Fuzzy Cognitive Research on Factors Influencing Technological Innovation – From Path Dependence Perspective

Jing Hu; Yong Zhang; Yilin Wang

doi:10.20965/jaciii.2016.p0543

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JACIII Vol.20 No.4 pp. 543-553

doi: 10.20965/jaciii.2016.p0543

(2016)

Paper:

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Fuzzy Cognitive Research on Factors Influencing Technological Innovation – From Path Dependence Perspective

Jing Hu^, Yong Zhang^, and Yilin Wang^**

^*College of Economics and Management, China Jiliang University
258 Xueyuan Street, Xiasha Higher Education District, Hangzhou 310018, China

^**Zhongchao Ink Co., Ltd.
No.288, Xuyan Road, Pudong New Area, Shanghai 201315, China

Received:

October 30, 2015

Accepted:

April 11, 2016

Published:

July 19, 2016

Keywords:

technological innovation, path dependence, influencing factors, fuzzy cognitive map (FCM)

Abstract

This paper aims to establish a framework for evaluating technological innovation and to emphasize the important influence of path dependence on technological innovation. The fuzzy cognitive map (FCM) method is used to identify causal relationships among factors that influence technological innovation, and a FCM structural diagram for evaluating enterprise technological innovation is described. Meanwhile, a fuzzy feedback system for the evaluation of technological innovation, integrated with a nonlinear Hebbian learning algorithm, is established; dependence on expert opinions may be avoided through learning and practice using the cognitive map. Finally, using a computer software platform, a dynamic simulation of any complex index system can be realized. From this simulation, stable conditions can provide path references by which an enterprise engaging in technological innovation can improve the integrative efficiency and the overall effect of any realistic technological innovation activity.

Cite this article as:

J. Hu, Y. Zhang, and Y. Wang, “Fuzzy Cognitive Research on Factors Influencing Technological Innovation – From Path Dependence Perspective,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.4, pp. 543-553, 2016.

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References

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[11] H. Hakansson and A. Waluszewski, “Path dependence: Restricting or facilitating technical development,” J. of Business Research, Vol.55, pp. 561-562, 2012.
[12] A. Balmann, M. Odening, H. Weikard, and W. Brandes, “Path-Dependence without Increasing Returns to Scale and Network Externalities,” J. of Economic Behavior and Organization, Vol.29, pp. 160, 1996.
[13] B. Kosko, “FCMs,” Int. J. of Man-Machine Studies, Vol.24, pp. 65-75, 1986.
[14] Z. Huiying and S. Ziwei, “Fuzzy Cognitive Research of the Influencing Factors During Scientific and Technological Achievements Transformation Based on Innovation Diffusion Perspective,” Science of science and management of S&T, Vol.35, No.5, pp. 28-35, 2013.
[15] D. E. Koulouriotis, I. E. Dialoulakis, and D. M. Emiris, “A fuzzy cognitive map-based stock market model: Synthesis, analysis and experimental results,” IEEE, pp. 238-245, 2001.
[16] K. Perusich, “Fuzzy cognitive maps for policy analysis,” IEEE, pp. 457-471, 1996.
[17] L. Sangjae, G. K. Byung, and L. Kidong, “Fuzzy cognitive map-based approach to evaluate EDI performance: A casual model,” Expert System with Applications, Vol.27, pp. 287-299, 2004
[18] E. Papageorgiou, C. Stylios, and P. Groumpos, “Fuzzy cognitive map learning based on nonlinear Hebbian rule,” Proc. of the Australian Conf. on Artificial Intelligence, Australian, pp. 256-268, 2013.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Paul A. David, “Clio and the economics of QWERTY,” American Economic Review, Vol.75, pp. 332-333, 1985.

[2] [2] W. Brian Arthur, “Competing technologies, Increasing returns and lock-in by historical events,” Economic J., Vol.99, pp. 116, 1989.

[3] [3] W. Brian Arthur, “Path-dependent processes and the emergence of macro-structure,” European J. of Operational Research, Vol.30, pp. 294, 1987.

[4] [4] Robert W. Rycroft and Don E. Kash, “Path Dependence in the innovation of complex technologies,” Technology Analysis & Strategic Management, Vol.114, pp. 204-206, 2012

[5] [5] G. Dosi, C. Freeman, R. Nelson, G. Silverberg, and L. Soete, Technical Change and Economic Theory, London: Pinter Publishers, 1988.

[6] [6] N. Rosenberg, Uncertainty and Technological Change, Stanford, CA: Standford University Press, 1996.

[7] [7] R. Stephen, “Path Dependence, Endogenous Innovation, and Growth,” Int. Economic Review, Vol.10, pp. 1215-1216, 2012.

[8] [8] P. Kep, T. Schot, and R. Hoogma, “Regime shifts to sustainability through process of niche formation: the approach of strategic in the management,” Technology Analysis and Strategic Management, Vol.10, pp. 175-195,1998.

[9] [9] W. Brian Arthur, “Positive feed backs in the economy,” Mc Kinsey Quarterly, Vol.1, pp. 81-82, 1994.

[10] [10] K. J. Arrow, “The economic implications of learning by doing,” Review of Economic Studies, Vol.29, pp. 155-173, 1962.

[11] [11] H. Hakansson and A. Waluszewski, “Path dependence: Restricting or facilitating technical development,” J. of Business Research, Vol.55, pp. 561-562, 2012.

[12] [12] A. Balmann, M. Odening, H. Weikard, and W. Brandes, “Path-Dependence without Increasing Returns to Scale and Network Externalities,” J. of Economic Behavior and Organization, Vol.29, pp. 160, 1996.

[13] [13] B. Kosko, “FCMs,” Int. J. of Man-Machine Studies, Vol.24, pp. 65-75, 1986.

[14] [14] Z. Huiying and S. Ziwei, “Fuzzy Cognitive Research of the Influencing Factors During Scientific and Technological Achievements Transformation Based on Innovation Diffusion Perspective,” Science of science and management of S&T, Vol.35, No.5, pp. 28-35, 2013.

[15] [15] D. E. Koulouriotis, I. E. Dialoulakis, and D. M. Emiris, “A fuzzy cognitive map-based stock market model: Synthesis, analysis and experimental results,” IEEE, pp. 238-245, 2001.

[16] [16] K. Perusich, “Fuzzy cognitive maps for policy analysis,” IEEE, pp. 457-471, 1996.

[17] [17] L. Sangjae, G. K. Byung, and L. Kidong, “Fuzzy cognitive map-based approach to evaluate EDI performance: A casual model,” Expert System with Applications, Vol.27, pp. 287-299, 2004

[18] [18] E. Papageorgiou, C. Stylios, and P. Groumpos, “Fuzzy cognitive map learning based on nonlinear Hebbian rule,” Proc. of the Australian Conf. on Artificial Intelligence, Australian, pp. 256-268, 2013.

Fuzzy Cognitive Research on Factors Influencing Technological Innovation – From Path Dependence Perspective

Jing Hu*, Yong Zhang*, and Yilin Wang**

Jing Hu^, Yong Zhang^, and Yilin Wang^**