Research ArticleEvolution of the Chinese Industry-University-ResearchCollaborative Innovation System

Jianyu Zhao12 and Guangdong Wu3

1School of Economics and Management Harbin Engineering University Heilongjiang Harbin 150001 China2School of Management Harbin Institute of Technology Heilongjiang Harbin 150001 China3School of Tourism and Urban Management Jiangxi University of Finance and Economics Nanchang 330013 China

Correspondence should be addressed to Jianyu Zhao jianyu64sinacom

Received 28 November 2016 Revised 19 March 2017 Accepted 29 March 2017 Published 9 April 2017

Academic Editor Katarzyna Musial

Copyright copy 2017 Jianyu Zhao and Guangdong Wu This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

The goal of this study was to reveal the mechanism of the Chinese industry-university-research collaborative innovation (IURCI)and interactions between the elements in the system and find issues that exist in the collaborative innovation process Basedon the theoretical perspective of innovation and complexity science we summarized the elements of the IURCI as innovationcapability research and development (RampD) configuration and knowledge transfer and established a theoretical model to describethe evolution of the IURCI systemWe used simulation technology to determine the interactions among variables and the evolutiontrend of the system The results showed that the RampD configuration can promote the evolution of innovation capability andknowledge transfer and that innovation capacity is the current dominant factor in the evolution of the Chinese IURCI system and ishighly positively correlated with RampD configurationThe evolutionary trend of knowledge transfer was gentler and its contributionto the evolution of the Chinese IURCI system was less than that of RampD configuration When innovation RampD configuration andknowledge transfer are relatively balanced the collaborative innovation system can achieve high speed and stable evolution

1 Introduction

Innovation is an important stimulator of economic develop-ment and is also a key element in the global competitivenessof the country As the worldrsquos second largest economy Chinahas made great progress along the road of independentinnovation research and development investment and thenumber of academic achievements and patents has beenranked at the top in the world But in spite of this progressthere is a disconnection between the economy and technol-ogy in the process of innovation in China On the one handthe Global Competitiveness Report developed by the WorldEconomic Forum according to the Global CompetitivenessIndex (GCI) showed that Chinarsquos technological readiness forinnovation (ranked at 88) has seriously hampered the coun-tryrsquos competitiveness ranking indicating that the innovationcapability of core technology in Chinese enterprises is stillrelatively backward failing to form an innovation-drivendevelopment model and many industryrsquos core technologies

with significantly shorter cycles of innovation are still heavilydependent on foreign countries On the other hand theconversion rate of technological achievements has been lowfor a long time in Chinese universities and research institu-tions therefore higher education training is lagging behind(ranked 62) Universities and research institutions cannoteffectively meet the knowledge requirements for innovationwhich is also an important factor that has led to delaysin the Chinese innovation system The Organization forEconomic Co-operation and Developmentrsquos China InnovationPolicy Research Report pointed out that coordination andintegration in Chinarsquos innovation system is not perfect andthe synergy between constituent subjects in the system is lowCompared with developed countries Chinese enterprisesnot only lack the RampD capabilities of core technology butalso the effects of knowledge accumulation are relativelypoor Enterprises tend to be more cost-oriented and lackthe motivation to carry out and use public research achieve-ments Meanwhile enterprises universities and research

HindawiComplexityVolume 2017 Article ID 4215805 13 pageshttpsdoiorg10115520174215805

2 Complexity

institutes rarely share innovation resources most types oftechnology transfer are carried out under the guidanceof government and universities and research institutionsdo not take the initiative to understand the technologyneeds of industry These problems have seriously hamperedknowledge spillover in the Chinese innovation system andhave become an obstacle that China must overcome tobuild an innovation-driven country through independentinnovation

An effective measure to solve the above problem isto establish a practical and effective Chinese coopera-tive research innovation system thus contributing to therapid transformation of public scientific and technologi-cal achievements of Chinese enterprises and universitiesas well as research institutions and to promote scientificresearch in Chinese universities and institutes that feedsthe demand for industrial innovation thus allowing tech-nological development and industrial development to moveforward together Research on the evolution of the Chineseindustry-university-research (IUR) collaborative innovation(IURCI) system can help identify the interactions betweenthe elements of Chinese IURCI systems and through iden-tifying problems in the process of collaborative innovationcan help Chinese enterprises and universities as well asresearch institutions emerge from the knowledge dilemmaof collaborative innovation This study therefore has the-oretical and practical value for enhancing collaborativeinnovation efficiency and promoting IURCI development inChina

In contrast to the past static perspective we establishedan evolutionary logisticsdynamics equation to describe theresearch collaborative innovation system in China usingrelevant methods from game theory to solve it On this basiswe collected indices and data that have influenced ChineseIURCI from 2005 to 2014 simulated the evolution mor-phology of related variables by MATLAB software analyzedthe interactions between elements and dynamic evolutionand demonstrated in detail the evolution mechanism ofthe research innovation system in China In this studywhile revealing the essence of Chinese IURCI we havetried to establish a new research framework and explain 2issues first there are different degrees of interaction amongvariables in the Chinese IURCI system with different levelsof contribution to the evolution of a collaborative innovationsystem and second when the default initial value of evo-lution changes an evolutionary trend develops among thevariables

In Section 2 of this paper we describe our analysis of theconstituent elements of the IURCI system and the synergyprinciple On this basis in Section 3 we describe how weestablished an IURCI system evolution model with a focuson innovation capacity RampD configuration and knowledgetransfer and conducted derivation and analysis In Section 4we conducted a simulation using actual data of relevantparameters and analyzed the results Finally in the conclu-sion we discuss the innovation and contribution of researchachievements and propose the main direction for futureresearch

2 Theory

IURCI refers to enterprises universities and research insti-tutes 3 main users of innovation to expand their resourcesand capabilities which jointly develop technology innovationactivities under the support of government science andtechnology intermediary service agencies financial institu-tions and other relevant organizations [1 2] In the processof IURCI enterprises universities and research institutesconvert science and technology into practical productiveforces for the purpose of innovation based on a clear divisionof functions through complex nonlinear interactions Thisrealizes mutual benefits between enterprises and universitiesas well as research institutes and produces a synergisticinnovation impact that each factor cannot achieve alone [3]Canhoto et al [4] believe that the essence of collaboration isthe complementary use of resources and capabilities betweencompetitive enterprises and universities as well as researchinstitutions The advantages of enterprises include the rapidcommercialization of technology relatively adequate inno-vation funding suitable production and test equipment andsites andmarket information andmarketing experience andtheir needs for innovation are in basic principles of knowl-edge and in scientific as well as technical human resources[5ndash7]The advantages of universities and research institutionsare theoretical research professionals scientific equipmentknowledge and technical information and research methodsand experience and their needs for innovation are resourcesupport and practical information [8ndash10] The needs ofenterprises for innovation in knowledge resources and theneeds of universities and research institutions for spreadingof scientific knowledge and practice demand constitute theIURCI system based on a retrieval mechanism and allocationrule for noncompetitive interests [11ndash13]

Improving collaborative innovation system performanceis a strategic goal and from the 1980s up to the present therehave been theories such as the innovation systems theory[14ndash16] triple helix [17ndash20] and open innovation theory [21ndash24] which have discussed the elements and principles of anIURCI system at different levels [25 26] The innovationsystem theory is that in the process of collaborative inno-vation and research knowledge reformation is the key toenhance collaborative innovation performance [27ndash29] Thisreformation mainly relies on the resource investment leveland the specificity of the RampD configuration and on knowl-edge accessibility as well as access of universities and researchinstitutions to enterprises In fact since technological innova-tion has become the key to business competition more andmore enterprises have started to pursue the development ofindustrial common technology or cutting-edge technologiesThis development on the one hand stems from the enhancedability of an RampD configuration to promote an innovationsystem and on the other hand it results from knowledgeflowing from the innovation platform of enterprises as theycooperate with universities and research institutions that isknowledge transfer within the IURCI system [30 31] Usingresource dependence theory and knowledge managementtheory the microperspective analysis of the Triple HelixModel found that the nature of evolution of the IURCI system

Complexity 3

involves nonlinear interactions among knowledge transferRampD configuration and innovation capability [32ndash34] Fur-ther the open innovation theory [35 36] which looked at theaspects of fitness and openness between subjects confirmedthat RampD resource configuration is the basic premise forIURCI system evolution and knowledge transfer is withinthe constraints of transaction cost law in the commoninterests of companies and universities well as researchinstitutions We therefore believe that the evolution of theIURCI system depends on innovation RampD configurationand knowledge transfer and the interactions among the 3 notonly surpass the general innovation paradigm of evolutionin previous evolutionary economics but also represent thecore element to decide and change collaborative innovationsystem performance

IURCI is a complex social system and the interac-tions among those constituent elements are the premise toenhance collaborative innovation performance InnovationRampD configuration and knowledge transfer become the keyto determine the evolution of a collaborative innovationsystem and determine its performance Therefore to analyzethe IURCImechanism we use the theory as well asmethod ofSynthetics [37] which illustrates the interactions among ele-ments in complex system and establish the logistic equationwhich analyzes and explains the interactions among innova-tion capacity RampD configuration and knowledge transferWe need to demonstrate precisely and comprehensivelythe interactions among the 3 elements in the collaborativeinnovation process innovation RampD configuration andknowledge transfer thus revealing the nature of IURCI

3 Model

31 Model Establishment

311 Logistic Evolution Equation of Innovation CapacityFaced with the fact of tight resources in order to improveinnovation performance universities and industry are boundto demand cooperation Knowledge transfer and flow inthe IURCI system eventually lead to improved innovationcapabilities in the system Meanwhile the IURCI systemin order to achieve higher innovation performance willcontinue to increase efforts to improve RampD configurationwhich to some extent will promote innovation capacity andthereby the evolution equation of innovation capacity iswritten as follows

1198891198991119889119905 = 12057211198991 + 120573111989911198993 + 12057411198992 (1)

In formula (1) 1205721 represents the influence of coefficientof innovation 1198991 itself and 12057311198993 stands for the influencefactor of RampD configuration on innovation capacity namelythe impact of increasing allocation of RampD configurationon innovation capacity Under normal circumstances 1205731 gt0 1205741 is the influence factor of knowledge transfer andrepresents the interaction between knowledge transfer andRampD configuration

312 Logistic Evolution Equation of RampD ConfigurationImproved innovation capacity of the IURCI system willappeal to the willingness of businesses universities andresearch institutes to improve RampD configuration thereforeinnovative ability 1198991 is also an influencing factor of RampDconfigurations 1198992 Meanwhile in the process of knowledgetransfer in the IURCI system enterprises will continuouslyincrease resources investment in RampD in order to continu-ously create and achieve high-value heterogeneity knowledgeand we can thereby establish the evolution equation of RampDconfiguration as follows

1198891198992119889119905 = minus12057221198992 + 120573211989911198992 + 12057421198993 (2)

In formula (2) minus1205722 represents the influence coefficient ofthe RampD configuration itself 12057321198991 represents the influencecoefficient of innovation capability on RampD configurationBecause there is positive feedback between RampD configura-tion 1198992 and innovation capability 1198991 the coefficient 1205732 is alsopositive Finally 1205742 represents the impact size of knowledgetransfer on RampD configuration

313 Logistic Evolution Equation of Knowledge TransferKnowledge transfer between enterprises and universities aswell as research institutions can have an impact on theinnovation capacity in the IURCI system Meanwhile as theRampD configuration increases the needs of the IURCI systemfor innovation and new knowledge continuously increasethereby facilitating improvement in the knowledge transfercapability of the IURCI systemThis demonstrated that thereis a positive correlation between the 2 variables knowledgetransfer andRampD configuration and therebywe can establishthe evolution equation of knowledge transfer thus

1198891198993119889119905 = 12057231198993 + 12057331198992 (3)

In formula (3) 1205723 represents the influence coefficient ofknowledge transfer itself and 1205733 represents the impact degreeof RampD configuration 1198992 on knowledge transfer capacity

Using innovation capacity RampD configuration andknowledge transfer of IUR as the 3 variables and usinga simultaneous logistics evolution equation we obtained adynamic evolution model as follows

1198891198991119889119905 = 12057211198991 + 120573111989911198993 + 120574111989921198891198992119889119905 = minus12057221198992 + 120573211989911198992 + 120574211989931198891198993119889119905 = 12057231198993 + 12057331198992

(4)

In formula (4) 1198991 1198992 1198993 are constants and the definitions of120572 120573 120574 are as follows120572 = 119894radicprod119901119894=1120572119894 (119894 = 1 2 3 119901) is an adjustment param-

eter of the state variable 1198991 corresponding to an innovationcapability index wherein 120572119894 is the resulting parameter after

4 Complexity

conversion of each index in the evaluation system wherein120572119894 is the resulting parameter after conversion of each index ininnovation capacity evaluation system

120573 = 119894radicprod119901119894=1120573119894 (119894 = 1 2 3 119901) is an adjustmentparameter of the state variable 1198992 corresponding to the RampDconfiguration index wherein 120573119894 is the resulting parameterafter conversion of each index in RampD configuration evalu-ation system

120574 = 119894radicprod119901119894=1120574119894 (119894 = 1 2 3 119901) is an adjustment parame-ter of the state variable 1198993 corresponding to the knowledgetransfer index wherein 120574119894 is the resulting parameter afterconversion of each index in knowledge transfer evaluationsystem

32 Model Analysis

321 Linearization According to Krasovskiirsquos method [38]we used a gradient vector matrix in the operation systemto solve the stable solution of evolution model wherein thesystematic coefficient matrix was solved using the TaylorFormula [39] After performingTaylor omitting higher-orderterms of a second and higher order we then obtained thelinearized coefficient matrix119872 (Jacobian matrix) as follows

119872 = nabla119865 =[[[[[[[[[[[[

1205971198911 (119883)1205971199091 sdot sdot sdot sdot sdot sdot 1205971198911 (119883)

1205971199091198991205971198912 (119883)1205971199091 d sdot sdot sdot

120597119891119899 (119883)1205971199091 sdot sdot sdot sdot sdot sdot 120597119891119899 (119883)

120597119909119899

]]]]]]]]]]]]

(5)

Thus according to Krasovskiirsquos method we rewrite for-mula (4) in the form of a matrix multiplication = 119872 sdot 119883and obtain the following

[[[[[

1119899

]]]]]

=[[[[[[[[

1205971198911 (119883)1205971199091 sdot sdot sdot 1205971198911 (119883)

120597119909119899 d

120597119891119899 (119883)1205971199091 sdot sdot sdot 120597119891119899 (119883)

120597119909119899

]]]]]]]]

[[[[[

1199091119909119899

]]]]] (6)

Formula (6) represents the initial state of the IURCIsystem with 0 input We can use linear system theory toobtain the time domain expression of state variable 119883 thatis the solution of system variable 119883 after linearization of theevolution equation

For evolution equations we solved using the Laplacetransformation [40] and formula (4) was converted to thefollowing

119904119883 (119904) minus 1198830 = 119872 sdot 119883 (119904) (119904119868 minus 119872)119883 (119904) = 1198830 (7)

Laplace transformation of119883 is obtained as follows

119883(119904) = (119904119868 minus119872)minus11198830 (8)

Thus we obtained an expression of the time domain of 119883as follows

119883 = 119871minus1 ((119904119868 minus 119872)minus11198830) (9)

In formula (9) 119904 is a pull complex symbol 1198830 representsthe initial state of the matrix 119883(119904) is the pull transformationof 119883(119905) (119904119868 minus 119872)minus1 represents the inverse matrix of matrix(119904119868 minus 119872) and 119871minus1 is the anti-Laplace transformation

322 Stable Solution According to the linearization resultsformula (4) is solved to obtain the Jacobian matrix as follows

119869 =[[[[[[[[[

1205971198911120597119899112059711989111205971198992

120597119891112059711989931205971198912120597119899112059711989121205971198992

120597119891212059711989931205971198913120597119899112059711989131205971198992

12059711989131205971198993

]]]]]]]]]

= [[[

1205721 + 12057311198993 1205741 1205731119899112057321198992 minus1205722 + 12057321198991 12057420 1205733 1205723

]]]

(10)

to obtain

|119869| =100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1205721 + 12057311198993 1205741 1205731119899112057321198992 minus1205722 + 12057321198991 12057420 1205733 1205723

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816 (11)

Meanwhile according to the result of formula (11) of Jacobianmatrix we set 1198891198991119889119905 = 0 1198891198992119889119905 = 0 1198891198993119889119905 = 0 andformula (12) was obtained as follows

12057211198991 + 120573111989911198993 + 12057411198992 = 0minus12057221198992 + 120573211989911198992 + 12057421198993 = 0

12057231198993 + 12057331198992 = 0(12)

According to this equation we use Routh-Hurwitz stabilitycriterion [41] and the stable solution of complex system tosolve formula (12) so that a partial equilibrium point can beobtained as follows

119860 (0 0 0)119861(12057221205723 + 1205733120574212057231205732 1205721120572212057223 + 12057211205723120573212057421205722312057321205741 minus 1205722120572312057311205733 minus 1205731120573231205742

1205721120572212057231205733 + 12057211205732120573312057421205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741) (13)

Bringing the equilibrium points 119860 and 119861 respectivelyinto the Jacobian matrix and judging equilibrium according

Complexity 5

to the trace and matrix determinant symbols of the Jacobianmatrix we obtained the following

tr (119869) = 1205723 + 12057321198991 + 12057311198991 minus 1205721 minus 1205722 (14)

For equilibrium point 119860tr (119869) = 1205723 minus 1205721 minus 1205722

|119869| =100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1205721 1205741 00 minus1205722 12057420 1205733 1205723

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(15)

For equilibrium point 119861

tr (119869) = 1205723 + 1205721 + 120573312057421205723 + 12057211205722120572312057311205733 + 120572112057311205732120573312057421205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741

|119869| =1003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

212057211205722120572312057311205733 + 12057211205731120573212057331205742 + 12057211205731120573231205742 minus 120572112057223120573212057411205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741 1205741 120572212057231205731 + 1205731120573312057421205723120573212057211205722120572231205732 + 120572112057231205732212057421205722312057321205741 minus 1205722120572312057311205733 minus 1205731120573231205742120573312057421205723 1205742

0 1205733 1205723

1003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(16)

Each point value of point 119860 is 0 representing the initialsteady state thus initial state 11989901 is substituted into expressionsfor 1198991 It can be understood that since the initial value is setto 0 the system has been in a steady state and the valueis always 0 Since there are nonlinear interactions amongthe 3 variables innovation capability RampD configurationand knowledge transfer in IURCI systems these caused anew stable state 119861 in the system A systematic state changefrom 119860 to 119861 represents the evolution of the IURCI systemInteractions among the variables and evolution law willdetermine the outcome of system evolution

4 Simulation

41 Parameter Value To find existing insufficiency in theChinese IURCI system we set specific values of parameterscalculated the coefficient and Jacobian matrix of the modelsimulated it using MATLAB and drew an evolution chartof the 3 variables in the IURCI system In contrast toa previous capability evaluation in the IURCI system wemeasured values of the original control parameters 120572 120573 and120574 of innovation capacity RampD configuration and knowledgetransfer through establishment of an index system Becauseinnovation capacity represents the presence of IURCI systemevolution we considered relevant content such as innovationeffects and innovation gains in the index selection RampDconfiguration represents a resource configuration in theIURCI system so index selection involved funding per-sonnel institutional settings and other aspects Knowledgetransfer represents knowledge flow between enterprises anduniversities as well as research institutions in IURCI systemsso indicator selectionwas based on the state of cooperation asthe core We learned from the studies of [18 26 31] and otherscholars to improve our procedures and ultimately arrived atthe indicators shown in Table 1

In Table 1 each index value was obtained from the ChinaStatistical Yearbook theChina Statistical Yearbook On Scienceand Technology theChina S amp T Paper Statistics and Analysis

the China Industry Economy Statistical Yearbook StatisticsYearbook On Science And Technology Activities of IndustrialEnterprise the Annual Report of Regional Innovation Capa-bility of China the NERI INDEX of Marketization of ChinarsquosProvinces Report and the SME Technology Innovation FundRelated public data from 2005 to 2014 were taken fromthe statistical yearbooks (2015 2016 information was notdisclosed) to give the final parameter values through datastandardization

According to the solvingmethod [37] of120572120573 120574 in formula(4) we bring in the data of 2005ndash2014 fromTable 1 and get theparameter values in Table 2

Table 2 shows the parameter values of the evolutionmodel and the values of 1205721 1205731 1205741 were calculated from thespecific data in the index systems shown in Table 1 In order toobtain the final parameter values of the evolutionary modelwe averaged values for 2005ndash2014 and obtained Table 3

We substituted values for the 9 parameters into theJacobian matrix calculated the determinant and trace of thematrix corresponding to the Jacobian matrix and analyzedsteady-state values of119860 and119861 by judging the determinant andtrace symbols

42 Tests and Analysis Using the Jacobian matrix as thecoefficient matrix of the equation the initial state matrixof the differential equation is [11989901 11989902 11989903] wherein 11989901 11989902 11989903represent the initial values of the 3 variables innovation RampDconfiguration and knowledge transfer respectively Point 119860represents the initial state and the initial state of the systemis 0 such that 1198730 = [0 0 0] therefore substituting this intothe expression we obtained the solution for the expression ofinnovation capacity of 0 that is 11989901 11989902 11989903 = [0 0 0] and theoutput response curve is 0 which indicates the initial state(Figure 1)

In all the figures (Figures 1ndash8) the 119909-axis representsthe input of three elements whereas the 119910-axis representsevolution performance For different values of the initial

6 Complexity

Table 1 Index system of Chinese industry-university-research collaborative innovation system evolution

Variable Indicators

Innovation capacity

Growth in the number of patent applications ()Enterprise market share ()Ratio of new products to total enterprise products ()Success rate of innovation projects ()New product sales margins ()

RampD configuration

Amount of money enterprises invest in product development (ten thousand RMB)Enterprisesrsquo business loan growth ()Growth of RampD funds of universities and research institutes from corporate ()Total number of RampD institutions of enterprises (set)The proportion of investment growth in RampD personnel ()

Knowledge transfer

Productivity of high quality achievements ()Number of high level articles of partner universities (piece)Number of staff entering into enterprises from universities (person)Number of patents enterprises purchased every year (set)Number of universities in industry-university-research cooperation (set)

Table 2 Parameter value of Chinese industry-university-researchcollaborative innovation system evolution from 2005 to 2014

Year 1205721 1205731 12057412005 00256 07732 084222006 00385 07985 089742007 00418 08279 093612008 00477 08741 097132009 00512 09278 102842010 00536 09516 129472011 00744 10479 158202012 00892 12346 180462013 00986 18512 192392014 01032 22017 21158

Table 3 Parameter value of evolution model

Variable 120572 120573 1205741198991 00624 11489 133961198992 01475 15267 194641198993 04587 12982 13983

conditions the resulting response curves are different rep-resenting the new steady state Substituting the relevant datavalues into the Jacobian matrix we used MATLAB softwareto simulate the evolution curves of the 3 variables 1198991 1198992 1198993When the initial values of 1198991 1198992 1198993 are different the outputof the system response curve is as shown in Figures 2ndash8

Figure 2 illustrates that at a high setting of innovation inthe Chinese IURCI system collaborative innovation systemvariables show a rapidly increasing trend in a short periodAmong them the simulation curve of innovation capacityappears as a slowly rising trend after the first drop we believethe reason for this phenomenon is that in the real situationof limited resources the original basis for the innovationcapacity of the Chinese IURCI system needs to quickly adjust

0 1

1

08

06

04

02

08060402

0

minus02

minus04

minus06

minus08

minus1

12 14 16 18

Figure 1 Steady state of industry-university-research collaborativeinnovation system (0 input)

0 1 2 30

5

10

15

20

25

n(3)

n(1)

n(2)

05 15 25

Figure 2 Evolution curve of variables when the initial state set as[1 0 0]

Complexity 7

0 1 20

10

20

30

40

50

60

n(3)

n(1) n(2)

05 15

Figure 3 Evolution curve of variables when the initial state set as[0 1 0]

00

5

10

15

20

25

30

35

40

n(3)

n(1) n(2)

108060402 12 14 16 18

Figure 4 Evolution curve of variables when the initial state set as[0 0 1]

0

5

10

15

20

25

30

35

40

n(3)

n(1)

n(2)

0 108060402 12 14 16 18

Figure 5 Evolution curve of variables when the initial state set as[0 1 1]

0

20

40

60

80

100

120

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 6 Evolution curve of variables when the initial state set as[1 1 0]

0

10

20

30

40

50

60

70

80

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 7 Evolution curve of variables when the initial state set as[1 0 1]

0

20

40

60

80

100

120

n(3)n(1) n(2)

0 108060402 12 14 16 18

Figure 8 Evolution curve of variables when the initial state set as[1 1 1]

8 Complexity

to market demand and devote effort towards knowledgetransfer activities resulting in the short-term declining trendof innovation capacity due to output of resources In thelong term due to the fact that the knowledge base of theIURCI system has been stabilized a complementary relation-ship between innovation capacity and RampD configuration isproduced resulting in a faster innovation capability upgradeThis indicates that since the Chinese IURCI system lagsbehind that of developed countries the ability to innovate andthe evolution of a collaborative innovation systemwill help toimprove targeting of the RampD configuration for businessesuniversities and research institutes and can also enhancepromotion of knowledge transfer capability But fundamen-tally the Chinese IURCI system more likely depends on theinnovation capacity for breakthrough innovations and oncomprehensively promoting the industrialization of acquiredknowledge In order to foster and enhance the innovativecapability the Chinese IURCI system not only needs to moreefficiently allocate limited resources by means of knowledgetransfer but also should provide enterprises with the high-value heterogeneity knowledge created by universities andresearch institutions form a sustained and stable directionalflow of knowledge establish a good feedback path formcomplementary advantages and establish risk sharing result-ing in mutual benefit and a win-win coinnovation situationMeanwhile long-term interactions between universities andresearch institutions and enterprises also contribute to themutual fit of collaborative innovation between variables thusknowledge of different attributes can interact to achieve anoptimal combination of knowledge resources achieve knowl-edge innovation and added value and ultimately produce thedesired collaborative innovation effect which will enhancethe overall competitive advantages of university and industry

Figure 3 shows that when the systemic RampD config-uration is set as 1 that is if the Chinese IURCI has amore comprehensive and adequate RampD configuration eachvariable in the same period has increased significantly andthe evolution speed of innovation capacity is closer to thatof the RampD configuration then the evolution speed ofknowledge transfer is relatively slow being increased onlyfrom 0 to about 52 Meanwhile we compared Figures 2 and3 and found that the evolution curve of knowledge transferhas always been lower than that of innovation capacityand RampD configuration this phenomenon reveals the factthat in the evolution process of the Chinese IURCI systemthe contribution of knowledge transfer to the evolution ofinnovative capacity is low which explains why the influencedegree of knowledge transfer on Chinese IURCI systemevolution is significantly low Thus companies can under-stand that in the current external environment and policycontext too much dependence on knowledge transfer fromuniversities and research institutions may not achieve thebest collaborative innovation strategy Therefore under thecurrent circumstances the Chinese IURCI system mainlyconsiders knowledge transfer as an assistance mode for col-laborative innovation and new knowledge access and focuseson enhancing their own innovation capacity and the rea-sonability of the RampD configuration Further as the ChineseIURCI system continues to evolve universities and research

institutions have clearer requirements for the distribution ofbenefits and coupled with a gradual improvement of theintellectual property system this could lead to a longer cycleof knowledge transfer between enterprises universities andresearch institutions At this time collaborative innovationand research will be more inclined to be aided by innovationcapability improvement so that the focus on knowledgetransfer is declining It can be seen that in the Chinese IURCIsystem enhancing the innovation capacity is more importantand knowledge transfer has not played its due role whichshows that the Chinese IURCI system has issues aroundweak knowledge transfer inadequate trust and cooperationmechanisms between enterprises and universities as well asresearch institutions and insufficient knowledge acquisitionby enterprises from universities and research institutions

Figure 4 illustrates in the context of a lack of innovationcapacity and incomplete RampD configuration that if the Chi-nese IURCI system was to have more prominent knowledgetransfer ability then knowledge transfer can to some extentcontribute to the evolution of collaborative innovation FromFigures 3 and 4 it can be seen that whether we set theRampD configuration as 1 and the knowledge transfer as 0or the knowledge transfer as 1 and the RampD configurationas 0 as long as these 2 variables have an initial valuethey will encourage the innovation capacity in the ChineseIURCI system to improve rapidly It means that in thiscontext innovation capacity is highly correlated with RampDconfiguration and knowledge transfer in the Chinese IURCIsystem Enhancing knowledge transfer capability can helpbusinesses more efficiently and in a more targeted wayobtain knowledge that universities and research institutionscreate and promulgate which can enhance the speed ofknowledge commercialization and help IURCI to achievehigh performance thus strengthening theChinese IURunionby maintaining internal synergies between knowledge supplyand knowledge applicationThis has a positive impact on pro-moting collaborative innovation activities and enhancing theconfidence of IUR cooperation At the same time comparedto Figures 2 and 3 it can be seen that although more promi-nent knowledge transfer can speed up innovation capacitythe enhancing effectiveness is still lower than that of RampDconfiguration The reason may be that knowledge transferfrom universities and research institutions is the main wayto promote Chinese IURCI system evolution since the basisof innovation and scientific and technical human capital onthe part of enterprises are poor knowledge input and supportof the collaborative innovation system are totally dependenton knowledge transfer from the universities and researchinstitutions side However due to the nature of knowledgetransfer there is a certain lag so in the absence of appropriateRampD configuration support the promotion of innovationcapability from universities and research institutions is notobvious so the Chinese IURCI requires a longer cooperationperiod

Figure 5 shows that when RampD configuration and knowl-edge transfer are more prominent in the Chinese IURCIsystem the increase in innovation capacity is more obviousthan the results of Figures 2 3 and 4 This demonstratesthat RampD configuration and knowledge transfer coaffect

Complexity 9

the evolution of innovation capacity in the Chinese IURCIsystem that is there are significant positive feedback effectsamong the 3 variables which mutually promote each otherIt means that at such time as the Chinese IURCI systemhas some input resources and knowledge transfer capabilitythese factors will be beneficial to rapidly increasing thecollaborative innovation knowledge level and innovationcapacity thus improving collaborative innovation perfor-mance We believe that the reasons for this may be twofoldon the one hand more effective knowledge transfer betweencorporations and universities as well as research institutionspromotes knowledge industrialization so the innovationcapability of enterprises quickly upgrades in the process ofrepeated practice and constant learning which to a certainextent promotes IURCI performance On the other handrational and targeted RampD configuration allows universitiesand research institutions under the premise of having astable knowledge base to reduce the time delay of knowledgetransfer in the process of collaborative innovation and putpart of their resources into the process of promotion ofknowledge transfer capacity thereby achieving a mutualfit with RampD configuration It should be noted that inthe initial stage of simulation namely the early periodof Chinese IURCI practice favorable Chinese governmentpolicies and attention to IURCI could promote the speedof knowledge industrialization But knowledge transfer fromuniversities and research institutions is subject to adversefactors in the cooperation run-in period and may producea situation of no improvement With the in-depth evolutionof the IURCI system cooperation between businesses anduniversities and research institutions can mature so thatuniversities and research institutions have clearer ideas aboutindustry needs and more accurately provide the necessaryknowledge to enterprises for knowledge industrializationUniversities and research institutions will also benefit fromcollaborative innovation as they better understand industrytechnology needs and market demand allowing them tooptimize their knowledge structure thus enhancing theIURCI performance

Figure 6 illustrates that in the evolution process ofIURCI that relies on innovation capacity and RampD con-figuration continuously increasing RampD configuration willmake evolution of innovation capacity significantly fasterAt the same time compared to Figure 5 it can be foundthat in the evolution of the Chinese IURCI system ifRampD configuration has an initial value to enhance theIURCI performance enterprises must operate on the basis ofcontinuously developing their RampD configuration obtainingas much as possible valuable heterogeneity knowledge fromuniversities and research institutions and universities andresearch institutions also need to enhance their abilities totransfer knowledge and fit the production needs of industryin order to obtain more significant innovation performanceIn addition this case also shows that although there maybe differences in their abilities to innovate for those enter-prises that lack the ability to innovate the input of externalresources and the gain of valuable knowledge by way ofknowledge transfer may change the current disadvantage andhelp them achieve rapid growth

Figures 5 and 6 both show that the innovation capacityand RampD configuration of the Chinese IURCI system canin a short period of time produce significant synergies andpromote each other indicating that Chinarsquos current IURCIdevelopment still requires a lot of resources investment as theprimary means to enhance innovation capability But whenRampD configuration is high the result of innovation capacityevolution [38] is lower than that when knowledge transferis relatively high which gives high values of innovationevolution [42] On the one hand this indicates that inthe current process of IURCI in China the universitiesand research institutions side creates and sends high-valueheterogeneity knowledge which has a fundamental role inpromoting innovation capacity On the other hand to acertain extent this confirms that if Chinese universities andresearch centers could face knowledge-oriented industrialdemand enterprises could accurately identify the marketvalue of accepted knowledge change the discrepancy thatexists between technology supply and technology demandand thereby reduce its dependency on RampD configurationwhile at the same time shortening the innovation cycleand avoiding the issues such as information asymmetryand transaction costs that may arise during collaborativeinnovation [42ndash44]

Figure 7 illustrates that when innovation capacity andknowledge transfer are more prominent and RampD con-figuration is lower variables of the Chinese IURCI couldpromote each other by interaction but the growth rate isrelatively small indicating that relying solely on knowledgetransfer to promote innovation capacity is not effective Onthe one hand this confirmed that the type of knowledgeproduced by Chinese universities and research institutionsmust have applicability to be promoted to companies forindustrialization On the other hand this maybe resultsfrom enterprises having an inadequate knowledge absorptivecapacity Meanwhile from Figures 7 5 and 6 it can befound that if the RampD configuration of the Chinese IURCIsystem is poor then there will be a very significant impacton the ability to enhance knowledge transfer speed Thepossible reasons are that when RampD configuration is notenough the Chinese IURCI system is already in the processof tight resource allocation and it is bound to devote moreresources into promotion activities that create a high-valueheterogeneity innovation capacity and it therefore lacksthe resources to facilitate knowledge transfer within thesystem Correspondingly if resources are relatively abundantenterprises must allow for a bottleneck phenomenon inenhancing innovation capacity or accept greater risks ininnovative activity and may assign the resources originallydevoted to innovative ability to universities and researchinstitutions supporting the knowledge transfer willingnessof universities and research institutions and thus enhancingknowledge transfer for collaborative innovation In this casethe evolution curve of knowledge transfer will be moresignificant

Figure 8 clearly shows that when the innovation capacityof the Chinese IURCI system is more prominent RampDconfiguration is more targeted knowledge transfer capabilityis better and the 3 state variables of collaborative innovation

10 Complexity

show rapid and steady evolution Among them the growthrate of innovation capability is the most significant so thatafter a new stable state is established knowledge transferand RampD configuration both can have a more substantialgrowth effect on innovation capability This means thatin order to achieve the objective of significantly improvedcollaborative innovation performance the Chinese IURCIsystem must adhere to long-term independent innovationactivities and in addition must maintain and increase thebasic RampD configuration constantly improve knowledgetransfer capacity andmore effectively and pertinently send toenterprises the complementary knowledge that universitiesand research institutions own With advances in IURCI thisapproachmay bring 2 advantages for enterprises universitiesand research institutions involved in Chinese IURCI butfrom a business perspective at present the awareness ofChinese enterprises to acquire complementary knowledgein an IUR-coordinated manner is poor and so initiative isvery low If enterprises realize that on the basis of improvingRampD configuration they can obtain complementary researchachievement by making use of knowledge transfer formthen this will greatly increase the willingness of Chineseenterprises to participate in IURCI thereby using new tech-nology developing newproducts and approaching universityRampD personnel which is beneficial to building a trust-basedcooperationmechanism between enterprises and universitiesas well as research institutions [45ndash48] From the perspectiveof Chinese universities and research institutes in the processof knowledge creation the biggest drawback is that newknowledge cannot be applied to production practice that isuniversities and research institutions pay too much attentionto academic value With periodic IURCI universities andresearch institutions can not only receive financial supportfrom the business according to the needs of the businessto carry out research activities but also fully explore newresearch areas which will also play a positive role in promot-ing more academic achievements [49 50]

From observing Figures 3ndash8 we found that in the currentChinese IURCIprocess RampDconfiguration alwaysmaintainsa high degree of positive correlationwith innovation capacityThe reasons for this may be that Chinarsquos current IURCIsystem is not perfect and enterprises and universities as wellas research institutions are always looking for cooperationmodes suitable for both sides Meanwhile the process ofknowledge transfer itself is relatively slow and betweenChinese enterprises and universities there might exist neg-ative factors affecting knowledge transfer speed (such asknowledge flow cost and familiarity running process betweenstaffs) and these factors will result in reduced efficiency ofknowledge transfer therefore growth of knowledge transferis always slow

In short in Chinarsquos IURCI process a consistent relation-ship indeed exists among innovation capability knowledgetransfer and RampD configuration In a higher knowledgetransfer and comprehensive RampD configuration scenarioenterprises can propose more targeted knowledge needsand initially provide financial and material support foruniversities and research institutions involved in innovationUniversities and research institutions from the strategic level

are also concerned about how to establish knowledge RampDto serve enterprises and under the premise of integratedresources establish a balanced benefits distribution activelycarry out scientific and technological achievement transfertrain technology and management personnel required forenterprises exert collaborative innovation effects by comple-mentary advantages and thus bring new benefits for bothsides

5 Conclusion

By combining methods of game theory and complex sciencewe studied the evolution of the Chinese IURCI systemand interactions among the variables We used interactionsamong variables in a collaborative innovation system toldquomaprdquo the perspective of collaborative innovation evolutionand study innovation capacity RampD configuration andknowledge transfer as key elements of collaborative inno-vation and on the basis of establishing a dynamic systemevolution model of collaborative innovation we collecteddata simulated discussed interactions among the variablesin the process of Chinese IURCI and analyzed principles ofcollaborative innovation We mainly obtained the followingconclusions(1) In the evolution of the Chinese IURCI systeminnovation capacity and knowledge transfer are dependenton RampD configuration and innovation capability is highlypositively correlated with RampD configurationThe higher thetarget and investment level of RampD configuration the fasterthe evolution of innovation capacity and knowledge transferIt is noted that currentlyChinese IURCI development is usinga lot of resource investment as its main factor Innovationcapacity has become the key variable to promote collaborativeinnovation system evolution(2) Strong knowledge transfer capability can acceleratethe evolution speed of innovation capacity but overall knowl-edge transfer remains at a relatively stable evolution state anddoes not change with evolution of collaborative innovationThis explains that in the evolution process of Chinarsquos currentIURCI system virtuous cooperation mechanisms based ontrust between enterprises and universities as well as researchinstitutions are lacking Universities and research institutionsalso pay more attention to the academic value of knowledgeleading to insufficient contribution of knowledge transferto knowledge industrialization by enterprises and reducedcollaborative innovation system evolution(3) In a relatively balanced state of innovation capacityRampD configuration and knowledge transfer the faster theevolution speed of Chinese IURCI systems the higher thestability This shows that the Chinese IURCI system mustadhere to long-term independent innovation and on thebasis of maintaining and improving RampD configurationconstantly improve internal knowledge transfer capability

Compared with early research this article has 3 mainfindings first we chose China as a developing country withrapid economic growth used a dynamic perspective ana-lyzed the evolutionmechanism of the Chinese IURCI systemand demonstrated the interactions among variables of thecollaborative innovation system Secondly we learned from

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

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Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Decision SciencesAdvances in

Journal of

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Complexity 3

involves nonlinear interactions among knowledge transferRampD configuration and innovation capability [32ndash34] Fur-ther the open innovation theory [35 36] which looked at theaspects of fitness and openness between subjects confirmedthat RampD resource configuration is the basic premise forIURCI system evolution and knowledge transfer is withinthe constraints of transaction cost law in the commoninterests of companies and universities well as researchinstitutions We therefore believe that the evolution of theIURCI system depends on innovation RampD configurationand knowledge transfer and the interactions among the 3 notonly surpass the general innovation paradigm of evolutionin previous evolutionary economics but also represent thecore element to decide and change collaborative innovationsystem performance

IURCI is a complex social system and the interac-tions among those constituent elements are the premise toenhance collaborative innovation performance InnovationRampD configuration and knowledge transfer become the keyto determine the evolution of a collaborative innovationsystem and determine its performance Therefore to analyzethe IURCImechanism we use the theory as well asmethod ofSynthetics [37] which illustrates the interactions among ele-ments in complex system and establish the logistic equationwhich analyzes and explains the interactions among innova-tion capacity RampD configuration and knowledge transferWe need to demonstrate precisely and comprehensivelythe interactions among the 3 elements in the collaborativeinnovation process innovation RampD configuration andknowledge transfer thus revealing the nature of IURCI

3 Model

31 Model Establishment

311 Logistic Evolution Equation of Innovation CapacityFaced with the fact of tight resources in order to improveinnovation performance universities and industry are boundto demand cooperation Knowledge transfer and flow inthe IURCI system eventually lead to improved innovationcapabilities in the system Meanwhile the IURCI systemin order to achieve higher innovation performance willcontinue to increase efforts to improve RampD configurationwhich to some extent will promote innovation capacity andthereby the evolution equation of innovation capacity iswritten as follows

1198891198991119889119905 = 12057211198991 + 120573111989911198993 + 12057411198992 (1)

In formula (1) 1205721 represents the influence of coefficientof innovation 1198991 itself and 12057311198993 stands for the influencefactor of RampD configuration on innovation capacity namelythe impact of increasing allocation of RampD configurationon innovation capacity Under normal circumstances 1205731 gt0 1205741 is the influence factor of knowledge transfer andrepresents the interaction between knowledge transfer andRampD configuration

312 Logistic Evolution Equation of RampD ConfigurationImproved innovation capacity of the IURCI system willappeal to the willingness of businesses universities andresearch institutes to improve RampD configuration thereforeinnovative ability 1198991 is also an influencing factor of RampDconfigurations 1198992 Meanwhile in the process of knowledgetransfer in the IURCI system enterprises will continuouslyincrease resources investment in RampD in order to continu-ously create and achieve high-value heterogeneity knowledgeand we can thereby establish the evolution equation of RampDconfiguration as follows

1198891198992119889119905 = minus12057221198992 + 120573211989911198992 + 12057421198993 (2)

In formula (2) minus1205722 represents the influence coefficient ofthe RampD configuration itself 12057321198991 represents the influencecoefficient of innovation capability on RampD configurationBecause there is positive feedback between RampD configura-tion 1198992 and innovation capability 1198991 the coefficient 1205732 is alsopositive Finally 1205742 represents the impact size of knowledgetransfer on RampD configuration

313 Logistic Evolution Equation of Knowledge TransferKnowledge transfer between enterprises and universities aswell as research institutions can have an impact on theinnovation capacity in the IURCI system Meanwhile as theRampD configuration increases the needs of the IURCI systemfor innovation and new knowledge continuously increasethereby facilitating improvement in the knowledge transfercapability of the IURCI systemThis demonstrated that thereis a positive correlation between the 2 variables knowledgetransfer andRampD configuration and therebywe can establishthe evolution equation of knowledge transfer thus

1198891198993119889119905 = 12057231198993 + 12057331198992 (3)

In formula (3) 1205723 represents the influence coefficient ofknowledge transfer itself and 1205733 represents the impact degreeof RampD configuration 1198992 on knowledge transfer capacity

Using innovation capacity RampD configuration andknowledge transfer of IUR as the 3 variables and usinga simultaneous logistics evolution equation we obtained adynamic evolution model as follows

1198891198991119889119905 = 12057211198991 + 120573111989911198993 + 120574111989921198891198992119889119905 = minus12057221198992 + 120573211989911198992 + 120574211989931198891198993119889119905 = 12057231198993 + 12057331198992

(4)

In formula (4) 1198991 1198992 1198993 are constants and the definitions of120572 120573 120574 are as follows120572 = 119894radicprod119901119894=1120572119894 (119894 = 1 2 3 119901) is an adjustment param-

eter of the state variable 1198991 corresponding to an innovationcapability index wherein 120572119894 is the resulting parameter after

4 Complexity

conversion of each index in the evaluation system wherein120572119894 is the resulting parameter after conversion of each index ininnovation capacity evaluation system

120573 = 119894radicprod119901119894=1120573119894 (119894 = 1 2 3 119901) is an adjustmentparameter of the state variable 1198992 corresponding to the RampDconfiguration index wherein 120573119894 is the resulting parameterafter conversion of each index in RampD configuration evalu-ation system

120574 = 119894radicprod119901119894=1120574119894 (119894 = 1 2 3 119901) is an adjustment parame-ter of the state variable 1198993 corresponding to the knowledgetransfer index wherein 120574119894 is the resulting parameter afterconversion of each index in knowledge transfer evaluationsystem

32 Model Analysis

321 Linearization According to Krasovskiirsquos method [38]we used a gradient vector matrix in the operation systemto solve the stable solution of evolution model wherein thesystematic coefficient matrix was solved using the TaylorFormula [39] After performingTaylor omitting higher-orderterms of a second and higher order we then obtained thelinearized coefficient matrix119872 (Jacobian matrix) as follows

119872 = nabla119865 =[[[[[[[[[[[[

1205971198911 (119883)1205971199091 sdot sdot sdot sdot sdot sdot 1205971198911 (119883)

1205971199091198991205971198912 (119883)1205971199091 d sdot sdot sdot

120597119891119899 (119883)1205971199091 sdot sdot sdot sdot sdot sdot 120597119891119899 (119883)

120597119909119899

]]]]]]]]]]]]

(5)

Thus according to Krasovskiirsquos method we rewrite for-mula (4) in the form of a matrix multiplication = 119872 sdot 119883and obtain the following

[[[[[

1119899

]]]]]

=[[[[[[[[

1205971198911 (119883)1205971199091 sdot sdot sdot 1205971198911 (119883)

120597119909119899 d

120597119891119899 (119883)1205971199091 sdot sdot sdot 120597119891119899 (119883)

120597119909119899

]]]]]]]]

[[[[[

1199091119909119899

]]]]] (6)

Formula (6) represents the initial state of the IURCIsystem with 0 input We can use linear system theory toobtain the time domain expression of state variable 119883 thatis the solution of system variable 119883 after linearization of theevolution equation

For evolution equations we solved using the Laplacetransformation [40] and formula (4) was converted to thefollowing

119904119883 (119904) minus 1198830 = 119872 sdot 119883 (119904) (119904119868 minus 119872)119883 (119904) = 1198830 (7)

Laplace transformation of119883 is obtained as follows

119883(119904) = (119904119868 minus119872)minus11198830 (8)

Thus we obtained an expression of the time domain of 119883as follows

119883 = 119871minus1 ((119904119868 minus 119872)minus11198830) (9)

In formula (9) 119904 is a pull complex symbol 1198830 representsthe initial state of the matrix 119883(119904) is the pull transformationof 119883(119905) (119904119868 minus 119872)minus1 represents the inverse matrix of matrix(119904119868 minus 119872) and 119871minus1 is the anti-Laplace transformation

322 Stable Solution According to the linearization resultsformula (4) is solved to obtain the Jacobian matrix as follows

119869 =[[[[[[[[[

1205971198911120597119899112059711989111205971198992

120597119891112059711989931205971198912120597119899112059711989121205971198992

120597119891212059711989931205971198913120597119899112059711989131205971198992

12059711989131205971198993

]]]]]]]]]

= [[[

1205721 + 12057311198993 1205741 1205731119899112057321198992 minus1205722 + 12057321198991 12057420 1205733 1205723

]]]

(10)

to obtain

|119869| =100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1205721 + 12057311198993 1205741 1205731119899112057321198992 minus1205722 + 12057321198991 12057420 1205733 1205723

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816 (11)

Meanwhile according to the result of formula (11) of Jacobianmatrix we set 1198891198991119889119905 = 0 1198891198992119889119905 = 0 1198891198993119889119905 = 0 andformula (12) was obtained as follows

12057211198991 + 120573111989911198993 + 12057411198992 = 0minus12057221198992 + 120573211989911198992 + 12057421198993 = 0

12057231198993 + 12057331198992 = 0(12)

According to this equation we use Routh-Hurwitz stabilitycriterion [41] and the stable solution of complex system tosolve formula (12) so that a partial equilibrium point can beobtained as follows

119860 (0 0 0)119861(12057221205723 + 1205733120574212057231205732 1205721120572212057223 + 12057211205723120573212057421205722312057321205741 minus 1205722120572312057311205733 minus 1205731120573231205742

1205721120572212057231205733 + 12057211205732120573312057421205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741) (13)

Bringing the equilibrium points 119860 and 119861 respectivelyinto the Jacobian matrix and judging equilibrium according

Complexity 5

to the trace and matrix determinant symbols of the Jacobianmatrix we obtained the following

tr (119869) = 1205723 + 12057321198991 + 12057311198991 minus 1205721 minus 1205722 (14)

For equilibrium point 119860tr (119869) = 1205723 minus 1205721 minus 1205722

|119869| =100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1205721 1205741 00 minus1205722 12057420 1205733 1205723

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(15)

For equilibrium point 119861

tr (119869) = 1205723 + 1205721 + 120573312057421205723 + 12057211205722120572312057311205733 + 120572112057311205732120573312057421205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741

|119869| =1003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

212057211205722120572312057311205733 + 12057211205731120573212057331205742 + 12057211205731120573231205742 minus 120572112057223120573212057411205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741 1205741 120572212057231205731 + 1205731120573312057421205723120573212057211205722120572231205732 + 120572112057231205732212057421205722312057321205741 minus 1205722120572312057311205733 minus 1205731120573231205742120573312057421205723 1205742

0 1205733 1205723

1003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(16)

Each point value of point 119860 is 0 representing the initialsteady state thus initial state 11989901 is substituted into expressionsfor 1198991 It can be understood that since the initial value is setto 0 the system has been in a steady state and the valueis always 0 Since there are nonlinear interactions amongthe 3 variables innovation capability RampD configurationand knowledge transfer in IURCI systems these caused anew stable state 119861 in the system A systematic state changefrom 119860 to 119861 represents the evolution of the IURCI systemInteractions among the variables and evolution law willdetermine the outcome of system evolution

4 Simulation

41 Parameter Value To find existing insufficiency in theChinese IURCI system we set specific values of parameterscalculated the coefficient and Jacobian matrix of the modelsimulated it using MATLAB and drew an evolution chartof the 3 variables in the IURCI system In contrast toa previous capability evaluation in the IURCI system wemeasured values of the original control parameters 120572 120573 and120574 of innovation capacity RampD configuration and knowledgetransfer through establishment of an index system Becauseinnovation capacity represents the presence of IURCI systemevolution we considered relevant content such as innovationeffects and innovation gains in the index selection RampDconfiguration represents a resource configuration in theIURCI system so index selection involved funding per-sonnel institutional settings and other aspects Knowledgetransfer represents knowledge flow between enterprises anduniversities as well as research institutions in IURCI systemsso indicator selectionwas based on the state of cooperation asthe core We learned from the studies of [18 26 31] and otherscholars to improve our procedures and ultimately arrived atthe indicators shown in Table 1

In Table 1 each index value was obtained from the ChinaStatistical Yearbook theChina Statistical Yearbook On Scienceand Technology theChina S amp T Paper Statistics and Analysis

the China Industry Economy Statistical Yearbook StatisticsYearbook On Science And Technology Activities of IndustrialEnterprise the Annual Report of Regional Innovation Capa-bility of China the NERI INDEX of Marketization of ChinarsquosProvinces Report and the SME Technology Innovation FundRelated public data from 2005 to 2014 were taken fromthe statistical yearbooks (2015 2016 information was notdisclosed) to give the final parameter values through datastandardization

According to the solvingmethod [37] of120572120573 120574 in formula(4) we bring in the data of 2005ndash2014 fromTable 1 and get theparameter values in Table 2

Table 2 shows the parameter values of the evolutionmodel and the values of 1205721 1205731 1205741 were calculated from thespecific data in the index systems shown in Table 1 In order toobtain the final parameter values of the evolutionary modelwe averaged values for 2005ndash2014 and obtained Table 3

We substituted values for the 9 parameters into theJacobian matrix calculated the determinant and trace of thematrix corresponding to the Jacobian matrix and analyzedsteady-state values of119860 and119861 by judging the determinant andtrace symbols

42 Tests and Analysis Using the Jacobian matrix as thecoefficient matrix of the equation the initial state matrixof the differential equation is [11989901 11989902 11989903] wherein 11989901 11989902 11989903represent the initial values of the 3 variables innovation RampDconfiguration and knowledge transfer respectively Point 119860represents the initial state and the initial state of the systemis 0 such that 1198730 = [0 0 0] therefore substituting this intothe expression we obtained the solution for the expression ofinnovation capacity of 0 that is 11989901 11989902 11989903 = [0 0 0] and theoutput response curve is 0 which indicates the initial state(Figure 1)

In all the figures (Figures 1ndash8) the 119909-axis representsthe input of three elements whereas the 119910-axis representsevolution performance For different values of the initial

6 Complexity

Table 1 Index system of Chinese industry-university-research collaborative innovation system evolution

Variable Indicators

Innovation capacity

Growth in the number of patent applications ()Enterprise market share ()Ratio of new products to total enterprise products ()Success rate of innovation projects ()New product sales margins ()

RampD configuration

Amount of money enterprises invest in product development (ten thousand RMB)Enterprisesrsquo business loan growth ()Growth of RampD funds of universities and research institutes from corporate ()Total number of RampD institutions of enterprises (set)The proportion of investment growth in RampD personnel ()

Knowledge transfer

Productivity of high quality achievements ()Number of high level articles of partner universities (piece)Number of staff entering into enterprises from universities (person)Number of patents enterprises purchased every year (set)Number of universities in industry-university-research cooperation (set)

Table 2 Parameter value of Chinese industry-university-researchcollaborative innovation system evolution from 2005 to 2014

Year 1205721 1205731 12057412005 00256 07732 084222006 00385 07985 089742007 00418 08279 093612008 00477 08741 097132009 00512 09278 102842010 00536 09516 129472011 00744 10479 158202012 00892 12346 180462013 00986 18512 192392014 01032 22017 21158

Table 3 Parameter value of evolution model

Variable 120572 120573 1205741198991 00624 11489 133961198992 01475 15267 194641198993 04587 12982 13983

conditions the resulting response curves are different rep-resenting the new steady state Substituting the relevant datavalues into the Jacobian matrix we used MATLAB softwareto simulate the evolution curves of the 3 variables 1198991 1198992 1198993When the initial values of 1198991 1198992 1198993 are different the outputof the system response curve is as shown in Figures 2ndash8

Figure 2 illustrates that at a high setting of innovation inthe Chinese IURCI system collaborative innovation systemvariables show a rapidly increasing trend in a short periodAmong them the simulation curve of innovation capacityappears as a slowly rising trend after the first drop we believethe reason for this phenomenon is that in the real situationof limited resources the original basis for the innovationcapacity of the Chinese IURCI system needs to quickly adjust

0 1

1

08

06

04

02

08060402

0

minus02

minus04

minus06

minus08

minus1

12 14 16 18

Figure 1 Steady state of industry-university-research collaborativeinnovation system (0 input)

0 1 2 30

5

10

15

20

25

n(3)

n(1)

n(2)

05 15 25

Figure 2 Evolution curve of variables when the initial state set as[1 0 0]

Complexity 7

0 1 20

10

20

30

40

50

60

n(3)

n(1) n(2)

05 15

Figure 3 Evolution curve of variables when the initial state set as[0 1 0]

00

5

10

15

20

25

30

35

40

n(3)

n(1) n(2)

108060402 12 14 16 18

Figure 4 Evolution curve of variables when the initial state set as[0 0 1]

0

5

10

15

20

25

30

35

40

n(3)

n(1)

n(2)

0 108060402 12 14 16 18

Figure 5 Evolution curve of variables when the initial state set as[0 1 1]

0

20

40

60

80

100

120

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 6 Evolution curve of variables when the initial state set as[1 1 0]

0

10

20

30

40

50

60

70

80

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 7 Evolution curve of variables when the initial state set as[1 0 1]

0

20

40

60

80

100

120

n(3)n(1) n(2)

0 108060402 12 14 16 18

Figure 8 Evolution curve of variables when the initial state set as[1 1 1]

8 Complexity

to market demand and devote effort towards knowledgetransfer activities resulting in the short-term declining trendof innovation capacity due to output of resources In thelong term due to the fact that the knowledge base of theIURCI system has been stabilized a complementary relation-ship between innovation capacity and RampD configuration isproduced resulting in a faster innovation capability upgradeThis indicates that since the Chinese IURCI system lagsbehind that of developed countries the ability to innovate andthe evolution of a collaborative innovation systemwill help toimprove targeting of the RampD configuration for businessesuniversities and research institutes and can also enhancepromotion of knowledge transfer capability But fundamen-tally the Chinese IURCI system more likely depends on theinnovation capacity for breakthrough innovations and oncomprehensively promoting the industrialization of acquiredknowledge In order to foster and enhance the innovativecapability the Chinese IURCI system not only needs to moreefficiently allocate limited resources by means of knowledgetransfer but also should provide enterprises with the high-value heterogeneity knowledge created by universities andresearch institutions form a sustained and stable directionalflow of knowledge establish a good feedback path formcomplementary advantages and establish risk sharing result-ing in mutual benefit and a win-win coinnovation situationMeanwhile long-term interactions between universities andresearch institutions and enterprises also contribute to themutual fit of collaborative innovation between variables thusknowledge of different attributes can interact to achieve anoptimal combination of knowledge resources achieve knowl-edge innovation and added value and ultimately produce thedesired collaborative innovation effect which will enhancethe overall competitive advantages of university and industry

Figure 3 shows that when the systemic RampD config-uration is set as 1 that is if the Chinese IURCI has amore comprehensive and adequate RampD configuration eachvariable in the same period has increased significantly andthe evolution speed of innovation capacity is closer to thatof the RampD configuration then the evolution speed ofknowledge transfer is relatively slow being increased onlyfrom 0 to about 52 Meanwhile we compared Figures 2 and3 and found that the evolution curve of knowledge transferhas always been lower than that of innovation capacityand RampD configuration this phenomenon reveals the factthat in the evolution process of the Chinese IURCI systemthe contribution of knowledge transfer to the evolution ofinnovative capacity is low which explains why the influencedegree of knowledge transfer on Chinese IURCI systemevolution is significantly low Thus companies can under-stand that in the current external environment and policycontext too much dependence on knowledge transfer fromuniversities and research institutions may not achieve thebest collaborative innovation strategy Therefore under thecurrent circumstances the Chinese IURCI system mainlyconsiders knowledge transfer as an assistance mode for col-laborative innovation and new knowledge access and focuseson enhancing their own innovation capacity and the rea-sonability of the RampD configuration Further as the ChineseIURCI system continues to evolve universities and research

institutions have clearer requirements for the distribution ofbenefits and coupled with a gradual improvement of theintellectual property system this could lead to a longer cycleof knowledge transfer between enterprises universities andresearch institutions At this time collaborative innovationand research will be more inclined to be aided by innovationcapability improvement so that the focus on knowledgetransfer is declining It can be seen that in the Chinese IURCIsystem enhancing the innovation capacity is more importantand knowledge transfer has not played its due role whichshows that the Chinese IURCI system has issues aroundweak knowledge transfer inadequate trust and cooperationmechanisms between enterprises and universities as well asresearch institutions and insufficient knowledge acquisitionby enterprises from universities and research institutions

Figure 4 illustrates in the context of a lack of innovationcapacity and incomplete RampD configuration that if the Chi-nese IURCI system was to have more prominent knowledgetransfer ability then knowledge transfer can to some extentcontribute to the evolution of collaborative innovation FromFigures 3 and 4 it can be seen that whether we set theRampD configuration as 1 and the knowledge transfer as 0or the knowledge transfer as 1 and the RampD configurationas 0 as long as these 2 variables have an initial valuethey will encourage the innovation capacity in the ChineseIURCI system to improve rapidly It means that in thiscontext innovation capacity is highly correlated with RampDconfiguration and knowledge transfer in the Chinese IURCIsystem Enhancing knowledge transfer capability can helpbusinesses more efficiently and in a more targeted wayobtain knowledge that universities and research institutionscreate and promulgate which can enhance the speed ofknowledge commercialization and help IURCI to achievehigh performance thus strengthening theChinese IURunionby maintaining internal synergies between knowledge supplyand knowledge applicationThis has a positive impact on pro-moting collaborative innovation activities and enhancing theconfidence of IUR cooperation At the same time comparedto Figures 2 and 3 it can be seen that although more promi-nent knowledge transfer can speed up innovation capacitythe enhancing effectiveness is still lower than that of RampDconfiguration The reason may be that knowledge transferfrom universities and research institutions is the main wayto promote Chinese IURCI system evolution since the basisof innovation and scientific and technical human capital onthe part of enterprises are poor knowledge input and supportof the collaborative innovation system are totally dependenton knowledge transfer from the universities and researchinstitutions side However due to the nature of knowledgetransfer there is a certain lag so in the absence of appropriateRampD configuration support the promotion of innovationcapability from universities and research institutions is notobvious so the Chinese IURCI requires a longer cooperationperiod

Figure 5 shows that when RampD configuration and knowl-edge transfer are more prominent in the Chinese IURCIsystem the increase in innovation capacity is more obviousthan the results of Figures 2 3 and 4 This demonstratesthat RampD configuration and knowledge transfer coaffect

Complexity 9

the evolution of innovation capacity in the Chinese IURCIsystem that is there are significant positive feedback effectsamong the 3 variables which mutually promote each otherIt means that at such time as the Chinese IURCI systemhas some input resources and knowledge transfer capabilitythese factors will be beneficial to rapidly increasing thecollaborative innovation knowledge level and innovationcapacity thus improving collaborative innovation perfor-mance We believe that the reasons for this may be twofoldon the one hand more effective knowledge transfer betweencorporations and universities as well as research institutionspromotes knowledge industrialization so the innovationcapability of enterprises quickly upgrades in the process ofrepeated practice and constant learning which to a certainextent promotes IURCI performance On the other handrational and targeted RampD configuration allows universitiesand research institutions under the premise of having astable knowledge base to reduce the time delay of knowledgetransfer in the process of collaborative innovation and putpart of their resources into the process of promotion ofknowledge transfer capacity thereby achieving a mutualfit with RampD configuration It should be noted that inthe initial stage of simulation namely the early periodof Chinese IURCI practice favorable Chinese governmentpolicies and attention to IURCI could promote the speedof knowledge industrialization But knowledge transfer fromuniversities and research institutions is subject to adversefactors in the cooperation run-in period and may producea situation of no improvement With the in-depth evolutionof the IURCI system cooperation between businesses anduniversities and research institutions can mature so thatuniversities and research institutions have clearer ideas aboutindustry needs and more accurately provide the necessaryknowledge to enterprises for knowledge industrializationUniversities and research institutions will also benefit fromcollaborative innovation as they better understand industrytechnology needs and market demand allowing them tooptimize their knowledge structure thus enhancing theIURCI performance

Figure 6 illustrates that in the evolution process ofIURCI that relies on innovation capacity and RampD con-figuration continuously increasing RampD configuration willmake evolution of innovation capacity significantly fasterAt the same time compared to Figure 5 it can be foundthat in the evolution of the Chinese IURCI system ifRampD configuration has an initial value to enhance theIURCI performance enterprises must operate on the basis ofcontinuously developing their RampD configuration obtainingas much as possible valuable heterogeneity knowledge fromuniversities and research institutions and universities andresearch institutions also need to enhance their abilities totransfer knowledge and fit the production needs of industryin order to obtain more significant innovation performanceIn addition this case also shows that although there maybe differences in their abilities to innovate for those enter-prises that lack the ability to innovate the input of externalresources and the gain of valuable knowledge by way ofknowledge transfer may change the current disadvantage andhelp them achieve rapid growth

Figures 5 and 6 both show that the innovation capacityand RampD configuration of the Chinese IURCI system canin a short period of time produce significant synergies andpromote each other indicating that Chinarsquos current IURCIdevelopment still requires a lot of resources investment as theprimary means to enhance innovation capability But whenRampD configuration is high the result of innovation capacityevolution [38] is lower than that when knowledge transferis relatively high which gives high values of innovationevolution [42] On the one hand this indicates that inthe current process of IURCI in China the universitiesand research institutions side creates and sends high-valueheterogeneity knowledge which has a fundamental role inpromoting innovation capacity On the other hand to acertain extent this confirms that if Chinese universities andresearch centers could face knowledge-oriented industrialdemand enterprises could accurately identify the marketvalue of accepted knowledge change the discrepancy thatexists between technology supply and technology demandand thereby reduce its dependency on RampD configurationwhile at the same time shortening the innovation cycleand avoiding the issues such as information asymmetryand transaction costs that may arise during collaborativeinnovation [42ndash44]

Figure 7 illustrates that when innovation capacity andknowledge transfer are more prominent and RampD con-figuration is lower variables of the Chinese IURCI couldpromote each other by interaction but the growth rate isrelatively small indicating that relying solely on knowledgetransfer to promote innovation capacity is not effective Onthe one hand this confirmed that the type of knowledgeproduced by Chinese universities and research institutionsmust have applicability to be promoted to companies forindustrialization On the other hand this maybe resultsfrom enterprises having an inadequate knowledge absorptivecapacity Meanwhile from Figures 7 5 and 6 it can befound that if the RampD configuration of the Chinese IURCIsystem is poor then there will be a very significant impacton the ability to enhance knowledge transfer speed Thepossible reasons are that when RampD configuration is notenough the Chinese IURCI system is already in the processof tight resource allocation and it is bound to devote moreresources into promotion activities that create a high-valueheterogeneity innovation capacity and it therefore lacksthe resources to facilitate knowledge transfer within thesystem Correspondingly if resources are relatively abundantenterprises must allow for a bottleneck phenomenon inenhancing innovation capacity or accept greater risks ininnovative activity and may assign the resources originallydevoted to innovative ability to universities and researchinstitutions supporting the knowledge transfer willingnessof universities and research institutions and thus enhancingknowledge transfer for collaborative innovation In this casethe evolution curve of knowledge transfer will be moresignificant

Figure 8 clearly shows that when the innovation capacityof the Chinese IURCI system is more prominent RampDconfiguration is more targeted knowledge transfer capabilityis better and the 3 state variables of collaborative innovation

10 Complexity

show rapid and steady evolution Among them the growthrate of innovation capability is the most significant so thatafter a new stable state is established knowledge transferand RampD configuration both can have a more substantialgrowth effect on innovation capability This means thatin order to achieve the objective of significantly improvedcollaborative innovation performance the Chinese IURCIsystem must adhere to long-term independent innovationactivities and in addition must maintain and increase thebasic RampD configuration constantly improve knowledgetransfer capacity andmore effectively and pertinently send toenterprises the complementary knowledge that universitiesand research institutions own With advances in IURCI thisapproachmay bring 2 advantages for enterprises universitiesand research institutions involved in Chinese IURCI butfrom a business perspective at present the awareness ofChinese enterprises to acquire complementary knowledgein an IUR-coordinated manner is poor and so initiative isvery low If enterprises realize that on the basis of improvingRampD configuration they can obtain complementary researchachievement by making use of knowledge transfer formthen this will greatly increase the willingness of Chineseenterprises to participate in IURCI thereby using new tech-nology developing newproducts and approaching universityRampD personnel which is beneficial to building a trust-basedcooperationmechanism between enterprises and universitiesas well as research institutions [45ndash48] From the perspectiveof Chinese universities and research institutes in the processof knowledge creation the biggest drawback is that newknowledge cannot be applied to production practice that isuniversities and research institutions pay too much attentionto academic value With periodic IURCI universities andresearch institutions can not only receive financial supportfrom the business according to the needs of the businessto carry out research activities but also fully explore newresearch areas which will also play a positive role in promot-ing more academic achievements [49 50]

From observing Figures 3ndash8 we found that in the currentChinese IURCIprocess RampDconfiguration alwaysmaintainsa high degree of positive correlationwith innovation capacityThe reasons for this may be that Chinarsquos current IURCIsystem is not perfect and enterprises and universities as wellas research institutions are always looking for cooperationmodes suitable for both sides Meanwhile the process ofknowledge transfer itself is relatively slow and betweenChinese enterprises and universities there might exist neg-ative factors affecting knowledge transfer speed (such asknowledge flow cost and familiarity running process betweenstaffs) and these factors will result in reduced efficiency ofknowledge transfer therefore growth of knowledge transferis always slow

In short in Chinarsquos IURCI process a consistent relation-ship indeed exists among innovation capability knowledgetransfer and RampD configuration In a higher knowledgetransfer and comprehensive RampD configuration scenarioenterprises can propose more targeted knowledge needsand initially provide financial and material support foruniversities and research institutions involved in innovationUniversities and research institutions from the strategic level

are also concerned about how to establish knowledge RampDto serve enterprises and under the premise of integratedresources establish a balanced benefits distribution activelycarry out scientific and technological achievement transfertrain technology and management personnel required forenterprises exert collaborative innovation effects by comple-mentary advantages and thus bring new benefits for bothsides

5 Conclusion

By combining methods of game theory and complex sciencewe studied the evolution of the Chinese IURCI systemand interactions among the variables We used interactionsamong variables in a collaborative innovation system toldquomaprdquo the perspective of collaborative innovation evolutionand study innovation capacity RampD configuration andknowledge transfer as key elements of collaborative inno-vation and on the basis of establishing a dynamic systemevolution model of collaborative innovation we collecteddata simulated discussed interactions among the variablesin the process of Chinese IURCI and analyzed principles ofcollaborative innovation We mainly obtained the followingconclusions(1) In the evolution of the Chinese IURCI systeminnovation capacity and knowledge transfer are dependenton RampD configuration and innovation capability is highlypositively correlated with RampD configurationThe higher thetarget and investment level of RampD configuration the fasterthe evolution of innovation capacity and knowledge transferIt is noted that currentlyChinese IURCI development is usinga lot of resource investment as its main factor Innovationcapacity has become the key variable to promote collaborativeinnovation system evolution(2) Strong knowledge transfer capability can acceleratethe evolution speed of innovation capacity but overall knowl-edge transfer remains at a relatively stable evolution state anddoes not change with evolution of collaborative innovationThis explains that in the evolution process of Chinarsquos currentIURCI system virtuous cooperation mechanisms based ontrust between enterprises and universities as well as researchinstitutions are lacking Universities and research institutionsalso pay more attention to the academic value of knowledgeleading to insufficient contribution of knowledge transferto knowledge industrialization by enterprises and reducedcollaborative innovation system evolution(3) In a relatively balanced state of innovation capacityRampD configuration and knowledge transfer the faster theevolution speed of Chinese IURCI systems the higher thestability This shows that the Chinese IURCI system mustadhere to long-term independent innovation and on thebasis of maintaining and improving RampD configurationconstantly improve internal knowledge transfer capability

Compared with early research this article has 3 mainfindings first we chose China as a developing country withrapid economic growth used a dynamic perspective ana-lyzed the evolutionmechanism of the Chinese IURCI systemand demonstrated the interactions among variables of thecollaborative innovation system Secondly we learned from

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

4 Complexity

conversion of each index in the evaluation system wherein120572119894 is the resulting parameter after conversion of each index ininnovation capacity evaluation system

120573 = 119894radicprod119901119894=1120573119894 (119894 = 1 2 3 119901) is an adjustmentparameter of the state variable 1198992 corresponding to the RampDconfiguration index wherein 120573119894 is the resulting parameterafter conversion of each index in RampD configuration evalu-ation system

120574 = 119894radicprod119901119894=1120574119894 (119894 = 1 2 3 119901) is an adjustment parame-ter of the state variable 1198993 corresponding to the knowledgetransfer index wherein 120574119894 is the resulting parameter afterconversion of each index in knowledge transfer evaluationsystem

32 Model Analysis

321 Linearization According to Krasovskiirsquos method [38]we used a gradient vector matrix in the operation systemto solve the stable solution of evolution model wherein thesystematic coefficient matrix was solved using the TaylorFormula [39] After performingTaylor omitting higher-orderterms of a second and higher order we then obtained thelinearized coefficient matrix119872 (Jacobian matrix) as follows

119872 = nabla119865 =[[[[[[[[[[[[

1205971198911 (119883)1205971199091 sdot sdot sdot sdot sdot sdot 1205971198911 (119883)

1205971199091198991205971198912 (119883)1205971199091 d sdot sdot sdot

120597119891119899 (119883)1205971199091 sdot sdot sdot sdot sdot sdot 120597119891119899 (119883)

120597119909119899

]]]]]]]]]]]]

(5)

Thus according to Krasovskiirsquos method we rewrite for-mula (4) in the form of a matrix multiplication = 119872 sdot 119883and obtain the following

[[[[[

1119899

]]]]]

=[[[[[[[[

1205971198911 (119883)1205971199091 sdot sdot sdot 1205971198911 (119883)

120597119909119899 d

120597119891119899 (119883)1205971199091 sdot sdot sdot 120597119891119899 (119883)

120597119909119899

]]]]]]]]

[[[[[

1199091119909119899

]]]]] (6)

Formula (6) represents the initial state of the IURCIsystem with 0 input We can use linear system theory toobtain the time domain expression of state variable 119883 thatis the solution of system variable 119883 after linearization of theevolution equation

For evolution equations we solved using the Laplacetransformation [40] and formula (4) was converted to thefollowing

119904119883 (119904) minus 1198830 = 119872 sdot 119883 (119904) (119904119868 minus 119872)119883 (119904) = 1198830 (7)

Laplace transformation of119883 is obtained as follows

119883(119904) = (119904119868 minus119872)minus11198830 (8)

Thus we obtained an expression of the time domain of 119883as follows

119883 = 119871minus1 ((119904119868 minus 119872)minus11198830) (9)

In formula (9) 119904 is a pull complex symbol 1198830 representsthe initial state of the matrix 119883(119904) is the pull transformationof 119883(119905) (119904119868 minus 119872)minus1 represents the inverse matrix of matrix(119904119868 minus 119872) and 119871minus1 is the anti-Laplace transformation

322 Stable Solution According to the linearization resultsformula (4) is solved to obtain the Jacobian matrix as follows

119869 =[[[[[[[[[

1205971198911120597119899112059711989111205971198992

120597119891112059711989931205971198912120597119899112059711989121205971198992

120597119891212059711989931205971198913120597119899112059711989131205971198992

12059711989131205971198993

]]]]]]]]]

= [[[

1205721 + 12057311198993 1205741 1205731119899112057321198992 minus1205722 + 12057321198991 12057420 1205733 1205723

]]]

(10)

to obtain

|119869| =100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1205721 + 12057311198993 1205741 1205731119899112057321198992 minus1205722 + 12057321198991 12057420 1205733 1205723

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816 (11)

Meanwhile according to the result of formula (11) of Jacobianmatrix we set 1198891198991119889119905 = 0 1198891198992119889119905 = 0 1198891198993119889119905 = 0 andformula (12) was obtained as follows

12057211198991 + 120573111989911198993 + 12057411198992 = 0minus12057221198992 + 120573211989911198992 + 12057421198993 = 0

12057231198993 + 12057331198992 = 0(12)

According to this equation we use Routh-Hurwitz stabilitycriterion [41] and the stable solution of complex system tosolve formula (12) so that a partial equilibrium point can beobtained as follows

119860 (0 0 0)119861(12057221205723 + 1205733120574212057231205732 1205721120572212057223 + 12057211205723120573212057421205722312057321205741 minus 1205722120572312057311205733 minus 1205731120573231205742

1205721120572212057231205733 + 12057211205732120573312057421205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741) (13)

Bringing the equilibrium points 119860 and 119861 respectivelyinto the Jacobian matrix and judging equilibrium according

Complexity 5

to the trace and matrix determinant symbols of the Jacobianmatrix we obtained the following

tr (119869) = 1205723 + 12057321198991 + 12057311198991 minus 1205721 minus 1205722 (14)

For equilibrium point 119860tr (119869) = 1205723 minus 1205721 minus 1205722

|119869| =100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1205721 1205741 00 minus1205722 12057420 1205733 1205723

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(15)

For equilibrium point 119861

tr (119869) = 1205723 + 1205721 + 120573312057421205723 + 12057211205722120572312057311205733 + 120572112057311205732120573312057421205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741

|119869| =1003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

212057211205722120572312057311205733 + 12057211205731120573212057331205742 + 12057211205731120573231205742 minus 120572112057223120573212057411205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741 1205741 120572212057231205731 + 1205731120573312057421205723120573212057211205722120572231205732 + 120572112057231205732212057421205722312057321205741 minus 1205722120572312057311205733 minus 1205731120573231205742120573312057421205723 1205742

0 1205733 1205723

1003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(16)

Each point value of point 119860 is 0 representing the initialsteady state thus initial state 11989901 is substituted into expressionsfor 1198991 It can be understood that since the initial value is setto 0 the system has been in a steady state and the valueis always 0 Since there are nonlinear interactions amongthe 3 variables innovation capability RampD configurationand knowledge transfer in IURCI systems these caused anew stable state 119861 in the system A systematic state changefrom 119860 to 119861 represents the evolution of the IURCI systemInteractions among the variables and evolution law willdetermine the outcome of system evolution

4 Simulation

41 Parameter Value To find existing insufficiency in theChinese IURCI system we set specific values of parameterscalculated the coefficient and Jacobian matrix of the modelsimulated it using MATLAB and drew an evolution chartof the 3 variables in the IURCI system In contrast toa previous capability evaluation in the IURCI system wemeasured values of the original control parameters 120572 120573 and120574 of innovation capacity RampD configuration and knowledgetransfer through establishment of an index system Becauseinnovation capacity represents the presence of IURCI systemevolution we considered relevant content such as innovationeffects and innovation gains in the index selection RampDconfiguration represents a resource configuration in theIURCI system so index selection involved funding per-sonnel institutional settings and other aspects Knowledgetransfer represents knowledge flow between enterprises anduniversities as well as research institutions in IURCI systemsso indicator selectionwas based on the state of cooperation asthe core We learned from the studies of [18 26 31] and otherscholars to improve our procedures and ultimately arrived atthe indicators shown in Table 1

In Table 1 each index value was obtained from the ChinaStatistical Yearbook theChina Statistical Yearbook On Scienceand Technology theChina S amp T Paper Statistics and Analysis

the China Industry Economy Statistical Yearbook StatisticsYearbook On Science And Technology Activities of IndustrialEnterprise the Annual Report of Regional Innovation Capa-bility of China the NERI INDEX of Marketization of ChinarsquosProvinces Report and the SME Technology Innovation FundRelated public data from 2005 to 2014 were taken fromthe statistical yearbooks (2015 2016 information was notdisclosed) to give the final parameter values through datastandardization

According to the solvingmethod [37] of120572120573 120574 in formula(4) we bring in the data of 2005ndash2014 fromTable 1 and get theparameter values in Table 2

Table 2 shows the parameter values of the evolutionmodel and the values of 1205721 1205731 1205741 were calculated from thespecific data in the index systems shown in Table 1 In order toobtain the final parameter values of the evolutionary modelwe averaged values for 2005ndash2014 and obtained Table 3

We substituted values for the 9 parameters into theJacobian matrix calculated the determinant and trace of thematrix corresponding to the Jacobian matrix and analyzedsteady-state values of119860 and119861 by judging the determinant andtrace symbols

42 Tests and Analysis Using the Jacobian matrix as thecoefficient matrix of the equation the initial state matrixof the differential equation is [11989901 11989902 11989903] wherein 11989901 11989902 11989903represent the initial values of the 3 variables innovation RampDconfiguration and knowledge transfer respectively Point 119860represents the initial state and the initial state of the systemis 0 such that 1198730 = [0 0 0] therefore substituting this intothe expression we obtained the solution for the expression ofinnovation capacity of 0 that is 11989901 11989902 11989903 = [0 0 0] and theoutput response curve is 0 which indicates the initial state(Figure 1)

In all the figures (Figures 1ndash8) the 119909-axis representsthe input of three elements whereas the 119910-axis representsevolution performance For different values of the initial

6 Complexity

Table 1 Index system of Chinese industry-university-research collaborative innovation system evolution

Variable Indicators

Innovation capacity

Growth in the number of patent applications ()Enterprise market share ()Ratio of new products to total enterprise products ()Success rate of innovation projects ()New product sales margins ()

RampD configuration

Amount of money enterprises invest in product development (ten thousand RMB)Enterprisesrsquo business loan growth ()Growth of RampD funds of universities and research institutes from corporate ()Total number of RampD institutions of enterprises (set)The proportion of investment growth in RampD personnel ()

Knowledge transfer

Productivity of high quality achievements ()Number of high level articles of partner universities (piece)Number of staff entering into enterprises from universities (person)Number of patents enterprises purchased every year (set)Number of universities in industry-university-research cooperation (set)

Table 2 Parameter value of Chinese industry-university-researchcollaborative innovation system evolution from 2005 to 2014

Year 1205721 1205731 12057412005 00256 07732 084222006 00385 07985 089742007 00418 08279 093612008 00477 08741 097132009 00512 09278 102842010 00536 09516 129472011 00744 10479 158202012 00892 12346 180462013 00986 18512 192392014 01032 22017 21158

Table 3 Parameter value of evolution model

Variable 120572 120573 1205741198991 00624 11489 133961198992 01475 15267 194641198993 04587 12982 13983

conditions the resulting response curves are different rep-resenting the new steady state Substituting the relevant datavalues into the Jacobian matrix we used MATLAB softwareto simulate the evolution curves of the 3 variables 1198991 1198992 1198993When the initial values of 1198991 1198992 1198993 are different the outputof the system response curve is as shown in Figures 2ndash8

Figure 2 illustrates that at a high setting of innovation inthe Chinese IURCI system collaborative innovation systemvariables show a rapidly increasing trend in a short periodAmong them the simulation curve of innovation capacityappears as a slowly rising trend after the first drop we believethe reason for this phenomenon is that in the real situationof limited resources the original basis for the innovationcapacity of the Chinese IURCI system needs to quickly adjust

0 1

1

08

06

04

02

08060402

0

minus02

minus04

minus06

minus08

minus1

12 14 16 18

Figure 1 Steady state of industry-university-research collaborativeinnovation system (0 input)

0 1 2 30

5

10

15

20

25

n(3)

n(1)

n(2)

05 15 25

Figure 2 Evolution curve of variables when the initial state set as[1 0 0]

Complexity 7

0 1 20

10

20

30

40

50

60

n(3)

n(1) n(2)

05 15

Figure 3 Evolution curve of variables when the initial state set as[0 1 0]

00

5

10

15

20

25

30

35

40

n(3)

n(1) n(2)

108060402 12 14 16 18

Figure 4 Evolution curve of variables when the initial state set as[0 0 1]

0

5

10

15

20

25

30

35

40

n(3)

n(1)

n(2)

0 108060402 12 14 16 18

Figure 5 Evolution curve of variables when the initial state set as[0 1 1]

0

20

40

60

80

100

120

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 6 Evolution curve of variables when the initial state set as[1 1 0]

0

10

20

30

40

50

60

70

80

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 7 Evolution curve of variables when the initial state set as[1 0 1]

0

20

40

60

80

100

120

n(3)n(1) n(2)

0 108060402 12 14 16 18

Figure 8 Evolution curve of variables when the initial state set as[1 1 1]

8 Complexity

to market demand and devote effort towards knowledgetransfer activities resulting in the short-term declining trendof innovation capacity due to output of resources In thelong term due to the fact that the knowledge base of theIURCI system has been stabilized a complementary relation-ship between innovation capacity and RampD configuration isproduced resulting in a faster innovation capability upgradeThis indicates that since the Chinese IURCI system lagsbehind that of developed countries the ability to innovate andthe evolution of a collaborative innovation systemwill help toimprove targeting of the RampD configuration for businessesuniversities and research institutes and can also enhancepromotion of knowledge transfer capability But fundamen-tally the Chinese IURCI system more likely depends on theinnovation capacity for breakthrough innovations and oncomprehensively promoting the industrialization of acquiredknowledge In order to foster and enhance the innovativecapability the Chinese IURCI system not only needs to moreefficiently allocate limited resources by means of knowledgetransfer but also should provide enterprises with the high-value heterogeneity knowledge created by universities andresearch institutions form a sustained and stable directionalflow of knowledge establish a good feedback path formcomplementary advantages and establish risk sharing result-ing in mutual benefit and a win-win coinnovation situationMeanwhile long-term interactions between universities andresearch institutions and enterprises also contribute to themutual fit of collaborative innovation between variables thusknowledge of different attributes can interact to achieve anoptimal combination of knowledge resources achieve knowl-edge innovation and added value and ultimately produce thedesired collaborative innovation effect which will enhancethe overall competitive advantages of university and industry

Figure 3 shows that when the systemic RampD config-uration is set as 1 that is if the Chinese IURCI has amore comprehensive and adequate RampD configuration eachvariable in the same period has increased significantly andthe evolution speed of innovation capacity is closer to thatof the RampD configuration then the evolution speed ofknowledge transfer is relatively slow being increased onlyfrom 0 to about 52 Meanwhile we compared Figures 2 and3 and found that the evolution curve of knowledge transferhas always been lower than that of innovation capacityand RampD configuration this phenomenon reveals the factthat in the evolution process of the Chinese IURCI systemthe contribution of knowledge transfer to the evolution ofinnovative capacity is low which explains why the influencedegree of knowledge transfer on Chinese IURCI systemevolution is significantly low Thus companies can under-stand that in the current external environment and policycontext too much dependence on knowledge transfer fromuniversities and research institutions may not achieve thebest collaborative innovation strategy Therefore under thecurrent circumstances the Chinese IURCI system mainlyconsiders knowledge transfer as an assistance mode for col-laborative innovation and new knowledge access and focuseson enhancing their own innovation capacity and the rea-sonability of the RampD configuration Further as the ChineseIURCI system continues to evolve universities and research

institutions have clearer requirements for the distribution ofbenefits and coupled with a gradual improvement of theintellectual property system this could lead to a longer cycleof knowledge transfer between enterprises universities andresearch institutions At this time collaborative innovationand research will be more inclined to be aided by innovationcapability improvement so that the focus on knowledgetransfer is declining It can be seen that in the Chinese IURCIsystem enhancing the innovation capacity is more importantand knowledge transfer has not played its due role whichshows that the Chinese IURCI system has issues aroundweak knowledge transfer inadequate trust and cooperationmechanisms between enterprises and universities as well asresearch institutions and insufficient knowledge acquisitionby enterprises from universities and research institutions

Figure 4 illustrates in the context of a lack of innovationcapacity and incomplete RampD configuration that if the Chi-nese IURCI system was to have more prominent knowledgetransfer ability then knowledge transfer can to some extentcontribute to the evolution of collaborative innovation FromFigures 3 and 4 it can be seen that whether we set theRampD configuration as 1 and the knowledge transfer as 0or the knowledge transfer as 1 and the RampD configurationas 0 as long as these 2 variables have an initial valuethey will encourage the innovation capacity in the ChineseIURCI system to improve rapidly It means that in thiscontext innovation capacity is highly correlated with RampDconfiguration and knowledge transfer in the Chinese IURCIsystem Enhancing knowledge transfer capability can helpbusinesses more efficiently and in a more targeted wayobtain knowledge that universities and research institutionscreate and promulgate which can enhance the speed ofknowledge commercialization and help IURCI to achievehigh performance thus strengthening theChinese IURunionby maintaining internal synergies between knowledge supplyand knowledge applicationThis has a positive impact on pro-moting collaborative innovation activities and enhancing theconfidence of IUR cooperation At the same time comparedto Figures 2 and 3 it can be seen that although more promi-nent knowledge transfer can speed up innovation capacitythe enhancing effectiveness is still lower than that of RampDconfiguration The reason may be that knowledge transferfrom universities and research institutions is the main wayto promote Chinese IURCI system evolution since the basisof innovation and scientific and technical human capital onthe part of enterprises are poor knowledge input and supportof the collaborative innovation system are totally dependenton knowledge transfer from the universities and researchinstitutions side However due to the nature of knowledgetransfer there is a certain lag so in the absence of appropriateRampD configuration support the promotion of innovationcapability from universities and research institutions is notobvious so the Chinese IURCI requires a longer cooperationperiod

Figure 5 shows that when RampD configuration and knowl-edge transfer are more prominent in the Chinese IURCIsystem the increase in innovation capacity is more obviousthan the results of Figures 2 3 and 4 This demonstratesthat RampD configuration and knowledge transfer coaffect

Complexity 9

the evolution of innovation capacity in the Chinese IURCIsystem that is there are significant positive feedback effectsamong the 3 variables which mutually promote each otherIt means that at such time as the Chinese IURCI systemhas some input resources and knowledge transfer capabilitythese factors will be beneficial to rapidly increasing thecollaborative innovation knowledge level and innovationcapacity thus improving collaborative innovation perfor-mance We believe that the reasons for this may be twofoldon the one hand more effective knowledge transfer betweencorporations and universities as well as research institutionspromotes knowledge industrialization so the innovationcapability of enterprises quickly upgrades in the process ofrepeated practice and constant learning which to a certainextent promotes IURCI performance On the other handrational and targeted RampD configuration allows universitiesand research institutions under the premise of having astable knowledge base to reduce the time delay of knowledgetransfer in the process of collaborative innovation and putpart of their resources into the process of promotion ofknowledge transfer capacity thereby achieving a mutualfit with RampD configuration It should be noted that inthe initial stage of simulation namely the early periodof Chinese IURCI practice favorable Chinese governmentpolicies and attention to IURCI could promote the speedof knowledge industrialization But knowledge transfer fromuniversities and research institutions is subject to adversefactors in the cooperation run-in period and may producea situation of no improvement With the in-depth evolutionof the IURCI system cooperation between businesses anduniversities and research institutions can mature so thatuniversities and research institutions have clearer ideas aboutindustry needs and more accurately provide the necessaryknowledge to enterprises for knowledge industrializationUniversities and research institutions will also benefit fromcollaborative innovation as they better understand industrytechnology needs and market demand allowing them tooptimize their knowledge structure thus enhancing theIURCI performance

Figure 6 illustrates that in the evolution process ofIURCI that relies on innovation capacity and RampD con-figuration continuously increasing RampD configuration willmake evolution of innovation capacity significantly fasterAt the same time compared to Figure 5 it can be foundthat in the evolution of the Chinese IURCI system ifRampD configuration has an initial value to enhance theIURCI performance enterprises must operate on the basis ofcontinuously developing their RampD configuration obtainingas much as possible valuable heterogeneity knowledge fromuniversities and research institutions and universities andresearch institutions also need to enhance their abilities totransfer knowledge and fit the production needs of industryin order to obtain more significant innovation performanceIn addition this case also shows that although there maybe differences in their abilities to innovate for those enter-prises that lack the ability to innovate the input of externalresources and the gain of valuable knowledge by way ofknowledge transfer may change the current disadvantage andhelp them achieve rapid growth

Figures 5 and 6 both show that the innovation capacityand RampD configuration of the Chinese IURCI system canin a short period of time produce significant synergies andpromote each other indicating that Chinarsquos current IURCIdevelopment still requires a lot of resources investment as theprimary means to enhance innovation capability But whenRampD configuration is high the result of innovation capacityevolution [38] is lower than that when knowledge transferis relatively high which gives high values of innovationevolution [42] On the one hand this indicates that inthe current process of IURCI in China the universitiesand research institutions side creates and sends high-valueheterogeneity knowledge which has a fundamental role inpromoting innovation capacity On the other hand to acertain extent this confirms that if Chinese universities andresearch centers could face knowledge-oriented industrialdemand enterprises could accurately identify the marketvalue of accepted knowledge change the discrepancy thatexists between technology supply and technology demandand thereby reduce its dependency on RampD configurationwhile at the same time shortening the innovation cycleand avoiding the issues such as information asymmetryand transaction costs that may arise during collaborativeinnovation [42ndash44]

Figure 7 illustrates that when innovation capacity andknowledge transfer are more prominent and RampD con-figuration is lower variables of the Chinese IURCI couldpromote each other by interaction but the growth rate isrelatively small indicating that relying solely on knowledgetransfer to promote innovation capacity is not effective Onthe one hand this confirmed that the type of knowledgeproduced by Chinese universities and research institutionsmust have applicability to be promoted to companies forindustrialization On the other hand this maybe resultsfrom enterprises having an inadequate knowledge absorptivecapacity Meanwhile from Figures 7 5 and 6 it can befound that if the RampD configuration of the Chinese IURCIsystem is poor then there will be a very significant impacton the ability to enhance knowledge transfer speed Thepossible reasons are that when RampD configuration is notenough the Chinese IURCI system is already in the processof tight resource allocation and it is bound to devote moreresources into promotion activities that create a high-valueheterogeneity innovation capacity and it therefore lacksthe resources to facilitate knowledge transfer within thesystem Correspondingly if resources are relatively abundantenterprises must allow for a bottleneck phenomenon inenhancing innovation capacity or accept greater risks ininnovative activity and may assign the resources originallydevoted to innovative ability to universities and researchinstitutions supporting the knowledge transfer willingnessof universities and research institutions and thus enhancingknowledge transfer for collaborative innovation In this casethe evolution curve of knowledge transfer will be moresignificant

Figure 8 clearly shows that when the innovation capacityof the Chinese IURCI system is more prominent RampDconfiguration is more targeted knowledge transfer capabilityis better and the 3 state variables of collaborative innovation

10 Complexity

show rapid and steady evolution Among them the growthrate of innovation capability is the most significant so thatafter a new stable state is established knowledge transferand RampD configuration both can have a more substantialgrowth effect on innovation capability This means thatin order to achieve the objective of significantly improvedcollaborative innovation performance the Chinese IURCIsystem must adhere to long-term independent innovationactivities and in addition must maintain and increase thebasic RampD configuration constantly improve knowledgetransfer capacity andmore effectively and pertinently send toenterprises the complementary knowledge that universitiesand research institutions own With advances in IURCI thisapproachmay bring 2 advantages for enterprises universitiesand research institutions involved in Chinese IURCI butfrom a business perspective at present the awareness ofChinese enterprises to acquire complementary knowledgein an IUR-coordinated manner is poor and so initiative isvery low If enterprises realize that on the basis of improvingRampD configuration they can obtain complementary researchachievement by making use of knowledge transfer formthen this will greatly increase the willingness of Chineseenterprises to participate in IURCI thereby using new tech-nology developing newproducts and approaching universityRampD personnel which is beneficial to building a trust-basedcooperationmechanism between enterprises and universitiesas well as research institutions [45ndash48] From the perspectiveof Chinese universities and research institutes in the processof knowledge creation the biggest drawback is that newknowledge cannot be applied to production practice that isuniversities and research institutions pay too much attentionto academic value With periodic IURCI universities andresearch institutions can not only receive financial supportfrom the business according to the needs of the businessto carry out research activities but also fully explore newresearch areas which will also play a positive role in promot-ing more academic achievements [49 50]

From observing Figures 3ndash8 we found that in the currentChinese IURCIprocess RampDconfiguration alwaysmaintainsa high degree of positive correlationwith innovation capacityThe reasons for this may be that Chinarsquos current IURCIsystem is not perfect and enterprises and universities as wellas research institutions are always looking for cooperationmodes suitable for both sides Meanwhile the process ofknowledge transfer itself is relatively slow and betweenChinese enterprises and universities there might exist neg-ative factors affecting knowledge transfer speed (such asknowledge flow cost and familiarity running process betweenstaffs) and these factors will result in reduced efficiency ofknowledge transfer therefore growth of knowledge transferis always slow

In short in Chinarsquos IURCI process a consistent relation-ship indeed exists among innovation capability knowledgetransfer and RampD configuration In a higher knowledgetransfer and comprehensive RampD configuration scenarioenterprises can propose more targeted knowledge needsand initially provide financial and material support foruniversities and research institutions involved in innovationUniversities and research institutions from the strategic level

are also concerned about how to establish knowledge RampDto serve enterprises and under the premise of integratedresources establish a balanced benefits distribution activelycarry out scientific and technological achievement transfertrain technology and management personnel required forenterprises exert collaborative innovation effects by comple-mentary advantages and thus bring new benefits for bothsides

5 Conclusion

By combining methods of game theory and complex sciencewe studied the evolution of the Chinese IURCI systemand interactions among the variables We used interactionsamong variables in a collaborative innovation system toldquomaprdquo the perspective of collaborative innovation evolutionand study innovation capacity RampD configuration andknowledge transfer as key elements of collaborative inno-vation and on the basis of establishing a dynamic systemevolution model of collaborative innovation we collecteddata simulated discussed interactions among the variablesin the process of Chinese IURCI and analyzed principles ofcollaborative innovation We mainly obtained the followingconclusions(1) In the evolution of the Chinese IURCI systeminnovation capacity and knowledge transfer are dependenton RampD configuration and innovation capability is highlypositively correlated with RampD configurationThe higher thetarget and investment level of RampD configuration the fasterthe evolution of innovation capacity and knowledge transferIt is noted that currentlyChinese IURCI development is usinga lot of resource investment as its main factor Innovationcapacity has become the key variable to promote collaborativeinnovation system evolution(2) Strong knowledge transfer capability can acceleratethe evolution speed of innovation capacity but overall knowl-edge transfer remains at a relatively stable evolution state anddoes not change with evolution of collaborative innovationThis explains that in the evolution process of Chinarsquos currentIURCI system virtuous cooperation mechanisms based ontrust between enterprises and universities as well as researchinstitutions are lacking Universities and research institutionsalso pay more attention to the academic value of knowledgeleading to insufficient contribution of knowledge transferto knowledge industrialization by enterprises and reducedcollaborative innovation system evolution(3) In a relatively balanced state of innovation capacityRampD configuration and knowledge transfer the faster theevolution speed of Chinese IURCI systems the higher thestability This shows that the Chinese IURCI system mustadhere to long-term independent innovation and on thebasis of maintaining and improving RampD configurationconstantly improve internal knowledge transfer capability

Compared with early research this article has 3 mainfindings first we chose China as a developing country withrapid economic growth used a dynamic perspective ana-lyzed the evolutionmechanism of the Chinese IURCI systemand demonstrated the interactions among variables of thecollaborative innovation system Secondly we learned from

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Complexity 5

to the trace and matrix determinant symbols of the Jacobianmatrix we obtained the following

tr (119869) = 1205723 + 12057321198991 + 12057311198991 minus 1205721 minus 1205722 (14)

For equilibrium point 119860tr (119869) = 1205723 minus 1205721 minus 1205722

|119869| =100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1205721 1205741 00 minus1205722 12057420 1205733 1205723

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(15)

For equilibrium point 119861

tr (119869) = 1205723 + 1205721 + 120573312057421205723 + 12057211205722120572312057311205733 + 120572112057311205732120573312057421205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741

|119869| =1003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

212057211205722120572312057311205733 + 12057211205731120573212057331205742 + 12057211205731120573231205742 minus 120572112057223120573212057411205722120572312057311205733 + 1205731120573231205742 minus 1205722312057321205741 1205741 120572212057231205731 + 1205731120573312057421205723120573212057211205722120572231205732 + 120572112057231205732212057421205722312057321205741 minus 1205722120572312057311205733 minus 1205731120573231205742120573312057421205723 1205742

0 1205733 1205723

1003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(16)

Each point value of point 119860 is 0 representing the initialsteady state thus initial state 11989901 is substituted into expressionsfor 1198991 It can be understood that since the initial value is setto 0 the system has been in a steady state and the valueis always 0 Since there are nonlinear interactions amongthe 3 variables innovation capability RampD configurationand knowledge transfer in IURCI systems these caused anew stable state 119861 in the system A systematic state changefrom 119860 to 119861 represents the evolution of the IURCI systemInteractions among the variables and evolution law willdetermine the outcome of system evolution

4 Simulation

41 Parameter Value To find existing insufficiency in theChinese IURCI system we set specific values of parameterscalculated the coefficient and Jacobian matrix of the modelsimulated it using MATLAB and drew an evolution chartof the 3 variables in the IURCI system In contrast toa previous capability evaluation in the IURCI system wemeasured values of the original control parameters 120572 120573 and120574 of innovation capacity RampD configuration and knowledgetransfer through establishment of an index system Becauseinnovation capacity represents the presence of IURCI systemevolution we considered relevant content such as innovationeffects and innovation gains in the index selection RampDconfiguration represents a resource configuration in theIURCI system so index selection involved funding per-sonnel institutional settings and other aspects Knowledgetransfer represents knowledge flow between enterprises anduniversities as well as research institutions in IURCI systemsso indicator selectionwas based on the state of cooperation asthe core We learned from the studies of [18 26 31] and otherscholars to improve our procedures and ultimately arrived atthe indicators shown in Table 1

In Table 1 each index value was obtained from the ChinaStatistical Yearbook theChina Statistical Yearbook On Scienceand Technology theChina S amp T Paper Statistics and Analysis

the China Industry Economy Statistical Yearbook StatisticsYearbook On Science And Technology Activities of IndustrialEnterprise the Annual Report of Regional Innovation Capa-bility of China the NERI INDEX of Marketization of ChinarsquosProvinces Report and the SME Technology Innovation FundRelated public data from 2005 to 2014 were taken fromthe statistical yearbooks (2015 2016 information was notdisclosed) to give the final parameter values through datastandardization

According to the solvingmethod [37] of120572120573 120574 in formula(4) we bring in the data of 2005ndash2014 fromTable 1 and get theparameter values in Table 2

Table 2 shows the parameter values of the evolutionmodel and the values of 1205721 1205731 1205741 were calculated from thespecific data in the index systems shown in Table 1 In order toobtain the final parameter values of the evolutionary modelwe averaged values for 2005ndash2014 and obtained Table 3

We substituted values for the 9 parameters into theJacobian matrix calculated the determinant and trace of thematrix corresponding to the Jacobian matrix and analyzedsteady-state values of119860 and119861 by judging the determinant andtrace symbols

42 Tests and Analysis Using the Jacobian matrix as thecoefficient matrix of the equation the initial state matrixof the differential equation is [11989901 11989902 11989903] wherein 11989901 11989902 11989903represent the initial values of the 3 variables innovation RampDconfiguration and knowledge transfer respectively Point 119860represents the initial state and the initial state of the systemis 0 such that 1198730 = [0 0 0] therefore substituting this intothe expression we obtained the solution for the expression ofinnovation capacity of 0 that is 11989901 11989902 11989903 = [0 0 0] and theoutput response curve is 0 which indicates the initial state(Figure 1)

In all the figures (Figures 1ndash8) the 119909-axis representsthe input of three elements whereas the 119910-axis representsevolution performance For different values of the initial

6 Complexity

Table 1 Index system of Chinese industry-university-research collaborative innovation system evolution

Variable Indicators

Innovation capacity

Growth in the number of patent applications ()Enterprise market share ()Ratio of new products to total enterprise products ()Success rate of innovation projects ()New product sales margins ()

RampD configuration

Amount of money enterprises invest in product development (ten thousand RMB)Enterprisesrsquo business loan growth ()Growth of RampD funds of universities and research institutes from corporate ()Total number of RampD institutions of enterprises (set)The proportion of investment growth in RampD personnel ()

Knowledge transfer

Productivity of high quality achievements ()Number of high level articles of partner universities (piece)Number of staff entering into enterprises from universities (person)Number of patents enterprises purchased every year (set)Number of universities in industry-university-research cooperation (set)

Table 2 Parameter value of Chinese industry-university-researchcollaborative innovation system evolution from 2005 to 2014

Year 1205721 1205731 12057412005 00256 07732 084222006 00385 07985 089742007 00418 08279 093612008 00477 08741 097132009 00512 09278 102842010 00536 09516 129472011 00744 10479 158202012 00892 12346 180462013 00986 18512 192392014 01032 22017 21158

Table 3 Parameter value of evolution model

Variable 120572 120573 1205741198991 00624 11489 133961198992 01475 15267 194641198993 04587 12982 13983

conditions the resulting response curves are different rep-resenting the new steady state Substituting the relevant datavalues into the Jacobian matrix we used MATLAB softwareto simulate the evolution curves of the 3 variables 1198991 1198992 1198993When the initial values of 1198991 1198992 1198993 are different the outputof the system response curve is as shown in Figures 2ndash8

Figure 2 illustrates that at a high setting of innovation inthe Chinese IURCI system collaborative innovation systemvariables show a rapidly increasing trend in a short periodAmong them the simulation curve of innovation capacityappears as a slowly rising trend after the first drop we believethe reason for this phenomenon is that in the real situationof limited resources the original basis for the innovationcapacity of the Chinese IURCI system needs to quickly adjust

0 1

1

08

06

04

02

08060402

0

minus02

minus04

minus06

minus08

minus1

12 14 16 18

Figure 1 Steady state of industry-university-research collaborativeinnovation system (0 input)

0 1 2 30

5

10

15

20

25

n(3)

n(1)

n(2)

05 15 25

Figure 2 Evolution curve of variables when the initial state set as[1 0 0]

Complexity 7

0 1 20

10

20

30

40

50

60

n(3)

n(1) n(2)

05 15

Figure 3 Evolution curve of variables when the initial state set as[0 1 0]

00

5

10

15

20

25

30

35

40

n(3)

n(1) n(2)

108060402 12 14 16 18

Figure 4 Evolution curve of variables when the initial state set as[0 0 1]

0

5

10

15

20

25

30

35

40

n(3)

n(1)

n(2)

0 108060402 12 14 16 18

Figure 5 Evolution curve of variables when the initial state set as[0 1 1]

0

20

40

60

80

100

120

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 6 Evolution curve of variables when the initial state set as[1 1 0]

0

10

20

30

40

50

60

70

80

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 7 Evolution curve of variables when the initial state set as[1 0 1]

0

20

40

60

80

100

120

n(3)n(1) n(2)

0 108060402 12 14 16 18

Figure 8 Evolution curve of variables when the initial state set as[1 1 1]

8 Complexity

to market demand and devote effort towards knowledgetransfer activities resulting in the short-term declining trendof innovation capacity due to output of resources In thelong term due to the fact that the knowledge base of theIURCI system has been stabilized a complementary relation-ship between innovation capacity and RampD configuration isproduced resulting in a faster innovation capability upgradeThis indicates that since the Chinese IURCI system lagsbehind that of developed countries the ability to innovate andthe evolution of a collaborative innovation systemwill help toimprove targeting of the RampD configuration for businessesuniversities and research institutes and can also enhancepromotion of knowledge transfer capability But fundamen-tally the Chinese IURCI system more likely depends on theinnovation capacity for breakthrough innovations and oncomprehensively promoting the industrialization of acquiredknowledge In order to foster and enhance the innovativecapability the Chinese IURCI system not only needs to moreefficiently allocate limited resources by means of knowledgetransfer but also should provide enterprises with the high-value heterogeneity knowledge created by universities andresearch institutions form a sustained and stable directionalflow of knowledge establish a good feedback path formcomplementary advantages and establish risk sharing result-ing in mutual benefit and a win-win coinnovation situationMeanwhile long-term interactions between universities andresearch institutions and enterprises also contribute to themutual fit of collaborative innovation between variables thusknowledge of different attributes can interact to achieve anoptimal combination of knowledge resources achieve knowl-edge innovation and added value and ultimately produce thedesired collaborative innovation effect which will enhancethe overall competitive advantages of university and industry

Figure 3 shows that when the systemic RampD config-uration is set as 1 that is if the Chinese IURCI has amore comprehensive and adequate RampD configuration eachvariable in the same period has increased significantly andthe evolution speed of innovation capacity is closer to thatof the RampD configuration then the evolution speed ofknowledge transfer is relatively slow being increased onlyfrom 0 to about 52 Meanwhile we compared Figures 2 and3 and found that the evolution curve of knowledge transferhas always been lower than that of innovation capacityand RampD configuration this phenomenon reveals the factthat in the evolution process of the Chinese IURCI systemthe contribution of knowledge transfer to the evolution ofinnovative capacity is low which explains why the influencedegree of knowledge transfer on Chinese IURCI systemevolution is significantly low Thus companies can under-stand that in the current external environment and policycontext too much dependence on knowledge transfer fromuniversities and research institutions may not achieve thebest collaborative innovation strategy Therefore under thecurrent circumstances the Chinese IURCI system mainlyconsiders knowledge transfer as an assistance mode for col-laborative innovation and new knowledge access and focuseson enhancing their own innovation capacity and the rea-sonability of the RampD configuration Further as the ChineseIURCI system continues to evolve universities and research

institutions have clearer requirements for the distribution ofbenefits and coupled with a gradual improvement of theintellectual property system this could lead to a longer cycleof knowledge transfer between enterprises universities andresearch institutions At this time collaborative innovationand research will be more inclined to be aided by innovationcapability improvement so that the focus on knowledgetransfer is declining It can be seen that in the Chinese IURCIsystem enhancing the innovation capacity is more importantand knowledge transfer has not played its due role whichshows that the Chinese IURCI system has issues aroundweak knowledge transfer inadequate trust and cooperationmechanisms between enterprises and universities as well asresearch institutions and insufficient knowledge acquisitionby enterprises from universities and research institutions

Figure 4 illustrates in the context of a lack of innovationcapacity and incomplete RampD configuration that if the Chi-nese IURCI system was to have more prominent knowledgetransfer ability then knowledge transfer can to some extentcontribute to the evolution of collaborative innovation FromFigures 3 and 4 it can be seen that whether we set theRampD configuration as 1 and the knowledge transfer as 0or the knowledge transfer as 1 and the RampD configurationas 0 as long as these 2 variables have an initial valuethey will encourage the innovation capacity in the ChineseIURCI system to improve rapidly It means that in thiscontext innovation capacity is highly correlated with RampDconfiguration and knowledge transfer in the Chinese IURCIsystem Enhancing knowledge transfer capability can helpbusinesses more efficiently and in a more targeted wayobtain knowledge that universities and research institutionscreate and promulgate which can enhance the speed ofknowledge commercialization and help IURCI to achievehigh performance thus strengthening theChinese IURunionby maintaining internal synergies between knowledge supplyand knowledge applicationThis has a positive impact on pro-moting collaborative innovation activities and enhancing theconfidence of IUR cooperation At the same time comparedto Figures 2 and 3 it can be seen that although more promi-nent knowledge transfer can speed up innovation capacitythe enhancing effectiveness is still lower than that of RampDconfiguration The reason may be that knowledge transferfrom universities and research institutions is the main wayto promote Chinese IURCI system evolution since the basisof innovation and scientific and technical human capital onthe part of enterprises are poor knowledge input and supportof the collaborative innovation system are totally dependenton knowledge transfer from the universities and researchinstitutions side However due to the nature of knowledgetransfer there is a certain lag so in the absence of appropriateRampD configuration support the promotion of innovationcapability from universities and research institutions is notobvious so the Chinese IURCI requires a longer cooperationperiod

Figure 5 shows that when RampD configuration and knowl-edge transfer are more prominent in the Chinese IURCIsystem the increase in innovation capacity is more obviousthan the results of Figures 2 3 and 4 This demonstratesthat RampD configuration and knowledge transfer coaffect

Complexity 9

the evolution of innovation capacity in the Chinese IURCIsystem that is there are significant positive feedback effectsamong the 3 variables which mutually promote each otherIt means that at such time as the Chinese IURCI systemhas some input resources and knowledge transfer capabilitythese factors will be beneficial to rapidly increasing thecollaborative innovation knowledge level and innovationcapacity thus improving collaborative innovation perfor-mance We believe that the reasons for this may be twofoldon the one hand more effective knowledge transfer betweencorporations and universities as well as research institutionspromotes knowledge industrialization so the innovationcapability of enterprises quickly upgrades in the process ofrepeated practice and constant learning which to a certainextent promotes IURCI performance On the other handrational and targeted RampD configuration allows universitiesand research institutions under the premise of having astable knowledge base to reduce the time delay of knowledgetransfer in the process of collaborative innovation and putpart of their resources into the process of promotion ofknowledge transfer capacity thereby achieving a mutualfit with RampD configuration It should be noted that inthe initial stage of simulation namely the early periodof Chinese IURCI practice favorable Chinese governmentpolicies and attention to IURCI could promote the speedof knowledge industrialization But knowledge transfer fromuniversities and research institutions is subject to adversefactors in the cooperation run-in period and may producea situation of no improvement With the in-depth evolutionof the IURCI system cooperation between businesses anduniversities and research institutions can mature so thatuniversities and research institutions have clearer ideas aboutindustry needs and more accurately provide the necessaryknowledge to enterprises for knowledge industrializationUniversities and research institutions will also benefit fromcollaborative innovation as they better understand industrytechnology needs and market demand allowing them tooptimize their knowledge structure thus enhancing theIURCI performance

Figure 6 illustrates that in the evolution process ofIURCI that relies on innovation capacity and RampD con-figuration continuously increasing RampD configuration willmake evolution of innovation capacity significantly fasterAt the same time compared to Figure 5 it can be foundthat in the evolution of the Chinese IURCI system ifRampD configuration has an initial value to enhance theIURCI performance enterprises must operate on the basis ofcontinuously developing their RampD configuration obtainingas much as possible valuable heterogeneity knowledge fromuniversities and research institutions and universities andresearch institutions also need to enhance their abilities totransfer knowledge and fit the production needs of industryin order to obtain more significant innovation performanceIn addition this case also shows that although there maybe differences in their abilities to innovate for those enter-prises that lack the ability to innovate the input of externalresources and the gain of valuable knowledge by way ofknowledge transfer may change the current disadvantage andhelp them achieve rapid growth

Figures 5 and 6 both show that the innovation capacityand RampD configuration of the Chinese IURCI system canin a short period of time produce significant synergies andpromote each other indicating that Chinarsquos current IURCIdevelopment still requires a lot of resources investment as theprimary means to enhance innovation capability But whenRampD configuration is high the result of innovation capacityevolution [38] is lower than that when knowledge transferis relatively high which gives high values of innovationevolution [42] On the one hand this indicates that inthe current process of IURCI in China the universitiesand research institutions side creates and sends high-valueheterogeneity knowledge which has a fundamental role inpromoting innovation capacity On the other hand to acertain extent this confirms that if Chinese universities andresearch centers could face knowledge-oriented industrialdemand enterprises could accurately identify the marketvalue of accepted knowledge change the discrepancy thatexists between technology supply and technology demandand thereby reduce its dependency on RampD configurationwhile at the same time shortening the innovation cycleand avoiding the issues such as information asymmetryand transaction costs that may arise during collaborativeinnovation [42ndash44]

Figure 7 illustrates that when innovation capacity andknowledge transfer are more prominent and RampD con-figuration is lower variables of the Chinese IURCI couldpromote each other by interaction but the growth rate isrelatively small indicating that relying solely on knowledgetransfer to promote innovation capacity is not effective Onthe one hand this confirmed that the type of knowledgeproduced by Chinese universities and research institutionsmust have applicability to be promoted to companies forindustrialization On the other hand this maybe resultsfrom enterprises having an inadequate knowledge absorptivecapacity Meanwhile from Figures 7 5 and 6 it can befound that if the RampD configuration of the Chinese IURCIsystem is poor then there will be a very significant impacton the ability to enhance knowledge transfer speed Thepossible reasons are that when RampD configuration is notenough the Chinese IURCI system is already in the processof tight resource allocation and it is bound to devote moreresources into promotion activities that create a high-valueheterogeneity innovation capacity and it therefore lacksthe resources to facilitate knowledge transfer within thesystem Correspondingly if resources are relatively abundantenterprises must allow for a bottleneck phenomenon inenhancing innovation capacity or accept greater risks ininnovative activity and may assign the resources originallydevoted to innovative ability to universities and researchinstitutions supporting the knowledge transfer willingnessof universities and research institutions and thus enhancingknowledge transfer for collaborative innovation In this casethe evolution curve of knowledge transfer will be moresignificant

Figure 8 clearly shows that when the innovation capacityof the Chinese IURCI system is more prominent RampDconfiguration is more targeted knowledge transfer capabilityis better and the 3 state variables of collaborative innovation

10 Complexity

show rapid and steady evolution Among them the growthrate of innovation capability is the most significant so thatafter a new stable state is established knowledge transferand RampD configuration both can have a more substantialgrowth effect on innovation capability This means thatin order to achieve the objective of significantly improvedcollaborative innovation performance the Chinese IURCIsystem must adhere to long-term independent innovationactivities and in addition must maintain and increase thebasic RampD configuration constantly improve knowledgetransfer capacity andmore effectively and pertinently send toenterprises the complementary knowledge that universitiesand research institutions own With advances in IURCI thisapproachmay bring 2 advantages for enterprises universitiesand research institutions involved in Chinese IURCI butfrom a business perspective at present the awareness ofChinese enterprises to acquire complementary knowledgein an IUR-coordinated manner is poor and so initiative isvery low If enterprises realize that on the basis of improvingRampD configuration they can obtain complementary researchachievement by making use of knowledge transfer formthen this will greatly increase the willingness of Chineseenterprises to participate in IURCI thereby using new tech-nology developing newproducts and approaching universityRampD personnel which is beneficial to building a trust-basedcooperationmechanism between enterprises and universitiesas well as research institutions [45ndash48] From the perspectiveof Chinese universities and research institutes in the processof knowledge creation the biggest drawback is that newknowledge cannot be applied to production practice that isuniversities and research institutions pay too much attentionto academic value With periodic IURCI universities andresearch institutions can not only receive financial supportfrom the business according to the needs of the businessto carry out research activities but also fully explore newresearch areas which will also play a positive role in promot-ing more academic achievements [49 50]

From observing Figures 3ndash8 we found that in the currentChinese IURCIprocess RampDconfiguration alwaysmaintainsa high degree of positive correlationwith innovation capacityThe reasons for this may be that Chinarsquos current IURCIsystem is not perfect and enterprises and universities as wellas research institutions are always looking for cooperationmodes suitable for both sides Meanwhile the process ofknowledge transfer itself is relatively slow and betweenChinese enterprises and universities there might exist neg-ative factors affecting knowledge transfer speed (such asknowledge flow cost and familiarity running process betweenstaffs) and these factors will result in reduced efficiency ofknowledge transfer therefore growth of knowledge transferis always slow

In short in Chinarsquos IURCI process a consistent relation-ship indeed exists among innovation capability knowledgetransfer and RampD configuration In a higher knowledgetransfer and comprehensive RampD configuration scenarioenterprises can propose more targeted knowledge needsand initially provide financial and material support foruniversities and research institutions involved in innovationUniversities and research institutions from the strategic level

are also concerned about how to establish knowledge RampDto serve enterprises and under the premise of integratedresources establish a balanced benefits distribution activelycarry out scientific and technological achievement transfertrain technology and management personnel required forenterprises exert collaborative innovation effects by comple-mentary advantages and thus bring new benefits for bothsides

5 Conclusion

By combining methods of game theory and complex sciencewe studied the evolution of the Chinese IURCI systemand interactions among the variables We used interactionsamong variables in a collaborative innovation system toldquomaprdquo the perspective of collaborative innovation evolutionand study innovation capacity RampD configuration andknowledge transfer as key elements of collaborative inno-vation and on the basis of establishing a dynamic systemevolution model of collaborative innovation we collecteddata simulated discussed interactions among the variablesin the process of Chinese IURCI and analyzed principles ofcollaborative innovation We mainly obtained the followingconclusions(1) In the evolution of the Chinese IURCI systeminnovation capacity and knowledge transfer are dependenton RampD configuration and innovation capability is highlypositively correlated with RampD configurationThe higher thetarget and investment level of RampD configuration the fasterthe evolution of innovation capacity and knowledge transferIt is noted that currentlyChinese IURCI development is usinga lot of resource investment as its main factor Innovationcapacity has become the key variable to promote collaborativeinnovation system evolution(2) Strong knowledge transfer capability can acceleratethe evolution speed of innovation capacity but overall knowl-edge transfer remains at a relatively stable evolution state anddoes not change with evolution of collaborative innovationThis explains that in the evolution process of Chinarsquos currentIURCI system virtuous cooperation mechanisms based ontrust between enterprises and universities as well as researchinstitutions are lacking Universities and research institutionsalso pay more attention to the academic value of knowledgeleading to insufficient contribution of knowledge transferto knowledge industrialization by enterprises and reducedcollaborative innovation system evolution(3) In a relatively balanced state of innovation capacityRampD configuration and knowledge transfer the faster theevolution speed of Chinese IURCI systems the higher thestability This shows that the Chinese IURCI system mustadhere to long-term independent innovation and on thebasis of maintaining and improving RampD configurationconstantly improve internal knowledge transfer capability

Compared with early research this article has 3 mainfindings first we chose China as a developing country withrapid economic growth used a dynamic perspective ana-lyzed the evolutionmechanism of the Chinese IURCI systemand demonstrated the interactions among variables of thecollaborative innovation system Secondly we learned from

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

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Mathematical Problems in Engineering

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Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

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Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

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Complex AnalysisJournal of

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OptimizationJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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6 Complexity

Table 1 Index system of Chinese industry-university-research collaborative innovation system evolution

Variable Indicators

Innovation capacity

Growth in the number of patent applications ()Enterprise market share ()Ratio of new products to total enterprise products ()Success rate of innovation projects ()New product sales margins ()

RampD configuration

Amount of money enterprises invest in product development (ten thousand RMB)Enterprisesrsquo business loan growth ()Growth of RampD funds of universities and research institutes from corporate ()Total number of RampD institutions of enterprises (set)The proportion of investment growth in RampD personnel ()

Knowledge transfer

Productivity of high quality achievements ()Number of high level articles of partner universities (piece)Number of staff entering into enterprises from universities (person)Number of patents enterprises purchased every year (set)Number of universities in industry-university-research cooperation (set)

Table 2 Parameter value of Chinese industry-university-researchcollaborative innovation system evolution from 2005 to 2014

Year 1205721 1205731 12057412005 00256 07732 084222006 00385 07985 089742007 00418 08279 093612008 00477 08741 097132009 00512 09278 102842010 00536 09516 129472011 00744 10479 158202012 00892 12346 180462013 00986 18512 192392014 01032 22017 21158

Table 3 Parameter value of evolution model

Variable 120572 120573 1205741198991 00624 11489 133961198992 01475 15267 194641198993 04587 12982 13983

conditions the resulting response curves are different rep-resenting the new steady state Substituting the relevant datavalues into the Jacobian matrix we used MATLAB softwareto simulate the evolution curves of the 3 variables 1198991 1198992 1198993When the initial values of 1198991 1198992 1198993 are different the outputof the system response curve is as shown in Figures 2ndash8

Figure 2 illustrates that at a high setting of innovation inthe Chinese IURCI system collaborative innovation systemvariables show a rapidly increasing trend in a short periodAmong them the simulation curve of innovation capacityappears as a slowly rising trend after the first drop we believethe reason for this phenomenon is that in the real situationof limited resources the original basis for the innovationcapacity of the Chinese IURCI system needs to quickly adjust

0 1

1

08

06

04

02

08060402

0

minus02

minus04

minus06

minus08

minus1

12 14 16 18

Figure 1 Steady state of industry-university-research collaborativeinnovation system (0 input)

0 1 2 30

5

10

15

20

25

n(3)

n(1)

n(2)

05 15 25

Figure 2 Evolution curve of variables when the initial state set as[1 0 0]

Complexity 7

0 1 20

10

20

30

40

50

60

n(3)

n(1) n(2)

05 15

Figure 3 Evolution curve of variables when the initial state set as[0 1 0]

00

5

10

15

20

25

30

35

40

n(3)

n(1) n(2)

108060402 12 14 16 18

Figure 4 Evolution curve of variables when the initial state set as[0 0 1]

0

5

10

15

20

25

30

35

40

n(3)

n(1)

n(2)

0 108060402 12 14 16 18

Figure 5 Evolution curve of variables when the initial state set as[0 1 1]

0

20

40

60

80

100

120

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 6 Evolution curve of variables when the initial state set as[1 1 0]

0

10

20

30

40

50

60

70

80

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 7 Evolution curve of variables when the initial state set as[1 0 1]

0

20

40

60

80

100

120

n(3)n(1) n(2)

0 108060402 12 14 16 18

Figure 8 Evolution curve of variables when the initial state set as[1 1 1]

8 Complexity

to market demand and devote effort towards knowledgetransfer activities resulting in the short-term declining trendof innovation capacity due to output of resources In thelong term due to the fact that the knowledge base of theIURCI system has been stabilized a complementary relation-ship between innovation capacity and RampD configuration isproduced resulting in a faster innovation capability upgradeThis indicates that since the Chinese IURCI system lagsbehind that of developed countries the ability to innovate andthe evolution of a collaborative innovation systemwill help toimprove targeting of the RampD configuration for businessesuniversities and research institutes and can also enhancepromotion of knowledge transfer capability But fundamen-tally the Chinese IURCI system more likely depends on theinnovation capacity for breakthrough innovations and oncomprehensively promoting the industrialization of acquiredknowledge In order to foster and enhance the innovativecapability the Chinese IURCI system not only needs to moreefficiently allocate limited resources by means of knowledgetransfer but also should provide enterprises with the high-value heterogeneity knowledge created by universities andresearch institutions form a sustained and stable directionalflow of knowledge establish a good feedback path formcomplementary advantages and establish risk sharing result-ing in mutual benefit and a win-win coinnovation situationMeanwhile long-term interactions between universities andresearch institutions and enterprises also contribute to themutual fit of collaborative innovation between variables thusknowledge of different attributes can interact to achieve anoptimal combination of knowledge resources achieve knowl-edge innovation and added value and ultimately produce thedesired collaborative innovation effect which will enhancethe overall competitive advantages of university and industry

Figure 3 shows that when the systemic RampD config-uration is set as 1 that is if the Chinese IURCI has amore comprehensive and adequate RampD configuration eachvariable in the same period has increased significantly andthe evolution speed of innovation capacity is closer to thatof the RampD configuration then the evolution speed ofknowledge transfer is relatively slow being increased onlyfrom 0 to about 52 Meanwhile we compared Figures 2 and3 and found that the evolution curve of knowledge transferhas always been lower than that of innovation capacityand RampD configuration this phenomenon reveals the factthat in the evolution process of the Chinese IURCI systemthe contribution of knowledge transfer to the evolution ofinnovative capacity is low which explains why the influencedegree of knowledge transfer on Chinese IURCI systemevolution is significantly low Thus companies can under-stand that in the current external environment and policycontext too much dependence on knowledge transfer fromuniversities and research institutions may not achieve thebest collaborative innovation strategy Therefore under thecurrent circumstances the Chinese IURCI system mainlyconsiders knowledge transfer as an assistance mode for col-laborative innovation and new knowledge access and focuseson enhancing their own innovation capacity and the rea-sonability of the RampD configuration Further as the ChineseIURCI system continues to evolve universities and research

institutions have clearer requirements for the distribution ofbenefits and coupled with a gradual improvement of theintellectual property system this could lead to a longer cycleof knowledge transfer between enterprises universities andresearch institutions At this time collaborative innovationand research will be more inclined to be aided by innovationcapability improvement so that the focus on knowledgetransfer is declining It can be seen that in the Chinese IURCIsystem enhancing the innovation capacity is more importantand knowledge transfer has not played its due role whichshows that the Chinese IURCI system has issues aroundweak knowledge transfer inadequate trust and cooperationmechanisms between enterprises and universities as well asresearch institutions and insufficient knowledge acquisitionby enterprises from universities and research institutions

Figure 4 illustrates in the context of a lack of innovationcapacity and incomplete RampD configuration that if the Chi-nese IURCI system was to have more prominent knowledgetransfer ability then knowledge transfer can to some extentcontribute to the evolution of collaborative innovation FromFigures 3 and 4 it can be seen that whether we set theRampD configuration as 1 and the knowledge transfer as 0or the knowledge transfer as 1 and the RampD configurationas 0 as long as these 2 variables have an initial valuethey will encourage the innovation capacity in the ChineseIURCI system to improve rapidly It means that in thiscontext innovation capacity is highly correlated with RampDconfiguration and knowledge transfer in the Chinese IURCIsystem Enhancing knowledge transfer capability can helpbusinesses more efficiently and in a more targeted wayobtain knowledge that universities and research institutionscreate and promulgate which can enhance the speed ofknowledge commercialization and help IURCI to achievehigh performance thus strengthening theChinese IURunionby maintaining internal synergies between knowledge supplyand knowledge applicationThis has a positive impact on pro-moting collaborative innovation activities and enhancing theconfidence of IUR cooperation At the same time comparedto Figures 2 and 3 it can be seen that although more promi-nent knowledge transfer can speed up innovation capacitythe enhancing effectiveness is still lower than that of RampDconfiguration The reason may be that knowledge transferfrom universities and research institutions is the main wayto promote Chinese IURCI system evolution since the basisof innovation and scientific and technical human capital onthe part of enterprises are poor knowledge input and supportof the collaborative innovation system are totally dependenton knowledge transfer from the universities and researchinstitutions side However due to the nature of knowledgetransfer there is a certain lag so in the absence of appropriateRampD configuration support the promotion of innovationcapability from universities and research institutions is notobvious so the Chinese IURCI requires a longer cooperationperiod

Figure 5 shows that when RampD configuration and knowl-edge transfer are more prominent in the Chinese IURCIsystem the increase in innovation capacity is more obviousthan the results of Figures 2 3 and 4 This demonstratesthat RampD configuration and knowledge transfer coaffect

Complexity 9

the evolution of innovation capacity in the Chinese IURCIsystem that is there are significant positive feedback effectsamong the 3 variables which mutually promote each otherIt means that at such time as the Chinese IURCI systemhas some input resources and knowledge transfer capabilitythese factors will be beneficial to rapidly increasing thecollaborative innovation knowledge level and innovationcapacity thus improving collaborative innovation perfor-mance We believe that the reasons for this may be twofoldon the one hand more effective knowledge transfer betweencorporations and universities as well as research institutionspromotes knowledge industrialization so the innovationcapability of enterprises quickly upgrades in the process ofrepeated practice and constant learning which to a certainextent promotes IURCI performance On the other handrational and targeted RampD configuration allows universitiesand research institutions under the premise of having astable knowledge base to reduce the time delay of knowledgetransfer in the process of collaborative innovation and putpart of their resources into the process of promotion ofknowledge transfer capacity thereby achieving a mutualfit with RampD configuration It should be noted that inthe initial stage of simulation namely the early periodof Chinese IURCI practice favorable Chinese governmentpolicies and attention to IURCI could promote the speedof knowledge industrialization But knowledge transfer fromuniversities and research institutions is subject to adversefactors in the cooperation run-in period and may producea situation of no improvement With the in-depth evolutionof the IURCI system cooperation between businesses anduniversities and research institutions can mature so thatuniversities and research institutions have clearer ideas aboutindustry needs and more accurately provide the necessaryknowledge to enterprises for knowledge industrializationUniversities and research institutions will also benefit fromcollaborative innovation as they better understand industrytechnology needs and market demand allowing them tooptimize their knowledge structure thus enhancing theIURCI performance

Figure 6 illustrates that in the evolution process ofIURCI that relies on innovation capacity and RampD con-figuration continuously increasing RampD configuration willmake evolution of innovation capacity significantly fasterAt the same time compared to Figure 5 it can be foundthat in the evolution of the Chinese IURCI system ifRampD configuration has an initial value to enhance theIURCI performance enterprises must operate on the basis ofcontinuously developing their RampD configuration obtainingas much as possible valuable heterogeneity knowledge fromuniversities and research institutions and universities andresearch institutions also need to enhance their abilities totransfer knowledge and fit the production needs of industryin order to obtain more significant innovation performanceIn addition this case also shows that although there maybe differences in their abilities to innovate for those enter-prises that lack the ability to innovate the input of externalresources and the gain of valuable knowledge by way ofknowledge transfer may change the current disadvantage andhelp them achieve rapid growth

Figures 5 and 6 both show that the innovation capacityand RampD configuration of the Chinese IURCI system canin a short period of time produce significant synergies andpromote each other indicating that Chinarsquos current IURCIdevelopment still requires a lot of resources investment as theprimary means to enhance innovation capability But whenRampD configuration is high the result of innovation capacityevolution [38] is lower than that when knowledge transferis relatively high which gives high values of innovationevolution [42] On the one hand this indicates that inthe current process of IURCI in China the universitiesand research institutions side creates and sends high-valueheterogeneity knowledge which has a fundamental role inpromoting innovation capacity On the other hand to acertain extent this confirms that if Chinese universities andresearch centers could face knowledge-oriented industrialdemand enterprises could accurately identify the marketvalue of accepted knowledge change the discrepancy thatexists between technology supply and technology demandand thereby reduce its dependency on RampD configurationwhile at the same time shortening the innovation cycleand avoiding the issues such as information asymmetryand transaction costs that may arise during collaborativeinnovation [42ndash44]

Figure 7 illustrates that when innovation capacity andknowledge transfer are more prominent and RampD con-figuration is lower variables of the Chinese IURCI couldpromote each other by interaction but the growth rate isrelatively small indicating that relying solely on knowledgetransfer to promote innovation capacity is not effective Onthe one hand this confirmed that the type of knowledgeproduced by Chinese universities and research institutionsmust have applicability to be promoted to companies forindustrialization On the other hand this maybe resultsfrom enterprises having an inadequate knowledge absorptivecapacity Meanwhile from Figures 7 5 and 6 it can befound that if the RampD configuration of the Chinese IURCIsystem is poor then there will be a very significant impacton the ability to enhance knowledge transfer speed Thepossible reasons are that when RampD configuration is notenough the Chinese IURCI system is already in the processof tight resource allocation and it is bound to devote moreresources into promotion activities that create a high-valueheterogeneity innovation capacity and it therefore lacksthe resources to facilitate knowledge transfer within thesystem Correspondingly if resources are relatively abundantenterprises must allow for a bottleneck phenomenon inenhancing innovation capacity or accept greater risks ininnovative activity and may assign the resources originallydevoted to innovative ability to universities and researchinstitutions supporting the knowledge transfer willingnessof universities and research institutions and thus enhancingknowledge transfer for collaborative innovation In this casethe evolution curve of knowledge transfer will be moresignificant

Figure 8 clearly shows that when the innovation capacityof the Chinese IURCI system is more prominent RampDconfiguration is more targeted knowledge transfer capabilityis better and the 3 state variables of collaborative innovation

10 Complexity

show rapid and steady evolution Among them the growthrate of innovation capability is the most significant so thatafter a new stable state is established knowledge transferand RampD configuration both can have a more substantialgrowth effect on innovation capability This means thatin order to achieve the objective of significantly improvedcollaborative innovation performance the Chinese IURCIsystem must adhere to long-term independent innovationactivities and in addition must maintain and increase thebasic RampD configuration constantly improve knowledgetransfer capacity andmore effectively and pertinently send toenterprises the complementary knowledge that universitiesand research institutions own With advances in IURCI thisapproachmay bring 2 advantages for enterprises universitiesand research institutions involved in Chinese IURCI butfrom a business perspective at present the awareness ofChinese enterprises to acquire complementary knowledgein an IUR-coordinated manner is poor and so initiative isvery low If enterprises realize that on the basis of improvingRampD configuration they can obtain complementary researchachievement by making use of knowledge transfer formthen this will greatly increase the willingness of Chineseenterprises to participate in IURCI thereby using new tech-nology developing newproducts and approaching universityRampD personnel which is beneficial to building a trust-basedcooperationmechanism between enterprises and universitiesas well as research institutions [45ndash48] From the perspectiveof Chinese universities and research institutes in the processof knowledge creation the biggest drawback is that newknowledge cannot be applied to production practice that isuniversities and research institutions pay too much attentionto academic value With periodic IURCI universities andresearch institutions can not only receive financial supportfrom the business according to the needs of the businessto carry out research activities but also fully explore newresearch areas which will also play a positive role in promot-ing more academic achievements [49 50]

From observing Figures 3ndash8 we found that in the currentChinese IURCIprocess RampDconfiguration alwaysmaintainsa high degree of positive correlationwith innovation capacityThe reasons for this may be that Chinarsquos current IURCIsystem is not perfect and enterprises and universities as wellas research institutions are always looking for cooperationmodes suitable for both sides Meanwhile the process ofknowledge transfer itself is relatively slow and betweenChinese enterprises and universities there might exist neg-ative factors affecting knowledge transfer speed (such asknowledge flow cost and familiarity running process betweenstaffs) and these factors will result in reduced efficiency ofknowledge transfer therefore growth of knowledge transferis always slow

In short in Chinarsquos IURCI process a consistent relation-ship indeed exists among innovation capability knowledgetransfer and RampD configuration In a higher knowledgetransfer and comprehensive RampD configuration scenarioenterprises can propose more targeted knowledge needsand initially provide financial and material support foruniversities and research institutions involved in innovationUniversities and research institutions from the strategic level

are also concerned about how to establish knowledge RampDto serve enterprises and under the premise of integratedresources establish a balanced benefits distribution activelycarry out scientific and technological achievement transfertrain technology and management personnel required forenterprises exert collaborative innovation effects by comple-mentary advantages and thus bring new benefits for bothsides

5 Conclusion

By combining methods of game theory and complex sciencewe studied the evolution of the Chinese IURCI systemand interactions among the variables We used interactionsamong variables in a collaborative innovation system toldquomaprdquo the perspective of collaborative innovation evolutionand study innovation capacity RampD configuration andknowledge transfer as key elements of collaborative inno-vation and on the basis of establishing a dynamic systemevolution model of collaborative innovation we collecteddata simulated discussed interactions among the variablesin the process of Chinese IURCI and analyzed principles ofcollaborative innovation We mainly obtained the followingconclusions(1) In the evolution of the Chinese IURCI systeminnovation capacity and knowledge transfer are dependenton RampD configuration and innovation capability is highlypositively correlated with RampD configurationThe higher thetarget and investment level of RampD configuration the fasterthe evolution of innovation capacity and knowledge transferIt is noted that currentlyChinese IURCI development is usinga lot of resource investment as its main factor Innovationcapacity has become the key variable to promote collaborativeinnovation system evolution(2) Strong knowledge transfer capability can acceleratethe evolution speed of innovation capacity but overall knowl-edge transfer remains at a relatively stable evolution state anddoes not change with evolution of collaborative innovationThis explains that in the evolution process of Chinarsquos currentIURCI system virtuous cooperation mechanisms based ontrust between enterprises and universities as well as researchinstitutions are lacking Universities and research institutionsalso pay more attention to the academic value of knowledgeleading to insufficient contribution of knowledge transferto knowledge industrialization by enterprises and reducedcollaborative innovation system evolution(3) In a relatively balanced state of innovation capacityRampD configuration and knowledge transfer the faster theevolution speed of Chinese IURCI systems the higher thestability This shows that the Chinese IURCI system mustadhere to long-term independent innovation and on thebasis of maintaining and improving RampD configurationconstantly improve internal knowledge transfer capability

Compared with early research this article has 3 mainfindings first we chose China as a developing country withrapid economic growth used a dynamic perspective ana-lyzed the evolutionmechanism of the Chinese IURCI systemand demonstrated the interactions among variables of thecollaborative innovation system Secondly we learned from

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Complexity 7

0 1 20

10

20

30

40

50

60

n(3)

n(1) n(2)

05 15

Figure 3 Evolution curve of variables when the initial state set as[0 1 0]

00

5

10

15

20

25

30

35

40

n(3)

n(1) n(2)

108060402 12 14 16 18

Figure 4 Evolution curve of variables when the initial state set as[0 0 1]

0

5

10

15

20

25

30

35

40

n(3)

n(1)

n(2)

0 108060402 12 14 16 18

Figure 5 Evolution curve of variables when the initial state set as[0 1 1]

0

20

40

60

80

100

120

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 6 Evolution curve of variables when the initial state set as[1 1 0]

0

10

20

30

40

50

60

70

80

n(3)

n(1) n(2)

0 108060402 12 14 16 18

Figure 7 Evolution curve of variables when the initial state set as[1 0 1]

0

20

40

60

80

100

120

n(3)n(1) n(2)

0 108060402 12 14 16 18

Figure 8 Evolution curve of variables when the initial state set as[1 1 1]

8 Complexity

to market demand and devote effort towards knowledgetransfer activities resulting in the short-term declining trendof innovation capacity due to output of resources In thelong term due to the fact that the knowledge base of theIURCI system has been stabilized a complementary relation-ship between innovation capacity and RampD configuration isproduced resulting in a faster innovation capability upgradeThis indicates that since the Chinese IURCI system lagsbehind that of developed countries the ability to innovate andthe evolution of a collaborative innovation systemwill help toimprove targeting of the RampD configuration for businessesuniversities and research institutes and can also enhancepromotion of knowledge transfer capability But fundamen-tally the Chinese IURCI system more likely depends on theinnovation capacity for breakthrough innovations and oncomprehensively promoting the industrialization of acquiredknowledge In order to foster and enhance the innovativecapability the Chinese IURCI system not only needs to moreefficiently allocate limited resources by means of knowledgetransfer but also should provide enterprises with the high-value heterogeneity knowledge created by universities andresearch institutions form a sustained and stable directionalflow of knowledge establish a good feedback path formcomplementary advantages and establish risk sharing result-ing in mutual benefit and a win-win coinnovation situationMeanwhile long-term interactions between universities andresearch institutions and enterprises also contribute to themutual fit of collaborative innovation between variables thusknowledge of different attributes can interact to achieve anoptimal combination of knowledge resources achieve knowl-edge innovation and added value and ultimately produce thedesired collaborative innovation effect which will enhancethe overall competitive advantages of university and industry

Figure 3 shows that when the systemic RampD config-uration is set as 1 that is if the Chinese IURCI has amore comprehensive and adequate RampD configuration eachvariable in the same period has increased significantly andthe evolution speed of innovation capacity is closer to thatof the RampD configuration then the evolution speed ofknowledge transfer is relatively slow being increased onlyfrom 0 to about 52 Meanwhile we compared Figures 2 and3 and found that the evolution curve of knowledge transferhas always been lower than that of innovation capacityand RampD configuration this phenomenon reveals the factthat in the evolution process of the Chinese IURCI systemthe contribution of knowledge transfer to the evolution ofinnovative capacity is low which explains why the influencedegree of knowledge transfer on Chinese IURCI systemevolution is significantly low Thus companies can under-stand that in the current external environment and policycontext too much dependence on knowledge transfer fromuniversities and research institutions may not achieve thebest collaborative innovation strategy Therefore under thecurrent circumstances the Chinese IURCI system mainlyconsiders knowledge transfer as an assistance mode for col-laborative innovation and new knowledge access and focuseson enhancing their own innovation capacity and the rea-sonability of the RampD configuration Further as the ChineseIURCI system continues to evolve universities and research

institutions have clearer requirements for the distribution ofbenefits and coupled with a gradual improvement of theintellectual property system this could lead to a longer cycleof knowledge transfer between enterprises universities andresearch institutions At this time collaborative innovationand research will be more inclined to be aided by innovationcapability improvement so that the focus on knowledgetransfer is declining It can be seen that in the Chinese IURCIsystem enhancing the innovation capacity is more importantand knowledge transfer has not played its due role whichshows that the Chinese IURCI system has issues aroundweak knowledge transfer inadequate trust and cooperationmechanisms between enterprises and universities as well asresearch institutions and insufficient knowledge acquisitionby enterprises from universities and research institutions

Figure 4 illustrates in the context of a lack of innovationcapacity and incomplete RampD configuration that if the Chi-nese IURCI system was to have more prominent knowledgetransfer ability then knowledge transfer can to some extentcontribute to the evolution of collaborative innovation FromFigures 3 and 4 it can be seen that whether we set theRampD configuration as 1 and the knowledge transfer as 0or the knowledge transfer as 1 and the RampD configurationas 0 as long as these 2 variables have an initial valuethey will encourage the innovation capacity in the ChineseIURCI system to improve rapidly It means that in thiscontext innovation capacity is highly correlated with RampDconfiguration and knowledge transfer in the Chinese IURCIsystem Enhancing knowledge transfer capability can helpbusinesses more efficiently and in a more targeted wayobtain knowledge that universities and research institutionscreate and promulgate which can enhance the speed ofknowledge commercialization and help IURCI to achievehigh performance thus strengthening theChinese IURunionby maintaining internal synergies between knowledge supplyand knowledge applicationThis has a positive impact on pro-moting collaborative innovation activities and enhancing theconfidence of IUR cooperation At the same time comparedto Figures 2 and 3 it can be seen that although more promi-nent knowledge transfer can speed up innovation capacitythe enhancing effectiveness is still lower than that of RampDconfiguration The reason may be that knowledge transferfrom universities and research institutions is the main wayto promote Chinese IURCI system evolution since the basisof innovation and scientific and technical human capital onthe part of enterprises are poor knowledge input and supportof the collaborative innovation system are totally dependenton knowledge transfer from the universities and researchinstitutions side However due to the nature of knowledgetransfer there is a certain lag so in the absence of appropriateRampD configuration support the promotion of innovationcapability from universities and research institutions is notobvious so the Chinese IURCI requires a longer cooperationperiod

Figure 5 shows that when RampD configuration and knowl-edge transfer are more prominent in the Chinese IURCIsystem the increase in innovation capacity is more obviousthan the results of Figures 2 3 and 4 This demonstratesthat RampD configuration and knowledge transfer coaffect

Complexity 9

the evolution of innovation capacity in the Chinese IURCIsystem that is there are significant positive feedback effectsamong the 3 variables which mutually promote each otherIt means that at such time as the Chinese IURCI systemhas some input resources and knowledge transfer capabilitythese factors will be beneficial to rapidly increasing thecollaborative innovation knowledge level and innovationcapacity thus improving collaborative innovation perfor-mance We believe that the reasons for this may be twofoldon the one hand more effective knowledge transfer betweencorporations and universities as well as research institutionspromotes knowledge industrialization so the innovationcapability of enterprises quickly upgrades in the process ofrepeated practice and constant learning which to a certainextent promotes IURCI performance On the other handrational and targeted RampD configuration allows universitiesand research institutions under the premise of having astable knowledge base to reduce the time delay of knowledgetransfer in the process of collaborative innovation and putpart of their resources into the process of promotion ofknowledge transfer capacity thereby achieving a mutualfit with RampD configuration It should be noted that inthe initial stage of simulation namely the early periodof Chinese IURCI practice favorable Chinese governmentpolicies and attention to IURCI could promote the speedof knowledge industrialization But knowledge transfer fromuniversities and research institutions is subject to adversefactors in the cooperation run-in period and may producea situation of no improvement With the in-depth evolutionof the IURCI system cooperation between businesses anduniversities and research institutions can mature so thatuniversities and research institutions have clearer ideas aboutindustry needs and more accurately provide the necessaryknowledge to enterprises for knowledge industrializationUniversities and research institutions will also benefit fromcollaborative innovation as they better understand industrytechnology needs and market demand allowing them tooptimize their knowledge structure thus enhancing theIURCI performance

Figure 6 illustrates that in the evolution process ofIURCI that relies on innovation capacity and RampD con-figuration continuously increasing RampD configuration willmake evolution of innovation capacity significantly fasterAt the same time compared to Figure 5 it can be foundthat in the evolution of the Chinese IURCI system ifRampD configuration has an initial value to enhance theIURCI performance enterprises must operate on the basis ofcontinuously developing their RampD configuration obtainingas much as possible valuable heterogeneity knowledge fromuniversities and research institutions and universities andresearch institutions also need to enhance their abilities totransfer knowledge and fit the production needs of industryin order to obtain more significant innovation performanceIn addition this case also shows that although there maybe differences in their abilities to innovate for those enter-prises that lack the ability to innovate the input of externalresources and the gain of valuable knowledge by way ofknowledge transfer may change the current disadvantage andhelp them achieve rapid growth

Figures 5 and 6 both show that the innovation capacityand RampD configuration of the Chinese IURCI system canin a short period of time produce significant synergies andpromote each other indicating that Chinarsquos current IURCIdevelopment still requires a lot of resources investment as theprimary means to enhance innovation capability But whenRampD configuration is high the result of innovation capacityevolution [38] is lower than that when knowledge transferis relatively high which gives high values of innovationevolution [42] On the one hand this indicates that inthe current process of IURCI in China the universitiesand research institutions side creates and sends high-valueheterogeneity knowledge which has a fundamental role inpromoting innovation capacity On the other hand to acertain extent this confirms that if Chinese universities andresearch centers could face knowledge-oriented industrialdemand enterprises could accurately identify the marketvalue of accepted knowledge change the discrepancy thatexists between technology supply and technology demandand thereby reduce its dependency on RampD configurationwhile at the same time shortening the innovation cycleand avoiding the issues such as information asymmetryand transaction costs that may arise during collaborativeinnovation [42ndash44]

Figure 7 illustrates that when innovation capacity andknowledge transfer are more prominent and RampD con-figuration is lower variables of the Chinese IURCI couldpromote each other by interaction but the growth rate isrelatively small indicating that relying solely on knowledgetransfer to promote innovation capacity is not effective Onthe one hand this confirmed that the type of knowledgeproduced by Chinese universities and research institutionsmust have applicability to be promoted to companies forindustrialization On the other hand this maybe resultsfrom enterprises having an inadequate knowledge absorptivecapacity Meanwhile from Figures 7 5 and 6 it can befound that if the RampD configuration of the Chinese IURCIsystem is poor then there will be a very significant impacton the ability to enhance knowledge transfer speed Thepossible reasons are that when RampD configuration is notenough the Chinese IURCI system is already in the processof tight resource allocation and it is bound to devote moreresources into promotion activities that create a high-valueheterogeneity innovation capacity and it therefore lacksthe resources to facilitate knowledge transfer within thesystem Correspondingly if resources are relatively abundantenterprises must allow for a bottleneck phenomenon inenhancing innovation capacity or accept greater risks ininnovative activity and may assign the resources originallydevoted to innovative ability to universities and researchinstitutions supporting the knowledge transfer willingnessof universities and research institutions and thus enhancingknowledge transfer for collaborative innovation In this casethe evolution curve of knowledge transfer will be moresignificant

Figure 8 clearly shows that when the innovation capacityof the Chinese IURCI system is more prominent RampDconfiguration is more targeted knowledge transfer capabilityis better and the 3 state variables of collaborative innovation

10 Complexity

show rapid and steady evolution Among them the growthrate of innovation capability is the most significant so thatafter a new stable state is established knowledge transferand RampD configuration both can have a more substantialgrowth effect on innovation capability This means thatin order to achieve the objective of significantly improvedcollaborative innovation performance the Chinese IURCIsystem must adhere to long-term independent innovationactivities and in addition must maintain and increase thebasic RampD configuration constantly improve knowledgetransfer capacity andmore effectively and pertinently send toenterprises the complementary knowledge that universitiesand research institutions own With advances in IURCI thisapproachmay bring 2 advantages for enterprises universitiesand research institutions involved in Chinese IURCI butfrom a business perspective at present the awareness ofChinese enterprises to acquire complementary knowledgein an IUR-coordinated manner is poor and so initiative isvery low If enterprises realize that on the basis of improvingRampD configuration they can obtain complementary researchachievement by making use of knowledge transfer formthen this will greatly increase the willingness of Chineseenterprises to participate in IURCI thereby using new tech-nology developing newproducts and approaching universityRampD personnel which is beneficial to building a trust-basedcooperationmechanism between enterprises and universitiesas well as research institutions [45ndash48] From the perspectiveof Chinese universities and research institutes in the processof knowledge creation the biggest drawback is that newknowledge cannot be applied to production practice that isuniversities and research institutions pay too much attentionto academic value With periodic IURCI universities andresearch institutions can not only receive financial supportfrom the business according to the needs of the businessto carry out research activities but also fully explore newresearch areas which will also play a positive role in promot-ing more academic achievements [49 50]

From observing Figures 3ndash8 we found that in the currentChinese IURCIprocess RampDconfiguration alwaysmaintainsa high degree of positive correlationwith innovation capacityThe reasons for this may be that Chinarsquos current IURCIsystem is not perfect and enterprises and universities as wellas research institutions are always looking for cooperationmodes suitable for both sides Meanwhile the process ofknowledge transfer itself is relatively slow and betweenChinese enterprises and universities there might exist neg-ative factors affecting knowledge transfer speed (such asknowledge flow cost and familiarity running process betweenstaffs) and these factors will result in reduced efficiency ofknowledge transfer therefore growth of knowledge transferis always slow

In short in Chinarsquos IURCI process a consistent relation-ship indeed exists among innovation capability knowledgetransfer and RampD configuration In a higher knowledgetransfer and comprehensive RampD configuration scenarioenterprises can propose more targeted knowledge needsand initially provide financial and material support foruniversities and research institutions involved in innovationUniversities and research institutions from the strategic level

are also concerned about how to establish knowledge RampDto serve enterprises and under the premise of integratedresources establish a balanced benefits distribution activelycarry out scientific and technological achievement transfertrain technology and management personnel required forenterprises exert collaborative innovation effects by comple-mentary advantages and thus bring new benefits for bothsides

5 Conclusion

By combining methods of game theory and complex sciencewe studied the evolution of the Chinese IURCI systemand interactions among the variables We used interactionsamong variables in a collaborative innovation system toldquomaprdquo the perspective of collaborative innovation evolutionand study innovation capacity RampD configuration andknowledge transfer as key elements of collaborative inno-vation and on the basis of establishing a dynamic systemevolution model of collaborative innovation we collecteddata simulated discussed interactions among the variablesin the process of Chinese IURCI and analyzed principles ofcollaborative innovation We mainly obtained the followingconclusions(1) In the evolution of the Chinese IURCI systeminnovation capacity and knowledge transfer are dependenton RampD configuration and innovation capability is highlypositively correlated with RampD configurationThe higher thetarget and investment level of RampD configuration the fasterthe evolution of innovation capacity and knowledge transferIt is noted that currentlyChinese IURCI development is usinga lot of resource investment as its main factor Innovationcapacity has become the key variable to promote collaborativeinnovation system evolution(2) Strong knowledge transfer capability can acceleratethe evolution speed of innovation capacity but overall knowl-edge transfer remains at a relatively stable evolution state anddoes not change with evolution of collaborative innovationThis explains that in the evolution process of Chinarsquos currentIURCI system virtuous cooperation mechanisms based ontrust between enterprises and universities as well as researchinstitutions are lacking Universities and research institutionsalso pay more attention to the academic value of knowledgeleading to insufficient contribution of knowledge transferto knowledge industrialization by enterprises and reducedcollaborative innovation system evolution(3) In a relatively balanced state of innovation capacityRampD configuration and knowledge transfer the faster theevolution speed of Chinese IURCI systems the higher thestability This shows that the Chinese IURCI system mustadhere to long-term independent innovation and on thebasis of maintaining and improving RampD configurationconstantly improve internal knowledge transfer capability

Compared with early research this article has 3 mainfindings first we chose China as a developing country withrapid economic growth used a dynamic perspective ana-lyzed the evolutionmechanism of the Chinese IURCI systemand demonstrated the interactions among variables of thecollaborative innovation system Secondly we learned from

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

8 Complexity

to market demand and devote effort towards knowledgetransfer activities resulting in the short-term declining trendof innovation capacity due to output of resources In thelong term due to the fact that the knowledge base of theIURCI system has been stabilized a complementary relation-ship between innovation capacity and RampD configuration isproduced resulting in a faster innovation capability upgradeThis indicates that since the Chinese IURCI system lagsbehind that of developed countries the ability to innovate andthe evolution of a collaborative innovation systemwill help toimprove targeting of the RampD configuration for businessesuniversities and research institutes and can also enhancepromotion of knowledge transfer capability But fundamen-tally the Chinese IURCI system more likely depends on theinnovation capacity for breakthrough innovations and oncomprehensively promoting the industrialization of acquiredknowledge In order to foster and enhance the innovativecapability the Chinese IURCI system not only needs to moreefficiently allocate limited resources by means of knowledgetransfer but also should provide enterprises with the high-value heterogeneity knowledge created by universities andresearch institutions form a sustained and stable directionalflow of knowledge establish a good feedback path formcomplementary advantages and establish risk sharing result-ing in mutual benefit and a win-win coinnovation situationMeanwhile long-term interactions between universities andresearch institutions and enterprises also contribute to themutual fit of collaborative innovation between variables thusknowledge of different attributes can interact to achieve anoptimal combination of knowledge resources achieve knowl-edge innovation and added value and ultimately produce thedesired collaborative innovation effect which will enhancethe overall competitive advantages of university and industry

Figure 3 shows that when the systemic RampD config-uration is set as 1 that is if the Chinese IURCI has amore comprehensive and adequate RampD configuration eachvariable in the same period has increased significantly andthe evolution speed of innovation capacity is closer to thatof the RampD configuration then the evolution speed ofknowledge transfer is relatively slow being increased onlyfrom 0 to about 52 Meanwhile we compared Figures 2 and3 and found that the evolution curve of knowledge transferhas always been lower than that of innovation capacityand RampD configuration this phenomenon reveals the factthat in the evolution process of the Chinese IURCI systemthe contribution of knowledge transfer to the evolution ofinnovative capacity is low which explains why the influencedegree of knowledge transfer on Chinese IURCI systemevolution is significantly low Thus companies can under-stand that in the current external environment and policycontext too much dependence on knowledge transfer fromuniversities and research institutions may not achieve thebest collaborative innovation strategy Therefore under thecurrent circumstances the Chinese IURCI system mainlyconsiders knowledge transfer as an assistance mode for col-laborative innovation and new knowledge access and focuseson enhancing their own innovation capacity and the rea-sonability of the RampD configuration Further as the ChineseIURCI system continues to evolve universities and research

institutions have clearer requirements for the distribution ofbenefits and coupled with a gradual improvement of theintellectual property system this could lead to a longer cycleof knowledge transfer between enterprises universities andresearch institutions At this time collaborative innovationand research will be more inclined to be aided by innovationcapability improvement so that the focus on knowledgetransfer is declining It can be seen that in the Chinese IURCIsystem enhancing the innovation capacity is more importantand knowledge transfer has not played its due role whichshows that the Chinese IURCI system has issues aroundweak knowledge transfer inadequate trust and cooperationmechanisms between enterprises and universities as well asresearch institutions and insufficient knowledge acquisitionby enterprises from universities and research institutions

Figure 4 illustrates in the context of a lack of innovationcapacity and incomplete RampD configuration that if the Chi-nese IURCI system was to have more prominent knowledgetransfer ability then knowledge transfer can to some extentcontribute to the evolution of collaborative innovation FromFigures 3 and 4 it can be seen that whether we set theRampD configuration as 1 and the knowledge transfer as 0or the knowledge transfer as 1 and the RampD configurationas 0 as long as these 2 variables have an initial valuethey will encourage the innovation capacity in the ChineseIURCI system to improve rapidly It means that in thiscontext innovation capacity is highly correlated with RampDconfiguration and knowledge transfer in the Chinese IURCIsystem Enhancing knowledge transfer capability can helpbusinesses more efficiently and in a more targeted wayobtain knowledge that universities and research institutionscreate and promulgate which can enhance the speed ofknowledge commercialization and help IURCI to achievehigh performance thus strengthening theChinese IURunionby maintaining internal synergies between knowledge supplyand knowledge applicationThis has a positive impact on pro-moting collaborative innovation activities and enhancing theconfidence of IUR cooperation At the same time comparedto Figures 2 and 3 it can be seen that although more promi-nent knowledge transfer can speed up innovation capacitythe enhancing effectiveness is still lower than that of RampDconfiguration The reason may be that knowledge transferfrom universities and research institutions is the main wayto promote Chinese IURCI system evolution since the basisof innovation and scientific and technical human capital onthe part of enterprises are poor knowledge input and supportof the collaborative innovation system are totally dependenton knowledge transfer from the universities and researchinstitutions side However due to the nature of knowledgetransfer there is a certain lag so in the absence of appropriateRampD configuration support the promotion of innovationcapability from universities and research institutions is notobvious so the Chinese IURCI requires a longer cooperationperiod

Figure 5 shows that when RampD configuration and knowl-edge transfer are more prominent in the Chinese IURCIsystem the increase in innovation capacity is more obviousthan the results of Figures 2 3 and 4 This demonstratesthat RampD configuration and knowledge transfer coaffect

Complexity 9

the evolution of innovation capacity in the Chinese IURCIsystem that is there are significant positive feedback effectsamong the 3 variables which mutually promote each otherIt means that at such time as the Chinese IURCI systemhas some input resources and knowledge transfer capabilitythese factors will be beneficial to rapidly increasing thecollaborative innovation knowledge level and innovationcapacity thus improving collaborative innovation perfor-mance We believe that the reasons for this may be twofoldon the one hand more effective knowledge transfer betweencorporations and universities as well as research institutionspromotes knowledge industrialization so the innovationcapability of enterprises quickly upgrades in the process ofrepeated practice and constant learning which to a certainextent promotes IURCI performance On the other handrational and targeted RampD configuration allows universitiesand research institutions under the premise of having astable knowledge base to reduce the time delay of knowledgetransfer in the process of collaborative innovation and putpart of their resources into the process of promotion ofknowledge transfer capacity thereby achieving a mutualfit with RampD configuration It should be noted that inthe initial stage of simulation namely the early periodof Chinese IURCI practice favorable Chinese governmentpolicies and attention to IURCI could promote the speedof knowledge industrialization But knowledge transfer fromuniversities and research institutions is subject to adversefactors in the cooperation run-in period and may producea situation of no improvement With the in-depth evolutionof the IURCI system cooperation between businesses anduniversities and research institutions can mature so thatuniversities and research institutions have clearer ideas aboutindustry needs and more accurately provide the necessaryknowledge to enterprises for knowledge industrializationUniversities and research institutions will also benefit fromcollaborative innovation as they better understand industrytechnology needs and market demand allowing them tooptimize their knowledge structure thus enhancing theIURCI performance

Figure 6 illustrates that in the evolution process ofIURCI that relies on innovation capacity and RampD con-figuration continuously increasing RampD configuration willmake evolution of innovation capacity significantly fasterAt the same time compared to Figure 5 it can be foundthat in the evolution of the Chinese IURCI system ifRampD configuration has an initial value to enhance theIURCI performance enterprises must operate on the basis ofcontinuously developing their RampD configuration obtainingas much as possible valuable heterogeneity knowledge fromuniversities and research institutions and universities andresearch institutions also need to enhance their abilities totransfer knowledge and fit the production needs of industryin order to obtain more significant innovation performanceIn addition this case also shows that although there maybe differences in their abilities to innovate for those enter-prises that lack the ability to innovate the input of externalresources and the gain of valuable knowledge by way ofknowledge transfer may change the current disadvantage andhelp them achieve rapid growth

Figures 5 and 6 both show that the innovation capacityand RampD configuration of the Chinese IURCI system canin a short period of time produce significant synergies andpromote each other indicating that Chinarsquos current IURCIdevelopment still requires a lot of resources investment as theprimary means to enhance innovation capability But whenRampD configuration is high the result of innovation capacityevolution [38] is lower than that when knowledge transferis relatively high which gives high values of innovationevolution [42] On the one hand this indicates that inthe current process of IURCI in China the universitiesand research institutions side creates and sends high-valueheterogeneity knowledge which has a fundamental role inpromoting innovation capacity On the other hand to acertain extent this confirms that if Chinese universities andresearch centers could face knowledge-oriented industrialdemand enterprises could accurately identify the marketvalue of accepted knowledge change the discrepancy thatexists between technology supply and technology demandand thereby reduce its dependency on RampD configurationwhile at the same time shortening the innovation cycleand avoiding the issues such as information asymmetryand transaction costs that may arise during collaborativeinnovation [42ndash44]

Figure 7 illustrates that when innovation capacity andknowledge transfer are more prominent and RampD con-figuration is lower variables of the Chinese IURCI couldpromote each other by interaction but the growth rate isrelatively small indicating that relying solely on knowledgetransfer to promote innovation capacity is not effective Onthe one hand this confirmed that the type of knowledgeproduced by Chinese universities and research institutionsmust have applicability to be promoted to companies forindustrialization On the other hand this maybe resultsfrom enterprises having an inadequate knowledge absorptivecapacity Meanwhile from Figures 7 5 and 6 it can befound that if the RampD configuration of the Chinese IURCIsystem is poor then there will be a very significant impacton the ability to enhance knowledge transfer speed Thepossible reasons are that when RampD configuration is notenough the Chinese IURCI system is already in the processof tight resource allocation and it is bound to devote moreresources into promotion activities that create a high-valueheterogeneity innovation capacity and it therefore lacksthe resources to facilitate knowledge transfer within thesystem Correspondingly if resources are relatively abundantenterprises must allow for a bottleneck phenomenon inenhancing innovation capacity or accept greater risks ininnovative activity and may assign the resources originallydevoted to innovative ability to universities and researchinstitutions supporting the knowledge transfer willingnessof universities and research institutions and thus enhancingknowledge transfer for collaborative innovation In this casethe evolution curve of knowledge transfer will be moresignificant

Figure 8 clearly shows that when the innovation capacityof the Chinese IURCI system is more prominent RampDconfiguration is more targeted knowledge transfer capabilityis better and the 3 state variables of collaborative innovation

10 Complexity

show rapid and steady evolution Among them the growthrate of innovation capability is the most significant so thatafter a new stable state is established knowledge transferand RampD configuration both can have a more substantialgrowth effect on innovation capability This means thatin order to achieve the objective of significantly improvedcollaborative innovation performance the Chinese IURCIsystem must adhere to long-term independent innovationactivities and in addition must maintain and increase thebasic RampD configuration constantly improve knowledgetransfer capacity andmore effectively and pertinently send toenterprises the complementary knowledge that universitiesand research institutions own With advances in IURCI thisapproachmay bring 2 advantages for enterprises universitiesand research institutions involved in Chinese IURCI butfrom a business perspective at present the awareness ofChinese enterprises to acquire complementary knowledgein an IUR-coordinated manner is poor and so initiative isvery low If enterprises realize that on the basis of improvingRampD configuration they can obtain complementary researchachievement by making use of knowledge transfer formthen this will greatly increase the willingness of Chineseenterprises to participate in IURCI thereby using new tech-nology developing newproducts and approaching universityRampD personnel which is beneficial to building a trust-basedcooperationmechanism between enterprises and universitiesas well as research institutions [45ndash48] From the perspectiveof Chinese universities and research institutes in the processof knowledge creation the biggest drawback is that newknowledge cannot be applied to production practice that isuniversities and research institutions pay too much attentionto academic value With periodic IURCI universities andresearch institutions can not only receive financial supportfrom the business according to the needs of the businessto carry out research activities but also fully explore newresearch areas which will also play a positive role in promot-ing more academic achievements [49 50]

From observing Figures 3ndash8 we found that in the currentChinese IURCIprocess RampDconfiguration alwaysmaintainsa high degree of positive correlationwith innovation capacityThe reasons for this may be that Chinarsquos current IURCIsystem is not perfect and enterprises and universities as wellas research institutions are always looking for cooperationmodes suitable for both sides Meanwhile the process ofknowledge transfer itself is relatively slow and betweenChinese enterprises and universities there might exist neg-ative factors affecting knowledge transfer speed (such asknowledge flow cost and familiarity running process betweenstaffs) and these factors will result in reduced efficiency ofknowledge transfer therefore growth of knowledge transferis always slow

In short in Chinarsquos IURCI process a consistent relation-ship indeed exists among innovation capability knowledgetransfer and RampD configuration In a higher knowledgetransfer and comprehensive RampD configuration scenarioenterprises can propose more targeted knowledge needsand initially provide financial and material support foruniversities and research institutions involved in innovationUniversities and research institutions from the strategic level

are also concerned about how to establish knowledge RampDto serve enterprises and under the premise of integratedresources establish a balanced benefits distribution activelycarry out scientific and technological achievement transfertrain technology and management personnel required forenterprises exert collaborative innovation effects by comple-mentary advantages and thus bring new benefits for bothsides

5 Conclusion

By combining methods of game theory and complex sciencewe studied the evolution of the Chinese IURCI systemand interactions among the variables We used interactionsamong variables in a collaborative innovation system toldquomaprdquo the perspective of collaborative innovation evolutionand study innovation capacity RampD configuration andknowledge transfer as key elements of collaborative inno-vation and on the basis of establishing a dynamic systemevolution model of collaborative innovation we collecteddata simulated discussed interactions among the variablesin the process of Chinese IURCI and analyzed principles ofcollaborative innovation We mainly obtained the followingconclusions(1) In the evolution of the Chinese IURCI systeminnovation capacity and knowledge transfer are dependenton RampD configuration and innovation capability is highlypositively correlated with RampD configurationThe higher thetarget and investment level of RampD configuration the fasterthe evolution of innovation capacity and knowledge transferIt is noted that currentlyChinese IURCI development is usinga lot of resource investment as its main factor Innovationcapacity has become the key variable to promote collaborativeinnovation system evolution(2) Strong knowledge transfer capability can acceleratethe evolution speed of innovation capacity but overall knowl-edge transfer remains at a relatively stable evolution state anddoes not change with evolution of collaborative innovationThis explains that in the evolution process of Chinarsquos currentIURCI system virtuous cooperation mechanisms based ontrust between enterprises and universities as well as researchinstitutions are lacking Universities and research institutionsalso pay more attention to the academic value of knowledgeleading to insufficient contribution of knowledge transferto knowledge industrialization by enterprises and reducedcollaborative innovation system evolution(3) In a relatively balanced state of innovation capacityRampD configuration and knowledge transfer the faster theevolution speed of Chinese IURCI systems the higher thestability This shows that the Chinese IURCI system mustadhere to long-term independent innovation and on thebasis of maintaining and improving RampD configurationconstantly improve internal knowledge transfer capability

Compared with early research this article has 3 mainfindings first we chose China as a developing country withrapid economic growth used a dynamic perspective ana-lyzed the evolutionmechanism of the Chinese IURCI systemand demonstrated the interactions among variables of thecollaborative innovation system Secondly we learned from

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Complexity 9

the evolution of innovation capacity in the Chinese IURCIsystem that is there are significant positive feedback effectsamong the 3 variables which mutually promote each otherIt means that at such time as the Chinese IURCI systemhas some input resources and knowledge transfer capabilitythese factors will be beneficial to rapidly increasing thecollaborative innovation knowledge level and innovationcapacity thus improving collaborative innovation perfor-mance We believe that the reasons for this may be twofoldon the one hand more effective knowledge transfer betweencorporations and universities as well as research institutionspromotes knowledge industrialization so the innovationcapability of enterprises quickly upgrades in the process ofrepeated practice and constant learning which to a certainextent promotes IURCI performance On the other handrational and targeted RampD configuration allows universitiesand research institutions under the premise of having astable knowledge base to reduce the time delay of knowledgetransfer in the process of collaborative innovation and putpart of their resources into the process of promotion ofknowledge transfer capacity thereby achieving a mutualfit with RampD configuration It should be noted that inthe initial stage of simulation namely the early periodof Chinese IURCI practice favorable Chinese governmentpolicies and attention to IURCI could promote the speedof knowledge industrialization But knowledge transfer fromuniversities and research institutions is subject to adversefactors in the cooperation run-in period and may producea situation of no improvement With the in-depth evolutionof the IURCI system cooperation between businesses anduniversities and research institutions can mature so thatuniversities and research institutions have clearer ideas aboutindustry needs and more accurately provide the necessaryknowledge to enterprises for knowledge industrializationUniversities and research institutions will also benefit fromcollaborative innovation as they better understand industrytechnology needs and market demand allowing them tooptimize their knowledge structure thus enhancing theIURCI performance

Figure 6 illustrates that in the evolution process ofIURCI that relies on innovation capacity and RampD con-figuration continuously increasing RampD configuration willmake evolution of innovation capacity significantly fasterAt the same time compared to Figure 5 it can be foundthat in the evolution of the Chinese IURCI system ifRampD configuration has an initial value to enhance theIURCI performance enterprises must operate on the basis ofcontinuously developing their RampD configuration obtainingas much as possible valuable heterogeneity knowledge fromuniversities and research institutions and universities andresearch institutions also need to enhance their abilities totransfer knowledge and fit the production needs of industryin order to obtain more significant innovation performanceIn addition this case also shows that although there maybe differences in their abilities to innovate for those enter-prises that lack the ability to innovate the input of externalresources and the gain of valuable knowledge by way ofknowledge transfer may change the current disadvantage andhelp them achieve rapid growth

Figures 5 and 6 both show that the innovation capacityand RampD configuration of the Chinese IURCI system canin a short period of time produce significant synergies andpromote each other indicating that Chinarsquos current IURCIdevelopment still requires a lot of resources investment as theprimary means to enhance innovation capability But whenRampD configuration is high the result of innovation capacityevolution [38] is lower than that when knowledge transferis relatively high which gives high values of innovationevolution [42] On the one hand this indicates that inthe current process of IURCI in China the universitiesand research institutions side creates and sends high-valueheterogeneity knowledge which has a fundamental role inpromoting innovation capacity On the other hand to acertain extent this confirms that if Chinese universities andresearch centers could face knowledge-oriented industrialdemand enterprises could accurately identify the marketvalue of accepted knowledge change the discrepancy thatexists between technology supply and technology demandand thereby reduce its dependency on RampD configurationwhile at the same time shortening the innovation cycleand avoiding the issues such as information asymmetryand transaction costs that may arise during collaborativeinnovation [42ndash44]

Figure 7 illustrates that when innovation capacity andknowledge transfer are more prominent and RampD con-figuration is lower variables of the Chinese IURCI couldpromote each other by interaction but the growth rate isrelatively small indicating that relying solely on knowledgetransfer to promote innovation capacity is not effective Onthe one hand this confirmed that the type of knowledgeproduced by Chinese universities and research institutionsmust have applicability to be promoted to companies forindustrialization On the other hand this maybe resultsfrom enterprises having an inadequate knowledge absorptivecapacity Meanwhile from Figures 7 5 and 6 it can befound that if the RampD configuration of the Chinese IURCIsystem is poor then there will be a very significant impacton the ability to enhance knowledge transfer speed Thepossible reasons are that when RampD configuration is notenough the Chinese IURCI system is already in the processof tight resource allocation and it is bound to devote moreresources into promotion activities that create a high-valueheterogeneity innovation capacity and it therefore lacksthe resources to facilitate knowledge transfer within thesystem Correspondingly if resources are relatively abundantenterprises must allow for a bottleneck phenomenon inenhancing innovation capacity or accept greater risks ininnovative activity and may assign the resources originallydevoted to innovative ability to universities and researchinstitutions supporting the knowledge transfer willingnessof universities and research institutions and thus enhancingknowledge transfer for collaborative innovation In this casethe evolution curve of knowledge transfer will be moresignificant

Figure 8 clearly shows that when the innovation capacityof the Chinese IURCI system is more prominent RampDconfiguration is more targeted knowledge transfer capabilityis better and the 3 state variables of collaborative innovation

10 Complexity

show rapid and steady evolution Among them the growthrate of innovation capability is the most significant so thatafter a new stable state is established knowledge transferand RampD configuration both can have a more substantialgrowth effect on innovation capability This means thatin order to achieve the objective of significantly improvedcollaborative innovation performance the Chinese IURCIsystem must adhere to long-term independent innovationactivities and in addition must maintain and increase thebasic RampD configuration constantly improve knowledgetransfer capacity andmore effectively and pertinently send toenterprises the complementary knowledge that universitiesand research institutions own With advances in IURCI thisapproachmay bring 2 advantages for enterprises universitiesand research institutions involved in Chinese IURCI butfrom a business perspective at present the awareness ofChinese enterprises to acquire complementary knowledgein an IUR-coordinated manner is poor and so initiative isvery low If enterprises realize that on the basis of improvingRampD configuration they can obtain complementary researchachievement by making use of knowledge transfer formthen this will greatly increase the willingness of Chineseenterprises to participate in IURCI thereby using new tech-nology developing newproducts and approaching universityRampD personnel which is beneficial to building a trust-basedcooperationmechanism between enterprises and universitiesas well as research institutions [45ndash48] From the perspectiveof Chinese universities and research institutes in the processof knowledge creation the biggest drawback is that newknowledge cannot be applied to production practice that isuniversities and research institutions pay too much attentionto academic value With periodic IURCI universities andresearch institutions can not only receive financial supportfrom the business according to the needs of the businessto carry out research activities but also fully explore newresearch areas which will also play a positive role in promot-ing more academic achievements [49 50]

From observing Figures 3ndash8 we found that in the currentChinese IURCIprocess RampDconfiguration alwaysmaintainsa high degree of positive correlationwith innovation capacityThe reasons for this may be that Chinarsquos current IURCIsystem is not perfect and enterprises and universities as wellas research institutions are always looking for cooperationmodes suitable for both sides Meanwhile the process ofknowledge transfer itself is relatively slow and betweenChinese enterprises and universities there might exist neg-ative factors affecting knowledge transfer speed (such asknowledge flow cost and familiarity running process betweenstaffs) and these factors will result in reduced efficiency ofknowledge transfer therefore growth of knowledge transferis always slow

In short in Chinarsquos IURCI process a consistent relation-ship indeed exists among innovation capability knowledgetransfer and RampD configuration In a higher knowledgetransfer and comprehensive RampD configuration scenarioenterprises can propose more targeted knowledge needsand initially provide financial and material support foruniversities and research institutions involved in innovationUniversities and research institutions from the strategic level

are also concerned about how to establish knowledge RampDto serve enterprises and under the premise of integratedresources establish a balanced benefits distribution activelycarry out scientific and technological achievement transfertrain technology and management personnel required forenterprises exert collaborative innovation effects by comple-mentary advantages and thus bring new benefits for bothsides

5 Conclusion

By combining methods of game theory and complex sciencewe studied the evolution of the Chinese IURCI systemand interactions among the variables We used interactionsamong variables in a collaborative innovation system toldquomaprdquo the perspective of collaborative innovation evolutionand study innovation capacity RampD configuration andknowledge transfer as key elements of collaborative inno-vation and on the basis of establishing a dynamic systemevolution model of collaborative innovation we collecteddata simulated discussed interactions among the variablesin the process of Chinese IURCI and analyzed principles ofcollaborative innovation We mainly obtained the followingconclusions(1) In the evolution of the Chinese IURCI systeminnovation capacity and knowledge transfer are dependenton RampD configuration and innovation capability is highlypositively correlated with RampD configurationThe higher thetarget and investment level of RampD configuration the fasterthe evolution of innovation capacity and knowledge transferIt is noted that currentlyChinese IURCI development is usinga lot of resource investment as its main factor Innovationcapacity has become the key variable to promote collaborativeinnovation system evolution(2) Strong knowledge transfer capability can acceleratethe evolution speed of innovation capacity but overall knowl-edge transfer remains at a relatively stable evolution state anddoes not change with evolution of collaborative innovationThis explains that in the evolution process of Chinarsquos currentIURCI system virtuous cooperation mechanisms based ontrust between enterprises and universities as well as researchinstitutions are lacking Universities and research institutionsalso pay more attention to the academic value of knowledgeleading to insufficient contribution of knowledge transferto knowledge industrialization by enterprises and reducedcollaborative innovation system evolution(3) In a relatively balanced state of innovation capacityRampD configuration and knowledge transfer the faster theevolution speed of Chinese IURCI systems the higher thestability This shows that the Chinese IURCI system mustadhere to long-term independent innovation and on thebasis of maintaining and improving RampD configurationconstantly improve internal knowledge transfer capability

Compared with early research this article has 3 mainfindings first we chose China as a developing country withrapid economic growth used a dynamic perspective ana-lyzed the evolutionmechanism of the Chinese IURCI systemand demonstrated the interactions among variables of thecollaborative innovation system Secondly we learned from

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

10 Complexity

show rapid and steady evolution Among them the growthrate of innovation capability is the most significant so thatafter a new stable state is established knowledge transferand RampD configuration both can have a more substantialgrowth effect on innovation capability This means thatin order to achieve the objective of significantly improvedcollaborative innovation performance the Chinese IURCIsystem must adhere to long-term independent innovationactivities and in addition must maintain and increase thebasic RampD configuration constantly improve knowledgetransfer capacity andmore effectively and pertinently send toenterprises the complementary knowledge that universitiesand research institutions own With advances in IURCI thisapproachmay bring 2 advantages for enterprises universitiesand research institutions involved in Chinese IURCI butfrom a business perspective at present the awareness ofChinese enterprises to acquire complementary knowledgein an IUR-coordinated manner is poor and so initiative isvery low If enterprises realize that on the basis of improvingRampD configuration they can obtain complementary researchachievement by making use of knowledge transfer formthen this will greatly increase the willingness of Chineseenterprises to participate in IURCI thereby using new tech-nology developing newproducts and approaching universityRampD personnel which is beneficial to building a trust-basedcooperationmechanism between enterprises and universitiesas well as research institutions [45ndash48] From the perspectiveof Chinese universities and research institutes in the processof knowledge creation the biggest drawback is that newknowledge cannot be applied to production practice that isuniversities and research institutions pay too much attentionto academic value With periodic IURCI universities andresearch institutions can not only receive financial supportfrom the business according to the needs of the businessto carry out research activities but also fully explore newresearch areas which will also play a positive role in promot-ing more academic achievements [49 50]

From observing Figures 3ndash8 we found that in the currentChinese IURCIprocess RampDconfiguration alwaysmaintainsa high degree of positive correlationwith innovation capacityThe reasons for this may be that Chinarsquos current IURCIsystem is not perfect and enterprises and universities as wellas research institutions are always looking for cooperationmodes suitable for both sides Meanwhile the process ofknowledge transfer itself is relatively slow and betweenChinese enterprises and universities there might exist neg-ative factors affecting knowledge transfer speed (such asknowledge flow cost and familiarity running process betweenstaffs) and these factors will result in reduced efficiency ofknowledge transfer therefore growth of knowledge transferis always slow

In short in Chinarsquos IURCI process a consistent relation-ship indeed exists among innovation capability knowledgetransfer and RampD configuration In a higher knowledgetransfer and comprehensive RampD configuration scenarioenterprises can propose more targeted knowledge needsand initially provide financial and material support foruniversities and research institutions involved in innovationUniversities and research institutions from the strategic level

are also concerned about how to establish knowledge RampDto serve enterprises and under the premise of integratedresources establish a balanced benefits distribution activelycarry out scientific and technological achievement transfertrain technology and management personnel required forenterprises exert collaborative innovation effects by comple-mentary advantages and thus bring new benefits for bothsides

5 Conclusion

By combining methods of game theory and complex sciencewe studied the evolution of the Chinese IURCI systemand interactions among the variables We used interactionsamong variables in a collaborative innovation system toldquomaprdquo the perspective of collaborative innovation evolutionand study innovation capacity RampD configuration andknowledge transfer as key elements of collaborative inno-vation and on the basis of establishing a dynamic systemevolution model of collaborative innovation we collecteddata simulated discussed interactions among the variablesin the process of Chinese IURCI and analyzed principles ofcollaborative innovation We mainly obtained the followingconclusions(1) In the evolution of the Chinese IURCI systeminnovation capacity and knowledge transfer are dependenton RampD configuration and innovation capability is highlypositively correlated with RampD configurationThe higher thetarget and investment level of RampD configuration the fasterthe evolution of innovation capacity and knowledge transferIt is noted that currentlyChinese IURCI development is usinga lot of resource investment as its main factor Innovationcapacity has become the key variable to promote collaborativeinnovation system evolution(2) Strong knowledge transfer capability can acceleratethe evolution speed of innovation capacity but overall knowl-edge transfer remains at a relatively stable evolution state anddoes not change with evolution of collaborative innovationThis explains that in the evolution process of Chinarsquos currentIURCI system virtuous cooperation mechanisms based ontrust between enterprises and universities as well as researchinstitutions are lacking Universities and research institutionsalso pay more attention to the academic value of knowledgeleading to insufficient contribution of knowledge transferto knowledge industrialization by enterprises and reducedcollaborative innovation system evolution(3) In a relatively balanced state of innovation capacityRampD configuration and knowledge transfer the faster theevolution speed of Chinese IURCI systems the higher thestability This shows that the Chinese IURCI system mustadhere to long-term independent innovation and on thebasis of maintaining and improving RampD configurationconstantly improve internal knowledge transfer capability

Compared with early research this article has 3 mainfindings first we chose China as a developing country withrapid economic growth used a dynamic perspective ana-lyzed the evolutionmechanism of the Chinese IURCI systemand demonstrated the interactions among variables of thecollaborative innovation system Secondly we learned from

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Complexity 11

the macroinnovation system theory the meso-Triple HelixModel and microopen organization innovation theory sep-arated the components of the IURCI system and confirmedcontribution degrees of different variables to collaborativeinnovation system evolution with particular emphasis ondifferent initial values Our rationale of analyzing 3 variableswith different evolutionary trends not only theoreticallycontributed to enrich the innovative theory system but alsomade developmental strategies for Chinese enterprises anduniversities as well as research institutions and providedan optimized direction to enhance collaborative innovationperformance Thirdly in this study we considered IURCI asa complex system with dynamic evolution and by analysis ofsystemic evolution and cooperative status among variableswe effectively revealed some issues existing in the currentChinese IURCI

This study made 2 contributions to studies of IURCIbased on resources and knowledgemanagement theory firstthis study overcame the disadvantage of only using trans-action cost theory and organization management theory toanalyze comprehensiveness and complexity of collaborativeinnovation The research framework and methodology pro-posed in this study will help researchers deal with challengesso that in a dynamic environment the synergistic effect ofdifferent variables of the IURCI system results in complexityof collaborative innovative evolution Secondly the methodchosen for this study is different frompast collaborative inno-vation research processes that were narrower and divided bylimited dimensions We started from the variables that deter-mine the IURCI system established a nonlinear model basedon the interactions among variables confirmed the impactlevel of different state variables on collaborative innovationand defined the mechanism of interactions between statevariables and evolution trend The use of this new method toobtain new conclusions is necessary for further research oncollaborative innovation

This study is also of great practical implication Based on10 years of data published by Chinese government interac-tions and evolution trend among innovation capacity RampDconfiguration and knowledge transfer in different contextsare given by means of simulation thus specifying existingproblems in Chinarsquos IURCI system Conclusions obtainedare beneficial in helping enterprises managers take measuresto promote knowledge absorption in helping universitiesand research institutions clarify knowledge transfer dilemmain certain knowledge demand condition and helping gov-ernment form proper resource allocation mode Thereforethe new conceptualization provides a useful guide for man-agers to make rational knowledge developing strategy foruniversities and research institutes to diagnose barriers inknowledge transfer and for government to take necessarypolicy-making

Selecting data from different countries to conduct com-parisons and discuss classification is the main research direc-tion in the future Because the framework proposed in thisstudy can be used in many fields this study plays a guidingrole to some extent in practical sample collection and analysiswhen trying to select future strategic alliances practicecommunities and other subjects Meanwhile we hoped to

bring the phenomenon of knowledge diffusion into researchand by improving existing models achieve a completeinterpretation of IURCI issues on aspects of theory andpractice thus making research more focused

Conflicts of Interest

The authors declare no conflicts of interest of regarding thepublication of this paper

Acknowledgments

This study is supported by theNationalNatural Science Foun-dation of China (71602041 71602042 and 71301065) NationalSocial Science Key Project of China (ID 14AGL004)China Postdoctoral Science Foundation (2015M570299 and2016M590605) Fundamental Research Funds for the Cen-tral Universities of China (ID HEUCF150901) PostdoctoralScience Foundation of Heilongjiang Province (LBH-15075)and Postdoctoral Science Foundation of Jiangxi Province(2016KY27)

References

[1] O W Maietta ldquoDeterminants of university-firm RampD collabo-ration and its impact on innovation a perspective from a low-tech industryrdquo Research Policy vol 44 no 7 pp 1341ndash1359 2015

[2] M D Santoro and P E Bierly III ldquoFacilitators of knowledgetransfer in university-industry collaborations a knowledge-based perspectiverdquo IEEE Transactions on Engineering Manage-ment vol 53 no 4 pp 495ndash507 2006

[3] O Al-Tabbaa and S Ankrah ldquoSocial capital to facilitateldquoengineeredrdquo university-industry collaboration for technologytransfer a dynamic perspectiverdquo Technological Forecasting andSocial Change vol 104 pp 1ndash15 2016

[4] A I Canhoto S Quinton P Jackson and S Dibb ldquoTheco-production of value in digital university-industry RampDcollaborative projectsrdquo Industrial Marketing Management vol56 pp 86ndash96 2016

[5] S Arvanitis U Kubli and M Woerter ldquoUniversity-industryknowledge and technology transfer in Switzerland what uni-versity scientists think about co-operation with private enter-prisesrdquo Research Policy vol 37 no 10 pp 1865ndash1883 2008

[6] M Perkmann and K Walsh ldquoUniversity-industry relationshipsand open innovation towards a research agendardquo InternationalJournal of Management Reviews vol 9 no 4 pp 259ndash280 2007

[7] K Koschatzky ldquoNetworking and knowledge transfer betweenresearch and industry in transition countries empirical evi-dence from the Slovenian innovation systemrdquo The Journal ofTechnology Transfer vol 27 no 1 pp 27ndash38 2002

[8] A Inzelt ldquoThe evolution of university-industry-governmentrelationships during transitionrdquo Research Policy vol 33 no 6-7pp 975ndash995 2004

[9] N Yumusak I Ozcelik M Iskefiyeli M F Adak and TKırktepeli ldquoUniversity industry linkage projects managementsystemrdquo ProcediamdashSocial and Behavioral Sciences vol 174 pp3254ndash3259 2015

[10] J Zhao X Xi and S Yi ldquoResource allocation under a strategicalliance how a cooperative network with knowledge flow spurs

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

12 Complexity

co-evolutionrdquo Knowledge-Based Systems vol 89 pp 497ndash5082015

[11] J Berbegal-Mirabent J L Sanchez Garcıa and D E Ribeiro-Soriano ldquoUniversity-industry partnerships for the provision ofRampD servicesrdquo Journal of Business Research vol 68 no 7 pp1407ndash1413 2015

[12] R Fontana A Geuna and M Matt ldquoFactors affectinguniversity-industry R andD projects the importance of search-ing screening and signallingrdquo Research Policy vol 35 no 2 pp309ndash323 2006

[13] S-H Chen M-H Huang and D-Z Chen ldquoDriving factors ofexternal funding and funding effects on academic innovationperformance in university-industry-government linkagesrdquo Sci-entometrics vol 94 no 3 pp 1077ndash1098 2013

[14] B A Lundvall National Innovation System Toward aTheory ofInnovation and Interactive Learning Pinter London UK 1992

[15] P Cooke M G Uranga and G Etxebarria ldquoRegional systemsof innovation an evolutionary perspectiverdquo Environment andPlanning A vol 30 no 9 pp 1563ndash1584 1998

[16] A K W Lau and W Lo ldquoRegional innovation system absorp-tive capacity and innovation performance an empirical studyrdquoTechnological Forecasting and Social Change vol 92 pp 99ndash1142015

[17] I A Ivanova and L Leydesdorff ldquoRotational symmetry andthe transformation of innovation systems in a Triple Helix ofuniversity-industry-government relationsrdquo Technological Fore-casting and Social Change vol 86 pp 143ndash156 2014

[18] I A Ivanova and L Leydesdorff ldquoKnowledge-generating effi-ciency in innovation systems the acceleration of technologicalparadigm changes with increasing complexityrdquo TechnologicalForecasting and Social Change vol 96 pp 254ndash265 2015

[19] L Leydesdorff and M Meyer ldquoTriple Helix indicators ofknowledge-based innovation systems introduction to the spe-cial issuerdquo Research Policy vol 35 no 10 pp 1441ndash1449 2006

[20] H Etzkowitz and L Leydesdorff ldquoThe triple helix University-industry government relations a laboratory for knowledgebased economic developmentrdquo EASSR Review vol 14 no 1 pp11ndash19 1995

[21] H W Chesbrough Open Innovation The New Imperative forCreating and Profiting from Technology Harvard Business Press2003

[22] J West and K R Lakhani ldquoGetting clear about communitiesin open innovationrdquo Industry and Innovation vol 15 no 2 pp223ndash231 2008

[23] A Draghici C Baban M Gogan and L Ivascu ldquoA knowledgemanagement approach for the university-industry collabora-tion in open innovationrdquo Procedia Economics and Finance vol23 pp 23ndash32 2015

[24] L Striukova and T Rayna ldquoUniversity-industry knowledgeexchange an exploratory study of Open Innovation in UKuniversitiesrdquo European Journal of Innovation Management vol18 no 4 pp 471ndash492 2015

[25] G George S A Zahra and D R Wood Jr ldquoThe effects ofbusiness-university alliances on innovative output and financialperformance a study of publicly traded biotechnology compa-niesrdquo Journal of Business Venturing vol 17 no 6 pp 577ndash6092002

[26] K Laursen and A Salter ldquoOpen for innovation the role ofopenness in explaining innovation performance among UKmanufacturing firmsrdquo StrategicManagement Journal vol 27 no2 pp 131ndash150 2006

[27] A Winkelbach and A Walter ldquoComplex technological knowl-edge and value creation in science-to-industry technologytransfer projects the moderating effect of absorptive capacityrdquoIndustrial Marketing Management vol 47 pp 98ndash108 2015

[28] Y-J Chen Y-MChen andM-SWu ldquoAn empirical knowledgemanagement framework for professional virtual community inknowledge-intensive service industriesrdquo Expert Systems withApplications vol 39 no 18 pp 13135ndash13147 2012

[29] M C J Caniels and B Verspagen ldquoBarriers to knowledgespillovers and regional convergence in an evolutionary modelrdquoJournal of Evolutionary Economics vol 11 no 3 pp 307ndash3292001

[30] M-C Hu ldquoKnowledge flows and innovation capability thepatenting trajectory of Taiwanrsquos thin film transistor-liquidcrystal display industryrdquo Technological Forecasting and SocialChange vol 75 no 9 pp 1423ndash1438 2008

[31] J Bercovitz andM Feldmann ldquoEntpreprenerial universities andtechnology transfer a conceptual framework for understandingknowledge-based economic developmentrdquo Journal of Technol-ogy Transfer vol 31 no 1 pp 175ndash188 2006

[32] A Bonaccorsi and A Piccaluga ldquoA theoretical frameworkfor the evaluation of university-industry relationshipsrdquo RampDManagement vol 24 no 3 pp 229ndash247 1994

[33] E G Carayannis J Alexander and A Ioannidis ldquoLeverag-ing knowledge learning and innovation in forming strategicgovernment-university-industry (GUI) RampD partnerships inthe US Germany and Francerdquo Technovation vol 20 no 9 pp477ndash488 2000

[34] L Anatan ldquoConceptual issues in university to industry knowl-edge transfer studies a literature reviewrdquo ProcediamdashSocial andBehavioral Sciences vol 211 pp 711ndash717 2015

[35] H W Chesbrough W Vanhaverbeke and J West OpenInnovation Researching a New Paradigm Oxford UniversityPress Oxford UK 2008

[36] Y Baba N Shichijo and S R Sedita ldquoHow do collaborationswith universities affect firmsrsquo innovative performanceThe roleof lsquoPasteur scientistsrsquo in the advanced materials fieldrdquo ResearchPolicy vol 38 no 5 pp 756ndash764 2009

[37] H Haken The Science of Structure Synergetics Van NostrandReinhold Grantham UK 1981

[38] J L Kuang P A Meehan and A Y Leung ldquoSuppressing chaosvia Lyapunov-Krasovskiirsquos methodrdquo Chaos Solitons amp Fractalsvol 27 no 5 pp 1408ndash1414 2006

[39] G A Anastassiou ldquoDistributional Taylor formulardquo NonlinearAnalysis Theory Methods amp Applications vol 70 no 9 pp3195ndash3202 2009

[40] Y Khan H Vazquez-Leal and N Faraz ldquoAn auxiliary param-eter method using Adomian polynomials and Laplace transfor-mation for nonlinear differential equationsrdquoAppliedMathemat-ical Modelling vol 37 no 5 pp 2702ndash2708 2013

[41] S F AL-Azzawi ldquoStability and bifurcation of pan chaoticsystem by using Routh-Hurwitz and Gardan methodsrdquo AppliedMathematics and Computation vol 219 no 3 pp 1144ndash11522012

[42] S Owen and A Yawson ldquoInformation asymmetry and interna-tional strategic alliancesrdquo Journal of Banking and Finance vol37 no 10 pp 3890ndash3903 2013

[43] R A Jensen J G Thursby and M C Thursby ldquoDisclosureand licensing of University inventionsrdquo International Journal ofIndustrial Organization vol 21 no 9 pp 1271ndash1300 2003

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Complexity 13

[44] X Gao X Guo and J Guan ldquoAn analysis of the patent-ing activities and collaboration among industry-university-research institutes in the Chinese ICT sectorrdquo Scientometricsvol 98 no 1 pp 247ndash263 2014

[45] Y S Lee ldquoldquoTechnology transferrdquo and the research university asearch for the boundaries of university-industry collaborationrdquoResearch Policy vol 25 no 6 pp 843ndash863 1996

[46] M Hemmert L Bstieler and H Okamuro ldquoBridging thecultural divide trust formation in university-industry researchcollaborations in the US Japan and South Koreardquo Technova-tion vol 34 no 10 pp 605ndash616 2014

[47] M D Santoro and P A Saparito ldquoThe firmrsquos trust in its univer-sity partner as a key mediator in advancing knowledge and newtechnologiesrdquo IEEE Transactions on Engineering Managementvol 50 no 3 pp 362ndash373 2003

[48] K Blomqvist P Hurmelinna and R Seppanen ldquoPlaying thecollaboration game rightmdashbalancing trust and contractingrdquoTechnovation vol 25 no 5 pp 497ndash504 2005

[49] A Geuna and L J J Nesta ldquoUniversity patenting and itseffects on academic research the emerging European evidencerdquoResearch Policy vol 35 no 6 pp 790ndash807 2006

[50] Y S Lee ldquoThe sustainability of university-industry researchcollaboration an empirical assessmentrdquo Journal of TechnologyTransfer vol 25 no 2 pp 111ndash133 2000

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of