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System dynamic analysis on industry-university-research institute synergetic innovation process based on knowledge flow

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Abstract

The industry-university-research institute synergetic innovation (IUR-SI) is a contractual arrangement formed by the core subjects of enterprises, universities, and research institutes, with the cooperation and assistance of intermediary organizations and other auxiliary organizations. The purpose of knowledge appreciation, sharing, and creation, along with the collaborative interaction approach, can realize joint development of major scientific and technological innovations. By analyzing the key factors affecting knowledge flow in the IUR-SI process, this research explores how knowledge flow can be promoted more effectively and efficiently. First, the subjective, knowledge, and knowledge flow simulation factors which influence knowledge flow in the IUR-SI process are analyzed. Then the knowledge flow process is simulated using the system dynamics method. Finally, the corresponding system dynamics model is constructed, and its simulation is analyzed. The results show that in the IUR-SI process, knowledge sharing ability, knowledge hiding coefficient, knowledge failure rate, subject innovation willingness, trust relationship, and organizational distance have obvious influences on the overall knowledge stock of the system, meaning that the above factors have relatively high sensitivity. The continuous flow of knowledge will gradually strengthen their influence. Knowledge flow is more affected by internal driving mechanisms, such as knowledge transfer and knowledge sharing. Changes in the internal driving mechanism’s role will affect knowledge flow efficiency, while factors such as trust relationships and organizational distance will have a relatively small effect. These results indicate that improving knowledge flow efficiency, the internal environment, and strengthening knowledge transfer and sharing, should be of great importance.

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Notes

  1. The INTEG is the abbreviation of integration, which refers to integral. The equation of each state variable is an integral equation.

  2. The initial knowledge stock of academic institutions is set to 20, and the initial knowledge stock of the enterprise is set to 10.

  3. The DELAY1I is a delay function with an initial value. Before the knowledge transfer occurs, both parties need to communicate and prepare, which will consume a certain amount of time. First order delay function is adopted for simulation, and the initial value of both is assumed to be 0.

  4. The STEP is adopted to simulate the process of knowledge invalidation.

  5. The knowledge invalidation of enterprises and academic institutions is starts at 5 units, and the overall knowledge invalidation begins after 6 units.

  6. When all parties carry out knowledge transfer, they will hide and protect some core knowledge. The knowledge that can be transferred does not include this part of the hidden knowledge. When both parties transfer knowledge, they need to prepare for the preliminary work and thus the transfer will not happen immediately. First order delay function is adopted for simulation, and the initial value is set to 0.

  7. After the knowledge is transferred, the knowledge sharing between the two parties will not happen immediately. All parties need to learn and absorb the knowledge to effectively share it. The first-order delay function is used for simulation, and the initial value is set to 0.

  8. WITH LOOKUP, which refers to the table function, is used to describe the knowledge sharing ability of all parties.

  9. The knowledge sharing ability of the academic institution is higher than that of the enterprise. The initial values are set to 0.15 and 0.1 respectively. After 18-unit time, the knowledge sharing ability of the academic institution is improved by 35% and the knowledge sharing ability increases by 25%.

  10. The table function is used to describe the knowledge innovation rate of all parties and of the whole. The knowledge innovation ability of the academic institutions is higher than that of the enterprise. The overall knowledge innovation rate is between that of the academic institutions and enterprise. The initial values are set to 0.05, 0.04 and 0.045 respectively. After 18-unit time, the knowledge innovation rate of academic institutions increased by 5%, the knowledge innovation rate of enterprises increased by 2%, and the overall knowledge innovation rate increased by 4%.

  11. As enterprises and academic institutions participate in technical cooperation, the knowledge distance is more reflected in the quantitative gap. For the convenience of analysis, the difference between the two knowledge stocks is used to express the knowledge distance.

  12. IF THEN ELSE is the selection function, which is used to describe the complexity of knowledge flow situation factors. Various situational factors have an impact on the overall knowledge increment, and they all randomly take values between [0, 1].

  13. RANDOM NORMAL refers to the normal distribution function, which is a test function.

  14. The knowledge invalidation rate of all parties is set to be the same. Considering that there will be a certain path loss in knowledge transfer, the overall knowledge invalidation rate is slightly higher.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China under Grant No. 71971146 and Annual project of Sichuan Social Science Planning under Grant No. SC19C013.

Funding

This research was supported by the National Natural Science Foundation of China under Grant No. 71971146 and Annual project of Sichuan Social Science Planning under Grant No. SC19C013.

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Authors and Affiliations

  1. School of Business, Sichuan University, Chengdu, 610064, People’s Republic of China

    Yue Wu, Xin Gu & Zhaobohan Zhang

  2. Chengdu Soft Innovation and Intelligence Industry Research Association, Chengdu, 610023, People’s Republic of China

    Xin Gu

  3. School of Management, Chengdu University of Information Engineering, Chengdu, 610103, People’s Republic of China

    Zhenzhou Tu

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  1. Yue Wu
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Correspondence to Zhenzhou Tu.

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Wu, Y., Gu, X., Tu, Z. et al. System dynamic analysis on industry-university-research institute synergetic innovation process based on knowledge flow. Scientometrics 127, 1317–1338 (2022). https://doi.org/10.1007/s11192-021-04244-y

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