김병훈
2019-02-26T06:51:53Z
2019-02-26T06:51:53Z
2017-10
EXPERT SYSTEMS WITH APPLICATIONS, v. 84, Page. 37-48
0957-4174
1873-6793
https://www.sciencedirect.com/science/article/pii/S0957417417303159
https://repository.hanyang.ac.kr/handle/20.500.11754/99227
This study proposes a methodology for assessing the contribution of knowledge services (KSs) provided by a Korean public research institute to the business performance of firms. A new methodology based on a data mining-based variable assessment method in a regression model is proposed for the service-level assessment. The contribution of the KSs to firms' business performance is analyzed using their attributes and specific business performance indicators through the conditional variable permutation method in the random forest regression. This reduces the ambiguity in variable importance caused by the correlations among input variables. The proposed methodology is applied to the survey dataset collected from firms. The survey dataset is examined 1) for the whole data and 2) for a subset of the data, namely, small and medium-sized enterprises (SMEs). The empirical results show behavioral properties of firms with regard to the given KSs in general and SMEs in particular. Practical and user-friendly service product types increase the firms' expectation on business performance. Also, flexibility in the service products helps firms acquire much-needed knowledge and boosts their expectation on business performance. In particular, SMEs expect better business performance from the KSs that help them create business plans and strategies. (C) 2017 Elsevier Ltd. All rights reserved.
This work was supported by the Korea Institute of Science and Technology Information for the collaborative research project: Development of Data Mining-based Methodology for Assessing Contribution of Knowledge Services to Performance of Companies [project number C16014]. Also, the authors greatly appreciate valuable and constructive comments of anonymous reviewers.
en_US
PERGAMON-ELSEVIER SCIENCE LTD
Knowledge service assessment
Public research institute
Data mining
Variable importance
Relative contribution score
Data mining-based variable assessment methodology for evaluating the contribution of knowledge services of a public research institute to business performance of firms
Article
10.1016/j.eswa.2017.04.057
EXPERT SYSTEMS WITH APPLICATIONS
Choi, Jeongsub
Kim, Byunghoon
Hahn, Hyuk
Park, Hun
Jeong, Yongil
Yoo, Jaeyoung
Jeong, Myong Kee
2017008335
E
COLLEGE OF ENGINEERING SCIENCES[E]
DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING
byungkim