An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation
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 propo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
| Subjects: | |
| Online Access: | https://www.mdpi.com/0718-1876/16/5/88 |
| _version_ | 1797520657880186880 |
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| author | Jiafu Su Xu Chen Fengting Zhang Na Zhang Fei Li |
| author_facet | Jiafu Su Xu Chen Fengting Zhang Na Zhang Fei Li |
| author_sort | Jiafu Su |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:00:42Z |
| format | Article |
| id | doaj.art-14e43f058d64427fb4a9b9cb3ba62e3f |
| institution | Directory Open Access Journal |
| issn | 0718-1876 |
| language | English |
| last_indexed | 2024-03-10T08:00:42Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj.art-14e43f058d64427fb4a9b9cb3ba62e3f2023-11-22T11:30:02ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762021-05-011651571158310.3390/jtaer16050088An Intelligent Method for Lead User Identification in Customer Collaborative Product InnovationJiafu Su0Xu Chen1Fengting Zhang2Na Zhang3Fei Li4Research Center for Economy of Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing 400067, ChinaSchool of Management and Economics, University of Electronic Science and Technology of China, Chengdu 610054, ChinaResearch Center for Economy of Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing 400067, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Management and Economics, University of Electronic Science and Technology of China, Chengdu 610054, ChinaFor 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.https://www.mdpi.com/0718-1876/16/5/88lead usercustomer collaborative product innovationlead user identificationsupport vector machinecost-sensitive learning |
| spellingShingle | Jiafu Su Xu Chen Fengting Zhang Na Zhang Fei Li An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation Journal of Theoretical and Applied Electronic Commerce Research lead user customer collaborative product innovation lead user identification support vector machine cost-sensitive learning |
| title | An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation |
| title_full | An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation |
| title_fullStr | An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation |
| title_full_unstemmed | An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation |
| title_short | An Intelligent Method for Lead User Identification in Customer Collaborative Product Innovation |
| title_sort | intelligent method for lead user identification in customer collaborative product innovation |
| topic | lead user customer collaborative product innovation lead user identification support vector machine cost-sensitive learning |
| url | https://www.mdpi.com/0718-1876/16/5/88 |
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