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An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation

by 1,2,3, 2, 1, 4 and 2,5,*
1
Research Center for Economy of Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing 400067, China
2
School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 610054, China
3
International College, Krirk University, Bangkok 10220, Thailand
4
School of Mines, China University of Mining and Technology, Xuzhou 221116, China
5
CISDI Engineering Co., Ltd., Chongqing 400013, China
*
Author to whom correspondence should be addressed.
Academic Editor: Yung-Shen Yen
J. Theor. Appl. Electron. Commer. Res. 2021, 16(5), 1571-1583; https://doi.org/10.3390/jtaer16050088
Received: 26 April 2021 / Revised: 11 May 2021 / Accepted: 11 May 2021 / Published: 12 May 2021
(This article belongs to the Special Issue Customer Relationships in Electronic Commerce)
For customer collaborative product innovation (CCPI), lead users are powerful enablers of product innovation. Identifying lead users is vital to successfully carrying out CCPI. In this paper, in order to overcome the shortcomings of traditional evaluation methods, a novel intelligent method is proposed to identify lead users efficiently based on the cost-sensitive learning and support vector machine theory. To this end, the characteristics of lead users in CCPI are first analyzed and concluded in-depth. On its basis, considering the sample misidentification cost and identification accuracy rate, an improved cost-sensitive learning support vector machine (ICS-SVM) method for lead user identification in CCPI is further proposed. A real case is provided to illustrate the effectiveness and advantages of the ICS-SVM method on lead user identification in CCPI. The case results show that the ICS-SVM method can effectively identify lead users in CCPI. This work contributes to user innovation literature by proposing a new way of identifying highly valuable lead users and offers a decision support for the efficient user management in CCPI. View Full-Text
Keywords: lead user; customer collaborative product innovation; lead user identification; support vector machine; cost-sensitive learning lead user; customer collaborative product innovation; lead user identification; support vector machine; cost-sensitive learning
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MDPI and ACS Style

Su, J.; Chen, X.; Zhang, F.; Zhang, N.; Li, F. An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 1571-1583. https://doi.org/10.3390/jtaer16050088

AMA Style

Su J, Chen X, Zhang F, Zhang N, Li F. An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation. Journal of Theoretical and Applied Electronic Commerce Research. 2021; 16(5):1571-1583. https://doi.org/10.3390/jtaer16050088

Chicago/Turabian Style

Su, Jiafu; Chen, Xu; Zhang, Fengting; Zhang, Na; Li, Fei. 2021. "An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation" J. Theor. Appl. Electron. Commer. Res. 16, no. 5: 1571-1583. https://doi.org/10.3390/jtaer16050088

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