运筹与管理 ›› 2014, Vol. 23 ›› Issue (1): 143-150.

• 应用研究 • 上一篇    下一篇

基于知识转移的合作创新伙伴信任评价研究

程巧莲, 胡珑瑛, 崔双双   

  1. 哈尔滨工业大学 管理学院, 黑龙江 哈尔滨 150001
  • 收稿日期:2012-11-05 出版日期:2014-01-25
  • 作者简介:程巧莲,女,博士,讲师,硕士生导师,研究方向:创新管理,制造战略;胡珑瑛,男,教授,博士生导师,研究方向:创新管理。
  • 基金资助:
    国家教育部博士点基金资助项目(20092302110061);黑龙江省博士后资助项目 (LBH-Z11182)

Trust Evaluation of Cooperation Innovation Partners Based on Knowledge Transfer

CHENG Qiao-lian, HU Long-ying, CUI Shuang-shuang   

  1. School of Management, Harbin Institute of Technology, Harbin China, 150001
  • Received:2012-11-05 Online:2014-01-25

摘要: 在合作创新中,知识转移是合作创新的必要过程,也是合作创新取得成功的关键。同时,信任可以促进合作创新伙伴之间的知识分享和知识转移,并由此促进合作创新的效果。因此,本文从知识转移的视角进行了合作创新伙伴信任评价研究。首先,在已有研究基础上,结合专家咨询的方法,建立了基于知识转移的合作创新伙伴信任评价的指标体系;然后,构建了合作创新伙伴信任评价的RS-SVM模型;最后选择某研究所及其30个合作创新伙伴作为样本,对评价模型进行了应用研究,结果显示,RS-SVM模型的准确率和效率均比较高。本研究不仅可以拓展目前信任研究的视角和方法,也从实践上帮助合作创新主体进行有效的信任管理,以进一步促进伙伴之间的知识转移,达成合作创新的目标。

关键词: 合作创新, 信任评价, 知识转移, 粗糙集, 支持向量机

Abstract: Knowledge transfer is a necessary process, which is crucial for success in cooperation innovation. Meanwhile, trust among partners can promote knowledge sharing and knowledge transferring during cooperation innovation, and thus can increase the performance of cooperative innovation. This paper evaluates trust among cooperation partners from the perspective of knowledge transfer. Firstly, an indicator system for trust evaluation is constructed based on literature and expert interview. Secondly, a RS-SVM trust evaluation model is proposed based on knowledge transfer. Finally, we take an institute and its 30 partners as example and investigate the applicability of the RS-SVM model. The result shows that both the accuracy and efficiency of the model are higher than those of other evaluation methods. This study can not only extend the perspective of current studies on trust, but also assist enterprises to conduct trust management in practice, and thus promote knowledge transfer among partners, to further achieve the target of cooperation innovation.

Key words: cooperation innovation, trust evaluation, knowledge transfer, rough set, support vector machine

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