Chen-Tung Chen

Bio: Chen-Tung Chen is an academic researcher from National United University. The author has contributed to research in topics: Fuzzy logic & TOPSIS. The author has an hindex of 14, co-authored 51 publications receiving 5720 citations. Previous affiliations of Chen-Tung Chen include Dayeh University & Cork College of Commerce.

A new evaluation model for intellectual capital based on computing with linguistic variable

Abstract: In a knowledge era, intellectual capital has become a determinant resource for enterprise to retain and improve competitive advantage. Because the nature of intellectual capital is abstract, intangible, and difficult to measure, it becomes a challenge for business managers to evaluate intellectual capital performance effectively. Recently, several methods have been proposed to assist business managers in evaluating performance of intellectual capital. However, they also face information loss problems while the processes of subjective evaluation integration. Therefore, this paper proposes a suitable model for intellectual capital performance evaluation by combining 2-tuple fuzzy linguistic approach with multiple criteria decision-making (MCDM) method. It is feasible to manipulate the processes of evaluation integration and avoid the information loss effectively. Based on the proposed model, its feasibility is demonstrated by the result of intellectual capital performance evaluation for a high-technology company in Taiwan.

Review: A state-of the-art survey of TOPSIS applications

Abstract: Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.