December 2010, 17(Suppl. 2): 45–50 www.sciencedirect.com/science/journal/10058885 http://www.jcupt.com

The Journal of China Universities of Posts and Telecommunications

Diffusion of technical innovation based on industry-university-institute cooperation in industrial clusters

JIA Wei ( ), LIU Li-ran, XIE Xue-mei

School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract

In this paper, Cellular Automaton model is applied to analyze the diffusion of innovation in industrial clusters. By comparing the simulation of two approaches of technical innovation enterprise independently research and development and Industry-University-Institute Cooperation, we find that Industry-University-Institute Cooperation is an effective way to promotethe diffusion of technical innovation in industrial cluster. The result of the simulation suggests that Industry-University-Institute Cooperation is better especially when enterprises’ research and development (R&D) capability is very weak or the technology is very difficult. Intensifying the external support from local government, industry associations and financial institutions has apositive effect on the diffusion of innovation in industrial clusters.

Keywords technical innovation, diffusion, Industry-University-Institute Cooperation, industrial cluster, Cellular Automaton

1 Introduction1

Industrial cluster was defined as a group of establishments geographically centralized, which either share a common set of input needs, or rely on each other as supplier or customer [1]. In an industrial cluster, there is not only a core value system that includes suppliers, competitors, customers and associated enterprises of industry, but also a support value system that includes universities, research institutions, local governments, industry associations, financial institutions and intermediary organizations of science and technology.

Technical innovation is considered to be one of the most important internal factors that affect the development of the industrial cluster [2]. Without technical innovation, the scale of the industry cluster cannot be expanded; without technical innovation, the competitiveness of the industrial cluster cannot be enhanced; without technical innovation, the development of the industrial cluster cannot be sustained. Currently, although some enterprises have embarked on the road of independent innovation, most of the enterprises in the industrial cluster have no capacity and conditions for

Received date: 29-06-2010 Corresponding author: JIA Wei, E-mail: wjia@bupt.edu.cn DOI: 10.1016/S1005-8885(09)60583-5

independent innovation. A sample survey showed that less than 40% of the enterprises in the Pearl River Delta region are independent in R&D. Most of the enterprises didn’t have core technology and were not capable in independent R&D [3]. Another survey pointed out that the average R&D investment for Chinese companies only accounted for 3.8% of the sales revenue, while based on the experience of the developed counties, for a competitive company, it should be more than 5% [4]. Therefore, it’s quite valuable to find an effective way to improve the capability of independent innovation for enterprises.

In this paper, we want to research on a cooperation model of technical innovation: Industry-University-Institute Coope- ration. Industry-University-Institute Cooperation means the innovation combines three participants including enterprises, universities and institutes in the industrial cluster. In the process of innovation, local government will create a favorable institutional environment by providing policy support; science and technology intermediary organizations will improve the success rate of the transfer by effectively connecting universities, research institutes, enterprises and government; financial institutions realize the combination of the different subjects by providing an effective capital supply; venture investment institutions will resolve the lack of funds

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for each of the main bottlenecks, and properly avoid the investment risk. In the collaborative support of local government, industry associations, financial institutions and intermediary organizations of science and technology, the three main members can join in the innovation with their advantage resources. By the participation of all these members, the capability of independent innovation will be enhanced, and the development of the industry cluster will be faster and better [5].

The domestic and foreign scholars have proposed many methods and tools to research the innovation and diffusion in and between enterprises [6–7]. However, few researches are on the innovation that based on the participation of enterprises, universities, institutions, local governments, industry associa- tions, financial institutions and intermediary organizations of science and technology. To an individual enterprise, affected by the competition, Market environment, strategic planning, risk preference and many other factors, the other enterprises’ innovation will inevitably influence its choice. The diffusion of enterprises’ innovation in the industrial cluster will impact the innovation capability and competitiveness of the whole industry cluster.

In this paper, enterprises’ innovation behavior is simulated by cellular automata (CA) model. CA model will analyze how the Industry-University-Institute Cooperation influences the enterprises’ innovation from micro-level. An extended cellular automaton model on the technology diffusion in industrial cluster is formulated.

2 The extended CA model for diffusion of the innovation in industrial cluster

2.1 CA model

CA model is a micro-simulation model which is discrete in time, space and state. Different from the macro-simulation model such as the famous Bass model, CA model is a bottom-up diffusion model characterized by parallel simulating the individual behavior and complex system. CA model was first proposed by John Von Neumann in 1966 [8]. From then on, it was gradually used in the transportation [9], finance [10], infectious diseases [11], mass media [12] and many other areas.

Structurally, CA model can be presented as: CA ( , , , )dL S N f (1)

There are four parameters in the equation above: dL , S, N

and f.

dL is the cell space, decided by the scale of space (L) and

the dimension of the space (d). When L=10, d=2, it means a two-dimensional regular uniform lattice of 10 10.S is the state of cells. In the innovation diffusion model, S

usually has two different values: ‘0’ (representing someone has not adopt the new technology), ‘1’ (representing someone adopts the new technology). N is the number of neighbors on which cells interact. For

two dimensional cellular automata models, two types of cellular neighborhoods are usually considered: 5 cells, consisting of the cell along with its four immediate none diagonal neighbors (Von Neumann configuration, Fig. 1), and 9 cells, consisting of the cell along with its eight surrounding neighbors (Moore configuration, Fig. 2).

Fig. 1 Von Neumann configuration

Fig. 2 Moore configuration

f is the function of the evolution rules, by which the state of any single cell is determined by the previous states of the cells in its neighborhood.

In 2000, Goldenberg and Efroni [13] established a heterogeneous innovation diffusion CA model. They defined a two-dimensional model in which cells had two states and affected by all eight surrounding neighbors (Moore configuration). The evolution rules of the model were established as follows:

1 0

0 1

0

1 (1 )(1 )nPP p q

(2)

p the probability of spontaneous, discovery-driven transformation. q the probability of word of mouth-induced awareness. n the number of neighbors in the state of ‘1’. If cell’s state equals to ‘1’ at time t, then cell’s state will

keep stable ‘1’ at time t+l; if cell’s state equals to ‘0’ at time t,

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then cell’s state turn to ‘1’ at time t+l with probability of 0 1P . If all the neighbors in the state of ‘0’, 0 1P p , else ,

0 1P is decided by both p and q.

2.2 External factors that affect the innovation of enterprises in industry cluster

1) The behavior of competitors To an individual enterprise in the industrial cluster, the

more competitors adopt innovation, the greater pressure comes from the market. In an industry cluster represented by the cell space, each cell is an enterprise. The distance of the cell is not the geographical distance of the enterprise, but the gap of their capability. The neighbor of the cell is the most competitive enterprise which has the most considerable capability. Therefore, the innovation of neighbors has a major impact on the enterprise. The possibility of one enterprise adopt innovation is proportionate to the number of innovation neighbors.

2) Technical attributes Technical attributes are the features of technology,

including the maturity, complexity and operability of technology, etc. In CA model, technical attributes can be abstracted as a variable called technical difficulty. The possibility of one enterprise to adopt innovation is inversely proportional to the technical difficulty.

3) External supports In the process of innovation, enterprises in the industry

cluster need the support from governments, industry associations, financial institutions and intermediary organizations of science and technology. All of these supports are external supports. The possibility of one enterprise to adopt innovation is proportionate to the efforts of external supports.

4) Other factors In addition to the above factors, there are also some other

factors existed. In this paper, we just focus on the first three factors.

2.3 The model design

According to the CA model established by Goldenberg and Efroni and considering the actual conditions of the innovation diffusion in industrial clusters, the factors of the extended cellular automaton model are explained as follows:

1) Cells and the cell space: every cell in the CA model represents an enterprise in the industrial cluster. Our CA

model uses a two-dimensional regular uniform lattice (20x20) to represent a set of all existing and potential enterprises in the industrial clusters.

2) Cellular state: The state variable has two different values: ‘0’ (representing the enterprise has not adopt technical innovation), ‘1’ (representing the enterprise has adopt technical innovation).

3) Neighbors: The typical type of neighbors- Moore type is chosen in this model.

4) Evolution rules: in Eq. (2), the variables at each site are updated simultaneously, based on the values of the variables in their neighbors at the preceding time step (q), and according to the definite set of ‘local rules’ (p). As we discussed above, a cell’s probability of innovation adoption at time t depends on four forces: the behavior of competitors, technical attributes, external supports and other factors. Therefore, the extended CA model is established as follows:

1 ( , , , , )t tdS f L S N p q (3)

dL the cell space. 1tS and tS the state of cells in time t+1 and t;.

N neighbors in which cells interact. p the behavior of competitors. q the enterprise’s will of technical innovation.

( )(1 )c u ep hd

(4)

p is decided by these factors: c enterprise’s research and development (R&D) capability. u universities’ and institutions’ R&D capability. d technical difficulty. e external supports to the technical innovation. h other factors that influence the innovation.

and the weights of factors. If enterprises conduct the research and development

independently, u=0. If enterprises adopt Industry-University- Institute Cooperation, R&D capability is c+u.

3 Simulation and analysis

We conduct simulation experiments based on the input parameters and different changes of some parameters in order to find out the change of the diffusion speed of the innovation in industrial cluster. The purpose of the simulation does not lie on precise calculation of the influence by various factors, but on analyzing the effect of different innovation diffusion based on the Industry-University-Institute Cooperation. Therefore, the setting of the parameters in the simulation process is based on the principle of relativity.

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In order to set up the unified standard, it is supposed that 0.05q , 0.045 , 0.005 , 40u , 20c , d

200 , 0.001e , 0.001h .

3.1 The simulation of diffusion in different enterprise’s R&D capability

Based on Eq. (4), we change the value of c to observe the influence of different enterprise’s R&D capability. When the enterprise’s R&D capability decreases, if enterprises conduct the research and development independently, the speed of the innovation diffusion is reduced significantly. Else, if enterprises adopt Industry-University-Institute Cooperation, the speed of the innovation diffusion is little changed. The results of these simulations are showed in Figs. 3–8.

Fig. 3 Enterprises conduct the research and development independently (c=20)

Fig. 4 Enterprises adopt Industry-University-Institute Cooperation (c=20,u=40)

Fig. 5 Enterprises conduct the research and development independently (c=10)

Fig. 6 Enterprises adopt Industry-University-Institute Cooperation (c=10,u=40)

Fig. 7 Enterprises conduct the research and development independently (c=5)

Fig. 8 Enterprises adopt Industry-University-Institute Cooperation (c=5,u=40)

3.2 The simulation of diffusion in different technical difficulty

According to Eq. (4), we change the value of d to simulate the influence of different technical difficulty. The result of this simulation is showed in Figs. 9–14.

Fig. 9 Enterprises conduct the research and development independently (d=200,c=5)

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Fig. 10 Enterprises adopt Industry-University-Institute Cooperation (d=200,c=5,u=40)

Fig. 11 Enterprises conduct the research and development independently (d=400,c=5)

Fig. 12 Enterprises adopt Industry-University-Institute Cooperation (d=400,c=5,u=40)

Fig. 13 Enterprises conduct the research and development independently (d=800,c=5)

Fig. 14 Enterprises adopt Industry-University-Institute Cooperation (d=800,c=5,u=40)

When the difficulty is increasing, the speed of innovation behavior diffusion in industrial clusters decreases. The comparison of two technical innovation ways shows that Industry- University-Institute Cooperation is a better choice when the technology is very difficult.

3.3 The simulation of diffusion in different external supports

When the enterprise’s R&D capability is very low and the technical is very difficult, it is very hard for the innovation to diffuse in industry cluster. From Fig. 15, we can see that even when enterprises adopt Industry-University-Institute Cooperation, the diffusion of innovation is very slow. Especially at the very beginning of the diffusion, seldom enterprises adopt technical innovation (Fig. 15). Figs. 17–18 show that if we increase external support at the initial period of the diffusion, the result would be much better.

Fig. 15 Industry-University-Institute Cooperation for difficult technical projects (d=2000,c=1)

Fig. 16 The cell space at the time of t=10

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Fig. 17 Increase external support at the initial period of the diffusion (t<=10,e=2)

Fig. 18 The cell space at the time of t=10, (t<=10,e=2)

4 Conclusions

In the fierce market competition, whether an enterprise adopts technical innovation inevitably affected by its competitor’s behavior and other factors. The important contribution of this paper lies on analyzing the key influential factors by the simulation experiments based on CA and pointing out the Industry-University-Institute Cooperation is an effective way to promote the diffusion of technical innovation in industrial cluster. The simulation result indicated that when most of the enterprises have no R&D capability or limited capability, Industry-University-Institute Cooperation can promote the emergence of technical innovation in the industrial cluster. The study also shows that in high-tech industry where the technology is very difficult, Industry-University-Institute Cooperation is a better choice for enterprises in this industrial cluster. Furthermore, the external support from local government, industry associations, financial institutions and intermediary organizations of science and technology can promote the diffusion of innovation in industrial cluster. Local government should intensify the support to Industry-University-Institute Cooperation by issuing preferential policies and providing connecting platform. Industry associations, financial

institutions and intermediary organizations of science and technology should be encouraged to participate more actively in the cooperation.

The conclusion of this paper points out that Industry- University-Institute Cooperation encourage more enterprises to adopt technical innovation, and ultimately improve the capability of independent innovation of enterprises and industrial clusters, help to build an innovation-oriented country.

Acknowledgements

This work was supported by the Humanities and Social Sciences research Foundation. Ministry of Education (Project No: 08JA880010).

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