Decision-Making Analysis of Enterprises’ Adopting Innovation Technology
European Business & Management
Volume 2, Issue 2, November 2016, Pages: 73-79
Received: Oct. 7, 2016; Accepted: Oct. 29, 2016; Published: Jan. 21, 2017
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Guozhong Yang, Business School, Central South University, Changsha, China
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After analyzing the uncertainty of technology innovation diffusion (TID), this paper proposes the model of enterprises’ TID based on geometric Brownian motion with jump, and analyzes the optional timing and influence of adopting innovation technology on TID by each parameter. The results show that enterprise should immediately adopts the technology when its market demand is greater than the optimal investment threshold of enterprise; changes of market environment is conducive to TID; increasing of market uncertainty and the expected rate of return will accelerate TID, and the increasing of market interest rate will inhibit TID.
Uncertainty, Technology Innovation Diffusion, Geometric Brownian Motion, Model
To cite this article
Guozhong Yang, Decision-Making Analysis of Enterprises’ Adopting Innovation Technology, European Business & Management. Vol. 2, No. 2, 2016, pp. 73-79. doi: 10.11648/j.ebm.20160202.19
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Scholtz T W. 1990. Human Capital Investment. Beijing: Commercial Press.
F M Bass. 1969. “A New Product Growth Model for Consumer Durables”. Management Science, 15 (5): 215-277.
DUAN Mao-sheng, ZHANG Xi-liang, GU Shu-hua. An Innovation Diffusion Model Based on
Individual Decision-Making. Systems Engineering-theory & Practice, 2001, (06): 46-51.
Steffens PR. A model of multiple-unit ownership as a diffusion process [J]. TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2003, 70 (9): 901-917.
Hu BM, Wang LL, Yu XK. Stochastic diffusion models for substitutable technological innovation [J]. INTERNATIONAL JOURNAL OF TECHNOLOGY MANAGEMENT, 2004, 28 (7): 654-666.
MO Yun-qing, WU Tian-zu, WU Chan-jun. An innovation diffusion study based on social networks. Soft Science, 2004, 18 (3): 4-6.
HU Zhi-neng, XU Jiu-ping. Multi-stage Dynamical Model of Innovation Product Diffusion. Systems Engineering-theory & Practice, 2005, (4): 15-21.
XIA Hui, ZENG Yong. A Study on Firms Optimal Investment Strategies and Diffusion of New Technology under Multiple Generations of Future Innovations: A Real Option Approach. Journal of Industrial Engineering and Engineering Management, 2005, 19 (3): 21-27. Carayannis EG, Turner E. Innovation diffusion and technology acceptance: The case of PKI technology. TECHNOVATION [J]. 2006, 26 (7): 847-855.
HUANG Weiqiang ZHUANG Xintian. Study of Innovation Diffusion by Using Stochastic Network. Chinese Journal of Management, 2007, 4 (5): 622-627+635.
Emmanouilides Christos J., Davies Richard B. Modelling and estimation of social interaction effects in new product diffusion [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH. 2007, 177 (2): 1253-1274.
ZHANG Jing-wei, CUI Wen-tian, LIN Jun, Xu Xiao-qing. The Problem of Optimal Adoption Timing of New Technology Based on Bass Model. Systems Engineering, 2010, 28 (11): 38-42.
LU Mingkai, SHI Benshan. Optimal timing decision model for new technology application. Statistics & Decision. 2011, (5): 54-56.
HUANG Hai-yang, CHEN Ji-xiang. Games Analysis on the technology innovation Diffusion of University and Enterprises’ Innovation Adoption.. Scientific Management Research, 2012, 30 (6): 61-64.
Gao Tao (Tony), Leichter Gordon, Wei Yinghong. Countervailing effects of value and risk perceptions in manufacturers' adoption of expensive, discontinuous innovations [J]. INDUSTRIAL MARKETING MANAGEMENT. 2012, 41 (4): 659-668.
Risselada Hans, Verhoef Peter C., Bijmolt Tammo H. A. Dynamic Effects of Social Influence and Direct Marketing on the Adoption of High-Technology Products [J]. JOURNAL OF MARKETING. 2014, 78 (2): 52-68.
Wang Zhanzhao; Ma Yonghong; Zhang Fan. Study on Technology Innovation Diffusion Model and Simulation Based on System Dynamics. Science & Technology Progress and Policy, 2015, 32 (19): 13-19.
HUANG Wei-qiang; YAO Shuang; ZHUANG Xin-tian; XIN Wei. Innovation Diffusion Modeling Based on Scale-Free Networks. Journal of Northeastern University (Natural Science), 2015, 36 (8): 1212-1216.
Stummer Christian, Kiesling Elmar, Guenther Markus. Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH. 2015, 245 (1): 157-167.
Montazemi Ali Reza, Qahri-Saremi Hanied. Factors affecting adoption of online banking: A meta-analytic structural equation modeling study [J]. INFORMATION & MANAGEMENT. 2015, 52 (2): 210-226.
XU Ying-ying; QI Liang-qun. Research on low carbon technological innovation diffusion in enterprises clusters based on evolutionary game theory on complex networks. China Population, Resources and Environment, 2016, 26 (8): 16-24.
MA Yong-hong, WANG Zhan-zhao, LI Huan, ZHOU Wen. Network Structure, Adopters Preference and Innovation Diffusion: A Simulation Analysis of the S-D Model of Innovation Diffusion Based on the Decision Making Process of Adopters. Operations Research and Management Science, 2016, 25 (3): 106-116.
Anand Adarsh, Agarwal Mohini, Aggrawal Deepti. Unified approach for modeling innovation adoption and optimal model selection for the diffusion process [J]. JOURNAL OF ADVANCES IN MANAGEMENT RESEARCH, 2016, 13 (2): 154-178.
Merton R C. Option pricing when underlying stock returns discontinuous [J]. Journal of Financial Economic, 1976, 3 (1-2): 125-144.
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