지능정보연구 (Journal of Intelligence and Information Systems)

  • 제7권2호
  • /
  • Pages.95-115
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  • 2001
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  • 2288-4866(pISSN)
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  • 2288-4882(eISSN)
한국지능정보시스템학회 (Korea Intelligent Information System Society)
권호 표지

자기조직화 지도(SOM) 인공신경망 모형을 이용한 벤쳐기업의 지식경영 유형 세분화에 관한 연구-코스닥 상장기업을 대상으로-

A Self-Organizing Map Neural Network Approach to Segmenting Knowledge Management Type of Venture Businesses in KOSDAG

  • 이건창 (성균관대학교 경영학부) ;
  • 권순재 (성균관대학교 경영학부 대학원) ;
  • 이광용 (성균관대학교 경영학부 대학원)
  • 발행 : 2001.12.01
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초록

본 논문에서는 우리나라 코스닥시장에 상장된 벤처기업을 대상으로 하여 지식경영 유형을 세분화 하기 위한 방법론을 제시한다. 이 방법론은 우선, 해당 벤처기업에 대하여 설문조사를 통하여 이들 회사의 지식경영 요인을 도출한 다음, 이들 요인 값을 가지고 비감독학습 인공신경망 모형인 SOM을 가지고 4개의 의미 있는 군집을 유도하였다. 이들 군집은 벤처기업이 수행하는 다양한 지식경영 유형을 나타내는 것으로 판명되었으며, 이들 유형은 각각 하이테크형, 조직지식중심형, 정보기술 중심형, 단순형으로 분류된다.

We propose classifying the venture firms into four types of knowledge management. For this purpose, we collected questionnaire data from 101 venture firms listed in KOSDAQ, and applied a unsupervised neural network algorithm SOM to obtain four clusters representing knowledge management types-High Tech Type, Organizational Knowledge Type, Information Technology Type, and Beginner Type. Based on the results, we conclude that the venture firms listed in KOSDAQ should first know its own knowledge management type, and then apply appropriate strategies to take advantage of the knowledge management impacts on the competitiveness.

키워드

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