균형성과표(BSC)를 이용한 물류정보시스템 성과평가지표 개발

• 영문명: The Development of Performance Evaluation Measures for Logistics Information Systems on the Basis of BSC
• 발행기관: 한국IT서비스학회
• 저자명: 강희석(Hee-Suk Kang) 김상훈(Sang-Hoon Kim)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제12권 제3호, 273~287쪽, 전체 14쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2013.09.30

영문 초록

The objective of this study is to empirically develop performance evaluation measures for LIS (logistics information systems) based upon BSC (balanced scorecard) model and determine the relative importance among four perspectives of BSC using AHP (analytic hierarchy process) methodology. Above all, forty nine probable measures for evaluating LIS performance were identified through reviewing the previous research related with SCM (supply chain management), logistics management, and information systems evaluation. And then, these probable measures were examined by means of coincidence analyses using three mutually exclusive criteria(validity of content, ease of measurement and reliability of measurement). Data for conducting the coincidence analyses were collected from LIS users and LIS development personnel. As the results of the coincidence analyses, it was found that 31 ones among 49 probable performance evaluation measures theoretically derived could be qualified for LIS performance evaluation. And AHP analysis showed that the weight of each perspective was respectively as follows : 46.8% for financial perspective, 31.3% for customer perspective, 14.8% for internal business process perspective, and 7.1 % for learning and growth perspective. The academic contribution of this study is that performance evaluation measures for LIS could be systematically and empirically developed on the basis of BSC. Also, the results of this study are expected to be used as a practice guideline of evaluating and improving LIS.

목차

Abstract

1. 서론

2. 이론적 배경

3. 물류정보시스템 성과평가지표의 이론적 도출

4. 연구설계 및 자료수집

5. 연구 결과

6. 결론

참고문헌

키워드

LIS(Logistics Information Systems) Performance Evaluation Measures BSC(Balanced Scorecard) Coincidence Analysis