02

0

Net

wor

k gr

ade

Net

wor

k ef

ficie

ncy

Linear (network efficiency)

Network gradeNetwork efficiency

Figure 4 Network grade and efficiency

Table 3 Analysis of the centrality of the spatial correlation network of the innovation efficiency of the IM enterprises

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Henei Heis CompanyLimited 5 37 15 7 22222 25 61972 4 4526 11

Shandong GoertekInc 8 27 19 3 30000 9 61111 9 1604 25

Hunan Zoomlion 10 23 10 22 22222 25 58667 15 7071 4Liaoning Ansteel 6 36 9 26 16666 33 50575 35 1009 34Shanghai TiandiScience ampTechnology

13 13 16 6 32222 4 57895 17 3079 17

Zhengjiang Robam 13 13 13 14 28889 13 57895 17 1802 24Henan Yutong Bus 12 18 15 7 30000 9 61972 4 10161 2Hebei CRRC 8 27 14 12 24444 22 58667 15 2332 22Jiangxi Copper 10 23 8 29 20000 27 55000 26 2568 21XCMG ConstructionMachinery 19 1 20 1 43333 1 68750 1 8573 3

Jiangsu HTGD 17 2 15 7 35555 3 65672 2 3415 13Beijing Foton 11 20 12 18 25555 19 57895 17 3242 15China XD Group 7 32 11 20 20000 27 56410 21 12609 1

500

450

400

350

300

250

200

150

100

50

0

Rela

tions

hip

num

ber

2015 2016 2017 2018 2019 2020Year

Net

wor

k de

nsity

Relationship numberNetwork densityLinear (relationship number)

03

025

02

015

01

005

0

Figure 3 Network density and relationship number

8 Complexity

432 Closeness Centrality Closeness centrality can be usedas an index to judge the degree of the correlation difficulty ofeach IM enterprise in the spatial association network In2020 the average closeness degree of the IM enterprises was54891 e top five enterprises in this regard were XCMGConstruction Machinery Jiangsu HTGD Shandong SailunJinyu Group Henan Yutong Bus and Jiangxi ChanghongHuayi which were located in Mideastern China where IM

enterprises were concentrated and had a short networkdistance from the other IM enterprises in the networkerefore they could more easily spatially correlate with theother IM enterprises in the spatial correlation network ebottom five enterprises were Xinjiang TBEA Guizhou SpaceAppliance Guangxi Hytera Xinjiang West-Constructionand Xinjiang Goldwind Science amp Technology which werelocated in Western China at the edge of the network

Table 3 Continued

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Guangxi Liugong 5 37 2 43 77775 44 45361 40 0559 39Guodian NanjingAutomation 13 13 20 1 36666 2 60274 11 2671 18

Chongqing ChuanyiAutomation 8 27 9 26 18889 30 56410 21 6018 5

Zhejiang Jushi 7 32 17 5 26667 16 53012 32 0435 41Xinjiang TBEA 4 40 5 37 10000 41 43564 41 338 14Xinjiang West-Construction 3 43 4 41 7778 42 37931 44 1468 27

Shandong SailunJinyu Group 11 20 18 4 32222 5 64706 3 4894 9

Chongqing Yahua 4 40 9 26 14444 36 47312 38 2009 23North ChinaPharmaceutical 7 32 5 37 13333 37 51163 34 087 36

Gansu Great WallElectrical 3 43 7 30 11111 39 48889 37 1112 33

Xinjiang GoldwindScience ampTechnology

2 45 2 43 4444 45 30769 45 0 45

Jiangsu VictoryPrecision 14 10 15 7 32222 5 60274 11 132 30

Jiangsu TongdingConnection 15 6 12 18 30000 9 55000 26 0643 38

HT-SAAE 15 6 13 14 31111 8 60274 11 1593 26Hubei Accelink 16 4 7 30 25556 18 61972 4 5366 6CITIC HeavyIndustries 12 18 5 37 18889 30 54321 28 1377 28

Rapoo Technology 7 32 7 30 15556 35 53659 31 522 8Guangxi Hytera 4 40 3 42 7778 42 40000 43 0872 35Zhejiang Eastcom 10 23 14 12 26666 17 55696 25 0518 40Fujian Changelight 8 27 13 14 25555 19 56410 21 1208 31Guizhou SpaceAppliance 5 37 6 35 12222 38 43564 41 2582 20

Shanghai Brightdairy 10 23 5 37 16666 33 50575 35 0123 44Jiangxi ChanghongHuayi 14 10 15 7 32222 5 61972 4 4709 10

Jiangxi JZJT 13 13 10 22 25555 19 60274 11 3701 12ShangdongDoublestar 11 20 11 20 24444 23 57895 17 1341 29

Shandong TianrunCrankshaft 13 13 13 14 28889 13 61111 9 3212 16

Zhejiang Wynca 17 2 10 22 30000 9 56410 21 1126 32Jiangsu HengshunVinegar Industry 15 6 7 30 20000 27 54321 28 0169 42

Zhengjiang SaintAngelo 15 6 6 35 18888 32 52381 33 0129 43

Anhui HuamaoGroup 16 4 10 22 28889 13 61972 4 5302 7

Jiangsu KanionPharmaceutical 14 10 7 30 23333 24 54321 28 2653 19

Shaanxi Xiagu Power 8 27 2 43 11111 40 45833 39 0658 37

Complexity 9

erefore these enterprises had difficulty obtaining inno-vation efficiency from the other IM enterprises and they hadno obvious impact and driving effect on the other IM en-terprises erefore their independence was stronger in theinnovation efficiency correlation

433 Intermediary Centrality Intermediary centrality canbe used to measure the ability of IM enterprises to controlresources in a spatial correlation network In 2020 theaverage intermediary centrality of the IM enterprises was2872e top five enterprises were China XDGroup HenanYutong Bus XCMG Construction Machinery HunanZoomlion and Chongqing Chuanyi Automation whichshowed that these IM enterprises were in the middle of theinnovation efficiency correlation network and controlled theconstruction and development of the correlation relation-ships of the innovation efficiency of the other IM enterprisesContrary to expectations the index was insufficient of theIM enterprises located in the economically developed areasof Eastern China which indicated that the IM enterpriseslocated in economically developed areas had no drivingeffect on the other IM enterprises

44 Module-Modeling Analysis To better show the networkrole and functional rule of the IM enterprises a socialnetwork analysis was introduced in this paper to perform aclustering analysis In this paper the CONCOR method inUCINET software was used to study the network structureand 45 IM enterprises were divided into four modules with amaximum segmentation depth of 2 and a concentrationstandard of 02 as shown in Table 4 Module I had 11 IMenterprises which were mainly located in Northeastern andEastern China Module II had 21 IM enterprises which weremainly located in Central and Eastern China Module III had8 IM enterprises which were mainly located in Central andWestern China Module IV had 5 IM enterprises whichwere mainly located in Western China

Table 5 shows that the entrepreneurial performance ofthe IM enterprises in 2020 had 465 spatial network rela-tionships Of these 338 were within the module accountingfor 7269 of the total and 127 were outside the moduleaccounting for 2731 of the total As a result intramoduleoverflow was the primary factor in the spatial networkoverflow of the innovation efficiency of the IM enterprisese network module nature was judged by using theWasserman evaluation method e number of sending-outrelationships inModule I was 104 among which the numberof relationships inside the module was 78 that receivedoutside the module was 58 and that overflowing outside was26 the proportion of expected internal relationships was2273 and the proportion of actual internal relationshipswas 75 As a result Module I had more overflow rela-tionships with the IM enterprises both inside and outsidethe module so it was a ldquotwo-way overflow modulerdquo enumber of sending-out relationships in Module II was 282among which the number of relationships inside the modulewas 220 that received outside the module was 34 and thatoverflowing outside the module was 62 the proportion of

expected internal relationships was 4545 and the pro-portion of actual internal relationships was 7801 eseresults indicated that Module II could improve its owninnovation efficiency while spilling innovation efficiency tomembers of other modules us Module II was designatedthe ldquonet overflow modulerdquo e number of sending-outrelationships in Module III was 60 among which thenumber of relationships inside the module was 27 thatreceived outside the module was 19 and that overflowingoutside the module was 33 the proportion of expectedinternal relationships was 1591 and the proportion ofactual internal relationships was 4500 us the con-nection between the module and the outside was similar tothat within the module which played the role of a ldquobrokerrdquoso Module III was designated the ldquobroker modulerdquo enumber of sending-out relationships in Module IV was 19among which the number of relationships inside the modulewas 13 that received outside the module was 16 and thatoverflowing outside the module was 6 the proportion ofexpected internal relationships was 909 and the pro-portion of actual internal relationships was 6842 enumber of external relationships received by the module wasfar greater than that overflowing from the module so themodule was a typical ldquonet income modulerdquo erefore in2020 Chinarsquos IM enterprises were unevenly distributed byregion and each module was composed of enterprises fromthe same or adjacent provinces e module regions weregeographically concentrated and when they were closer toeach other they were more likely to have innovation cor-relation Module II showed that the Yangtze River Delta andits surrounding economically developed regions accountedfor more innovation correlation and the internal correlationwas strong so they could meet the development needs oftheir own innovation efficiencies However the enterprisesin Module IV located in Western China needed to absorbthe overflow effect of the other modules to develop inno-vation efficiency because the module had a small number ofenterprises

To further study the overflow relationships and pathamong the modules UCINET software was used to cal-culate the network density and image matrices of eachmodule (see Table 6) Figure 4 indicates that the density ofthe whole network in 2020 was 02383 which was taken asthe critical value A module density greater than 02383indicated a concentration trend in the module e densityvalue was assigned to 1 and a density value less than 02383was assigned to 0 to convert the density matrix of eachmodule into an image matrix e evolutionary processshowed that the spatial correlation network of the inno-vation efficiency of the IM enterprises did not show theexpected core-edge structure but presented a significantcohesive subgroup characteristic e IM enterpriseswithin the module were closely connected while there werefewer external relations Only Module III sent relationshipsto Module IV that is the innovation efficiency of BrokerModule III flowed to Net IncomeModule IVerefore theIM enterprises in Module IV had to absorb the overfloweffects of the other modules to meet the development needsof their own innovation efficiency However they could

10 Complexity

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

China is finding showed that these five IM enterpriseshad more innovation efficiency overflow which promotedthe innovation efficiency of the other IM enterprisese fiveIM enterprises with the poorest outdegrees were XinjiangGoldwind Science amp Technology Xinjiang West-Con-struction Gansu Great Wall Electrical Xinjiang TBEA andGuangxi Liugong which were located in NorthwesternChina and had little correlation with the other IM enter-prises e five IM enterprises with leading indegrees wereXCMG Construction Machinery Guodian Nanjing Auto-mation Shandong Goertek Inc Shandong Sailun JinyuGroup and Zhejiang Jushi which were all located in EasternChina e higher outdegrees indicated that the innovationefficiencies of the other IM enterprises overflowed to thesefive enterprises e five IM enterprises with the lowestindegrees were Xinjiang Goldwind Science amp TechnologyGuangxi Liugong Shaanxi Xiagu Power Xinjiang TBEAand Xinjiang West-Construction which were located in

Northwestern China indicating that these enterprises un-dertook fewer network relationships A further analysisshowed that the degree centrality was the highest for the IMenterprises in Eastern China indicating that these enter-prises had a higher degree of external innovation efficiencyIn contrast the degree centrality of the IM enterprises inWestern China ranked lower indicating that these enter-prises had a lower degree of external innovation efficiencyAccording to the Report on the IM Development Index(2020) the IM enterprises in China are unevenly distributedand more IM enterprises are concentrated in Jiangsu andZhejiang provinces and other parts of Eastern China einnovation efficiencies in Eastern China produced a mutualaid effect with a high density which caused the spatialnetwork relationships to become increasingly closerHowever few IM enterprises were located inWestern Chinaand these were far from the correlation degree of the in-novation efficiency of other enterprises in the network

Figure 1 Spatial correlation network of the innovation efficiency of the IM enterprises in 2015

Figure 2 Spatial correlation network of the innovation efficiency of the IM enterprises in 2020

Complexity 7

2015 2016 2017 2018 2019 2020Years

052

05

048

046

044

042

08

06

04

02

0

Net

wor

k gr

ade

Net

wor

k ef

ficie

ncy

Linear (network efficiency)

Network gradeNetwork efficiency

Figure 4 Network grade and efficiency

Table 3 Analysis of the centrality of the spatial correlation network of the innovation efficiency of the IM enterprises

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Henei Heis CompanyLimited 5 37 15 7 22222 25 61972 4 4526 11

Shandong GoertekInc 8 27 19 3 30000 9 61111 9 1604 25

Hunan Zoomlion 10 23 10 22 22222 25 58667 15 7071 4Liaoning Ansteel 6 36 9 26 16666 33 50575 35 1009 34Shanghai TiandiScience ampTechnology

13 13 16 6 32222 4 57895 17 3079 17

Zhengjiang Robam 13 13 13 14 28889 13 57895 17 1802 24Henan Yutong Bus 12 18 15 7 30000 9 61972 4 10161 2Hebei CRRC 8 27 14 12 24444 22 58667 15 2332 22Jiangxi Copper 10 23 8 29 20000 27 55000 26 2568 21XCMG ConstructionMachinery 19 1 20 1 43333 1 68750 1 8573 3

Jiangsu HTGD 17 2 15 7 35555 3 65672 2 3415 13Beijing Foton 11 20 12 18 25555 19 57895 17 3242 15China XD Group 7 32 11 20 20000 27 56410 21 12609 1

500

450

400

350

300

250

200

150

100

50

0

Rela

tions

hip

num

ber

2015 2016 2017 2018 2019 2020Year

Net

wor

k de

nsity

Relationship numberNetwork densityLinear (relationship number)

03

025

02

015

01

005

0

Figure 3 Network density and relationship number

8 Complexity

432 Closeness Centrality Closeness centrality can be usedas an index to judge the degree of the correlation difficulty ofeach IM enterprise in the spatial association network In2020 the average closeness degree of the IM enterprises was54891 e top five enterprises in this regard were XCMGConstruction Machinery Jiangsu HTGD Shandong SailunJinyu Group Henan Yutong Bus and Jiangxi ChanghongHuayi which were located in Mideastern China where IM

enterprises were concentrated and had a short networkdistance from the other IM enterprises in the networkerefore they could more easily spatially correlate with theother IM enterprises in the spatial correlation network ebottom five enterprises were Xinjiang TBEA Guizhou SpaceAppliance Guangxi Hytera Xinjiang West-Constructionand Xinjiang Goldwind Science amp Technology which werelocated in Western China at the edge of the network

Table 3 Continued

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Guangxi Liugong 5 37 2 43 77775 44 45361 40 0559 39Guodian NanjingAutomation 13 13 20 1 36666 2 60274 11 2671 18

Chongqing ChuanyiAutomation 8 27 9 26 18889 30 56410 21 6018 5

Zhejiang Jushi 7 32 17 5 26667 16 53012 32 0435 41Xinjiang TBEA 4 40 5 37 10000 41 43564 41 338 14Xinjiang West-Construction 3 43 4 41 7778 42 37931 44 1468 27

Shandong SailunJinyu Group 11 20 18 4 32222 5 64706 3 4894 9

Chongqing Yahua 4 40 9 26 14444 36 47312 38 2009 23North ChinaPharmaceutical 7 32 5 37 13333 37 51163 34 087 36

Gansu Great WallElectrical 3 43 7 30 11111 39 48889 37 1112 33

Xinjiang GoldwindScience ampTechnology

2 45 2 43 4444 45 30769 45 0 45

Jiangsu VictoryPrecision 14 10 15 7 32222 5 60274 11 132 30

Jiangsu TongdingConnection 15 6 12 18 30000 9 55000 26 0643 38

HT-SAAE 15 6 13 14 31111 8 60274 11 1593 26Hubei Accelink 16 4 7 30 25556 18 61972 4 5366 6CITIC HeavyIndustries 12 18 5 37 18889 30 54321 28 1377 28

Rapoo Technology 7 32 7 30 15556 35 53659 31 522 8Guangxi Hytera 4 40 3 42 7778 42 40000 43 0872 35Zhejiang Eastcom 10 23 14 12 26666 17 55696 25 0518 40Fujian Changelight 8 27 13 14 25555 19 56410 21 1208 31Guizhou SpaceAppliance 5 37 6 35 12222 38 43564 41 2582 20

Shanghai Brightdairy 10 23 5 37 16666 33 50575 35 0123 44Jiangxi ChanghongHuayi 14 10 15 7 32222 5 61972 4 4709 10

Jiangxi JZJT 13 13 10 22 25555 19 60274 11 3701 12ShangdongDoublestar 11 20 11 20 24444 23 57895 17 1341 29

Shandong TianrunCrankshaft 13 13 13 14 28889 13 61111 9 3212 16

Zhejiang Wynca 17 2 10 22 30000 9 56410 21 1126 32Jiangsu HengshunVinegar Industry 15 6 7 30 20000 27 54321 28 0169 42

Zhengjiang SaintAngelo 15 6 6 35 18888 32 52381 33 0129 43

Anhui HuamaoGroup 16 4 10 22 28889 13 61972 4 5302 7

Jiangsu KanionPharmaceutical 14 10 7 30 23333 24 54321 28 2653 19

Shaanxi Xiagu Power 8 27 2 43 11111 40 45833 39 0658 37

Complexity 9

erefore these enterprises had difficulty obtaining inno-vation efficiency from the other IM enterprises and they hadno obvious impact and driving effect on the other IM en-terprises erefore their independence was stronger in theinnovation efficiency correlation

433 Intermediary Centrality Intermediary centrality canbe used to measure the ability of IM enterprises to controlresources in a spatial correlation network In 2020 theaverage intermediary centrality of the IM enterprises was2872e top five enterprises were China XDGroup HenanYutong Bus XCMG Construction Machinery HunanZoomlion and Chongqing Chuanyi Automation whichshowed that these IM enterprises were in the middle of theinnovation efficiency correlation network and controlled theconstruction and development of the correlation relation-ships of the innovation efficiency of the other IM enterprisesContrary to expectations the index was insufficient of theIM enterprises located in the economically developed areasof Eastern China which indicated that the IM enterpriseslocated in economically developed areas had no drivingeffect on the other IM enterprises

44 Module-Modeling Analysis To better show the networkrole and functional rule of the IM enterprises a socialnetwork analysis was introduced in this paper to perform aclustering analysis In this paper the CONCOR method inUCINET software was used to study the network structureand 45 IM enterprises were divided into four modules with amaximum segmentation depth of 2 and a concentrationstandard of 02 as shown in Table 4 Module I had 11 IMenterprises which were mainly located in Northeastern andEastern China Module II had 21 IM enterprises which weremainly located in Central and Eastern China Module III had8 IM enterprises which were mainly located in Central andWestern China Module IV had 5 IM enterprises whichwere mainly located in Western China

Table 5 shows that the entrepreneurial performance ofthe IM enterprises in 2020 had 465 spatial network rela-tionships Of these 338 were within the module accountingfor 7269 of the total and 127 were outside the moduleaccounting for 2731 of the total As a result intramoduleoverflow was the primary factor in the spatial networkoverflow of the innovation efficiency of the IM enterprisese network module nature was judged by using theWasserman evaluation method e number of sending-outrelationships inModule I was 104 among which the numberof relationships inside the module was 78 that receivedoutside the module was 58 and that overflowing outside was26 the proportion of expected internal relationships was2273 and the proportion of actual internal relationshipswas 75 As a result Module I had more overflow rela-tionships with the IM enterprises both inside and outsidethe module so it was a ldquotwo-way overflow modulerdquo enumber of sending-out relationships in Module II was 282among which the number of relationships inside the modulewas 220 that received outside the module was 34 and thatoverflowing outside the module was 62 the proportion of

expected internal relationships was 4545 and the pro-portion of actual internal relationships was 7801 eseresults indicated that Module II could improve its owninnovation efficiency while spilling innovation efficiency tomembers of other modules us Module II was designatedthe ldquonet overflow modulerdquo e number of sending-outrelationships in Module III was 60 among which thenumber of relationships inside the module was 27 thatreceived outside the module was 19 and that overflowingoutside the module was 33 the proportion of expectedinternal relationships was 1591 and the proportion ofactual internal relationships was 4500 us the con-nection between the module and the outside was similar tothat within the module which played the role of a ldquobrokerrdquoso Module III was designated the ldquobroker modulerdquo enumber of sending-out relationships in Module IV was 19among which the number of relationships inside the modulewas 13 that received outside the module was 16 and thatoverflowing outside the module was 6 the proportion ofexpected internal relationships was 909 and the pro-portion of actual internal relationships was 6842 enumber of external relationships received by the module wasfar greater than that overflowing from the module so themodule was a typical ldquonet income modulerdquo erefore in2020 Chinarsquos IM enterprises were unevenly distributed byregion and each module was composed of enterprises fromthe same or adjacent provinces e module regions weregeographically concentrated and when they were closer toeach other they were more likely to have innovation cor-relation Module II showed that the Yangtze River Delta andits surrounding economically developed regions accountedfor more innovation correlation and the internal correlationwas strong so they could meet the development needs oftheir own innovation efficiencies However the enterprisesin Module IV located in Western China needed to absorbthe overflow effect of the other modules to develop inno-vation efficiency because the module had a small number ofenterprises

To further study the overflow relationships and pathamong the modules UCINET software was used to cal-culate the network density and image matrices of eachmodule (see Table 6) Figure 4 indicates that the density ofthe whole network in 2020 was 02383 which was taken asthe critical value A module density greater than 02383indicated a concentration trend in the module e densityvalue was assigned to 1 and a density value less than 02383was assigned to 0 to convert the density matrix of eachmodule into an image matrix e evolutionary processshowed that the spatial correlation network of the inno-vation efficiency of the IM enterprises did not show theexpected core-edge structure but presented a significantcohesive subgroup characteristic e IM enterpriseswithin the module were closely connected while there werefewer external relations Only Module III sent relationshipsto Module IV that is the innovation efficiency of BrokerModule III flowed to Net IncomeModule IVerefore theIM enterprises in Module IV had to absorb the overfloweffects of the other modules to meet the development needsof their own innovation efficiency However they could

10 Complexity

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

2015 2016 2017 2018 2019 2020Years

052

05

048

046

044

042

08

06

04

02

0

Net

wor

k gr

ade

Net

wor

k ef

ficie

ncy

Linear (network efficiency)

Network gradeNetwork efficiency

Figure 4 Network grade and efficiency

Table 3 Analysis of the centrality of the spatial correlation network of the innovation efficiency of the IM enterprises

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Henei Heis CompanyLimited 5 37 15 7 22222 25 61972 4 4526 11

Shandong GoertekInc 8 27 19 3 30000 9 61111 9 1604 25

Hunan Zoomlion 10 23 10 22 22222 25 58667 15 7071 4Liaoning Ansteel 6 36 9 26 16666 33 50575 35 1009 34Shanghai TiandiScience ampTechnology

13 13 16 6 32222 4 57895 17 3079 17

Zhengjiang Robam 13 13 13 14 28889 13 57895 17 1802 24Henan Yutong Bus 12 18 15 7 30000 9 61972 4 10161 2Hebei CRRC 8 27 14 12 24444 22 58667 15 2332 22Jiangxi Copper 10 23 8 29 20000 27 55000 26 2568 21XCMG ConstructionMachinery 19 1 20 1 43333 1 68750 1 8573 3

Jiangsu HTGD 17 2 15 7 35555 3 65672 2 3415 13Beijing Foton 11 20 12 18 25555 19 57895 17 3242 15China XD Group 7 32 11 20 20000 27 56410 21 12609 1

500

450

400

350

300

250

200

150

100

50

0

Rela

tions

hip

num

ber

2015 2016 2017 2018 2019 2020Year

Net

wor

k de

nsity

Relationship numberNetwork densityLinear (relationship number)

03

025

02

015

01

005

0

Figure 3 Network density and relationship number

8 Complexity

432 Closeness Centrality Closeness centrality can be usedas an index to judge the degree of the correlation difficulty ofeach IM enterprise in the spatial association network In2020 the average closeness degree of the IM enterprises was54891 e top five enterprises in this regard were XCMGConstruction Machinery Jiangsu HTGD Shandong SailunJinyu Group Henan Yutong Bus and Jiangxi ChanghongHuayi which were located in Mideastern China where IM

enterprises were concentrated and had a short networkdistance from the other IM enterprises in the networkerefore they could more easily spatially correlate with theother IM enterprises in the spatial correlation network ebottom five enterprises were Xinjiang TBEA Guizhou SpaceAppliance Guangxi Hytera Xinjiang West-Constructionand Xinjiang Goldwind Science amp Technology which werelocated in Western China at the edge of the network

Table 3 Continued

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Guangxi Liugong 5 37 2 43 77775 44 45361 40 0559 39Guodian NanjingAutomation 13 13 20 1 36666 2 60274 11 2671 18

Chongqing ChuanyiAutomation 8 27 9 26 18889 30 56410 21 6018 5

Zhejiang Jushi 7 32 17 5 26667 16 53012 32 0435 41Xinjiang TBEA 4 40 5 37 10000 41 43564 41 338 14Xinjiang West-Construction 3 43 4 41 7778 42 37931 44 1468 27

Shandong SailunJinyu Group 11 20 18 4 32222 5 64706 3 4894 9

Chongqing Yahua 4 40 9 26 14444 36 47312 38 2009 23North ChinaPharmaceutical 7 32 5 37 13333 37 51163 34 087 36

Gansu Great WallElectrical 3 43 7 30 11111 39 48889 37 1112 33

Xinjiang GoldwindScience ampTechnology

2 45 2 43 4444 45 30769 45 0 45

Jiangsu VictoryPrecision 14 10 15 7 32222 5 60274 11 132 30

Jiangsu TongdingConnection 15 6 12 18 30000 9 55000 26 0643 38

HT-SAAE 15 6 13 14 31111 8 60274 11 1593 26Hubei Accelink 16 4 7 30 25556 18 61972 4 5366 6CITIC HeavyIndustries 12 18 5 37 18889 30 54321 28 1377 28

Rapoo Technology 7 32 7 30 15556 35 53659 31 522 8Guangxi Hytera 4 40 3 42 7778 42 40000 43 0872 35Zhejiang Eastcom 10 23 14 12 26666 17 55696 25 0518 40Fujian Changelight 8 27 13 14 25555 19 56410 21 1208 31Guizhou SpaceAppliance 5 37 6 35 12222 38 43564 41 2582 20

Shanghai Brightdairy 10 23 5 37 16666 33 50575 35 0123 44Jiangxi ChanghongHuayi 14 10 15 7 32222 5 61972 4 4709 10

Jiangxi JZJT 13 13 10 22 25555 19 60274 11 3701 12ShangdongDoublestar 11 20 11 20 24444 23 57895 17 1341 29

Shandong TianrunCrankshaft 13 13 13 14 28889 13 61111 9 3212 16

Zhejiang Wynca 17 2 10 22 30000 9 56410 21 1126 32Jiangsu HengshunVinegar Industry 15 6 7 30 20000 27 54321 28 0169 42

Zhengjiang SaintAngelo 15 6 6 35 18888 32 52381 33 0129 43

Anhui HuamaoGroup 16 4 10 22 28889 13 61972 4 5302 7

Jiangsu KanionPharmaceutical 14 10 7 30 23333 24 54321 28 2653 19

Shaanxi Xiagu Power 8 27 2 43 11111 40 45833 39 0658 37

Complexity 9

erefore these enterprises had difficulty obtaining inno-vation efficiency from the other IM enterprises and they hadno obvious impact and driving effect on the other IM en-terprises erefore their independence was stronger in theinnovation efficiency correlation

433 Intermediary Centrality Intermediary centrality canbe used to measure the ability of IM enterprises to controlresources in a spatial correlation network In 2020 theaverage intermediary centrality of the IM enterprises was2872e top five enterprises were China XDGroup HenanYutong Bus XCMG Construction Machinery HunanZoomlion and Chongqing Chuanyi Automation whichshowed that these IM enterprises were in the middle of theinnovation efficiency correlation network and controlled theconstruction and development of the correlation relation-ships of the innovation efficiency of the other IM enterprisesContrary to expectations the index was insufficient of theIM enterprises located in the economically developed areasof Eastern China which indicated that the IM enterpriseslocated in economically developed areas had no drivingeffect on the other IM enterprises

44 Module-Modeling Analysis To better show the networkrole and functional rule of the IM enterprises a socialnetwork analysis was introduced in this paper to perform aclustering analysis In this paper the CONCOR method inUCINET software was used to study the network structureand 45 IM enterprises were divided into four modules with amaximum segmentation depth of 2 and a concentrationstandard of 02 as shown in Table 4 Module I had 11 IMenterprises which were mainly located in Northeastern andEastern China Module II had 21 IM enterprises which weremainly located in Central and Eastern China Module III had8 IM enterprises which were mainly located in Central andWestern China Module IV had 5 IM enterprises whichwere mainly located in Western China

Table 5 shows that the entrepreneurial performance ofthe IM enterprises in 2020 had 465 spatial network rela-tionships Of these 338 were within the module accountingfor 7269 of the total and 127 were outside the moduleaccounting for 2731 of the total As a result intramoduleoverflow was the primary factor in the spatial networkoverflow of the innovation efficiency of the IM enterprisese network module nature was judged by using theWasserman evaluation method e number of sending-outrelationships inModule I was 104 among which the numberof relationships inside the module was 78 that receivedoutside the module was 58 and that overflowing outside was26 the proportion of expected internal relationships was2273 and the proportion of actual internal relationshipswas 75 As a result Module I had more overflow rela-tionships with the IM enterprises both inside and outsidethe module so it was a ldquotwo-way overflow modulerdquo enumber of sending-out relationships in Module II was 282among which the number of relationships inside the modulewas 220 that received outside the module was 34 and thatoverflowing outside the module was 62 the proportion of

expected internal relationships was 4545 and the pro-portion of actual internal relationships was 7801 eseresults indicated that Module II could improve its owninnovation efficiency while spilling innovation efficiency tomembers of other modules us Module II was designatedthe ldquonet overflow modulerdquo e number of sending-outrelationships in Module III was 60 among which thenumber of relationships inside the module was 27 thatreceived outside the module was 19 and that overflowingoutside the module was 33 the proportion of expectedinternal relationships was 1591 and the proportion ofactual internal relationships was 4500 us the con-nection between the module and the outside was similar tothat within the module which played the role of a ldquobrokerrdquoso Module III was designated the ldquobroker modulerdquo enumber of sending-out relationships in Module IV was 19among which the number of relationships inside the modulewas 13 that received outside the module was 16 and thatoverflowing outside the module was 6 the proportion ofexpected internal relationships was 909 and the pro-portion of actual internal relationships was 6842 enumber of external relationships received by the module wasfar greater than that overflowing from the module so themodule was a typical ldquonet income modulerdquo erefore in2020 Chinarsquos IM enterprises were unevenly distributed byregion and each module was composed of enterprises fromthe same or adjacent provinces e module regions weregeographically concentrated and when they were closer toeach other they were more likely to have innovation cor-relation Module II showed that the Yangtze River Delta andits surrounding economically developed regions accountedfor more innovation correlation and the internal correlationwas strong so they could meet the development needs oftheir own innovation efficiencies However the enterprisesin Module IV located in Western China needed to absorbthe overflow effect of the other modules to develop inno-vation efficiency because the module had a small number ofenterprises

To further study the overflow relationships and pathamong the modules UCINET software was used to cal-culate the network density and image matrices of eachmodule (see Table 6) Figure 4 indicates that the density ofthe whole network in 2020 was 02383 which was taken asthe critical value A module density greater than 02383indicated a concentration trend in the module e densityvalue was assigned to 1 and a density value less than 02383was assigned to 0 to convert the density matrix of eachmodule into an image matrix e evolutionary processshowed that the spatial correlation network of the inno-vation efficiency of the IM enterprises did not show theexpected core-edge structure but presented a significantcohesive subgroup characteristic e IM enterpriseswithin the module were closely connected while there werefewer external relations Only Module III sent relationshipsto Module IV that is the innovation efficiency of BrokerModule III flowed to Net IncomeModule IVerefore theIM enterprises in Module IV had to absorb the overfloweffects of the other modules to meet the development needsof their own innovation efficiency However they could

10 Complexity

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

432 Closeness Centrality Closeness centrality can be usedas an index to judge the degree of the correlation difficulty ofeach IM enterprise in the spatial association network In2020 the average closeness degree of the IM enterprises was54891 e top five enterprises in this regard were XCMGConstruction Machinery Jiangsu HTGD Shandong SailunJinyu Group Henan Yutong Bus and Jiangxi ChanghongHuayi which were located in Mideastern China where IM

enterprises were concentrated and had a short networkdistance from the other IM enterprises in the networkerefore they could more easily spatially correlate with theother IM enterprises in the spatial correlation network ebottom five enterprises were Xinjiang TBEA Guizhou SpaceAppliance Guangxi Hytera Xinjiang West-Constructionand Xinjiang Goldwind Science amp Technology which werelocated in Western China at the edge of the network

Table 3 Continued

Enterpriseabbreviation Outdegree Ranking Indegree Ranking Degree

centrality Ranking Closenesscentrality Ranking Intermediary

centralityRanking

Guangxi Liugong 5 37 2 43 77775 44 45361 40 0559 39Guodian NanjingAutomation 13 13 20 1 36666 2 60274 11 2671 18

Chongqing ChuanyiAutomation 8 27 9 26 18889 30 56410 21 6018 5

Zhejiang Jushi 7 32 17 5 26667 16 53012 32 0435 41Xinjiang TBEA 4 40 5 37 10000 41 43564 41 338 14Xinjiang West-Construction 3 43 4 41 7778 42 37931 44 1468 27

Shandong SailunJinyu Group 11 20 18 4 32222 5 64706 3 4894 9

Chongqing Yahua 4 40 9 26 14444 36 47312 38 2009 23North ChinaPharmaceutical 7 32 5 37 13333 37 51163 34 087 36

Gansu Great WallElectrical 3 43 7 30 11111 39 48889 37 1112 33

Xinjiang GoldwindScience ampTechnology

2 45 2 43 4444 45 30769 45 0 45

Jiangsu VictoryPrecision 14 10 15 7 32222 5 60274 11 132 30

Jiangsu TongdingConnection 15 6 12 18 30000 9 55000 26 0643 38

HT-SAAE 15 6 13 14 31111 8 60274 11 1593 26Hubei Accelink 16 4 7 30 25556 18 61972 4 5366 6CITIC HeavyIndustries 12 18 5 37 18889 30 54321 28 1377 28

Rapoo Technology 7 32 7 30 15556 35 53659 31 522 8Guangxi Hytera 4 40 3 42 7778 42 40000 43 0872 35Zhejiang Eastcom 10 23 14 12 26666 17 55696 25 0518 40Fujian Changelight 8 27 13 14 25555 19 56410 21 1208 31Guizhou SpaceAppliance 5 37 6 35 12222 38 43564 41 2582 20

Shanghai Brightdairy 10 23 5 37 16666 33 50575 35 0123 44Jiangxi ChanghongHuayi 14 10 15 7 32222 5 61972 4 4709 10

Jiangxi JZJT 13 13 10 22 25555 19 60274 11 3701 12ShangdongDoublestar 11 20 11 20 24444 23 57895 17 1341 29

Shandong TianrunCrankshaft 13 13 13 14 28889 13 61111 9 3212 16

Zhejiang Wynca 17 2 10 22 30000 9 56410 21 1126 32Jiangsu HengshunVinegar Industry 15 6 7 30 20000 27 54321 28 0169 42

Zhengjiang SaintAngelo 15 6 6 35 18888 32 52381 33 0129 43

Anhui HuamaoGroup 16 4 10 22 28889 13 61972 4 5302 7

Jiangsu KanionPharmaceutical 14 10 7 30 23333 24 54321 28 2653 19

Shaanxi Xiagu Power 8 27 2 43 11111 40 45833 39 0658 37

Complexity 9

erefore these enterprises had difficulty obtaining inno-vation efficiency from the other IM enterprises and they hadno obvious impact and driving effect on the other IM en-terprises erefore their independence was stronger in theinnovation efficiency correlation

433 Intermediary Centrality Intermediary centrality canbe used to measure the ability of IM enterprises to controlresources in a spatial correlation network In 2020 theaverage intermediary centrality of the IM enterprises was2872e top five enterprises were China XDGroup HenanYutong Bus XCMG Construction Machinery HunanZoomlion and Chongqing Chuanyi Automation whichshowed that these IM enterprises were in the middle of theinnovation efficiency correlation network and controlled theconstruction and development of the correlation relation-ships of the innovation efficiency of the other IM enterprisesContrary to expectations the index was insufficient of theIM enterprises located in the economically developed areasof Eastern China which indicated that the IM enterpriseslocated in economically developed areas had no drivingeffect on the other IM enterprises

44 Module-Modeling Analysis To better show the networkrole and functional rule of the IM enterprises a socialnetwork analysis was introduced in this paper to perform aclustering analysis In this paper the CONCOR method inUCINET software was used to study the network structureand 45 IM enterprises were divided into four modules with amaximum segmentation depth of 2 and a concentrationstandard of 02 as shown in Table 4 Module I had 11 IMenterprises which were mainly located in Northeastern andEastern China Module II had 21 IM enterprises which weremainly located in Central and Eastern China Module III had8 IM enterprises which were mainly located in Central andWestern China Module IV had 5 IM enterprises whichwere mainly located in Western China

Table 5 shows that the entrepreneurial performance ofthe IM enterprises in 2020 had 465 spatial network rela-tionships Of these 338 were within the module accountingfor 7269 of the total and 127 were outside the moduleaccounting for 2731 of the total As a result intramoduleoverflow was the primary factor in the spatial networkoverflow of the innovation efficiency of the IM enterprisese network module nature was judged by using theWasserman evaluation method e number of sending-outrelationships inModule I was 104 among which the numberof relationships inside the module was 78 that receivedoutside the module was 58 and that overflowing outside was26 the proportion of expected internal relationships was2273 and the proportion of actual internal relationshipswas 75 As a result Module I had more overflow rela-tionships with the IM enterprises both inside and outsidethe module so it was a ldquotwo-way overflow modulerdquo enumber of sending-out relationships in Module II was 282among which the number of relationships inside the modulewas 220 that received outside the module was 34 and thatoverflowing outside the module was 62 the proportion of

expected internal relationships was 4545 and the pro-portion of actual internal relationships was 7801 eseresults indicated that Module II could improve its owninnovation efficiency while spilling innovation efficiency tomembers of other modules us Module II was designatedthe ldquonet overflow modulerdquo e number of sending-outrelationships in Module III was 60 among which thenumber of relationships inside the module was 27 thatreceived outside the module was 19 and that overflowingoutside the module was 33 the proportion of expectedinternal relationships was 1591 and the proportion ofactual internal relationships was 4500 us the con-nection between the module and the outside was similar tothat within the module which played the role of a ldquobrokerrdquoso Module III was designated the ldquobroker modulerdquo enumber of sending-out relationships in Module IV was 19among which the number of relationships inside the modulewas 13 that received outside the module was 16 and thatoverflowing outside the module was 6 the proportion ofexpected internal relationships was 909 and the pro-portion of actual internal relationships was 6842 enumber of external relationships received by the module wasfar greater than that overflowing from the module so themodule was a typical ldquonet income modulerdquo erefore in2020 Chinarsquos IM enterprises were unevenly distributed byregion and each module was composed of enterprises fromthe same or adjacent provinces e module regions weregeographically concentrated and when they were closer toeach other they were more likely to have innovation cor-relation Module II showed that the Yangtze River Delta andits surrounding economically developed regions accountedfor more innovation correlation and the internal correlationwas strong so they could meet the development needs oftheir own innovation efficiencies However the enterprisesin Module IV located in Western China needed to absorbthe overflow effect of the other modules to develop inno-vation efficiency because the module had a small number ofenterprises

To further study the overflow relationships and pathamong the modules UCINET software was used to cal-culate the network density and image matrices of eachmodule (see Table 6) Figure 4 indicates that the density ofthe whole network in 2020 was 02383 which was taken asthe critical value A module density greater than 02383indicated a concentration trend in the module e densityvalue was assigned to 1 and a density value less than 02383was assigned to 0 to convert the density matrix of eachmodule into an image matrix e evolutionary processshowed that the spatial correlation network of the inno-vation efficiency of the IM enterprises did not show theexpected core-edge structure but presented a significantcohesive subgroup characteristic e IM enterpriseswithin the module were closely connected while there werefewer external relations Only Module III sent relationshipsto Module IV that is the innovation efficiency of BrokerModule III flowed to Net IncomeModule IVerefore theIM enterprises in Module IV had to absorb the overfloweffects of the other modules to meet the development needsof their own innovation efficiency However they could

10 Complexity

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

erefore these enterprises had difficulty obtaining inno-vation efficiency from the other IM enterprises and they hadno obvious impact and driving effect on the other IM en-terprises erefore their independence was stronger in theinnovation efficiency correlation

433 Intermediary Centrality Intermediary centrality canbe used to measure the ability of IM enterprises to controlresources in a spatial correlation network In 2020 theaverage intermediary centrality of the IM enterprises was2872e top five enterprises were China XDGroup HenanYutong Bus XCMG Construction Machinery HunanZoomlion and Chongqing Chuanyi Automation whichshowed that these IM enterprises were in the middle of theinnovation efficiency correlation network and controlled theconstruction and development of the correlation relation-ships of the innovation efficiency of the other IM enterprisesContrary to expectations the index was insufficient of theIM enterprises located in the economically developed areasof Eastern China which indicated that the IM enterpriseslocated in economically developed areas had no drivingeffect on the other IM enterprises

44 Module-Modeling Analysis To better show the networkrole and functional rule of the IM enterprises a socialnetwork analysis was introduced in this paper to perform aclustering analysis In this paper the CONCOR method inUCINET software was used to study the network structureand 45 IM enterprises were divided into four modules with amaximum segmentation depth of 2 and a concentrationstandard of 02 as shown in Table 4 Module I had 11 IMenterprises which were mainly located in Northeastern andEastern China Module II had 21 IM enterprises which weremainly located in Central and Eastern China Module III had8 IM enterprises which were mainly located in Central andWestern China Module IV had 5 IM enterprises whichwere mainly located in Western China

Table 5 shows that the entrepreneurial performance ofthe IM enterprises in 2020 had 465 spatial network rela-tionships Of these 338 were within the module accountingfor 7269 of the total and 127 were outside the moduleaccounting for 2731 of the total As a result intramoduleoverflow was the primary factor in the spatial networkoverflow of the innovation efficiency of the IM enterprisese network module nature was judged by using theWasserman evaluation method e number of sending-outrelationships inModule I was 104 among which the numberof relationships inside the module was 78 that receivedoutside the module was 58 and that overflowing outside was26 the proportion of expected internal relationships was2273 and the proportion of actual internal relationshipswas 75 As a result Module I had more overflow rela-tionships with the IM enterprises both inside and outsidethe module so it was a ldquotwo-way overflow modulerdquo enumber of sending-out relationships in Module II was 282among which the number of relationships inside the modulewas 220 that received outside the module was 34 and thatoverflowing outside the module was 62 the proportion of

expected internal relationships was 4545 and the pro-portion of actual internal relationships was 7801 eseresults indicated that Module II could improve its owninnovation efficiency while spilling innovation efficiency tomembers of other modules us Module II was designatedthe ldquonet overflow modulerdquo e number of sending-outrelationships in Module III was 60 among which thenumber of relationships inside the module was 27 thatreceived outside the module was 19 and that overflowingoutside the module was 33 the proportion of expectedinternal relationships was 1591 and the proportion ofactual internal relationships was 4500 us the con-nection between the module and the outside was similar tothat within the module which played the role of a ldquobrokerrdquoso Module III was designated the ldquobroker modulerdquo enumber of sending-out relationships in Module IV was 19among which the number of relationships inside the modulewas 13 that received outside the module was 16 and thatoverflowing outside the module was 6 the proportion ofexpected internal relationships was 909 and the pro-portion of actual internal relationships was 6842 enumber of external relationships received by the module wasfar greater than that overflowing from the module so themodule was a typical ldquonet income modulerdquo erefore in2020 Chinarsquos IM enterprises were unevenly distributed byregion and each module was composed of enterprises fromthe same or adjacent provinces e module regions weregeographically concentrated and when they were closer toeach other they were more likely to have innovation cor-relation Module II showed that the Yangtze River Delta andits surrounding economically developed regions accountedfor more innovation correlation and the internal correlationwas strong so they could meet the development needs oftheir own innovation efficiencies However the enterprisesin Module IV located in Western China needed to absorbthe overflow effect of the other modules to develop inno-vation efficiency because the module had a small number ofenterprises

To further study the overflow relationships and pathamong the modules UCINET software was used to cal-culate the network density and image matrices of eachmodule (see Table 6) Figure 4 indicates that the density ofthe whole network in 2020 was 02383 which was taken asthe critical value A module density greater than 02383indicated a concentration trend in the module e densityvalue was assigned to 1 and a density value less than 02383was assigned to 0 to convert the density matrix of eachmodule into an image matrix e evolutionary processshowed that the spatial correlation network of the inno-vation efficiency of the IM enterprises did not show theexpected core-edge structure but presented a significantcohesive subgroup characteristic e IM enterpriseswithin the module were closely connected while there werefewer external relations Only Module III sent relationshipsto Module IV that is the innovation efficiency of BrokerModule III flowed to Net IncomeModule IVerefore theIM enterprises in Module IV had to absorb the overfloweffects of the other modules to meet the development needsof their own innovation efficiency However they could

10 Complexity

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

only be affected by the innovation efficiency of the IMenterprises in neighboring provinces due to geographicalfactors Meanwhile Eastern China which has a developedeconomy and a large number of IM enterprises had less ofan effect on this relationship which is consistent with thecloseness centrality conclusion

45 Role Analysis of the Intermediary Table 7 shows that the45 IM enterprises acted as intermediaries in the submodulesand internal relationships of the module Regarding the

number of times the IM enterprises acted as intermediariesXCMG Construction Machinery Jiangsu HTGD andZhejiang Wynca did so more frequently ese three en-terprises were all from Module II and were important hubsin both the whole network and Module II XCMG Con-struction Machinery Shandong Goertek Inc and ShandongSailun Jinyu Group acted as gatekeepers most frequently andplayed the role of a gateway and window regarding theoverflow of the innovation efficiency of foreign IM enter-prises XCMG Construction Machinery HT-SAAE andJiangxi Changhong Huayi most frequently acted as agentsand were important output windows influencing the othermodules Hubei Accelink Henan Yutong Bus JiangsuHTGD and XCMG Construction Machinery mostly playedconsultant roles and were the main entry windows in theinnovation efficiency relationships of each module HunanZoomlion Hubei Accelink and Henan Yutong Bus actedmostly as contacts and played important roles that deepenedthe innovation efficiency and spatial correlation of ChinarsquosIM enterprises

Table 4 Module members and industries

Module Enterprise abbreviation Economic zone andnumber Industry

I

Henei Heis Company Limited Shandong GoertekInc Hebei CRRC Liaoning Ansteel ShangdongDoublestar Shandong Tianrun Crankshaft HenanYutong Bus Hebei CRRC Shandong Sailun Jinyu

Group Beijing Foton and North ChinaPharmaceutical

1 in Northeast and10 in Eastern China

Electrical machinery and equipment manufacturingautomobile manufacturing ferrous metal smelting

and rolling processing pharmaceuticalmanufacturing and rubber and plastic product

II

XCMG Construction Machinery Jiangsu VictoryPrecision Jiangxi Changhong Huayi GuodianNanjing Automation Shanghai Tiandi ScienceampTechnology Zhengjiang Robam Shanghai

Brightdairy Jiangsu Hengshun Vinegar IndustryJiangxi Copper Anhui Huamao Group JiangsuHTGD Zhejiang Jushi Zhejiang Wynca RapooTechnology Jiangsu Tongding Connection HT-

SAAE Fujian Changelight Jiangsu KanionPharmaceutical Zhejiang Eastcom Zhengjiang Saint

Angelo and Jiangxi JZJT

2 in Central and 19in Eastern China

Electrical machinery and equipment manufacturingscience and technology promotion and application

service computer communication and otherelectronic equipment manufacturing ferrous metal

smelting and rolling processing foodmanufacturing chemical raw material and chemicalproduct manufacturing textile pharmaceuticalmanufacturing nonmetallic mineral product

III

Hunan Zoomlion Guizhou Space ApplianceShaanxi Xiagu Power Guangxi Liugong HubeiAccelink Chongqing Chuanyi AutomationChongqing Yahua and Guangxi Hytera

1 in Central and 7in Western China

Electrical machinery and equipment manufacturingand science and technology promotion and

application service

IVChina XD Group Xinjiang TBEA Xinjiang West-

Construction Xinjiang Goldwind Science ampTechnology and Gansu Great Wall Electrical

5 in Western China

Electrical machinery and equipment manufacturingscience and technology promotion and applicationservice chemical raw material and chemical product

manufacturing

Table 5 Module overflow effect of the spatial correlation network of the innovation efficiency of the IM enterprises

Module

Receivingrelationship

matrixRelationship numberreceived outside the

module

Relationship number sentoutside the overflow

module

Proportion of theexpected internalrelationship ()

Proportion of the actualinternal relationship ()

I II III IVI 78 19 3 4 58 26 2273 7500II 49 220 13 0 34 62 4545 7801III 6 15 27 12 19 33 1591 4500IV 3 0 3 13 16 6 909 6842

Table 6 Density and image matrices

Module Density matrix Image matrixI II III IV I II III IV

I 0709 0082 0034 0073 1 0 0 0II 0212 0521 0077 0000 0 1 0 0III 0068 0089 0518 0300 0 0 1 1IV 0055 0000 0075 0650 0 0 0 1

Complexity 11

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

5 Main Research Conclusions and Implications

51 Research Conclusions e DEA cross-efficiency modelwas used in this paper to measure the innovation efficiencyof 45 IM enterprises in China from 2015 to 2020 In additionits spatial correlation network was built through the mod-ified gravity model Moreover its structural characteristicswere comprehensively analyzed from the aspects of thewhole network characteristics and the position clusteringand intermediary roles of various IM enterprises in thenetwork by using a social network analysis e mainconclusions are as follows

(1) e whole network analysis showed that the spatialnetwork accessibility was favorable for the innova-tion efficiency of Chinarsquos IM enterprises from 2015 to2020 e network density and the number of re-lationships showed an upward trend in fluctuationsand the network relationships tended to be closeenetwork grade presented a downward trend in thefluctuations which indicated that the relationshipnetwork of innovation efficiency was more complexand broke the regional restrictions Finally thespatial correlation of the innovation efficiencyamong the enterprises was gradually strengthened

Table 7 Intermediary role

Module Enterprise abbreviation Coordinator Gatekeeper Agent Consultant Contacts Total

I

Henei Heis Company Limited 5 34 0 0 0 39Shandong Goertek Inc 11 60 3 3 1 78

Hebei CRRC 17 21 8 0 4 50Liaoning Ansteel 4 5 13 3 0 25

Shangdong Doublestar 7 55 2 1 0 65Shandong Tianrun Crankshaft 14 51 9 3 7 84

Henan Yutong Bus 9 30 42 7 18 106CITIC Heavy Industries 7 0 10 0 0 17

Shandong Sailun Jinyu Group 17 59 20 2 9 107Beijing Foton 17 8 42 0 2 69

North China Pharmaceutical 5 0 5 0 0 10

II

XCMG Construction Machinery 83 84 68 7 9 251Jiangsu Victory Precision 41 15 22 1 2 81Jiangxi Changhong Huayi 49 12 52 0 3 116

Guodian Nanjing Automation 35 41 26 2 3 107Shanghai Tiandi 37 15 28 1 1 82

Zhengjiang Robam 53 0 17 0 0 70Shanghai Brightdairy 8 0 3 0 0 11

Jiangsu Hengshun Vinegar Industry 15 8 8 0 2 33Jiangxi Copper 8 12 16 2 6 44

Anhui Huamao Group 49 10 33 1 0 93Jiangsu HTGD 67 32 45 7 4 155Zhejiang Jushi 29 4 0 0 0 33Zhejiang Wynca 59 0 14 0 0 73Rapoo Technology 12 10 8 2 0 32

Jiangsu Tongding Connection 42 0 14 0 0 56HT-SAAE 29 0 64 0 0 93

Fujian Changelight 43 3 0 0 0 46Jiangsu Kanion Pharmaceutical 32 11 13 2 0 58

Zhejiang Eastcom 39 5 0 0 0 44Zhengjiang Saint Angelo 19 9 0 0 0 28

Jiangxi JZJT 36 6 36 0 2 80

III

Hunan Zoomlion 2 16 13 5 26 62Guizhou Space Appliance 7 3 3 0 1 14Shaanxi Xiagu Power 2 0 4 0 0 6Guangxi Liugong 0 0 3 0 0 3Hubei Accelink 2 15 22 21 21 81

Chongqing Chuanyi Automation 11 16 6 0 10 43Chongqing Yahua 8 5 3 0 0 16Guangxi Hytera 1 0 2 0 0 3

IV

China XD Group 3 17 11 0 14 45Xinjiang TBEA 4 1 4 0 1 10

Xinjiang West-Construction 2 1 0 0 0 3Xinjiang Goldwind Science amp Technology 0 0 0 0 0 0

Gansu Great Wall Electrical 0 6 0 0 1 7

12 Complexity

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

(2) e analysis of the individual networks revealedthat the IM enterprises in Jiangsu Province whichhad a high level of economic development andboth a high number and an intensive distributionIM enterprises were in a central position in theinnovation efficiency network However althoughthey had a high degree of correlation with the IMenterprises in their own and neighboring prov-inces they had no strong driving effect on theinnovation efficiency of the cross-regional IMenterprises and had overflow benefits only for theIM enterprises in neighboring provinces How-ever the IM enterprises in Xinjiang and Shaanxiprovinces and other parts of Western China wereat the edge of the network In addition they weremore independent in the innovation efficiencycorrelation and were not controlled by the otherIM enterprises

(3) e module analysis showed that the 4 modulesformed a correlation network for the innovationefficiency of the IM enterprisese gap between thenumbers of IM enterprises in each module waslarge which presented obvious characteristics ofcohesive subgroups and the correlation betweenmodules was weak Module I included the IM en-terprises in Eastern and Northeastern China eseenterprises could not only improve their own in-novation efficiencies but also greatly promote thatof the other modules erefore Module I wasdescribed as a two-way overflow module Module IIhad the largest number of members which werelocated in Central and Eastern China mainly in-cluding those located in Jiangsu and Zhejiangprovinces e enterprises in this module wereclosely related and formed highly cohesive sub-groups that could meet the development needs oftheir own innovation efficiency erefore it wasdescribed as the net overflow module e membersof Module III were mainly located in Central andWestern China and played ldquobridgerdquo and ldquointer-mediaryrdquo roles Module IVmdashwhich included en-terprises in Xinjiang Shaanxi and Gansuprovincesmdashseldom invited outside enterprises toengage in innovation relationships but frequentlyreceived invitations to engage with enterprises fromnearby Module III In summary the regional dis-tribution of IM enterprises in China affected thecharacteristics of the spatial correlation networkstructure

(4) e analysis of the intermediary role showed thatexcept for enterprises with outstanding innovationcapability the economically developed YangtzeRiver Delta played the most important role of theintermediary Henan Yutong Bus XCMG Con-struction Machinery Hunan Zoomlion and ChinaXD Group were the most active intermediaries

among the four modules and played an importantrole as bridges in establishing the relationshipbetween and within the modules

52 Research Implications Based on the above analysis thefollowing four aspects presented in this paper can promotethe development of the innovation efficiency network of IMenterprises in China

(1) e spatial network structure can be optimized of theinnovation efficiency of IM enterprises e spatialnetwork structure has become increasingly complexfor the innovation of IM enterprises in Chinathrough network analysis However compared withthe relationship density of Module II the complexityof the whole network still lags far behind and thewhole network spatial correlation still has muchroom for growth Network upgrading for the in-novation efficiency of IM enterprises is realized byspeeding information construction optimizing theinnovation environment constructing trans-portation infrastructure promoting the regionalinnovation exchange of IM enterprises and furtherbreaking regional barriers On the one hand IMenterprises must optimize their industrial structureensure the rational use of limited independent in-novation elements promote the rational flow ofvarious element resources among different industriesof IM enterprises and achieve the balanced devel-opment of IM enterprisesrsquo innovation On the otherhand in the context of coordinated industrial andregional development it is necessary not only to payattention to the efficiency problems reflected byattribute data in the innovation process of IM en-terprises but also to place in a prominent positionthe spatial structure and spillover effects reflectedthrough relational data

(2) Pay attention to the cyberspace spillover effect of theinnovation efficiency of IM companies in which keyleading companies play a leading innovation roleAccording to previous research enterprises withhigh innovation capabilitiesmdashsuch as XCMG Con-struction Machinery Jiangsu HTGD and ZhejiangWyncamdashhave a small innovation radiation scopeand more innovation overflow only to the IM en-terprises in their own module As an innovationresource-clustering area such enterprises shouldstrengthen the external output capability of theirown innovation efficiency and expand their inno-vation radiation scope while constantly improvingtheir own innovation capability

(3) Give full play to the role of the government in macrocontrol and balance the development level of IMenterprises in different regions e module-mod-eling analysis showed that the four modules in Chinahave an uneven number of members among which

Complexity 13

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

the 21 members in Module II are four times that inModule IV Moreover the innovation and devel-opment capability of IM enterprises gradually de-creases from economically developed Eastern Chinato Western China A new regional pattern must bebuilt of the innovation efficiency of IM enterprises atthe national level and different technological de-velopment strategies must be formulated based onthe different roles played in their spatial correlationnetwork of innovation efficiency ldquoSpilloverrdquo IMcompanies must achieve basic original critical andforward-looking technological breakthroughs andspread technology outwards with strong technicalstrength ldquoBrokerrdquo IM companies should undertakenew technologies and improve their own techno-logical innovation capabilities while expanding theirtechnologies ldquoNet incomerdquo IM companies shouldengage more in the introduction digestion ab-sorption and transformation of new technologiesand special attention should be given to the intro-duction and cultivation of talent

A social network analysis was used in this paper to studythe overall and individual characteristics and internalstructure of the spatial network for the innovation efficiencyof IM enterprises but some shortcomings remain First thebinary matrix of the correlation structure for the innovationefficiency of IM enterprises can only reveal the relationshipsof innovation efficiencies among enterprises but cannotdescribe their strength Second all IM enterprises were takenas the research object in this paper but no further researchwas conducted on the industry segmentation of IM enter-prises ird no further research was done on the impact ofcyberspace correlations that affect the innovation efficiencyof IM enterprises e author will further explore the aboveproblems in future research

Data Availability

e data used to support the findings of this study areavailable from the author upon request

Conflicts of Interest

e author declares no conflicts of interest

References

[1] P K Wright and D A Bourne Manufacturing IntelligenceVol 4 Addison-Wesley Boston MA USA 1988

[2] Research group of ldquoIntelligent manufacturing research led bynew generation artificial intelligencerdquo Research on the De-velopment Strategy of Intelligent Manufacturing in Chinavol 20 no 4 pp 1ndash8 2018

[3] Q Gong and L Yang ldquoResearch on the development strategyof manufacturing industry under the background of ldquoman-ufactured in China 2025rdquo-based on social network analysisand text miningrdquo Science Technology and Developmentvol 16 no 8 pp 917ndash923 2020

[4] Y Yang J Liu W Yao and J Yang ldquoResearch hotspot andfrontier exploration of intelligent manufacturing in the new

China in 70 years-analysis based on knowledge maprdquo Scienceand Technology Management Research vol 40 no 18pp 46ndash54 2020

[5] B Wang Y Xue J Yan X Yang and Y Zhou ldquoPeopleoriented intelligent manufacturing concept technology andapplicationrdquo Strategic Study of CAE vol 22 no 4pp 139ndash146 2020

[6] Z Hu H Ma J Zhang J Xiong and Wushan ldquoAnalysis onthe vertical synergy of transformation and upgrading policiesfor Chinarsquos manufacturing industryrdquo Studies in Science ofScience vol 39 no 3 pp 1ndash13 2021

[7] W Li K Yu and W Yi ldquoResearch on the innovation ofbusiness model of industrial enterprises based on the inte-gration of service and intelligent manufacturingrdquo TechnologyEconomics vol 39 no 6 pp 63ndash69 2020

[8] J Xiao andW Li ldquoe impact of intelligent manufacturing onenterprise strategic change and innovation-analysis on theperspective of resource base reformrdquo Research in Financialand Economic Issues vol 42 no 2 pp 38ndash46 2020

[9] S Zhang H Hu and L Sun ldquoIs intelligent manufacturingconducive to increasing technological innovation investmentof enterprises-quasi natural experiment based on intelligentmanufacturing pilotrdquo Science amp Technology Progress andPolicy vol 39 no 3 pp 1ndash10 2021

[10] W Lv L Xu L Jin and C Jin ldquoA decade review of artificialintelligence research in China-based on bibliometrics andknowledge mapping analysis from 2008 to 2017rdquo TechnologyEconomics vol 37 no 10 pp 73ndash78 2018

[11] M S Schioenning Larsen and A H Lassen ldquoDesign pa-rameters for smart manufacturing innovation processesrdquoProcedia CIRP vol 93 pp 365ndash370 2020

[12] A Bhattacharya and P K Dey ldquoAchieving sustainabilitythrough innovation-led lean approaches to manufacturingsupply chainsrdquo International Journal of Production Eco-nomics vol 219 pp 402ndash404 2020

[13] Y Chu L Pan and K Leng ldquoResearch on key technologies ofservice quality optimization for industrial IT 5G network forintelligent manufacturingrdquo International Journal of AdvancedManufacturing Technology vol 107 no 3 pp 1071ndash1080 2020

[14] YWang Y Zhou and J Luo ldquoResearch hotspots and frontiermining of intelligent manufacturing in recent 20 yearsrdquoComputer Engineering and Applications vol 57 no 6pp 49ndash57 2021

[15] J Zhao ldquoPromoting the integrative development of newgeneration information technology and real economy basedon the perspective of intelligent manufacturingrdquo Science ofScience and Management of SampT vol 41 no 3 pp 3ndash162020

[16] B Li B C Hou W T Yu X B Lu and C W YangldquoApplications of artificial intelligence in intelligentmanufacturing a reviewrdquo Frontiers of Information Technologyamp Electronic Engineering vol 18 no 1 pp 86ndash96 2017

[17] A Charnes W W Cooper and E Rhodes ldquoMeasuring theefficiency of decision making unitsrdquo North Holland vol 2no 6 pp 429ndash449 1978

[18] R Ali ldquoEfficiency ranking of decision making units in dataenvelopment analysis by using TOPSIS-DEA methodrdquoJournal of the Operational Research Society vol 68 no 8pp 906ndash918 2017

[19] C Franco F Pieri and F Venturini ldquoProduct market reg-ulation and innovation efficiencyrdquo Journal of ProductivityAnalysis vol 45 no 3 pp 299ndash315 2016

[20] Y Lu Q Yan and F Liu ldquoOverview of visualization clas-sification of research status of intelligent manufacturing in

14 Complexity

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15

China-scientometrics analysis based on CNKI (2005ndash2018)rdquoIndustrial Engineering amp Management vol 24 no 4pp 14ndash22 2019

[21] F Liu and J Ning ldquoTechnological innovation efficiency ofintelligent manufacturing enterprises and its influencingfactorsrdquo Business Economy vol 36 no 4 pp 142ndash147 2016

[22] H Qin ldquoTechnological innovation efficiency of intelligentmanufacturing enterprises and its influencing factorsrdquo Eco-nomic and Trade Practice vol 21 no 14 pp 8-9 2018

[23] G Liao ldquoProblems and development ideas of intelligentmanufacturing equipment industry in Chinardquo China PlantEngineering vol 37 no 21 pp 199-200 2018

[24] J Xiao X Wu K Xie and Y Wu ldquoInformation technologydrives the transformation and upgrading of Chinarsquomanufacturing a longitudinal case study of leapfrog strategicchange of Media in intelligent manufacturingrdquo Managementvol 37 no 3 pp 161ndash179 2021

[25] J Zhou P Li Y Zhou B Wang J Zang andM Liu ldquoTowardnew-generation intelligent manufacturingrdquo Engineeringvol 4 no 1 pp 10ndash20 2018

[26] V Jovanovic B Stevanov D Seslija S Dudic and Z TesicldquoEnergy efficiency optimization of air supply system in a waterbottle manufacturing systemrdquo Journal of Cleaner Productionvol 85 pp 306ndash317 2014

[27] S Zhang ldquoFull of opportunities and challenges for the futureof intelligent manufacturing under 5 grdquo High-Technology ampCommercialization vol 26 no 12 pp 44ndash47 2020

[28] Y Chen ldquoResearch on the collaborative development of re-gional industry promoted by intelligent manufacturing-takingthe manufacturing industry of Beijing Tianjin and Hebei asan examplerdquo Northern Economy vol 29 no 10 pp 35ndash382020

[29] Y Su X Jiang and Z Lin ldquoSimulation and relationshipstrength characteristics of knowledge flows among subjects ina regional innovation systemrdquo Science Technology amp Societyvol 26 no 3 pp 459ndash481 2021

[30] Y Zhang Y Zhu M Wu X Zhou H Zhang and X YinldquoSimulation of complex network model of innovation dif-fusion dynamics for intelligent manufacturing industrycluster of small and medium sized enterprisesrdquo Journal ofQingdao University of Science and Technology (Natural ScienceEdition) vol 35 no 1 pp 35ndash40 2019

[31] Y Zhao and C Zhou ldquoAn analysis of the scientific researchcooperation network of Chinese researchers a study based onthe perspective of the individual central networkrdquo ScienceResearch vol 29 no 7 pp 999ndash1006 2011

[32] M Kong X Wang and Q Wu ldquoe development efficiencyof Chinarsquos innovative industrial clusters-based on the DEA-malmquist modelrdquo Arabian Journal of Geosciences vol 14no 7 pp 1ndash15 2021

[33] F Fan H Lian and S Wang ldquoCan regional collaborativeinnovation improve innovation efficiency An empirical studyof Chinese citiesrdquo Growth and Change vol 51 no 1pp 440ndash463 2020

[34] M Kogut-Jaworska and E Ociepa-Kicinska ldquoSmart special-isation as a strategy for implementing the regional innovationdevelopment policy-Poland case studyrdquo Sustainabilityvol 12 no 19 p 7986 2020

[35] W Cai and X Chen ldquoe impact mechanism of the parkcluster network structure and resource acquisition on en-terprise performancerdquo System Engineering vol 28 no 8pp 31ndash38 2010

[36] R Chi ldquoe formation structural attributes and functionenhancement of regional SMEs innovation network an

empirical investigation in Zhejiang provincerdquo ManagementWorld vol 21 no 10 pp 102ndash112 2005

[37] Y Su and Y-Q Yu ldquoSpatial agglomeration of new energyindustries on the performance of regional pollution controlthrough spatial econometric analysisrdquoBe Science of the TotalEnvironment vol 704 Article ID 135261 2020

[38] R H Silkman Measuring Efficiency An Assessment of DataEnvelopment Analysis Vol 32 Jossey-Bass San FranciscoCA USA 1986

[39] H Liu C Liu and Y Sun ldquoStudy on the structural char-acteristics and effects of spatial correlation network of energyconsumption in Chinardquo China Industrial Economics vol 39no 5 pp 83ndash95 2015

[40] H Shao L Zhou and Y Liu ldquoSpatial correlation networkstructure and driving factors of innovation development inChinardquo Studies in Science of Science vol 36 no 11pp 2055ndash2069 2018

[41] L Lin and T Niu ldquoe evolution of spatial correlationnetwork structure of regional innovation output based onSNArdquo Economic Geography vol 37 no 9 pp 19ndash25+61 2017

[42] J LiuWhole Network Analysis A Practical Guide to UCINETTruth amp Wisdom Press Shanghai Peoplersquos Press ShanghaiChina 3rd edition 2019

[43] S Wasserman and K Faust Social Network AnalysisMethods and Applications Vol 3 London CambridgeUniversity Press London UK 1994

[44] L Jing C Shu G Wan and C Fu ldquoSpatial correlation ofChinarsquos regional economic growth and its explanation-basedon network analysis methodrdquo Economic Research Journalvol 49 no 11 pp 4ndash16 2014

[45] R V Gould and R M Fernandez ldquoStructures of mediation aformal approach to brokerage in transaction networksrdquo So-ciological Methodology vol 19 pp 89ndash126 1989

[46] Y Su and D Li ldquoInteraction effects of government subsidiesRampD input and innovation performance of Chinese energyindustry a panel vector autoregressive (PVAR) analysisrdquoTechnology Analysis amp Strategic Management vol 33 pp 1ndash152021

[47] H Liu ldquoResearch on Chinarsquos regional RampD efficiency and itsinfluencing factors-an empirical analysis based on stochasticfrontier functionrdquo Studies in Science of Science vol 29 no 4pp 548ndash556 2011

[48] Y Yuan and K Gao ldquoIndustrial collaborative agglomerationspatial knowledge spillover and regional innovation effi-ciencyrdquo Studies in Science of Science vol 38 no 11pp 1966ndash1975 2020

[49] X Cai X Cui J Xiong and T Liu ldquoResearch on RegionalRampD efficiency and its influencing factors in China- Based onthe perspective of scientific research output-achievementtransformationrdquo Soft Science vol 27 no 3 pp 80ndash84 2013

Complexity 15