Research ArticleCollaborative Innovation Efficiency and Influencing Factors ofCivil-Military Integration Enterprises

Zilong Wang,1 Zhiwen Zhang ,1 Xiaodi Xu,2 and Dandan Wang1

1College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China2School of Public Administration, Nanjing Normal University, Nanjing 210023, China

Correspondence should be addressed to Zhiwen Zhang; zzwnuaa@163.com

Received 23 September 2020; Revised 22 December 2020; Accepted 29 December 2020; Published 9 January 2021

Academic Editor: Emilio Jiménez Macı́as

Copyright © 2021 ZilongWang et al. -is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Military-civilian integration has developed into a national strategy. It helps military technology transform into civilian consumergoods but also lets civilian suppliers join the military supply chain. Collaborative innovation is a good method to drive thedevelopment of civil-military integration. -e measurement of the efficiency of military-civilian integration enterprises’ col-laborative innovation and analysis of influencing factors are an important basis for making relevant policies. -us, this paperbuilds up a two-stage StoNED model. It is used to measure the efficiency of collaborative innovation of civil-military integrationenterprises.-en, the Tobit model is used to analyze the influencing factors of innovation efficiency. Here, we show that the overallefficiency and substages efficiency are relatively high, and the low efficiency of the technology R&D stage is the limiting factor forachieving the optimal overall efficiency. Our results demonstrate that there are differences in the effect of the same factor onefficiency in different substages.

1. Introduction

Civil-military integration is a significant issue in nationaldefense and economic development. Against the backdropof the intensification and upgrading of competition amongmajor countries, making overall plans for development andsecurity is essential to consolidate China’s economic andnational strength. Collaborative innovation is a goodmethod to drive the development of civil-military integra-tion. Its essence is to promote the effective combination ofvarious production factors required by technological in-novation through the rational allocation of resources of allparties. -e flow of information, technologies, talents,capital, facilities, services, and other elements between themilitary and civilian gives emphasis to joint developmentand the sharing of military and civilian facilities to improvethe efficiency of innovation in military technologies andpromote the high-quality development of the economy.

Civil-military integration and collaborative innovationare an important part of the national innovation system. Forexample, the BAE system, Dassault, and Boeing are alltypical examples of military-civilian integration. It is the

process of achieving military-civilian innovation resourcesharing within the national innovation system and bybreaking through the barriers among innovation subjects inorder to meet the needs of both military and civilian in-novation [1, 2]. -at is to say, the military innovation andcivil innovation system are integrated, through the sharingof financial resources and technical resources, thus formingsynergies. -rough this process of integration, the innova-tion subject absorbs and learns from each other to achieveeffective use of resources [3]. Among the military andtechnological powers in the world, their national economicdevelopment has adopted various measures to promotemilitary-civilian technological integration and innovationactively. As a developing country, China’s military-civiliantechnology collaborative innovation more urgently requiresthe effective allocation of human, financial, and materialresources, and limited innovation resources can achieve asmuch output as possible. -erefore, how to improve theefficiency of collaborative innovation in civil-military inte-gration enterprises and how to avoid inadequate resourceinvestment and redundancy have become urgent problemsto be solved.

HindawiMathematical Problems in EngineeringVolume 2021, Article ID 8844453, 11 pageshttps://doi.org/10.1155/2021/8844453

mailto:zzwnuaa@163.comhttps://orcid.org/0000-0002-8479-1887https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://doi.org/10.1155/2021/8844453

-e previous research mainly focuses on military-civilianintegration management mechanism, collaborative innovationsystem, and innovation path selection. For example, Haico andSmit [4] thought that it is necessary to attach importance to thecooperation between the civilian and military parties in thedevelopment of new technologies. -ey suggested establishing a“dual-use capability network” to achieve military-civilian tech-nology integration. Brandt [5] pointed out that the biggestproblem in the transformation of civil-military integration wasto explore new business methods. Guerrero and Urbano [6]analyzed the effect of university-industry-government collabo-rative innovation on the innovation performance of enterprisesin the new economy, especially the impact of collaborationbetween enterprises and between universities and the govern-ment on innovation performance. Dong [2] believed that therewas no specialized division of the labor cooperation networksystem among China’s civil-military integration enterprises.-ecollaborative innovation process was affected by the techno-logical complementarity, interest compatibility, risk controlla-bility, and operational complexity among the innovation entities.Zhao et al. [7] believed that when military-industrial enterprisesand civil enterprises shared the cost of technology sharing, theoptimal benefits of both parties and the overall benefits of thesystem would increase. Shao [8] proposed that enterprisesshould establish a military-civilian interactive communicationmechanism and strengthen two-way cooperation.

In the field of innovation efficiency measurement, there aremany studies based on DEA and SFA. Zhang et al. [9] appliedthe SFA method to measure the technical efficiency of listedcompanies in the national defense science and technology in-dustry and concluded that most enterprises had inefficiencies.Among them, property rights structure was an important factorthat affected the technical efficiency of enterprises. Xiao and Lin[10] used the SFA method to measure the efficiency of tech-nological innovation in the industrial sector. -ey believed thatneither direct nor indirect support from the government wasconducive to the improvement of technological innovationefficiency. Zhou [11] used the SE-SBM model to measure theeconomic efficiency of the civil-military integration industrydemonstration center. He found that external policies, devel-opment environment, and the internal relationship of innova-tion subjects have obvious positive effects on industrialtechnology collaborative innovation. In recent years, somestudies have considered the staged issues of “technology R&D”and “technology transformation” in the innovation process.Zhao andYang [12] used a two-stageDEAmodel tomeasure theefficiency of regional innovation networks in the western regionfrom two stages of technological R&D and economic trans-formation. -e results showed that there were significant re-gional and stage differences in regional innovation efficiency.Huang [13] used the chain network DEA model to evaluate theuniversity-industry collaborative innovation efficiency. It foundthat the efficiency of industry-university-research collaborativeinnovation is low, mainly due to the low efficiency of knowledgetransformation. Li et al. [14] distinguished the relationship anddifference between “R&D efficiency,” “conversion efficiency,”and “innovation efficiency” based on an innovation efficiencyevaluation model under the decoupling perspective of “R&Dtransformation”.

Scholars have also carried out a wealth of research on thefactors affecting the implementation of civil-military integrationpolicies and the effectiveness of innovation. -orgren et al. [15]pointed out that the innovation effect of larger interorganiza-tional collaboration networks was better than that of smallinterorganizational networks. Also, relevant government policieswere conducive to promoting collaborative innovation of en-terprises. Burch et al. [16] pointed out two constraints that affectthe consistency of civil-military integration policies. One is theexpected length of time.-e civil sector usually formulated long-term and stable development plans, which lacks sensitivity tounexpected situations.-e secondwas that the complexity of theenvironment itself would affect the strategic decision-makingprocess. Xie [17] studied the interactive relationship between thefactors influencing corporate collaborative innovation and thedegree of collaboration. -e research results showed that thepolicy environment factors had the most obvious impact on thedegree of collaboration. Yu et al. [18] believed that the marketplayed a decisive role in the depth of collaborative innovation,the intensity of government R&D investment had nothing to dowith the depth of corporate collaborative innovation, and thesize of the company was inversely related to the depth of col-laborative innovation. Wang and Li [19] measured the tech-nological efficiency of the civil-military integration enterprises inChina’s “top ten military industry groups” and concluded thatthe state-owned shareholding ratio with a high concentration ofequity is not conducive to the improvement of enterprisetechnical efficiency. Dai et al. [20] discussed the current prob-lems in the development of civil-military integration from theperspectives of partner selection, benefit distribution, and ex-change mechanisms. -e study believed that there was a largegap between China and the developed countries, the depth ofcivil-military integration is insufficient, the scope of cooperationis not wide, and there was a lack of coordinationmechanisms forbenefit distribution and risk-taking.

Based on related literature, the current research on thecollaborative innovation of civil-military integration has thefollowing limitations: (1) most of the existing literature is aqualitative research study on the civil-military integrationmanagement mechanism, innovation system construction, andpolicy formulation. -ere is a lack of systematic scientificquantitative research, especially there is not much literature onresearch from the perspective of efficiency measurement. (2)Most of the discussions on civil-military integration and col-laborative innovation are conducted at the macrolevel of re-gions and industries, and there are still few empirical analysesbased on the microlevel of enterprises. (3) In terms of researchmethods, scholars mostly use the SFA model and the DEAmodel. -e SFA model can effectively separate random errorsand nonefficiency terms, but inappropriate production functionforms or distribution assumptions of error terms may confusesetting errors and efficiency estimates. -ere is no need to setthe production function form for the DEAmodel, which avoidsthe setting error, but it cannot separate the random error [21].

Overall, our article contributes to the existing literaturein two important ways. First, breaking through the limita-tions of the sample, we adopted a microperspective andconducted research based on enterprise-level data. Second,using the two-stage StoNED model to conduct an empirical

2 Mathematical Problems in Engineering

study on the efficiency of collaborative innovation of mili-tary-civilian enterprises has reference significance in themethod.-e research results will help reveal the internal andexternal influencing factors of military-civilian integratedcollaborative innovation and provide a scientific basis for thegovernment to formulate relevant policies.

-e remainder of this paper is organized as follows. InSection 2, we introduce the methods and models. Section3demonstrates the data source and the construction process.Section 4 presents the results of our empirical analysis on theevolution of collaborative innovation efficiency and itsinfluencing factors. Finally, Section 5 concludes.

2. Methods and Models

2.1. Dynamic StoNED Model. StoNED (stochastic non-parametric envelopment of data) was proposed by Kwos-manen [22] in 2006, and this method was developed basedon the traditional DEA model and SFA model. -e modeldetermines the components using the nonparametric pro-cessing method of the DEA model and uses the SFA settingmethod for the introduced random components. -eStoNED model first researched the cross-sectional data andbelieved that the production technology and efficiency itemsdid not change with time. To explain the changes in pro-duction technology and efficiency over time, a dynamicStoNED has been developed based on the static modelsmethod applied to panel data [22]. Now suppose that theproduction function can show the changes in productiontechnology and efficiency with time. Incorporate the timevariable t into the production functionf, write the pro-duction function as f(x, t), t � 0, 1, . . . , T, record the ran-dom error term as vit, and record the nonefficiency term asui(t), i � 1, 2, . . . , n. To reduce the influence of possibleheteroscedasticity, the combined residuals adopt the mul-tiplication form instead of the additional form of the originalmodel and then obtain the StoNED model as follows:

yit �f xit, t(

1 + ui(t) − Vit( , (1)

which is

yit � f xit, t( − ui(t)yit + vityit. (2)

If technological progress is an enhanced type of factorinput, the production function can be written as

f xit, t( � f xit, 0( + M

m�1Am(t)xmit. (3)

In the above formula,f(xit, 0) represents the base pro-duction function, and function Am(t) refers to the input-type technological change. It can be proved that the fol-lowing properties exist between the base production func-tion and the function f(xit, t): if f(xit, 0) is a monotoneincreasing function andAm(t)≥ 0, f(xit, t) is also amonotone increasing function; if f(xit, 0) is a concavefunction, f(xit, t) is also a concave function. -e aboveproperties show that when setting technological progressunder the framework of StoNED, a nonparametric situationcan be used to estimate the base productionfunctionf(xit, 0), and the nonparametric or parametricsituations can be used to set the technological progress, andthe setting of Am(t) will not affect the concavity of f(xit, t).

-is article uses quadratic equations to set the technicalprogress, thereby reducing the introduction of too manyunnecessary unknown parameters as follows:

Am(t) � θm + ψmt2. (4)

In the framework of concave nonparametric leastsquares (CNLS), setting efficiency changes requires im-posing many strict constraints to distinguish efficiencychanges from random changes. -erefore, we use theparametric equation to estimate the nonefficiency termui(t),referring to the processing method of Cornwell et al. [23],and setting ui(t) to the form of a quadratic polynomial asfollows:

ui(t) � ai + bit + cit2. (5)

If bi � ci � 0, the change level of the nonefficiency term isconstant; if bi > 0 and ci � 0, the nonefficiency term increaseslinearly. If ci ≠ 0 , it means that the nonefficiency termchanges nonlinearly.

To estimate the production function of the base periodf(xit, 0) and parameters a, b, c, α, β, θ,ψ, and v, the CNLSestimation model is constructed as follows:

mina,b,c,α,β,θ,ψ,]

T

t�1

n

i�1v2it.

s.t.

yit � αit + M

m�1βmitxmit +

M

m�1θmt + ψmt

2 xmit − ai + bit + cit

2 yit + vityit,

αit + M

m�1βmitxmit ≤ αhs +

M

m�1βmhsxmit,

βmit ≥ 0,θmt + ψmt

2 ≥ 0,∀h, i � 1, . . . , n; ∀s, t � 1, . . . , T,∀m, i � 1, . . . , M.

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(6)

Mathematical Problems in Engineering 3

Among them, the objective function is to find theminimum value of the sum of squares of random error termsvit. -e first constraint represents the production regressionequation, and αit + βitxit represents its production functionf(xit, 0). -e second constraint is the concave restriction onthe tangent hyperplane. -e third constraint represents themonotonic production function increasing, and the fourthconstraint imposes nonnegative restrictions on technolog-ical changes. According to the above constraints, the con-cave and monotonicity of the production function areguaranteed.

-e efficiency E is derived indirectly according to themodel (6) and is expressed in the following standardizedform as follows:

Ei(t) �1

1 + ui(t). (7)

2.2. Tobit Model. To further explore the influencing factorsof the collaborative innovation efficiency of civil-militaryintegration enterprises, this paper takes the substage in-novation efficiency and overall innovation efficiency mea-sured by the two-stage StoNED model as the explainedvariables and uses each influencing factor as the explanatoryvariable to set the multiple linear regression equation. Be-cause the efficiency value is limited to a specific interval,which is a truncation problem, this paper uses the Tobitmodel to analyze the significant influencing factors of thecollaborative innovation efficiency of civil-military inte-gration enterprises. -e specific model is constructed asfollows:

Yk �Xkβ + εk, Xkβ + εk > 0,

0, Xkβ + εk ≤ 0. (8)

Among them, Yk is the limited explained variable, whichrepresents the comprehensive efficiency value of the col-laborative innovation of the class k civil-military integrationenterprise. Xk is the explanatory variable, which representsthe k-th influencing factor of collaborative innovation ef-ficiency, and εk is the error term.

3. Data Source and Variable Selection

3.1. Data Source. -is paper is based on the data of 86 listedcompanies in the civil-military integration category ofChina’s twelve major military groups. After excludingcompanies that did not publish R&D-related data within theresearch time (2015–2017) and whose listing time was tooshort (after 2016), a total of 45 listed companies are selectedas the research sample. -e original data sources of theresearch mainly come from the China Stock Market andAccounting Research Database, the company’s annual re-port, the China Statistical Yearbook, and China and mul-tinational patent examination information query system ofthe State Intellectual Property Office. According to the in-dustry attributes of the sample enterprises, they are dividedinto six categories: nuclear industry enterprises, aerospaceindustry enterprises, aviation industry enterprises,

shipbuilding industry enterprises, weapon industry enter-prises, and electronic technology enterprises. -e specificclassification is shown in Table 1, which shows the numberand proportion of various civil-military integration enter-prises in the research sample. Among the research samples,the quantity of electronic technology companies is thelargest (28.89%).

3.2. Variable Selection. Enterprise innovation should be acomplex systematic project. -e research on innovationefficiency mainly includes two perspectives: one is to analyzeinnovation activities, and the other is to divide the inno-vation process into two stages for analysis. -e traditionaldata envelopment model treats the entire system as a “blackbox”, without considering the intermediate process ofproduction activities. It is impossible to analyze the oper-ational efficiency of the intermediate stage of the productionprocess and the impact of each stage on the overall efficiency.-e correlation two-stage analytical model can effectivelymake up for the above deficiencies [24]. Since the two stagesof technology R&D and technology transformation areclosely linked and both have an impact on innovation ef-ficiency, this paper uses a two-stage research approach tobuild the input-output indicators for the substages of col-laborative innovation efficiency of civil-military integrationenterprises (see Table 2).

In the technology R&D stage, the input indicators aremainly considered from the two aspects: human resources andcapital [25]. R&D personnel and R&D investment are im-portant components of collaborative innovation activities[26, 27]. -erefore, this paper selects the proportion of R&Dpeople and the proportion of R&D investment to operatingincome as input variables in this stage. Patent is the source ofcore competitiveness of military-civilian integrated enterprises.-e number of patent applications can more comprehensivelyreflect the scientific and technological output of the enterprise’sinnovation activities [28], so this variable is selected as theoutput indicator of the technology R&D stage [29].

In the technology transformation stage, resource in-vestment is considered from three aspects: human resources,capital, and technology [13]. In terms of human resources,the number of employees at the end of the year is used as theinput variable [30]; in terms of capital, the increase in in-tangible assets is selected as the input variable [31]; in termsof technology, the number of patent applications is used[29]; this variable is the output variable of the technologyR&D stage, which is used as the technology input indicatorat this stage. -e main output of Stage 2 is the benefits of theapplication and commercialization of collaborative inno-vation [32]. Considering the availability of data, we draw onthe practices of some scholars and select operating income asthe final output indicator [33].

4. Empirical Analysis

4.1. Collaborative Innovation Efficiency of Civil-Military In-tegration Enterprises. -is paper takes six major civil-mil-itary integration companies of the twelve military-industrial

4 Mathematical Problems in Engineering

groups as the research object and uses the StoNED model tomeasure technology R&D, technology transformation, and

overall collaborative innovation efficiency. Based on model(6), the two-stage StoNED model for measuring the effi-ciency of collaborative innovation in civil-military inte-gration enterprises is expressed in the following form:

S1 mina,b,c,α,β,θ,ψ,]

3

t�1

45

i�1v2it,

s.t.

PAit � βPitPit + βQitQit +[ θPt + ψPt2

Pit +( θQt

− ai + bit + cit2

PAit + vitPAit,

βPitPit + βQitQit ≤ βPhsPit + βQhsQit,

βPitβQit ≥ 0,

θPt + ψPt2 ≥ 0,

θQt + ψQt2 ≥ 0,

∀h, i � 1, . . . , 45,

∀s, t � 1, 2, 3,

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(9)

S2 mina,b,c,α,β,θ,ψ,]

3

t�1

45

i�1v2it,

s.t.

ORit � βLitLit + βKitKit + βPAitPAit +[ θLt + ψLt2

Lit + θKt + ψKt2

Kit

+ θPAt + ψPAt2

PAit ] − ai + bit + cit2

ORit + vitORit,

βLitLit + βKitKit + βPAitPAit ≤ βLhsLit + βKhsKit + βPAhsPAit,βLitβKitβPAit ≥ 0,θLt + ψLt

2 ≥ 0,θKt + ψKt

2 ≥ 0,θPAt + ψPAt

2 ≥ 0,∀h, i � 1, . . . , 45,∀s, t � 1, 2, 3.

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(10)

-e model (9) and model (10) are solved using GAMSsoftware to estimate the relevant parameter values. Finally,the technology R&D efficiency ES1, technology transfor-mation efficiency ES2, and overall efficiency value of col-laborative innovation of civil-military integrationenterprises are obtained. -e specific data are shown inTable 3.

Table 3 shows the collaborative innovation efficiencyvalue, average value, and coefficient of variation of varioustypes of civil-military integration enterprises from 2015 to2017. From the overall perspective of the civil-military in-tegration enterprise, the average efficiency of the overall

efficiency is 0.8443, the average efficiency of the technologyR&D stage is 0.5014, and the average efficiency of thetechnology transformation stage is 0.8204, indicating thatthe overall collaborative innovation efficiency and tech-nology transformation efficiency of the civil-military inte-gration enterprise are both high. -e level of technologyR&D efficiency is in the midstream, and the efficiency of thetechnology transformation stage is higher than the tech-nology R&D efficiency.

From the perspective of various types of civil-militaryintegration, the overall efficiency shows industry heteroge-neity, the difference is obvious; but the efficiency difference

Table 1: Classification of sample enterprises.

Type of enterprise Quantity Proportion (%)Nuclear industry enterprises 2 4.44Aerospace industry enterprises 10 22.22Aviation industry enterprises 7 15.56Shipbuilding industry enterprises 3 6.67Weapon industry enterprises 10 22.22Electronic technology enterprises 13 28.89

Mathematical Problems in Engineering 5

in the substage is not obvious, especially in the technologyR&D stage, the difference between technology developmentefficiency of various types of civil-military integration is verysmall. Among them, electronic technology enterprises havethe highest technology R&D efficiency, which is 0.5032;shipbuilding industry enterprises have the highest tech-nology transformation efficiency and overall efficiency ofcollaborative innovation, respectively 0.9040 and 0.9750, butthe technology R&D efficiency of such enterprises is thelowest among all sub-industries, which is 0.5000; the effi-ciency of technology transformation and the overall effi-ciency of collaborative innovation in nuclear industryenterprises are the lowest among all types of civil-militaryintegration enterprises, and their technology R&D efficiencyis also lower than the average level of collaborative inno-vation technology R&D efficiency of civil-military integra-tion enterprises. To further analyze the changes in theefficiency of collaborative innovation of civil-military inte-gration enterprises over time and the difference in resourceinvestment, the following gives the trends and changes in theefficiency of overall and substage efficiency of various typesof civil-military integration enterprises in collaborative in-novation (see Figure 1).

Figure 1 shows the changing characteristics of the effi-ciency of various civil-military integration enterprises’collaborative innovation in the technology R&D stage overtime. Overall, the trend of the technology R&D efficiency ofthe civil-military integration enterprises showed a trend ofincreasing first and then decreasing. Among them, the

Table 2: Input-output indicator system.

Stage Index category Indicator name Indicator symbol

Technology R&D Input variableProportion of R&D personnel (%) P

Proportion of R&D investment to operating income（%） QOutput variable Number of patent applications (item) PA

Technology transformation Input variableNumber of employees at year end L

Increase in intangible assets KNumber of patent applications (item) PA

Output variable Operating income (Ten thousand yuan) OR

Table 3: Collaborative innovation efficiency value of civil-military integration enterprises.

Year Efficiencyvalue

Nuclearindustry

enterprises

Aerospaceindustry

enterprises

Aviationindustry

enterprises

Shipbuildingindustry

enterprises

Weaponindustry

enterprises

Electronictechnologyenterprises

CV Mean

2015ES1 0.5007 0.5009 0.5018 0.5000 0.5016 0.5026 0.0018 0.5012ES2 0.7391 0.7721 0.7912 0.8496 0.7902 0.7887 0.0456 0.7885E 0.7283 0.8683 0.7798 1.0000 0.8089 0.8845 0.1126 0.8450

2016ES1 0.5007 0.5008 0.5042 0.5000 0.5018 0.5036 0.0034 0.5018ES2 0.7369 0.8062 0.8011 0.8850 0.8232 0.8048 0.0586 0.8095E 0.7267 0.8772 0.7788 1.0000 0.8235 0.8639 0.1113 0.8450

2017ES1 0.5005 0.5007 0.5014 0.5000 0.5008 0.5034 0.0024 0.5011ES2 0.7770 0.8728 0.8357 0.9775 0.8566 0.8601 0.0758 0.8633E 0.7358 0.8924 0.7785 0.9251 0.8286 0.8964 0.0886 0.8428

MeanES1 0.5006 0.5008 0.5025 0.5000 0.5014 0.5032 0.0024 0.5014ES2 0.7510 0.8171 0.8093 0.9040 0.8233 0.8179 0.0596 0.8204E 0.7303 0.8793 0.7790 0.9750 0.8204 0.8816 0.1026 0.8443

Note. CV (coefficient of variation) represents the coefficient of variation among subsectors.

2015 2016 2017

0.5050

0.5040

0.5030

0.5020

0.5010

0.5000

0.4990

0.4980

0.4970

Nuclear industry enterprises technologytransformation efficiencyAerospace industry enterprises technologytransformation efficiencyAviation industry enterprises technologytransformation efficiencyShipbuilding industry enterprises technologytransformation efficiencyWeapon industry enterprises technologytransformation efficiencyElectronic technology enterprises technologytransformation efficiencyMilitary-civilian integration enterprises averagetechnology transformation efficiency

Figure 1: Changes in R&D efficiency of collaborative innovationtechnology in civil-military integration enterprises.

6 Mathematical Problems in Engineering

efficiency of aviation industry enterprises and electronictechnology enterprises in the collaborative innovationtechnology R&D stage is higher than the average level oftechnology R&D efficiency of civil-military integration en-terprises. In 2016, aviation industry enterprises have thehighest technology R&D efficiency. In 2015 and 2017,electronic technology companies have higher technologyR&D efficiency than other types of military-civilian inte-gration companies. -e R&D efficiency of collaborativeinnovation technology of weapon industry enterprises isclose to the average value of technology R&D efficiency ofcivil-military integration enterprises. -e R&D efficienciesof collaborative innovation technology of the aerospaceindustry, nuclear industry, and shipbuilding industry arebelow average. And the trend of change over time is morestable. -e reason may be that these industry-relatedtechnologies are at the forefront of science and technology,technology research, and development space is large.However, the difficulty of research and development alsoincreases with the depth of research, so the efficiency oftechnology research and development is lower than othertypes of civil-military integration enterprises.

Figure 2 shows the various types of civil-military inte-gration enterprise collaborative innovation in technologytransfer stage time-varying characteristics of the passage ofefficiency; as a whole, civil-military integration stage com-panies in the technology transfer efficiency increased year byyear, indicating that the resource allocation of collaborativeinnovation is becoming more and more rational in theprocess of technology transformation. Among them, thetransformation efficiency of the collaborative innovationtechnology of the shipbuilding industry is significantlyhigher than that of other types of civil-military integrationenterprises. -e efficiency value in 2017 is 0.9775, indicatingthat the technology application of such enterprises is rela-tively mature, and new technologies can be quickly put intoproduction and market. -e technology transformationefficiencies of aerospace industry enterprises, weapon in-dustry enterprises, electronic technology enterprises, andaviation industry enterprises are close to the average level oftechnology transformation efficiency of civil-military inte-gration enterprises, and the efficiency value is in the upper-middle level. -e technology transformation efficiency ofnuclear industry enterprises is significantly lower than theaverage level of the technology transformation efficiency ofother civil-military integration enterprises. -e reason maybe that the technology conversion of these enterprises ismore difficult, and the transformation period is longer.

Figure 3 shows the trend of the overall efficiency ofcollaborative innovation of various types of civil-militaryintegration enterprises over time. -e overall efficiency ofvarious types of military-civilian integration enterprises hasa significant gap, but the overall efficiency of collaborativeinnovation has not changed significantly over time, and itschanges show four different trends. Shipbuilding industryenterprises have the highest overall efficiency of collabo-rative innovation, with an efficiency value of 1 in 2015 and2016, indicating that the shipbuilding industry enterpriseshave a deeper technological accumulation and the

development of collaborative innovation is superior to otherindustries, but in 2017 showed a downward trend, possiblybecause the redundant input of innovation resources beganto appear in the collaborative innovation process; the overallefficiency of collaborative innovation of electronic tech-nology companies first decrease and then increase, but thelevel of efficiency is still relatively high; the overall efficiencyof collaborative innovation of aerospace industrial enter-prises and weapons industrial enterprises is slowly in-creasing, indicating that the resource allocation of the twocollaborative innovation processes has improved; the overallefficiency of collaborative innovation in nuclear industryenterprises and aviation industry enterprises is lower thanthe overall efficiency of other types of civil-military inte-gration enterprises, and the efficiency value remains stableover time. Since these two types of civil-military integrationenterprises are both types of enterprises that are difficult totackle. Although the space for technological innovation islarge, the technical difficulty also continues to increase withthe deepening of exploration.

4.2. Analysis of Influencing Factors. In order to further ex-plore the impact of different factors on the efficiency ofcollaborative innovation from the enterprise level, this paperuses the Tobit model based on panel data to analyze theinfluencing factors of collaborative innovation efficiency of

1.00

0.95

0.90

0.85

0.80

0.75

0.70

0.65

0.602015 2016 2017

Nuclear industry enterprises technologytransformation efficiencyAerospace industry enterprises technologytransformation efficiencyAviation industry enterprises technologytransformation efficiencyShipbuilding industry enterprises technologytransformation efficiencyWeapon industry enterprises technologytransformation efficiencyElectronic technology enterprises technologytransformation efficiency

Military-civilian integration enterprises averagetechnology transformation efficiency

Figure 2: Changes in transformation efficiency of collaborativeinnovation in civil-military integration.

Mathematical Problems in Engineering 7

civil-military integration enterprises from the following fouraspects.

4.2.1. Size of Enterprises (SE). Large enterprises have betterresource endowments which are more conducive to theimprovement of collaborative innovation efficiency such assufficient R&D funds, good R&D equipment, and perfectmanagement system [34]. Generally speaking, the larger thescale of the enterprise, the more capable the enterprise is tooccupy the leading position in R&D and carry out con-tinuous innovation of knowledge and technology; this articleselects the total assets of the enterprise to represent the scaleof the enterprise [35].

4.2.2. Ownership Structure (OS). Ownership Structure willaffect the fairness and efficiency of enterprises decision-making and then affect the operation and stable operation ofthe enterprise. Over-dispersion of equity increases the risk ofR&D activities and reduces decision-making efficiency,which easily leads to insufficient R&D investment and lowerinnovation efficiency. Excessive concentration of equity caneasily make major shareholders make “dictatorial” decisionsin the production process, which is not conducive to thecontinuous improvement of production efficiency [36]. -isarticle selects the proportion of the five major shareholders

at the end of the period to represent the concentration ofenterprise equity.

4.2.3. Market Framework (MF). Monopoly benefits attractleading companies in my country’s military-civilian inte-gration industry to improve their own R&D efficiency [37].Market framework can reflect the degree of market mo-nopoly or competition in the market, and this paper uses theratio of the number of companies in the industry to the totalnumber of a-share listed companies to measure [38].

4.2.4. Government Favour (GF). Technological innovationactivities cannot be separated from government support, andchanges in the policy environment, science, and technologyinnovation policies are the most important leverage tool forthe government to promote enterprise scientific and tech-nological innovation [39]. -e various policy tools owned bythe government can have a significant impact on the choiceof collaborative innovation models. For example, the gov-ernment can influence the effect of collaborative innovationby increasing resource investment in scientific and tech-nological innovation activities [40]. -is paper selects theproportion of government subsidy funds in R&D investmentfunds to represent the influencing factor.

Based on the model (8), this paper constructs a Tobitmodel of the factors influencing the efficiency of collabo-rative innovation of civil-military integration companies andestablishes multiple linear regression equations for the civil-military integration companies in the overall, technologyR&D, and technology transformation stages as follows:

Eit � C + β1 ln ESit + β2 lnOSit + β3 lnMFit + β4 lnGFit + εit,

ES1it � C + β1 ln ESit + β2 lnOSit + β3 lnMFit + β4 lnGFit + εit,

ES2it � C + β1 ln ESit + β2 lnOSit + β3 lnMFit + β4 lnGFit + εit.

(11)

In the formula, the explained variables Eit,ES1it , and ES2it ,

respectively, represent the collaborative innovation effi-ciency of type i enterprises in the year t overall, technologyR&D, and technology transformation stages. To eliminatethe effect of heteroscedasticity, the explanatory variables areall processed logarithmically. C represents the constant term,β1, β2, β3, and β4 is the regression coefficient, andεitrepresents the random interference term. Using Eviews 8.0software for regression analysis, the results are shown inTable 4.

From the regression results, the influence of variousfactors on overall efficiency and substage efficiency is dif-ferent, and the market structure factors are not significant inthe overall and stage efficiency. In the collaborative inno-vation technology R&D stage of the enterprise, ownershipstructure factors and market framework factors have notpassed the significance test, indicating that these two factorshave not played a significant role in the collaborative in-novation technology R&D stage. -e regression coefficientof the enterprise-scale factor is −0.00111, which has passedthe 5% significance test, indicating that the enterprise scale is

1.00

0.95

0.90

0.85

0.80

0.75

0.702015 2016 2017

Nuclear industry enterprises technologytransformation efficiencyAerospace industry enterprises technologytransformation efficiencyAviation industry enterprises technologytransformation efficiencyShipbuilding industry enterprises technologytransformation efficiencyWeapon industry enterprises technologytransformation efficiencyElectronic technology enterprises technologytransformation efficiency

Military-civilian integration enterprises averagetechnology transformation efficiency

Figure 3: Changes in the overall efficiency of collaborative in-novation in civil-military integration enterprises.

8 Mathematical Problems in Engineering

inversely related to the R&D efficiency of collaborative in-novation technology of civil-military integration enterprises.-at is, the larger the enterprise size, the lower the R&Defficiency of collaborative innovation technology. Maybedue to the time lag, the high investment in the technologyR&D stage could not be converted to the correspondingoutput in a short period, resulting in low efficiency oftechnology R&D [41]. -e regression coefficient of gov-ernment favor factors is 0.00063, which has passed the 5%significance test, indicating that policy support is positivelycorrelated with the efficiency of collaborative innovationtechnology R&D in civil-military integration enterprises.

In the stage of enterprise collaborative innovationtechnology transformation, market framework factors andgovernment support factors have not passed the signifi-cance test, indicating that these two factors have not playeda significant role in the stage of collaborative innovationtechnology transformation. -e regression coefficient ofthe enterprise-scale factor is 0.03893, which passes thesignificance level at 1%, indicating that the enterprise scaleis positively related to the transformation efficiency of thecollaborative innovation technology of civil-military in-tegration enterprises. -e larger the scale of the enterprise,the higher the efficiency of technology transformation. -ereason may be that the larger the scale of the enterprise, theeasier it is for innovative technologies to be applied toproducts and marketed. -e regression coefficient of theownership structure factor is −0.06194, which passes thesignificance level at 1%, indicating that the ownershipstructure is negatively related to the transformation effi-ciency of collaborative innovation technology of civil-military integration enterprises. -e more concentrated theownership, the lower the efficiency of collaborative inno-vation technology transformation. Too concentrated equitywill lead to a lack of democracy in decision-making, whichwill inhibit the efficiency of collaborative innovationtechnology transformation.

As far as the overall efficiency of civil-military integrationcollaborative innovation is concerned, market frameworkfactors and government favor factors have no significant effecton the efficiency of collaborative innovation. -e regressioncoefficient of the enterprise-scale factor is 0.03711, whichpasses the significance level at 1%, indicating that the en-terprise-scale factor is positively related to the overall effi-ciency of civil-military integration collaborative innovation.-e larger the enterprise scale, the higher the overall efficiencyof collaborative innovation. -e regression coefficient of theownership structure factor is −0.07738, which passes the

significance level at 1%, indicating that the ownershipstructure is negatively related to the overall efficiency of civil-military integration innovation. -at is, the more concen-trated the ownership, the lower the efficiency.

5. Conclusions

-e calculation and analysis of the factors influencing theefficiency of collaborative innovation in civil-military inte-gration enterprises are the basis and premise for furtherimproving the level of collaborative innovation in civil-military integration. -is paper uses a two-stage StoNEDmodel to measure the overall efficiency and stage efficiencyof 45 Chinese listed civil-military integration listed com-panies from 2015 to 2017 and uses the Tobit model toperform regression analysis on the factors that affect effi-ciency. -e main conclusions were as follows:

(1) -e overall collaborative innovation efficiency ofcivil-military integration enterprises is at the upper-middle level, indicating that there is still more roomfor improvement in resource allocation, and effortsmust be made to solve problems such as resourceredundancy or waste. -e efficiency of the collabo-rative innovation technology R&D stage is low, andthe efficiency of the technology transformation stageis at a high level. -e efficiency difference of variouscivil-military integration companies in the tech-nology R&D stage is small, and the efficiency dif-ference is obvious in the technology transformationstage. During the sample period, the overall effi-ciency of collaborative innovation has changedsmoothly, with a slight improvement. TechnologyR&D efficiency shows a trend of increasing first andthen decreasing. -e transformation stage showed aclear upward trend.

(2) -e scale of the enterprise has a negative effect on theefficiency of the civil-military integration innovationtechnology R&D stage and has a positive effect on thetechnology transformation efficiency and the overallefficiency.-e expansion of the enterprise scale leadsto a reduction in intermediate output efficiency andpromotes the improvement of final output efficiency;ownership concentration degree has a negative effecton the overall innovation and technology transfor-mation efficiency of civil-military integration en-terprises. -e more concentrated the ownership, thelower the efficiency.

Table 4: Tobit regression results of factors affecting the efficiency of collaborative innovation in civil-military integration enterprises.

S1 S2 OverallCoefficient Standard deviation P value Coefficient Standard deviation P value Coefficient Standard deviation P value

β1 −0.00111∗∗ 0.00044 0.0118 0.03893∗∗∗ 0.00668 0.0001 0.03711∗∗∗ 0.00676 0.0001β2 0.00094 0.00130 0.4684 −0.06194∗∗∗ 0.01978 0.0017 −0.07738∗∗∗ 0.02001 0.0001β3 −0.00009 0.00052 0.8643 0.00910 0.00786 0.2471 −0.00450 0.00795 0.5714β4 0.00063∗∗ 0.00029 0.0313 −0.00348 0.00447 0.4360 −0.00482 0.00452 0.2864C 0.51253∗∗∗ 0.00850 0.0001 0.22522 0.12923 0.0814 0.35703∗∗∗ 0.13072 0.0063∗∗∗-e significance level at 1%, ∗∗the significance level at 5%, and ∗the significance level at 10%.

Mathematical Problems in Engineering 9

(3) Most of the sample enterprises of civil-military in-tegration within the study period are large-scalemilitary enterprises. Due to its characteristics, themarket framework has no significant effect on col-laborative innovation. Government favormainly has apositive impact on the efficiency of collaborative in-novation technology R&D, indicating that govern-ment favor in terms of funds and policies is conducivefor promoting collaborative innovation technologyR&D in civil-military integration enterprises.

In response to the research conclusions, this paper putsforward corresponding policy recommendations to improvethe efficiency of civil-military integration collaborative in-novation as follows:

(1) At present, there are still certain defects in the de-ployment of civil-military integration collaborativeinnovation resources. -e technology R&D stage isthe limiting factor for the failure to achieve theoptimal efficiency of civil-military integration col-laborative innovation. It is necessary to focus onimproving the scientific allocation of innovationresources at this stage, strengthening the techno-logical R&D capabilities of enterprises, and pro-moting the full use of innovation resources.

(2) Civil-military integration enterprises should ra-tionally optimize the scale structure of enterprises,attach importance to the innovation power ofsmall- and medium-sized enterprises, and canexpand the scale of enterprises through mergersand acquisitions, reorganization, and otherrestructuring methods and absorb small- andmedium-sized enterprises with strong innovationand R&D capabilities to enter the defense field andpromote the rational allocation of innovation re-sources; enterprises should also rationally diversifytheir ownership and avoid the concentration ofownership. -ey can take the form of shareholdingincentives to promote technological innovation.

(3) -e government should actively formulate relevantpreferential policies for collaborative innovation ofcivil-military integration enterprises, includingtaxation, preferential benefits, and priority in re-source allocation and ensure that scientific andtechnological R&D funds are effectively invested inthe field of corporate collaborative innovation. -egovernment can encourage “military to civilian”and promote military-industrial enterprises to ex-pand the civilian market, encourage “civilians tomilitary,” undertake some military R&D tasks, andavoid wasting resources between the military andthe local.

Data Availability

-e R&D-related data used to support the findings of thisstudy may be released upon application to the China Stock

Market&Accounting Research Database (https://www.gtarsc.com/).

Conflicts of Interest

-e authors declare that there are no conflicts of interestregarding the publication of this paper.

Acknowledgments

-is work was supported by the Chinese National Fundingof Social Sciences (Grant no. 18AGL028).

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