Mu-Yen Chen

Knowledge management performance evaluation: a decade review from 1995 to 2004

In this paper, the development of knowledge management (KM) was surveyed, using a literature review and classification of articles from 1995 to 2004. With a keyword index and article abstract, we explored how KM performance evaluation has developed during this period. Based on a scope of 108 articles from 80 academic KM journals (retrieved from six online databases), we surveyed and classified methods of KM measurement, using the following eight categories: qualitative analysis, quantitative analysis, financial indicator analysis, non-financial indicator analysis, internal performance analysis, external performance analysis, project-orientated analysis and organizationorientated analysis, together with their measurement matrices for different research and problem domains. Future development directions for KM performance evaluation are presented in our discussion. They include: (1) KM performance measurements have tended towards expertise orientation, while evaluation development is a problemorientated domain; (2) different information technology methodologies, such as expert systems, knowledge-based systems and case-based reasoning may be able to evaluate KM as simply another methodology; (3) the ability to continually change and obtain new understanding is the driving power behind KM methodologies, and should be the basis of KM performance evaluations in the future.

Integrating option model and knowledge management performance measures: an empirical study

The knowledge-based economy is coming, and knowledge management (KM) has rapidly disseminated in academic circles as well as in the business world. While an increasing number of companies have launched into knowledge management initiatives, a large proportion of these initiatives are limited to a technical focus. The problem with this type of focus is that it excludes and neglects the true potential benefits that can be derived from knowledge management. This paper develops a new metric, knowledge management performance index (KMPI), for evaluating the performance of a firm in its KM at a point in time. We therefore suggest that a KMPI can be used to determine KM activities from the following perspectives: knowledge creation, knowledge conversion, knowledge circulation and knowledge completion. When KM activities efficiency is increased, KMPI will also be expanded, enabling firms to become knowledge intensive. This paper makes three important contributions: (1) it provides a formal theoretical grounding for the validity of the Black-Scholes model that might be applied to KM; (2) it proposes a measurement framework to enable knowledge assets to be leveraged effectively and efficiently; and (3) it presents the first application of the Black-Scholes model that uses a real-world business situation involving KM as its test bed. The results prove the option pricing model can act as a measurement guideline to the whole range of KM activities.

Comparing with your main competitor: the single most important task of knowledge management performance measurement

The single most important task of knowledge management (KM) performance measurement is comparing your company with its main rivals. Most of the metrics and methods of knowledge measurement that have been developed are concentrated on measuring the knowledge within the organization, which may be nice to know, but is not critical. In this paper, we propose a methodology for comparing a firms knowledge management performance with its major rivals using the Analytical Network Process (ANP) to obtain a clear direction of the effort required to gain or maintain a competitive advantage. The ANP approach employed in the present study is a theory of multiple criteria decision making (MCDM), and is good at dealing with tangible and intangible information. Our methodology is designed to make a detailed comparison of a firms KM performance with that of its main rivals, in order to be able to provide effective information for improving its KM and to increase its decision-making quality. This paper makes three important contributions: (1) it develops a comprehensive model, which incorporates a variety of issues for conducting KM performance measurements in comparison with major rivals; (2) case experience is provided to help us understand the advantages and disadvantages of the methodology for KM performance measurement from a practical point of view, and (3) the results obtained from exploring the case firm present changes that the case firm can make, implying that the case firm must reinforce its knowledge creation and internalization so as to improve its position in comparison with its most competitive rivals. The method proposed by this paper is generic in nature and is applicable to benefit any firm.

Integrating extended classifier system and knowledge extraction model for financial investment prediction: An empirical study

Abstract Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules, however they can get trapped into a local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising for obtaining better results. This article adopts the learning classifier systems (LCS) technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it represents various rule sets that are derived from different sources and encoded as a fixed-length bit string in the knowledge encoding phase; (2) it uses three criteria (accuracy, coverage, and fitness) to select an optimal set of rules from a large population in the knowledge extraction phase; (3) it applies genetic operations to generate optimal rule sets in the knowledge integration phase. The experiments prove that the rule sets derived by the proposed approach is more accurate than other machine learning algorithm.

A unifying ontology modeling for knowledge management

The knowledge-based economy is approaching rapidly, and knowledge management (KM) has disseminated in leaps and bounds in academic circles as well as in the business world. This paper develops a unifying framework to evaluate KM activities for supporting intelligent knowledge-based system (KBS) using web interface, and expert system technology to help inexperienced administrators in insuring the smooth operation of KM performance. This paper makes three important contributions: (1) it proposes an efficient KM Ontology Construction Algorithm to fast conceptualize KM domain concept; (2) it provides a hybrid model used for knowledge acquisition through skeletal concept model and IDEF (Integrated DEFinition function modeling) analysis; and (3) it presents a methodology for using KM ontology in building a unifying framework and evaluation guideline for KM that works well and effective.

Measurement practices for knowledge management: an option perspective

This article develops an option pricing model to evaluate knowledge management (KM) activities from the following perspectives: knowledge creation, knowledge conversion, knowledge circulation, and knowledge carry out. This paper makes three important contributions: (1) it provides a formal theoretical grounding for the validity of the Black-Scholes model that might be employed to KM; (2) it proposes a measurement framework to enable leveraging knowledge assets effectively and efficiently; (3) it presents the first application of the Black-Scholes model that uses a real world business situation involving KM as its test bed. The results prove the option pricing model can be act as a measurement guideline to the whole KM activities.

An implementation of learning classifier systems for rule-based machine learning

Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules, however they can get trapped into a local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising for obtaining better results. This article adopts the learning classifier systems (LCS) technique to provide a hybrid knowledge integration strategy, which makes for continuous and instant learning while integrating multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it provides a knowledge encoding methodology to represent various rule sets that are derived from different sources, and that are encoded as a fixed-length bit string; (2) it proposes a knowledge integration methodology to apply genetic operations and credit assignment to generate optimal rule sets; (3) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process, which is very effective in selecting an optimal set of rules from a large population. The experiments prove that the rule sets derived by the proposed approach is more accurate than the Fuzzy ID3 algorithm.

The secret-open watermarking scheme for ownership verification

By adopting a private and public watermark methodology, an algorithm is proposed where the characteristic between the original image and the embedding watermark with visible content is utilised to form a two-stage verification process: secret-open watermarking. Performance analysis shows that the design scheme provides sufficient ownership right verification mechanism and strong robustness under attack.